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Hydraulics, Water Resources, Coastal and Environmental Engineering( HYDRO 2014 International)

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Abstract

Department of Civil Engineering, MANIT Bhopal Maulana Azad National Institute of Technology Bhopal (Madhya Pradesh) India Pin -462051

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SWAT is a river basin scale model that operates on a daily, monthly time-step. It was developed at the University of Texas, USA. Major components of the SWAT model include hydrology, weather, erosion, soil temperature, crop growth, nutrients, pesticides, and agricultural management (Neitsch et al; 2001D).
SWAT is a river basin scale model that operates on a daily, monthly time-step. It was developed at the University of Texas, USA. Major components of the SWAT model include hydrology, weather, erosion, soil temperature, crop growth, nutrients, pesticides, and agricultural management (Neitsch et al; 2001D).
Figure 2. Methodol ogy Flowchart 3.4 Estimating power output Understanding the model processes, checking the various components such as rainfall to runoff ratio, ET, base flow contribution, etc. are very important. To make sure all the major components are represented well for a watershed before attempting either manual or auto-calibration. The model contains both manual and auto-calibration tools. In this study, model parameters will calibrate using the observed daily flow. Sensitivity analysis will be conducted for the SWAT model to guide calibration process. Figure 2 represents the detailed methodology which was used during the research work.
Figure 2. Methodol ogy Flowchart 3.4 Estimating power output Understanding the model processes, checking the various components such as rainfall to runoff ratio, ET, base flow contribution, etc. are very important. To make sure all the major components are represented well for a watershed before attempting either manual or auto-calibration. The model contains both manual and auto-calibration tools. In this study, model parameters will calibrate using the observed daily flow. Sensitivity analysis will be conducted for the SWAT model to guide calibration process. Figure 2 represents the detailed methodology which was used during the research work.
From figure 3 represents sub-basin wise monthly average flow during the months of June to January. It can be observed that sub-basin 3 has the highest amount of discharge compared to the rest of the sub-basins. This is due to the presence of C group of HSG soil, which are moderately high runoff potential soil type and very high rainfall. Sub-basin 4 has the lowest amount of discharge. This is due to the presence of A group of HSG, which are low runoff potential soil type Figure 4, Monthly average flow (cms) Sub-basin wise
From figure 3 represents sub-basin wise monthly average flow during the months of June to January. It can be observed that sub-basin 3 has the highest amount of discharge compared to the rest of the sub-basins. This is due to the presence of C group of HSG soil, which are moderately high runoff potential soil type and very high rainfall. Sub-basin 4 has the lowest amount of discharge. This is due to the presence of A group of HSG, which are low runoff potential soil type Figure 4, Monthly average flow (cms) Sub-basin wise
Table 2. Sub-basin wise power potential 5. CONCLUSIONS He LOuMallull Ul PUWCL Uy The flow quantiles estimated from FDC and hydraulic head determined from DEM are substituted in equation 3 to estimate power in watts for each sub-basin. The turbine efficiency (n) was taken as 85% for Kaplan turbine. The value of power in mega watts is tabulated in Arc GIS, as shown i in Table 2. — 2a =: ret
Table 2. Sub-basin wise power potential 5. CONCLUSIONS He LOuMallull Ul PUWCL Uy The flow quantiles estimated from FDC and hydraulic head determined from DEM are substituted in equation 3 to estimate power in watts for each sub-basin. The turbine efficiency (n) was taken as 85% for Kaplan turbine. The value of power in mega watts is tabulated in Arc GIS, as shown i in Table 2. — 2a =: ret
The stream flow data are arranged in a descending order of discharges, using class intervals. The data used can be daily, weekly or monthly values. If N numbers of data points are used in this listing, the plotting position of any discharges Q in equation 2. Table 1 gives the dependable flows of all sub-basins, which are derived from flow duration curves. Table.1 shows the flow quantiles derived from the flow duration curves for 90%, 80% and 75% for each sub-basins. These flow quantiles were used for power estimation.
The stream flow data are arranged in a descending order of discharges, using class intervals. The data used can be daily, weekly or monthly values. If N numbers of data points are used in this listing, the plotting position of any discharges Q in equation 2. Table 1 gives the dependable flows of all sub-basins, which are derived from flow duration curves. Table.1 shows the flow quantiles derived from the flow duration curves for 90%, 80% and 75% for each sub-basins. These flow quantiles were used for power estimation.
Fig.1: Location Map of Study Area
Fig.1: Location Map of Study Area
Topography was det fined by a D EM that describes the elevation of any point in a given area at a specific spatial resolution. A 90 m by 90 m resolu tion DEM (Fig. 2) was downloaded from SRTM (Shuttle Radar Topography Mission). The DEM was used to delineate tl he watershed and to analyze the drainage patterns of the land surface terrain. Sub basin parameters such as slope gradient, slo pe length of the terrain, and the stream network characteristics such as channel slope, length, and width were derived from the DEM.
Topography was det fined by a D EM that describes the elevation of any point in a given area at a specific spatial resolution. A 90 m by 90 m resolu tion DEM (Fig. 2) was downloaded from SRTM (Shuttle Radar Topography Mission). The DEM was used to delineate tl he watershed and to analyze the drainage patterns of the land surface terrain. Sub basin parameters such as slope gradient, slo pe length of the terrain, and the stream network characteristics such as channel slope, length, and width were derived from the DEM.
Table No. 1 Spatial model input data for the Watershed.
Table No. 1 Spatial model input data for the Watershed.
Table No.2 Average monthly basin output
Table No.2 Average monthly basin output
Fig.3. Detailed LULC classification of study area
Fig.3. Detailed LULC classification of study area
SWAT gives the average monthly basin values of water and sediment yield in mm and Tonnes/Hector respectively. From the output it seems easier to estimate sediment yield using hydrological model i.e. SWAT. Using this model identification of the soil erosion area becomes easier from which management of sediment yield can be done. Thus SWAT gives each basin values present in watershed through which Soil and Water conservation practices can be done for sustainable development of water resources.
SWAT gives the average monthly basin values of water and sediment yield in mm and Tonnes/Hector respectively. From the output it seems easier to estimate sediment yield using hydrological model i.e. SWAT. Using this model identification of the soil erosion area becomes easier from which management of sediment yield can be done. Thus SWAT gives each basin values present in watershed through which Soil and Water conservation practices can be done for sustainable development of water resources.
The catchment of the Dal Lake was delineated using ASTER DEM of 30 m resolution using the Automatic Watershed Delineator of the hydrological model ArcSWAT. Based on the visually observed differences in the physical features of the andscape and thereby in the hydrological response, the catchment was broadly divided into three subbasins (Figure 3). The Dara-Dachhigam subbasin comprises of the mountains on the north of the lake and those extending far in the east behind the Zabarwan hills. The subbasin constitutes nearly 74 per cent of the total catchment area. The Zabarwan subbasin comprises of the steep slopes of the Zabarwan hills lying along the entire east coast of the lake. The urban subbasin on the west comprises of wetlands, floating gardens and urban settlements. PIQULe B. OUNNGolTs Ul WI Dadi CalOHinene 2.3 Hydrological characterization and selection of modeling approach —
The catchment of the Dal Lake was delineated using ASTER DEM of 30 m resolution using the Automatic Watershed Delineator of the hydrological model ArcSWAT. Based on the visually observed differences in the physical features of the andscape and thereby in the hydrological response, the catchment was broadly divided into three subbasins (Figure 3). The Dara-Dachhigam subbasin comprises of the mountains on the north of the lake and those extending far in the east behind the Zabarwan hills. The subbasin constitutes nearly 74 per cent of the total catchment area. The Zabarwan subbasin comprises of the steep slopes of the Zabarwan hills lying along the entire east coast of the lake. The urban subbasin on the west comprises of wetlands, floating gardens and urban settlements. PIQULe B. OUNNGolTs Ul WI Dadi CalOHinene 2.3 Hydrological characterization and selection of modeling approach —
Figure 4. Outflow hydrograph of the Dara-Dachhigam subbasin Hydrological modeling of the Zabarwan subbasin revealed that the entire precipitation falling on the unsaturated length of the hillslope is either absorbed by the soil matrix or bypassed through the macropores, leaving zero amount of precipitation to flow as surface runoff. Figure 5. Saturated slope length in the steep region of Zabarwan subbasin for different initial conditions of water table (Ls1:
Figure 4. Outflow hydrograph of the Dara-Dachhigam subbasin Hydrological modeling of the Zabarwan subbasin revealed that the entire precipitation falling on the unsaturated length of the hillslope is either absorbed by the soil matrix or bypassed through the macropores, leaving zero amount of precipitation to flow as surface runoff. Figure 5. Saturated slope length in the steep region of Zabarwan subbasin for different initial conditions of water table (Ls1:
The outflow hydrograph for the Dara- Dachhigam subbasin is shown in Figure 4. The hydrograph indicates that the subbasin shows a direct response to precipitation. Peak flows occur mostly during the rainy months of March and April. Occurrence of zero flows during the months of December and January indicates that the flows are intermittent.
The outflow hydrograph for the Dara- Dachhigam subbasin is shown in Figure 4. The hydrograph indicates that the subbasin shows a direct response to precipitation. Peak flows occur mostly during the rainy months of March and April. Occurrence of zero flows during the months of December and January indicates that the flows are intermittent.
Figure 6. Saturated slope length in the foothills of the Zabarwan subbasin For a number of initial conditions of the water table, it was observed that nearly 100 m slope length (out of an average slope ength of 2300 m) at the lower end of the steep zone always remains saturated (Figure 5), whereas the entire length of the foothills remains wet during all seasons (Figure 6). The same is also evident from the presence of a number of springs in the foothill region of this subbasin.
Figure 6. Saturated slope length in the foothills of the Zabarwan subbasin For a number of initial conditions of the water table, it was observed that nearly 100 m slope length (out of an average slope ength of 2300 m) at the lower end of the steep zone always remains saturated (Figure 5), whereas the entire length of the foothills remains wet during all seasons (Figure 6). The same is also evident from the presence of a number of springs in the foothill region of this subbasin.
Figure 7. Outflow hydrograph at the lower end of the steep region of Zabarwan subbasin Figure 8. Total outflow from the Zabarwan subbasin The urban subbasin exhibits a highly complex hydrology. The hydrological response is a result of a number of factors like land cover, soil type and depth of water table below the ground surface. Results
Figure 7. Outflow hydrograph at the lower end of the steep region of Zabarwan subbasin Figure 8. Total outflow from the Zabarwan subbasin The urban subbasin exhibits a highly complex hydrology. The hydrological response is a result of a number of factors like land cover, soil type and depth of water table below the ground surface. Results
The entire overland flow component (appearing as peaks) in the outflow hydrograp h of the steep region (Figure 7) is due to saturation excess overland flow occurring at the saturated lower end of the slope. T also represents the he outflow hydrograph of the foothills which outflow of the entire subbasin (Figure 8) has a constant return flow component and peaks due to overland flow during precipi tation events.
The entire overland flow component (appearing as peaks) in the outflow hydrograp h of the steep region (Figure 7) is due to saturation excess overland flow occurring at the saturated lower end of the slope. T also represents the he outflow hydrograph of the foothills which outflow of the entire subbasin (Figure 8) has a constant return flow component and peaks due to overland flow during precipi tation events.
The d annual aily rainfall data are sorted out and filtered to compute one day maximum. The maximum (189.1 mm) and minimum (548 mm) annual one day maximum rainfa l(AODMR) was recorded during the year 20" Sep 1962 and 26" August 1981, respectively. The mean value of AODMR was found to be 134.77 mm with coefficient of variation as 0.528 . The coefficient of skewness was observed to be 1.3464. August month received the highest amount of one day maximum rainfa 1 (53%) followed by September (17%) and July (13%). it can be observed that the estimated annual AODMR for different probability distributions are following the same trend of Table 1: One day maximum daily rainfall for the period of 1961 to 1990
The d annual aily rainfall data are sorted out and filtered to compute one day maximum. The maximum (189.1 mm) and minimum (548 mm) annual one day maximum rainfa l(AODMR) was recorded during the year 20" Sep 1962 and 26" August 1981, respectively. The mean value of AODMR was found to be 134.77 mm with coefficient of variation as 0.528 . The coefficient of skewness was observed to be 1.3464. August month received the highest amount of one day maximum rainfa 1 (53%) followed by September (17%) and July (13%). it can be observed that the estimated annual AODMR for different probability distributions are following the same trend of Table 1: One day maximum daily rainfall for the period of 1961 to 1990
Ply. be AU UNILN Il GU erent MOTUS Annual one day maximum rainfall was sorted out from the data collected and using statistical techniques for data analysis. The statistical behavior of any hydrological series can be described on the basis of certain parameters. The statistical tests were carried out in accordance with the procedure. The computation of statistical parameters includes mean, standard deviation; coefficient of variation and coefficient of skewness were taken as measures of variability of hydrological series. All the parameters have been used to describe the variability of rainfall in the present study. observed rainfall. The distribution of one day maximum rainfall received during different months in a year is presented in Fig. 1.
Ply. be AU UNILN Il GU erent MOTUS Annual one day maximum rainfall was sorted out from the data collected and using statistical techniques for data analysis. The statistical behavior of any hydrological series can be described on the basis of certain parameters. The statistical tests were carried out in accordance with the procedure. The computation of statistical parameters includes mean, standard deviation; coefficient of variation and coefficient of skewness were taken as measures of variability of hydrological series. All the parameters have been used to describe the variability of rainfall in the present study. observed rainfall. The distribution of one day maximum rainfall received during different months in a year is presented in Fig. 1.
The average, standard deviation, coefficient of variation and skewness of Annual One Day Maximum Rainfall for 30 years and their respective formulas are given in Table 2. These statistical parameters can be used to find the estimated one day maximum rainfall from different probability distribution functions. The variation of standard deviation over the mean is shown in Fig. 2. It was also observed that 10 years (33.3%) received one day maximum daily rainfall above the average.
The average, standard deviation, coefficient of variation and skewness of Annual One Day Maximum Rainfall for 30 years and their respective formulas are given in Table 2. These statistical parameters can be used to find the estimated one day maximum rainfall from different probability distribution functions. The variation of standard deviation over the mean is shown in Fig. 2. It was also observed that 10 years (33.3%) received one day maximum daily rainfall above the average.
Fig. 3: Predicted AODMR with different probability distribution vs return period
Fig. 3: Predicted AODMR with different probability distribution vs return period
Table 3: Observed and expected one day maximum rainfall at different probability levels
Table 3: Observed and expected one day maximum rainfall at different probability levels
Fig.4:- Annual One Day Maximum Rainfall with various return period The expected AODMR for different probabilities are graphically represented in Fig. 4.Regression models were developed from the observed AODMR against different return period by using Weibull's method. The trend analysis (Fig. 4.) for prediction of one day maximum rainfall for different return period was carried out and it is found that the exponential trend line gives better coefficient of determination (R’) = 0.9342 and the equation is: Y = 79,95X°*28 where Y is AODMR in mm and X is Retum period in Y ears.
Fig.4:- Annual One Day Maximum Rainfall with various return period The expected AODMR for different probabilities are graphically represented in Fig. 4.Regression models were developed from the observed AODMR against different return period by using Weibull's method. The trend analysis (Fig. 4.) for prediction of one day maximum rainfall for different return period was carried out and it is found that the exponential trend line gives better coefficient of determination (R’) = 0.9342 and the equation is: Y = 79,95X°*28 where Y is AODMR in mm and X is Retum period in Y ears.
Flow over the sluice spillway was simulated with CFD software FLUENT. FLUENT is a commercial computer program for modelling fluid flow and heat transfer in complex geometries. FLUENT provides complete mesh flexibility, including the ability to solve flow problems using unstructured meshes that can be generated about complex geometries with relative ease. It solves the full three dimensional equations of fluid motion in general orthogonal curvilinear coordinates for both laminar and turbulent flows.
Flow over the sluice spillway was simulated with CFD software FLUENT. FLUENT is a commercial computer program for modelling fluid flow and heat transfer in complex geometries. FLUENT provides complete mesh flexibility, including the ability to solve flow problems using unstructured meshes that can be generated about complex geometries with relative ease. It solves the full three dimensional equations of fluid motion in general orthogonal curvilinear coordinates for both laminar and turbulent flows.
Figure 4. Meshing of the domain 3.3 Boundary C onditions Three dimensional grid was developed in Gambit software itself. The 3-D mesh generation consists of the geometry generation and 3-D grid development over the spillway geometry, water tank upstream for reservoir and downstream region. The objective behind this grid generation is to provide the mesh to simulate flow through two spans, mixing of flows coming out of two spans and the flow over spillways. The full domain is decomposed into the smaller volumes, so that they can be meshed by structured mesh. The cells have been clustered near the sluice roof profile and spillway surface to capture wall bounded effects and predict the wall pressures in the flow simulation. The grid is made finer in those regions where the water and air have interface. Minimum height of the grid cell is 0.1 m and maximum height of the grid cell is 0.9 m in the domain. The hexahedral cells are used for grid generation with the cell count 885261. The surface grid is shown in Figure 4.
Figure 4. Meshing of the domain 3.3 Boundary C onditions Three dimensional grid was developed in Gambit software itself. The 3-D mesh generation consists of the geometry generation and 3-D grid development over the spillway geometry, water tank upstream for reservoir and downstream region. The objective behind this grid generation is to provide the mesh to simulate flow through two spans, mixing of flows coming out of two spans and the flow over spillways. The full domain is decomposed into the smaller volumes, so that they can be meshed by structured mesh. The cells have been clustered near the sluice roof profile and spillway surface to capture wall bounded effects and predict the wall pressures in the flow simulation. The grid is made finer in those regions where the water and air have interface. Minimum height of the grid cell is 0.1 m and maximum height of the grid cell is 0.9 m in the domain. The hexahedral cells are used for grid generation with the cell count 885261. The surface grid is shown in Figure 4.
Air was defined as a primary phase and water as a secondary phase. For the calculation of air water interface i.e. free water surface, volume of fluid (VOF) model was used. For simulation of spillway flow, two inlets were needed to define the water inflow to domain and air inflow over the top of domain. Water inflow was defined as a pressure inlet with the initial water leve and initial velocity at the inlet face. Also the air inflow over the domain upstream and downstream of the spillway was defined as pressure inlet boundary condition. The water outflow at the end of domain was defined as a pressure outflow boundary condition. All the solid boundaries including side walls, Sluice walls, piers and spillway bottom were defined as wal boundaries with no slip condition. Figure 5 shows boundary conditions of the numerical domain. Figure 5. Boundary conditions of the domain 4, SOLUTION PROCEDURE
Air was defined as a primary phase and water as a secondary phase. For the calculation of air water interface i.e. free water surface, volume of fluid (VOF) model was used. For simulation of spillway flow, two inlets were needed to define the water inflow to domain and air inflow over the top of domain. Water inflow was defined as a pressure inlet with the initial water leve and initial velocity at the inlet face. Also the air inflow over the domain upstream and downstream of the spillway was defined as pressure inlet boundary condition. The water outflow at the end of domain was defined as a pressure outflow boundary condition. All the solid boundaries including side walls, Sluice walls, piers and spillway bottom were defined as wal boundaries with no slip condition. Figure 5 shows boundary conditions of the numerical domain. Figure 5. Boundary conditions of the domain 4, SOLUTION PROCEDURE
The numerical model of sluice spillway was run with unsteady free surface calculations with pressure based solver, which enables the pressure-based Navier-Stokes solution algorithm. The VOF method was used to capture the interface between water and air and governing equations are solved by the Finite volume method. For the VOF - method the Body force weighted scheme is used for pressure interpolation as the gravity force is high and the modified HRIC scheme is used for the volume fraction equations in order to improve the sharpness of the interface between the two phases i.e. water and air. Second order upwind scheme is used for momentum and pressure equations. The k- ¢ turbulence model was used to simulate the three- dimensional turbulent flow. Figure 6 shows simulated flow after run of 27.36 seconds.
The numerical model of sluice spillway was run with unsteady free surface calculations with pressure based solver, which enables the pressure-based Navier-Stokes solution algorithm. The VOF method was used to capture the interface between water and air and governing equations are solved by the Finite volume method. For the VOF - method the Body force weighted scheme is used for pressure interpolation as the gravity force is high and the modified HRIC scheme is used for the volume fraction equations in order to improve the sharpness of the interface between the two phases i.e. water and air. Second order upwind scheme is used for momentum and pressure equations. The k- ¢ turbulence model was used to simulate the three- dimensional turbulent flow. Figure 6 shows simulated flow after run of 27.36 seconds.
SMM fe VY GLU OULLOUY VIVillv Pressure distribution were computed over the sluice bottom profile for numerical studies and compared with the physical model results. In both the studies water surface follow the sluice profile and corresponding pressures having same trend over the profile. Figure 8 shows the plots for pressure values of experimental and numerical modelling results over the sluice surface for comparison. It shows the good agreement between both the values. Pressure distribution over the sluice profile were
SMM fe VY GLU OULLOUY VIVillv Pressure distribution were computed over the sluice bottom profile for numerical studies and compared with the physical model results. In both the studies water surface follow the sluice profile and corresponding pressures having same trend over the profile. Figure 8 shows the plots for pressure values of experimental and numerical modelling results over the sluice surface for comparison. It shows the good agreement between both the values. Pressure distribution over the sluice profile were
Figure 8. Pressures over the sluice profile
Figure 8. Pressures over the sluice profile
In this paper, a finite volume-based CFD software FLUENT was used to investigate the hydraulic characteristics of flow through sluice spillway. The water surface profile, pressure distribution and discharge characteristics of the chosen spillway were computed and compared with existing physical model data. The computed and experimental values of the coefficient of discharge were 0-63 and 0-62, the computed value being 1.6 % higher than the experimental value observed on the physical model. As seen from the figures depicting pressure values and water surface elevations, it shows the good matching trend and values in case of breastwall bottom profile for both numerical as well as experimental studies. The upstream slope was not guiding the flow over the crest properly, as a result of which a mild separation zone was seen forming over the horizontal crest in the numerical model as depicted in the figure of velocity vectors, which was not predicted by physical model. Except the entrance the pressure distribution was found good agreement between the physical and numerical results. Reasonable agreement is observed with the numerical and physical mode results, showing the applicability of the CFD software for the numerical simulation of real case study of spillway. Further refinement in mesh generation and cell count may improve the results of the simulation of flow through a sluice spillway.
In this paper, a finite volume-based CFD software FLUENT was used to investigate the hydraulic characteristics of flow through sluice spillway. The water surface profile, pressure distribution and discharge characteristics of the chosen spillway were computed and compared with existing physical model data. The computed and experimental values of the coefficient of discharge were 0-63 and 0-62, the computed value being 1.6 % higher than the experimental value observed on the physical model. As seen from the figures depicting pressure values and water surface elevations, it shows the good matching trend and values in case of breastwall bottom profile for both numerical as well as experimental studies. The upstream slope was not guiding the flow over the crest properly, as a result of which a mild separation zone was seen forming over the horizontal crest in the numerical model as depicted in the figure of velocity vectors, which was not predicted by physical model. Except the entrance the pressure distribution was found good agreement between the physical and numerical results. Reasonable agreement is observed with the numerical and physical mode results, showing the applicability of the CFD software for the numerical simulation of real case study of spillway. Further refinement in mesh generation and cell count may improve the results of the simulation of flow through a sluice spillway.
eS, ee The model was constructed with the hydraulic components as per the design drawings and the downstream bed levels given by the Field officials. Figure- 1. Dry Model of Pachaiyar Spillway
eS, ee The model was constructed with the hydraulic components as per the design drawings and the downstream bed levels given by the Field officials. Figure- 1. Dry Model of Pachaiyar Spillway
ac: aT a a Table No.1 Statement t showing the observed velocity in’ in Trial No VI
ac: aT a a Table No.1 Statement t showing the observed velocity in’ in Trial No VI
Figure 4. View of Left side scour vents Hydraulic Jump at stilling basin
Figure 4. View of Left side scour vents Hydraulic Jump at stilling basin
Figure 1. Indicative cross section of stepped spillway A stepped spillway has conventional ogee spillway profile. However, it is provided with steps from just below the crest up to the toe of the spillway. The provision of steps on the downstream face of the spillway chute increases the rate of energy dissipation and in turn, reduces the size of energy dissipater downstream. A typical cross section of stepped spillway is indicated in Figure 1. Thus a stepped chute not only significantly increases the dissipation energy rate but also decreases the construction costs of the downstream stilling basin.
Figure 1. Indicative cross section of stepped spillway A stepped spillway has conventional ogee spillway profile. However, it is provided with steps from just below the crest up to the toe of the spillway. The provision of steps on the downstream face of the spillway chute increases the rate of energy dissipation and in turn, reduces the size of energy dissipater downstream. A typical cross section of stepped spillway is indicated in Figure 1. Thus a stepped chute not only significantly increases the dissipation energy rate but also decreases the construction costs of the downstream stilling basin.
Table 2. Experimental observations and computations for 6 = 45°, @ = 45° and H* = 20
Table 2. Experimental observations and computations for 6 = 45°, @ = 45° and H* = 20
Table 1. Experimental observations and computations for 0 = 45° @ = 45° and H* =10
Table 1. Experimental observations and computations for 0 = 45° @ = 45° and H* =10
Figure 2 shows the photographs of convergent stepped spillway experimental setup constructed at G. H. Raisoni College of Engineering, Nagpur in collaboration with VNIT, Nagpur, Maharashtra State, India.
Figure 2 shows the photographs of convergent stepped spillway experimental setup constructed at G. H. Raisoni College of Engineering, Nagpur in collaboration with VNIT, Nagpur, Maharashtra State, India.
Table 3. Experimental observations and computations for 6 = 452, @ = 452 and H* = 40 Table 4. Experimental observations and computations for 6 = 452, @ = 452 and H* = 80
Table 3. Experimental observations and computations for 6 = 452, @ = 452 and H* = 40 Table 4. Experimental observations and computations for 6 = 452, @ = 452 and H* = 80
Table 5. Experimental observations and computations for 6 = 45°, @ =452 and smooth ogee chute
Table 5. Experimental observations and computations for 6 = 45°, @ =452 and smooth ogee chute
1, ANALYSIS OF EXPERIMENTAL DATA :
1, ANALYSIS OF EXPERIMENTAL DATA :
Due to convergence of the chute walls, the required training wall height is governed by the flow run- up. Visual observations indicated that there were no transve rse waves for any of the step height ratios and the air entrainment occurred for nearly all the observations. Experimental data ha and also along the convergent wal s been collected for plotting the water surface profiles along the centerline of the spillway ls. As anticipated, the flow depths near the wall were more than those at the centerline of the spillway. The flow depths along wall shall form the basis for deciding the minimum training wal the flow does not overtop the discharge for a step height ratio H* ll height requirement so that convergent training walls endangering the safety of the structure. Figure (3) illustrates the observed water surface profiles along the wall for different = 40. As the maximum depth of flow along the wall (hy,,,) would determine the training wall height, a dimensionless plot showing its variation with dimensionless discharge is presented in Figure (4). The regression equations have been obtained and are as follows.
Due to convergence of the chute walls, the required training wall height is governed by the flow run- up. Visual observations indicated that there were no transve rse waves for any of the step height ratios and the air entrainment occurred for nearly all the observations. Experimental data ha and also along the convergent wal s been collected for plotting the water surface profiles along the centerline of the spillway ls. As anticipated, the flow depths near the wall were more than those at the centerline of the spillway. The flow depths along wall shall form the basis for deciding the minimum training wal the flow does not overtop the discharge for a step height ratio H* ll height requirement so that convergent training walls endangering the safety of the structure. Figure (3) illustrates the observed water surface profiles along the wall for different = 40. As the maximum depth of flow along the wall (hy,,,) would determine the training wall height, a dimensionless plot showing its variation with dimensionless discharge is presented in Figure (4). The regression equations have been obtained and are as follows.
Figure 3 (a). Water surface profiles along the side wall of spillway for step height ratio, H* = 40, Q1 = 0.024 cumec. Figure 3 (b). Water surface profiles along the side wall of spillway for step height ratio, H* = 40, Q2 = 0.035 cumec.
Figure 3 (a). Water surface profiles along the side wall of spillway for step height ratio, H* = 40, Q1 = 0.024 cumec. Figure 3 (b). Water surface profiles along the side wall of spillway for step height ratio, H* = 40, Q2 = 0.035 cumec.
Figure 3 (c). Water surface profiles along the side wall of spillway for step height ratio, H* =40, Q3 =0.042 cumec.
Figure 3 (c). Water surface profiles along the side wall of spillway for step height ratio, H* =40, Q3 =0.042 cumec.
The maximum flow depths along wall depths were compared with the corresponding critical depths. For H* =80, the maximum flow depth was found to be between 2.25Y, to 2.9Y,, for H*= 40, the maximum flow depth was found to between 3.5Y, to 5.6Y,, for H*= 20, the maximum flow depth was found to lie between 4.85Y, to 6.25Y, whereas for H*= 10, the maximum depth of flow was observed to be in the range of 5.35Y, to 7.6Y
The maximum flow depths along wall depths were compared with the corresponding critical depths. For H* =80, the maximum flow depth was found to be between 2.25Y, to 2.9Y,, for H*= 40, the maximum flow depth was found to between 3.5Y, to 5.6Y,, for H*= 20, the maximum flow depth was found to lie between 4.85Y, to 6.25Y, whereas for H*= 10, the maximum depth of flow was observed to be in the range of 5.35Y, to 7.6Y
Figure 4. Dimensionless maximum depth of flow along the side wall versus dimensionless discharge
Figure 4. Dimensionless maximum depth of flow along the side wall versus dimensionless discharge
Figure 3 (e). Water surface profiles along the side wall of spillway for step height ratio, H* = 40, Q5 =0.063 cumec.
Figure 3 (e). Water surface profiles along the side wall of spillway for step height ratio, H* = 40, Q5 =0.063 cumec.
Figure 3 (d). Water surface profiles along the side wall of spillway for step height ratio, H* =40, Q4 =0.052 cumec.
Figure 3 (d). Water surface profiles along the side wall of spillway for step height ratio, H* =40, Q4 =0.052 cumec.
4.0 MODEL STUDIES
4.0 MODEL STUDIES
In order to reproduce proper bed movement and roughness, the model bed was made mobile by laying sand having a mean diameter (Ds) of 0.34 mm. Figure 2 shows the grain size distribution of sand used. In order to establish flood slope and to observe water levels at various locations, gauges were installed on the right side upstream and downstream of Wazirabad barrage, upstream of ISBT road bridge and upstream of Indraprastha barrage and on the left side at Kailashnagar downstream of old rail-cum-road bridge, near Okhla weir and at the proposed road bridge site.
In order to reproduce proper bed movement and roughness, the model bed was made mobile by laying sand having a mean diameter (Ds) of 0.34 mm. Figure 2 shows the grain size distribution of sand used. In order to establish flood slope and to observe water levels at various locations, gauges were installed on the right side upstream and downstream of Wazirabad barrage, upstream of ISBT road bridge and upstream of Indraprastha barrage and on the left side at Kailashnagar downstream of old rail-cum-road bridge, near Okhla weir and at the proposed road bridge site.
Table 1. Maximum water levels and velocities observed during model studies
Table 1. Maximum water levels and velocities observed during model studies
The preliminary model studies include, assessing and understanding the flow conditions existing in and around the structures to be introduced. This study is conducted by passing the predecided Figure 4. Model set-up with existing conditions Figure 5. Flow pattern in the vicinity of proposed bridge with Q =12750 m°/s (Alignment-1)
The preliminary model studies include, assessing and understanding the flow conditions existing in and around the structures to be introduced. This study is conducted by passing the predecided Figure 4. Model set-up with existing conditions Figure 5. Flow pattern in the vicinity of proposed bridge with Q =12750 m°/s (Alignment-1)
Figure 8. Flow patter in the vicinity of pattern in the vicinity of proposed bridge with q = 12,750 m*/ ( Alignment —3) Figure 9. Flow proposed bridge with g = 12,750 m*/s (Alignment — 4)
Figure 8. Flow patter in the vicinity of pattern in the vicinity of proposed bridge with q = 12,750 m*/ ( Alignment —3) Figure 9. Flow proposed bridge with g = 12,750 m*/s (Alignment — 4)
Figure 6. Flow pattem in the vicinity of pattern in the vicinity of proposed bridge with Q = 12,750 m’/: (Alignment —1) Figure7. Flow proposed bridge with 0 =12,750m3/s (Alignment —2)
Figure 6. Flow pattem in the vicinity of pattern in the vicinity of proposed bridge with Q = 12,750 m’/: (Alignment —1) Figure7. Flow proposed bridge with 0 =12,750m3/s (Alignment —2)
Fig. 1: Experimental Setup The entire procedure was repeated for other weirs having different apex angles. Table - 1 gives an account of the different parameters of the triangular weir taken into consideration in this study. A total of 65 experimental runs were taken.
Fig. 1: Experimental Setup The entire procedure was repeated for other weirs having different apex angles. Table - 1 gives an account of the different parameters of the triangular weir taken into consideration in this study. A total of 65 experimental runs were taken.
3. ANALYSIS AND RESULTS: 3.1 Variation of discharge with head: The variation of discharge with head for five triangular weirs tested in the present study is shown in Fig. 2.
3. ANALYSIS AND RESULTS: 3.1 Variation of discharge with head: The variation of discharge with head for five triangular weirs tested in the present study is shown in Fig. 2.
Photo 1: Upstreamview of the channel Table -1: Range of parameters for the triangular weir
Photo 1: Upstreamview of the channel Table -1: Range of parameters for the triangular weir
Following best fit equations for Q — hhave been obtained:
Following best fit equations for Q — hhave been obtained:
SYMBOLS USED:
SYMBOLS USED:
Figure |, Eliipucal section or wier In the present study the above definite integral is solved by using the Gaussian Quadrature technique so that estimates are made till five places of decimals. The solution of the above integral is also checked by finding the elliptical integrals of the first and the second kind as per equation 1. The experimental discharges are calculated by taking actual observations in the experimental channel. The coefficient of discharge can thus be calculated. in = >) V2g(h—x) dA, where dAis the area of the small
Figure |, Eliipucal section or wier In the present study the above definite integral is solved by using the Gaussian Quadrature technique so that estimates are made till five places of decimals. The solution of the above integral is also checked by finding the elliptical integrals of the first and the second kind as per equation 1. The experimental discharges are calculated by taking actual observations in the experimental channel. The coefficient of discharge can thus be calculated. in = >) V2g(h—x) dA, where dAis the area of the small
Figure 2. Variation of Cd with Reynolds Number It is observed on comparing the experimental discharges for the three different weir sections that for lower eccentricity of ellipse that is for the highest value of minor axis of 0.34 m the discharges for the same and similar heads are higher. Figure 3. Variation of Cd with viscosity
Figure 2. Variation of Cd with Reynolds Number It is observed on comparing the experimental discharges for the three different weir sections that for lower eccentricity of ellipse that is for the highest value of minor axis of 0.34 m the discharges for the same and similar heads are higher. Figure 3. Variation of Cd with viscosity
Head discharge variations are plotted as shown in Figures 2 for Channel Reynolds number from 635 to 1837 which is the aminar transition zone. The computed head discharge curve lies above the experimental curve as expected. However in the given range of flow and Reynolds number it is further observed that the two curves have not remained parallel. With increasing Reynolds number the gap between the two curves of computed and experimental discharges goes on widening which means that the ratio of experimental discharge to the theoretical discharge and therefore the coefficient of discharge goes on reducing for the given range of Reynolds Number .
Head discharge variations are plotted as shown in Figures 2 for Channel Reynolds number from 635 to 1837 which is the aminar transition zone. The computed head discharge curve lies above the experimental curve as expected. However in the given range of flow and Reynolds number it is further observed that the two curves have not remained parallel. With increasing Reynolds number the gap between the two curves of computed and experimental discharges goes on widening which means that the ratio of experimental discharge to the theoretical discharge and therefore the coefficient of discharge goes on reducing for the given range of Reynolds Number .
Variation of Coefficient of Discharge with surface tension The properties of the elliptical section as a weir are studied. Discharge sensitivity of elliptically shaped weir section is found to be more than the rectangular section. In fact its sensitivity lies between that of a rectangular and parabolic weir section.
Variation of Coefficient of Discharge with surface tension The properties of the elliptical section as a weir are studied. Discharge sensitivity of elliptically shaped weir section is found to be more than the rectangular section. In fact its sensitivity lies between that of a rectangular and parabolic weir section.
Fig. 1. Submerged vane induced transverse irculations
Fig. 1. Submerged vane induced transverse irculations
Fig.4. Variation of various turbulence quantities and velocity profile for x = 3H for four vane rows
Fig.4. Variation of various turbulence quantities and velocity profile for x = 3H for four vane rows
Fig.5. Variation of various turbulence quantities and velocity profile for x = 3H for no vane row.
Fig.5. Variation of various turbulence quantities and velocity profile for x = 3H for no vane row.
Figure 2. Hydraulic jump on sloping apron and the relationship between D ’, and D, (Peterka, 1964) 4. With the apron design properly for the maximum discharge condition, it should then be determined that the tail water depth and length of basin available for energy dissipation are sufficient for, say,1/4,1/2 and 3/4 capacity.
Figure 2. Hydraulic jump on sloping apron and the relationship between D ’, and D, (Peterka, 1964) 4. With the apron design properly for the maximum discharge condition, it should then be determined that the tail water depth and length of basin available for energy dissipation are sufficient for, say,1/4,1/2 and 3/4 capacity.
G arudeshwar weir is located about 12 km downstream of Sardar Sarovar Dam in Gujarat. The reservoir created by the weir would function as the lower reservoir for reversible operation of the turbines of river bed power house of Sardar Sarovar Dam. Total length of the weir is 1137 m which includes 339 m long rockfill dam and non overflow blocks of length 189 m. The ungated overflow portion is 609 m long. It has an ogee profile with crest at El. 31.75 m. The design discharge is 62,807 m°/s and the high flood level is El. 44.65 m. The FRL and MDDL are at El. 31.5 m and El. 25.91 m respectively. The original design of weir consisted of roller bucket as an energy dissipater with a 40 m long apron downstream of bucket and since the solid roller bucket was not functioning satisfactorily for the entire range of discharges, the design was changed to 95 m long Stilling basin with horizontal apron as energy dissipator. As the horizontal stilling basin was not performing satisfactorily, it was provided with the sloping apron with dentate end sill.
G arudeshwar weir is located about 12 km downstream of Sardar Sarovar Dam in Gujarat. The reservoir created by the weir would function as the lower reservoir for reversible operation of the turbines of river bed power house of Sardar Sarovar Dam. Total length of the weir is 1137 m which includes 339 m long rockfill dam and non overflow blocks of length 189 m. The ungated overflow portion is 609 m long. It has an ogee profile with crest at El. 31.75 m. The design discharge is 62,807 m°/s and the high flood level is El. 44.65 m. The FRL and MDDL are at El. 31.5 m and El. 25.91 m respectively. The original design of weir consisted of roller bucket as an energy dissipater with a 40 m long apron downstream of bucket and since the solid roller bucket was not functioning satisfactorily for the entire range of discharges, the design was changed to 95 m long Stilling basin with horizontal apron as energy dissipator. As the horizontal stilling basin was not performing satisfactorily, it was provided with the sloping apron with dentate end sill.
Figure 5. Location plan of proposed Garudeshwar weir.
Figure 5. Location plan of proposed Garudeshwar weir.
Figure 3. Length of jump in terms of conjugate depth D, (IS: 4997- 1968)
Figure 3. Length of jump in terms of conjugate depth D, (IS: 4997- 1968)
5.0 STUDIES WITH SLOPING APRON (CWPRS T.R.N¢ 5027, 2012) 5.1 Studies with dentated end sill Table 1. Model Scale Relation forV arious Dimensions
5.0 STUDIES WITH SLOPING APRON (CWPRS T.R.N¢ 5027, 2012) 5.1 Studies with dentated end sill Table 1. Model Scale Relation forV arious Dimensions
Figure 6. Tail Water Rating Curve and Jump Height Curves for different aprons of Stilling Basin.
Figure 6. Tail Water Rating Curve and Jump Height Curves for different aprons of Stilling Basin.
Figure 8. Water surface profiles on Sloping Stilling Basin for the discharge of 15,700 m*/s
Figure 8. Water surface profiles on Sloping Stilling Basin for the discharge of 15,700 m*/s
Figure 7. Pressures on profile of Sloping Stilling Basin for the discharge of 15,700 m*/s
Figure 7. Pressures on profile of Sloping Stilling Basin for the discharge of 15,700 m*/s
6.0 NUMERICAL MODELLING
6.0 NUMERICAL MODELLING
Photo 3. Performance of Stilling Basin with solid endsill for discharge of 15,700 m°/s basin resulting in improved efficiency. To allow a shift of toe of jump further upstream for lower discharges, the existing dentated endsill was converted into solid endsill and studies were carried out. From the model studies, it was observed that the front of the jump shifted slightly towards toe with the provision of solid end sill as compared to the jump with dentated end sill, though it did not form at the toe of the weir. But for discharge of 15,700 m°/s, jump was forming exactly at the toe without showing any tendency of shifting down as shown in photo 3. Velocities observed downstream of sloping apron are of the order of 1.5 m/s and were slightly more than the one with dentated endsill as shown in Table 2.
Photo 3. Performance of Stilling Basin with solid endsill for discharge of 15,700 m°/s basin resulting in improved efficiency. To allow a shift of toe of jump further upstream for lower discharges, the existing dentated endsill was converted into solid endsill and studies were carried out. From the model studies, it was observed that the front of the jump shifted slightly towards toe with the provision of solid end sill as compared to the jump with dentated end sill, though it did not form at the toe of the weir. But for discharge of 15,700 m°/s, jump was forming exactly at the toe without showing any tendency of shifting down as shown in photo 3. Velocities observed downstream of sloping apron are of the order of 1.5 m/s and were slightly more than the one with dentated endsill as shown in Table 2.
upstream and downstream. There is an initial time gap, for which the hydraulic jump, still is not stabilised and characteristics flow parameters presents a great time fluctuation. When the jump becomes stable, these values have a small fluctuation around an average value. Simulation was carried out for 15,700 m°/s (25% of design discharge). Figure 9 shows numerical simulation in Flow- 3D for Garudeshwar weir for discharge of 15,700 m*/s with solid endsill.
upstream and downstream. There is an initial time gap, for which the hydraulic jump, still is not stabilised and characteristics flow parameters presents a great time fluctuation. When the jump becomes stable, these values have a small fluctuation around an average value. Simulation was carried out for 15,700 m°/s (25% of design discharge). Figure 9 shows numerical simulation in Flow- 3D for Garudeshwar weir for discharge of 15,700 m*/s with solid endsill.
Figure 12. Water surface profile from Numerical Simulation for discharge of 15,700 m*/s.
Figure 12. Water surface profile from Numerical Simulation for discharge of 15,700 m*/s.
Figure 11. Comparision of average mean Pressure for discharge of 15,700 m/s,
Figure 11. Comparision of average mean Pressure for discharge of 15,700 m/s,
Figure 10. Average mean Pressure from Numerical Simulation for discharge of 15,700 m’/s.
Figure 10. Average mean Pressure from Numerical Simulation for discharge of 15,700 m’/s.
Figure 1. Typical River in Bouldery Reach The paper describes in detail the hydraulic design of barrage carried out by the prevalent BIS guidelines and the formulae developed by many researchers for hydraulic design of barrage in montane regions.
Figure 1. Typical River in Bouldery Reach The paper describes in detail the hydraulic design of barrage carried out by the prevalent BIS guidelines and the formulae developed by many researchers for hydraulic design of barrage in montane regions.
eS Oye 8 ee Pee Me eee eee The intake design is mainly dependent on the river morphology adjacent to the intake. Since the intake is to be located upstream of a weir, the reservoir is subjected to sedimentation and the river tries to change its planform continuously. This change is due to the movement and deposition of sediment with respect to the flow passing downstream of the weir. To assist in proper location of the intake, morphological studies were conducted with the help of topo-sheet of 1970, Satellite Imageries for the years1989, 2000 and 2012 (Fig.2). In addition hydrographic survey data of Brahmani river, hydraulic data and observations made during site visit were used to locate the intake. —
eS Oye 8 ee Pee Me eee eee The intake design is mainly dependent on the river morphology adjacent to the intake. Since the intake is to be located upstream of a weir, the reservoir is subjected to sedimentation and the river tries to change its planform continuously. This change is due to the movement and deposition of sediment with respect to the flow passing downstream of the weir. To assist in proper location of the intake, morphological studies were conducted with the help of topo-sheet of 1970, Satellite Imageries for the years1989, 2000 and 2012 (Fig.2). In addition hydrographic survey data of Brahmani river, hydraulic data and observations made during site visit were used to locate the intake. —
Fig. 3: A close view of Satellite images for past 40 years
Fig. 3: A close view of Satellite images for past 40 years
FINALISING DIFFERENT WATER LEVELS
FINALISING DIFFERENT WATER LEVELS
Figure 9: Gauge — Discharge relation at Franposh gauging site From the Gumble extreme value analysis of the gauge- discharge data of Panposh gauging site for 26 years, it was revealed that for 50 years frequency, maximum discharge would be 14,138 m°/s and minimum discharge would be 7.8 m’/s. The maximum discharge with hundred year frequency was found to be 15,700 m°/s, which is considered for design of foundation level of Intake structure. The project authority informed that they had never faced shortage of water supply at Tarkera Pump house for last 40 years due to regular releases from Mandira dam in the upstream. The Mandira dam having storage capacity of 326 million cubic meter was solely constructed for RSP and the releases from the dam are governed by the requirement at Tarkera weir. The requirement of water for NSPCL intake is only 0.425m*/s, for which availability is ensured on the basis of above data.
Figure 9: Gauge — Discharge relation at Franposh gauging site From the Gumble extreme value analysis of the gauge- discharge data of Panposh gauging site for 26 years, it was revealed that for 50 years frequency, maximum discharge would be 14,138 m°/s and minimum discharge would be 7.8 m’/s. The maximum discharge with hundred year frequency was found to be 15,700 m°/s, which is considered for design of foundation level of Intake structure. The project authority informed that they had never faced shortage of water supply at Tarkera Pump house for last 40 years due to regular releases from Mandira dam in the upstream. The Mandira dam having storage capacity of 326 million cubic meter was solely constructed for RSP and the releases from the dam are governed by the requirement at Tarkera weir. The requirement of water for NSPCL intake is only 0.425m*/s, for which availability is ensured on the basis of above data.
HY DNAULIU DESIGN The one dimensional mathematical model HEC-RAS was used to predict water levels and velocities at the proposed Intake site. The Water level and corresponding discharge data available at Panposh gauging site of CWC about 4 km upstream of proposed ntake site was utilised for model calibration. For different discharges, the flow simulations were carried out by providing normal depth condition at the downstream boundary, for which the bed slope of river was taken as 1 in 4464 (as per available survey drawings) and about 1 in 2460 m in downstream of Tarkera weir. Discharges were used as the upstream boundary, and n value was taken as 0.04. The n value was decided considering lot of rock exposures in bed in this reach. It was seen from the extrapolated gauge — discharge data at Panposh that the water level was about RL 207.2 m for the discharge of 14,000 m*/s and matches well with the water level obtained by Mathematical model for this discharge (RL 207.49 m). Similarly water level at Panposh for discharge of 12,000 m*/s was RL 206.60 m from the G-Q curve and RL 206.50 m from the mathematical model, which shows a very good conformity. The high flood of 15,700 m/s was also simulated by providing normal depth as the downstream boundary condition and discharge at the upstream boundary. The Table-1 shows the water levels and velocities worked out with HEC-RAS at different locations. From this table it was seen that at proposed openings should neither be provided facing upstream nor facing downstream, but these should be provided at the sides making an angle of 30 to 40° to the flow direction.
HY DNAULIU DESIGN The one dimensional mathematical model HEC-RAS was used to predict water levels and velocities at the proposed Intake site. The Water level and corresponding discharge data available at Panposh gauging site of CWC about 4 km upstream of proposed ntake site was utilised for model calibration. For different discharges, the flow simulations were carried out by providing normal depth condition at the downstream boundary, for which the bed slope of river was taken as 1 in 4464 (as per available survey drawings) and about 1 in 2460 m in downstream of Tarkera weir. Discharges were used as the upstream boundary, and n value was taken as 0.04. The n value was decided considering lot of rock exposures in bed in this reach. It was seen from the extrapolated gauge — discharge data at Panposh that the water level was about RL 207.2 m for the discharge of 14,000 m*/s and matches well with the water level obtained by Mathematical model for this discharge (RL 207.49 m). Similarly water level at Panposh for discharge of 12,000 m*/s was RL 206.60 m from the G-Q curve and RL 206.50 m from the mathematical model, which shows a very good conformity. The high flood of 15,700 m/s was also simulated by providing normal depth as the downstream boundary condition and discharge at the upstream boundary. The Table-1 shows the water levels and velocities worked out with HEC-RAS at different locations. From this table it was seen that at proposed openings should neither be provided facing upstream nor facing downstream, but these should be provided at the sides making an angle of 30 to 40° to the flow direction.
Fig. 7 : Water Surface Profiles along Brahmani River
Fig. 7 : Water Surface Profiles along Brahmani River
Figure No. 1 : Typical diagram showing the parameters of dam By observing the Figure No. 1, it is evident that the upstream and downstream slopes have a great impact on the values of X» and X3 which in turn will govern the total base with of the dam. Whereas X ; remains unaffected as it is the Height which already is assumed to be constant. By varying the width of dam on both the upstream side and the downstream side the stresses are studied for all the different cases of the dam which are discussed in brief in result and discussion. The Design constant taken is the height of the dam which will depend on water level and free board. Design variables are the sloping projection of height on the upstream side of the dam considered as X,, width of the dam on the upstream side of the dam is taken as X. and width of the dam on the downstream side of the dam is considered as X3. A typical; Diagram Showing these parameters is in Figure No: 1.
Figure No. 1 : Typical diagram showing the parameters of dam By observing the Figure No. 1, it is evident that the upstream and downstream slopes have a great impact on the values of X» and X3 which in turn will govern the total base with of the dam. Whereas X ; remains unaffected as it is the Height which already is assumed to be constant. By varying the width of dam on both the upstream side and the downstream side the stresses are studied for all the different cases of the dam which are discussed in brief in result and discussion. The Design constant taken is the height of the dam which will depend on water level and free board. Design variables are the sloping projection of height on the upstream side of the dam considered as X,, width of the dam on the upstream side of the dam is taken as X. and width of the dam on the downstream side of the dam is considered as X3. A typical; Diagram Showing these parameters is in Figure No: 1.
change in the width of toe for reservoir full condition with uplift Consider case 2 of dam for reservoir full condition with uplift in which with the variation in parameter of dam & stresses are studied with change in the width of toe and as from the above Figure 3, the various parameter of dam like eccentricity, various stresses are checked for reservoir full condition with uplift case and the width of the dam at toe is reduced from 51m to 50m with height constant and the stresses are safe for this condition, but if we reduce further the width the stresses does not remain safe as per the Indian Standards. The factor of Safety against Sliding and Overturning satisfy the safety criteria for this case. change in the width of toe for reservoir Empty Case Consider Case 1 for reservoir empty condition in which with the variation in parameter of dam & stresses are studied with change in the width of toe and as from the above Figure No. 2, the various parameter of dam like eccentricity, various stresses are checked for reservoir empty condition and the width of the dam at toe is reduced from 51m to 50m with height constant and the stresses are safe for this condition, but if we reduce further the width the stresses does not remain safe as per the Indian Standards. The factor of Safety against Sliding and Overturning satisfy the safety criteria for this case.
change in the width of toe for reservoir full condition with uplift Consider case 2 of dam for reservoir full condition with uplift in which with the variation in parameter of dam & stresses are studied with change in the width of toe and as from the above Figure 3, the various parameter of dam like eccentricity, various stresses are checked for reservoir full condition with uplift case and the width of the dam at toe is reduced from 51m to 50m with height constant and the stresses are safe for this condition, but if we reduce further the width the stresses does not remain safe as per the Indian Standards. The factor of Safety against Sliding and Overturning satisfy the safety criteria for this case. change in the width of toe for reservoir Empty Case Consider Case 1 for reservoir empty condition in which with the variation in parameter of dam & stresses are studied with change in the width of toe and as from the above Figure No. 2, the various parameter of dam like eccentricity, various stresses are checked for reservoir empty condition and the width of the dam at toe is reduced from 51m to 50m with height constant and the stresses are safe for this condition, but if we reduce further the width the stresses does not remain safe as per the Indian Standards. The factor of Safety against Sliding and Overturning satisfy the safety criteria for this case.
Figure No. 2: Variation of Parameter of dam & Stresses with change in the wicth of toe for reservoir Empty Case which their the dam is not safe in sliding criteria. The detailed discussion of the result is as under.
Figure No. 2: Variation of Parameter of dam & Stresses with change in the wicth of toe for reservoir Empty Case which their the dam is not safe in sliding criteria. The detailed discussion of the result is as under.
Figure No. 5: Variation of Parameter of dam & Stresses with change in the width of toe for reservoir full condition drains chocked Consider case 3 of dam for reservoir full condition with no uplift in which with the variation in parameter of dam & stresses are studied with change in the width of toe and as from the above Figure 4, the various parameter of dam like eccentricity, various stresses are checked for reservoir full condition with uplift case and the width of the dam at toe is reduced from 51m to 50m with height constant and the stresses are safe for this condition, but if we reduce further the width the stresses does not remain safe as per the Indian Standards. The factor of Safety against Sliding and Overturning satisfy the safety criteria for this case.
Figure No. 5: Variation of Parameter of dam & Stresses with change in the width of toe for reservoir full condition drains chocked Consider case 3 of dam for reservoir full condition with no uplift in which with the variation in parameter of dam & stresses are studied with change in the width of toe and as from the above Figure 4, the various parameter of dam like eccentricity, various stresses are checked for reservoir full condition with uplift case and the width of the dam at toe is reduced from 51m to 50m with height constant and the stresses are safe for this condition, but if we reduce further the width the stresses does not remain safe as per the Indian Standards. The factor of Safety against Sliding and Overturning satisfy the safety criteria for this case.
Figure No. 4: Variation of Parameter of dam & Stresses with change in the width of toe for reservoir full condition with no uplift
Figure No. 4: Variation of Parameter of dam & Stresses with change in the width of toe for reservoir full condition with no uplift
Fig 1: Thessien polygon map of the study area ¢ Cid where, P, is the annual rainfall in mm and R, are annual R- factor in MJ mmha“yr’. The theissen map (Fig. 1) of Kodar catchment has been prepared using the ILWIS 3.0 software and it observed that Kodar catchment is affected by Kodar, Bagbahara and Bartunga R.G. stations. The weights and R-factor for different RG stations have been presented in The value of annual and seasonal R-factor for Kodar reservoir catchment has been obtained as 429.39 MJmmha''hr' and 402.94 MJmmha ‘hr ' respectively. The weights of Kodar, Bagbahara and Bartunga RG stations have been computed as 0.50, 0.48 and 0.02 respectively. The rainfall in the study area concentrated mainly in the month of July, August and September. By using the operation attribute map input as thessien polygon and table as R value output Rmap is generated is shown Fig 2.
Fig 1: Thessien polygon map of the study area ¢ Cid where, P, is the annual rainfall in mm and R, are annual R- factor in MJ mmha“yr’. The theissen map (Fig. 1) of Kodar catchment has been prepared using the ILWIS 3.0 software and it observed that Kodar catchment is affected by Kodar, Bagbahara and Bartunga R.G. stations. The weights and R-factor for different RG stations have been presented in The value of annual and seasonal R-factor for Kodar reservoir catchment has been obtained as 429.39 MJmmha''hr' and 402.94 MJmmha ‘hr ' respectively. The weights of Kodar, Bagbahara and Bartunga RG stations have been computed as 0.50, 0.48 and 0.02 respectively. The rainfall in the study area concentrated mainly in the month of July, August and September. By using the operation attribute map input as thessien polygon and table as R value output Rmap is generated is shown Fig 2.
Table 1: Computation of K-factor for soils in the study area
Table 1: Computation of K-factor for soils in the study area
Fig 2: Rmap for the Kodar catchment
Fig 2: Rmap for the Kodar catchment
The topographic index was calculated using the Digital Elevation Model which has been generated using contour map and point elevation map obtained from the seamless data distribution database. The use of DEM has been documented by Mitasova et al (1996) to be the most reliable elevation data when higher resolution data is unavailable because it allows for lower levels of systematic errors and artifacts of analysis compared to the lower resolution DEMs that are available. The interpolation for contour map and rasterize operation for point elevation has been performed to get two separate raster maps. The ‘iff statement of ILWIS has been used to combine both the raster maps to get the DEM. The points defining the flow line are computed as the points of intersection of a line constructed in the flow direction given by aspect angle a: and a grid cell edge. The Map Calculation option of raster operation in ILWIS has been used to determine topographic factor map (Fig. 4). 0.1.4 Cover and Management rfactor (U ) The main role of vegetation cover in the interception of the rain drops is that their kinetic energy is dissipated by them. The crop management factor is the expected ratio of soil loss from land cropped under specified conditions to soil loss from clean, tilled fallow or identical soil and slope and under the same rainfall. Available soil loss data from undisturbed land were not sufficient to derive C values by direct comparison of measured soil loss rates, as was done for the development of C values for cropland. The following equation suggested by Van der et al. 1999, 2000 has been used for estimation of C factor.
The topographic index was calculated using the Digital Elevation Model which has been generated using contour map and point elevation map obtained from the seamless data distribution database. The use of DEM has been documented by Mitasova et al (1996) to be the most reliable elevation data when higher resolution data is unavailable because it allows for lower levels of systematic errors and artifacts of analysis compared to the lower resolution DEMs that are available. The interpolation for contour map and rasterize operation for point elevation has been performed to get two separate raster maps. The ‘iff statement of ILWIS has been used to combine both the raster maps to get the DEM. The points defining the flow line are computed as the points of intersection of a line constructed in the flow direction given by aspect angle a: and a grid cell edge. The Map Calculation option of raster operation in ILWIS has been used to determine topographic factor map (Fig. 4). 0.1.4 Cover and Management rfactor (U ) The main role of vegetation cover in the interception of the rain drops is that their kinetic energy is dissipated by them. The crop management factor is the expected ratio of soil loss from land cropped under specified conditions to soil loss from clean, tilled fallow or identical soil and slope and under the same rainfall. Available soil loss data from undisturbed land were not sufficient to derive C values by direct comparison of measured soil loss rates, as was done for the development of C values for cropland. The following equation suggested by Van der et al. 1999, 2000 has been used for estimation of C factor.
RED is Band III and NIR is Band IV of IRS satellites (IRS ID and P6). For determination of C-factor map of the study area, the and the DVI image of LISS III data for the study area has been generated. The C-factor-map using equation 7 has been prepared a graph between NDVI and C-factor values has been plotted. From the analysis of graph, it has been observed that the some of C-factor values were going above the limiting value of C- fac to tor. Therefore, a correction factor of 0.6246 has been applied keep all the values between 0 and 1 (Fig. 5). Conservation practice conditions consist mainly in the method: of land use and tillage, and the agro technology. The amount ot soil loss from a given land is influenced by the land management practice adopted. The value of P ranges from 1.0 for up anc down cultivation to 0.25 for contour strip cropping of gentle slope. In case of USPED model, the agricultural area of catchment has been divided in different slope ranges and according to slope, the values of P-factor have been assigned (Fig. 6). For other land uses, standard values considering no conservation measures have been given. All the thematic maps have been generated in ILWIS GIS for USPED model. After multiplication of thematic maps R, K, LS, C and P-factors, the annual and seasonal soil loss maps giving spatial distribution of soil losses have been generated. It has been observed from the field visits that presently no conservation measures are beinc implemented in study area, P-factor map has been generatec using P-factor values for different land uses with no
RED is Band III and NIR is Band IV of IRS satellites (IRS ID and P6). For determination of C-factor map of the study area, the and the DVI image of LISS III data for the study area has been generated. The C-factor-map using equation 7 has been prepared a graph between NDVI and C-factor values has been plotted. From the analysis of graph, it has been observed that the some of C-factor values were going above the limiting value of C- fac to tor. Therefore, a correction factor of 0.6246 has been applied keep all the values between 0 and 1 (Fig. 5). Conservation practice conditions consist mainly in the method: of land use and tillage, and the agro technology. The amount ot soil loss from a given land is influenced by the land management practice adopted. The value of P ranges from 1.0 for up anc down cultivation to 0.25 for contour strip cropping of gentle slope. In case of USPED model, the agricultural area of catchment has been divided in different slope ranges and according to slope, the values of P-factor have been assigned (Fig. 6). For other land uses, standard values considering no conservation measures have been given. All the thematic maps have been generated in ILWIS GIS for USPED model. After multiplication of thematic maps R, K, LS, C and P-factors, the annual and seasonal soil loss maps giving spatial distribution of soil losses have been generated. It has been observed from the field visits that presently no conservation measures are beinc implemented in study area, P-factor map has been generatec using P-factor values for different land uses with no
conservation measures. The histogram of the resultant map has been used to estimate the rate of soil erosion from the catchment. The land use classification of the study area has been taken from IRS LISS IV data. Using spectral signatures of various land uses, sample sets for different land uses have been prepared. The maximum likelihood technique of classification has been used for generation of land use map of Kodar catchment. From the analysis, it has been observed that the Kodar catchment is an agriculture watershed covering nearly eighty percent of watershed with dense forest on the ridges only. Several smal water bodies in the form of village tanks have been found in Kodar catchment which is used for bathing, cattle, recreation and other house hold work. ora ST SOO eee oe—ornreaca The map of all the factors responsible for developing the USPED model is generated. The sediment flux is than calculated by multiplication of all the maps separately for both rill and sheet. The directional derivative of the sediment transport capacity is than computed using the command Mapfilter. Finally using the Map slicing command the Erosional Depositional map for sheet and rill is generated.
conservation measures. The histogram of the resultant map has been used to estimate the rate of soil erosion from the catchment. The land use classification of the study area has been taken from IRS LISS IV data. Using spectral signatures of various land uses, sample sets for different land uses have been prepared. The maximum likelihood technique of classification has been used for generation of land use map of Kodar catchment. From the analysis, it has been observed that the Kodar catchment is an agriculture watershed covering nearly eighty percent of watershed with dense forest on the ridges only. Several smal water bodies in the form of village tanks have been found in Kodar catchment which is used for bathing, cattle, recreation and other house hold work. ora ST SOO eee oe—ornreaca The map of all the factors responsible for developing the USPED model is generated. The sediment flux is than calculated by multiplication of all the maps separately for both rill and sheet. The directional derivative of the sediment transport capacity is than computed using the command Mapfilter. Finally using the Map slicing command the Erosional Depositional map for sheet and rill is generated.
Table 4. 1 Overall Prioritization of Sub Watershed
Table 4. 1 Overall Prioritization of Sub Watershed
Fig 7: Sheet and Rill Erosion and Deposition after map slicing in Kodar catchment.
Fig 7: Sheet and Rill Erosion and Deposition after map slicing in Kodar catchment.
Table 1: Distribution of G odavari river
Table 1: Distribution of G odavari river
Table 2: Tributaries of G odavari river The water resources potential in Godavari basin has been assessed to be 110.54 km*.The utilisable surface water is about 76.3 km? , the replenish able ground water is about 45 km’. There is a vast potential for irrigation development and hydropower generation in the basin. Prior to Independence only a few irrigation projects were constructed in Godavari basin. Important among these are Godavari delta system (with Dowlaiswaram weir as head works), Nizamsagar reservoir, Kadana dam and Pravara dam. After independence, under various five year plans a large number of multipurpose and irrigation projects have been taken up. Themost important among them are the Jaikwadi, Sriramsagar, Kadam, Upper Indravati, Singur and Godavari Barrage (by modernising the existing gated weir at Dowlaiswaram). Since the mid1960's, the Central Water Commission is conducting hydrological observations in Godavari basin. Hydrological observation stations have been established on main Godavari River as well as on all the important tributaries. During the year 2008-09,
Table 2: Tributaries of G odavari river The water resources potential in Godavari basin has been assessed to be 110.54 km*.The utilisable surface water is about 76.3 km? , the replenish able ground water is about 45 km’. There is a vast potential for irrigation development and hydropower generation in the basin. Prior to Independence only a few irrigation projects were constructed in Godavari basin. Important among these are Godavari delta system (with Dowlaiswaram weir as head works), Nizamsagar reservoir, Kadana dam and Pravara dam. After independence, under various five year plans a large number of multipurpose and irrigation projects have been taken up. Themost important among them are the Jaikwadi, Sriramsagar, Kadam, Upper Indravati, Singur and Godavari Barrage (by modernising the existing gated weir at Dowlaiswaram). Since the mid1960's, the Central Water Commission is conducting hydrological observations in Godavari basin. Hydrological observation stations have been established on main Godavari River as well as on all the important tributaries. During the year 2008-09,
Table 3: Management options 5. RESULTS OF WATER SHED MANAGEMENT MODEL
Table 3: Management options 5. RESULTS OF WATER SHED MANAGEMENT MODEL
The weather data such as daily rainfall, maximum and minimum temperature, morning and evening relative humidity, wind speed, pan evaporation and sun shine hours for a period of 5 years (2002-2006) were collected from the Agricultural Engineering Division, ICAR Research Complex for North East Hill Region and analyzed for making the model input files. The observed hydrological data such and daily sediment yield for the periods of five years (2002 to 2006) were collected from the Agricultural Engineering division, ICAR Research Complex for EH Region and analyzed for making model input file.
The weather data such as daily rainfall, maximum and minimum temperature, morning and evening relative humidity, wind speed, pan evaporation and sun shine hours for a period of 5 years (2002-2006) were collected from the Agricultural Engineering Division, ICAR Research Complex for North East Hill Region and analyzed for making the model input files. The observed hydrological data such and daily sediment yield for the periods of five years (2002 to 2006) were collected from the Agricultural Engineering division, ICAR Research Complex for EH Region and analyzed for making model input file.
The hydrological model was evaluated through a pair wise comparison of the observed and simulated data to determine the closeness of their match. Split sample calibration approach was adopted for model’s performance evaluation. Five-year’ data set pertaining to 2002 through 2006 was split into two parts. The data of 2002-2004 were used on trial-and-error procedure used in the study. Singh et al. (201 parameters namely; rill erodi hydraulic conductivity and sensitive in Meghalaya conditions. Therefore, only these for calibration. The calibrated parameters were considered values of these parameters re critica ported bility, interrill erodibility, effective for model calibration and that of 2005-2006 for model validation. The manual calibration based Soroos hian and Gupta, 1995) was 1) reported that soil related shear stress were most by Singh et al. (2011) were taken as base value and fine tuned for the Mawpun watershed. Figure-1: Location map of Mawpun watershed
The hydrological model was evaluated through a pair wise comparison of the observed and simulated data to determine the closeness of their match. Split sample calibration approach was adopted for model’s performance evaluation. Five-year’ data set pertaining to 2002 through 2006 was split into two parts. The data of 2002-2004 were used on trial-and-error procedure used in the study. Singh et al. (201 parameters namely; rill erodi hydraulic conductivity and sensitive in Meghalaya conditions. Therefore, only these for calibration. The calibrated parameters were considered values of these parameters re critica ported bility, interrill erodibility, effective for model calibration and that of 2005-2006 for model validation. The manual calibration based Soroos hian and Gupta, 1995) was 1) reported that soil related shear stress were most by Singh et al. (2011) were taken as base value and fine tuned for the Mawpun watershed. Figure-1: Location map of Mawpun watershed
Figure-2: Delineated hillslopes and channels of the Mawpun Watershed using WEPP model
Figure-2: Delineated hillslopes and channels of the Mawpun Watershed using WEPP model
Figure-4: Observed and simulated daily runoff hydrograph of Mawpun watershed during model calibration for the period of May to October 2003. Figure-3: Observed and simulated daily runoff hydrograph of Mawpun watershed during model calibration for the period of May to October 2002.
Figure-4: Observed and simulated daily runoff hydrograph of Mawpun watershed during model calibration for the period of May to October 2003. Figure-3: Observed and simulated daily runoff hydrograph of Mawpun watershed during model calibration for the period of May to October 2002.
The daily observed runoff and sediment yield hydrograp the calibration (May—October) 2002 to 2004 and the validation periods (May—October) 2005 and Figure-8 through Figure-12, respec the simulated values closely values for calibration periods through Figure-7 and It is observed that the trend of matches the trend of the measured and validation period sediment yield of hi model during simulations for cali s. However, the measured daily runo periods. Based on hs for 2006 are shown in Figure-3 tively. ff and gher magnitude is under-predicted by the bration periods and validation the goodness-of-fit test statistics (Table-1, Table-2, Table-3 and Table-4), it can be concluded that the WEPP model simulates daily runoff from the Mawpun watershed with acceptable accuracy.
The daily observed runoff and sediment yield hydrograp the calibration (May—October) 2002 to 2004 and the validation periods (May—October) 2005 and Figure-8 through Figure-12, respec the simulated values closely values for calibration periods through Figure-7 and It is observed that the trend of matches the trend of the measured and validation period sediment yield of hi model during simulations for cali s. However, the measured daily runo periods. Based on hs for 2006 are shown in Figure-3 tively. ff and gher magnitude is under-predicted by the bration periods and validation the goodness-of-fit test statistics (Table-1, Table-2, Table-3 and Table-4), it can be concluded that the WEPP model simulates daily runoff from the Mawpun watershed with acceptable accuracy.
Figure-7: Observed and simulated daily runoff hydrograph of Mawpun watershed during model validation for the period of May to October 2006. igure-8: Observed and simulated daily sediment yield of Mawpun watershed during model calibration for the period of May to October 2002.
Figure-7: Observed and simulated daily runoff hydrograph of Mawpun watershed during model validation for the period of May to October 2006. igure-8: Observed and simulated daily sediment yield of Mawpun watershed during model calibration for the period of May to October 2002.
Figure-6: Observed and simulated daily runoff hydrograph of Mawpun watershed during model validation for the period of May to October 2005. Figure-5: Observed and simulated daily runoff hydrograph of Mawpun watershed during model calibration for the period of May to October 2004.
Figure-6: Observed and simulated daily runoff hydrograph of Mawpun watershed during model validation for the period of May to October 2005. Figure-5: Observed and simulated daily runoff hydrograph of Mawpun watershed during model calibration for the period of May to October 2004.
Table-3: Goodness-of-fit statistics of observed and simulated daily sediment yield simulation during calibration periods 2002 through 900A (Mav to Octoher). ‘able-4: Goodness-of-fit statistics of observed and simulated daily sediment yield simulation during validation periods 2005 and 2006 (May to October).
Table-3: Goodness-of-fit statistics of observed and simulated daily sediment yield simulation during calibration periods 2002 through 900A (Mav to Octoher). ‘able-4: Goodness-of-fit statistics of observed and simulated daily sediment yield simulation during validation periods 2005 and 2006 (May to October).
Table-2: Goodness-of-fit statistics of observed and simulated daily runoff simulation during validation periods 2005 and 2006 (May to October).
Table-2: Goodness-of-fit statistics of observed and simulated daily runoff simulation during validation periods 2005 and 2006 (May to October).
Figure-11: Observed and simulated daily sediment yield of Mawpun watershed during model validation for the period of May to October 2005. Figure-10: Observed and simulated daily sediment yield of Mawpun watershed during model calibration for the period of May to October 2004. Figure-10: Observed and simulated daily sediment yield of Mawpun watershed during model calibration for the period of May to October 2004.
Figure-11: Observed and simulated daily sediment yield of Mawpun watershed during model validation for the period of May to October 2005. Figure-10: Observed and simulated daily sediment yield of Mawpun watershed during model calibration for the period of May to October 2004. Figure-10: Observed and simulated daily sediment yield of Mawpun watershed during model calibration for the period of May to October 2004.
Figure-9: Observed and simulated daily sediment yield of Mawpun watershed during model calibration for the period of May to October 2003.
Figure-9: Observed and simulated daily sediment yield of Mawpun watershed during model calibration for the period of May to October 2003.
4, CONCLUSSIONS:
4, CONCLUSSIONS:
Table: 1. Soil texture of Nagwan watershed
Table: 1. Soil texture of Nagwan watershed
undulating and maximum and the minimum elevations of the area are 667 m and 560 m, respectively. The area experiences sub-humid sub-tropical monsoon type of climate, characterized by hot summers (40°C) and mild winters (4°C). The watershed receives an average annual rainfall of 1256 mm, out of which more than 80% rainfall contributes during monsoon season (June—October). The average storm intensity, by considering storms of more than 30 min duration, is about 10 cm/hr. The daily mean relative humidity varies from a minimum of 40% in the month of April to a maximum of 85% in the month of July. The main agricultural crops grown during kharif season are paddy and maize and in rabi season are wheat, gram and mustard. The agriculture is mostly rainfed as only 20% irrigation is available in the area through sources other than rain and the cropping intensity is also quite low at 98%. The irrigation is received mainly by wells. Prevalence of conventional cultivation practices, characterized by conventional tillage or no tillage; low fertilizer/manure consumption and local varieties of the crops is mainly responsible for the low crop productivity in the area. All this information on the test area was obtained through secondary sources such as Directorate of Economics and Statistics, Ministry of Agriculture; Directorate of Census (data Center); DVC, Hazaribagh officials and Sadar block office of Hazaribagh district.
undulating and maximum and the minimum elevations of the area are 667 m and 560 m, respectively. The area experiences sub-humid sub-tropical monsoon type of climate, characterized by hot summers (40°C) and mild winters (4°C). The watershed receives an average annual rainfall of 1256 mm, out of which more than 80% rainfall contributes during monsoon season (June—October). The average storm intensity, by considering storms of more than 30 min duration, is about 10 cm/hr. The daily mean relative humidity varies from a minimum of 40% in the month of April to a maximum of 85% in the month of July. The main agricultural crops grown during kharif season are paddy and maize and in rabi season are wheat, gram and mustard. The agriculture is mostly rainfed as only 20% irrigation is available in the area through sources other than rain and the cropping intensity is also quite low at 98%. The irrigation is received mainly by wells. Prevalence of conventional cultivation practices, characterized by conventional tillage or no tillage; low fertilizer/manure consumption and local varieties of the crops is mainly responsible for the low crop productivity in the area. All this information on the test area was obtained through secondary sources such as Directorate of Economics and Statistics, Ministry of Agriculture; Directorate of Census (data Center); DVC, Hazaribagh officials and Sadar block office of Hazaribagh district.
4, 2. Final priority map
4, 2. Final priority map
Figure 4. SPI map of the Nagwan watershed Figure 5. Soil erodibility map of Nagwan watershed. The soil map of the catchment area was used to prepare the digitized soil map. The predominant soil textural classes were clay loam and silty loam type, found in the watershed. Soil group of the study area shown in Figure 5 and soil erodibility value given by Table 1.
Figure 4. SPI map of the Nagwan watershed Figure 5. Soil erodibility map of Nagwan watershed. The soil map of the catchment area was used to prepare the digitized soil map. The predominant soil textural classes were clay loam and silty loam type, found in the watershed. Soil group of the study area shown in Figure 5 and soil erodibility value given by Table 1.
Supervised classification techniques with maximum likelihood classifier were used for the land use classification with average accuracy 89.98 %, average reliability 85.09 % and overall Accuracy 90.78 %. Seven major land use categories namely agriculture land (with & without crop and grass land), barren land, builtup land, dense forest, open forest, scrubs, water bodies were identified and then assign CN value . The land use map of the watershed is shown in Figure 7 and the land use details are shown in Table 2. Table 2. Land use pattern of nagwan watershed.
Supervised classification techniques with maximum likelihood classifier were used for the land use classification with average accuracy 89.98 %, average reliability 85.09 % and overall Accuracy 90.78 %. Seven major land use categories namely agriculture land (with & without crop and grass land), barren land, builtup land, dense forest, open forest, scrubs, water bodies were identified and then assign CN value . The land use map of the watershed is shown in Figure 7 and the land use details are shown in Table 2. Table 2. Land use pattern of nagwan watershed.
5. CONCLUSIONS Table 3. Final priority of Nagwan watershed
5. CONCLUSIONS Table 3. Final priority of Nagwan watershed
Figure 8. Final priority map of the Nagwan watershed
Figure 8. Final priority map of the Nagwan watershed
Figure 1: Command Area of Nashik Left Bank Canal [NLBC Source: Nasik Irrigation Departmen Nashik district of Maharashtra state is one of the leading districts in the field of agriculture. The new experiments and use of advanced technology have empowered the farmers to increase export of agro based products. Gangapur Dam is most important and the oldest earthen dam in Nashik. It was constructed on Godavari River. Two canals namely Nashik [NRBC] and Nashik Left Bank Canal [NLBC] dam. The GRBC is closed due to high civiliz The present paper focuses on the case study of Canal of Nashik district, Maharashtra state w 20° 38’ Lattitude and 73° 19’ Longitude. The r is 64 km which is running open to atmosphere. take off ation in ashik L each of t in 1965 Right Bank Canal from the the area. eft Bank hich is located at his canal The alignment of canal and its command area is shown in Figure 1.
Figure 1: Command Area of Nashik Left Bank Canal [NLBC Source: Nasik Irrigation Departmen Nashik district of Maharashtra state is one of the leading districts in the field of agriculture. The new experiments and use of advanced technology have empowered the farmers to increase export of agro based products. Gangapur Dam is most important and the oldest earthen dam in Nashik. It was constructed on Godavari River. Two canals namely Nashik [NRBC] and Nashik Left Bank Canal [NLBC] dam. The GRBC is closed due to high civiliz The present paper focuses on the case study of Canal of Nashik district, Maharashtra state w 20° 38’ Lattitude and 73° 19’ Longitude. The r is 64 km which is running open to atmosphere. take off ation in ashik L each of t in 1965 Right Bank Canal from the the area. eft Bank hich is located at his canal The alignment of canal and its command area is shown in Figure 1.
TABLE II: Crop Pattern and Crop Water Requirement Details for NLBC [6]
TABLE II: Crop Pattern and Crop Water Requirement Details for NLBC [6]
It can be seen from the losses for the NLBC. N Table III, that there are huge conveyance LBC has yearly conveyance loss of 15.55 Mm*, in which rabi season has the conveyance loss of 10.69 Mm° and conveyance loss of season. It is clear from NLBC is more than 55 4.862 Mm? has observed in hot weather the Table III that the conveyance loss for %, which is very huge. The efficiency is calculated from the details of conveyance losses for rabi and hot weather season. The e season wherein 44.71% fficiency of NLBC is 43.02 % for rabi for hot weather season.
It can be seen from the losses for the NLBC. N Table III, that there are huge conveyance LBC has yearly conveyance loss of 15.55 Mm*, in which rabi season has the conveyance loss of 10.69 Mm° and conveyance loss of season. It is clear from NLBC is more than 55 4.862 Mm? has observed in hot weather the Table III that the conveyance loss for %, which is very huge. The efficiency is calculated from the details of conveyance losses for rabi and hot weather season. The e season wherein 44.71% fficiency of NLBC is 43.02 % for rabi for hot weather season.
Table III: Actual Details of Conveyance Losses and Efficiency for NLBC [6]
Table III: Actual Details of Conveyance Losses and Efficiency for NLBC [6]
Figure 2: Closed Circular Conduit [GRP] @ 1.82m
Figure 2: Closed Circular Conduit [GRP] @ 1.82m
2) For an open canal flow in NLBC, Figure 3: Open canal cross-section of NLBC
2) For an open canal flow in NLBC, Figure 3: Open canal cross-section of NLBC
Due to conversion of OCI into CCI, conveyance losses of 15.55 Mm’ are saved, which can be used for improving the duty of water. The following table IV shows the details of improvement in the duty. 2.8 Saving in water of NLBC by CCI
Due to conversion of OCI into CCI, conveyance losses of 15.55 Mm’ are saved, which can be used for improving the duty of water. The following table IV shows the details of improvement in the duty. 2.8 Saving in water of NLBC by CCI
Table1. Equation and Measured data required for ET, prediction for various methods.
Table1. Equation and Measured data required for ET, prediction for various methods.
T = Temperature, SS = Sun shine hours, RH = Relative Humidity, W = Wind, E = Evaporation, R,= Solar Radiation, Ry = Net Radiation.. PET = Potential evapotranspiration (mm day”), T,= Mean monthly temperature in ° C, R= Mean monthly Relative humidity, rf = ratio of monthly to annual radiation. CU= Annual consumptive use (inches), Hg= Effective heat, in degree days above 32° F. e = unadjusted potential ET cm/month)( month of 30 days each and 12 hrs daytime), t= mean air temperature(° C), I = annual or seasonal heat index, a= an empirical exponent. f= monthly consumptive use factor, T = mean monthly temperature (° F), p = monthly per cent of total daytime hrs of the year. ET= Seasonal crop water requirements (inches), k= monthly Blaney Cnddle coefficient , & = monthly consumptive use factor , = mean temperature for month i, (° F). ET,= Reference eepaneneyiaitan (mm day”), K,= Pan coefficient, Eyan = Pan evaporation (mm day"'). @, b = climatic calibration coefficients , = mean daily percentage of total annual daytime hours, T= mean daily temperature in ° C over the month considered. © = adjustment factor depending on mean humidity and daytime wind conditions, W = function of the temperature & altitude, R,= solar radiation (mm day”). @y = coefficient depending mean relative humidity, R,= solar radiation (MJ m” day”), A = latent heat of vaporization (MJ kg
T = Temperature, SS = Sun shine hours, RH = Relative Humidity, W = Wind, E = Evaporation, R,= Solar Radiation, Ry = Net Radiation.. PET = Potential evapotranspiration (mm day”), T,= Mean monthly temperature in ° C, R= Mean monthly Relative humidity, rf = ratio of monthly to annual radiation. CU= Annual consumptive use (inches), Hg= Effective heat, in degree days above 32° F. e = unadjusted potential ET cm/month)( month of 30 days each and 12 hrs daytime), t= mean air temperature(° C), I = annual or seasonal heat index, a= an empirical exponent. f= monthly consumptive use factor, T = mean monthly temperature (° F), p = monthly per cent of total daytime hrs of the year. ET= Seasonal crop water requirements (inches), k= monthly Blaney Cnddle coefficient , & = monthly consumptive use factor , = mean temperature for month i, (° F). ET,= Reference eepaneneyiaitan (mm day”), K,= Pan coefficient, Eyan = Pan evaporation (mm day"'). @, b = climatic calibration coefficients , = mean daily percentage of total annual daytime hours, T= mean daily temperature in ° C over the month considered. © = adjustment factor depending on mean humidity and daytime wind conditions, W = function of the temperature & altitude, R,= solar radiation (mm day”). @y = coefficient depending mean relative humidity, R,= solar radiation (MJ m” day”), A = latent heat of vaporization (MJ kg
Table 2. Values for C, and Cain Equation for the FAO-PM and ASCE-EWRI standardized PM equations (as reported in Allen et al., (1998) and ASCE-EWRI (2005)) 7.0 DISCUSSION
Table 2. Values for C, and Cain Equation for the FAO-PM and ASCE-EWRI standardized PM equations (as reported in Allen et al., (1998) and ASCE-EWRI (2005)) 7.0 DISCUSSION
? Ground level 3 USDA=United States Dep’t of Agriculture Table 1. Soil physical characteristics of the experimental plot
? Ground level 3 USDA=United States Dep’t of Agriculture Table 1. Soil physical characteristics of the experimental plot
ee Lad sore. Figure-1. L Lysimeter set up ) details (All dimensions are in mm) Where ET, for standard conditions assumes hypothetical conditions where there is no short of water, actively growing disease free crops in an extensive area. However, this imaginary condition seldom occurs in a practical field condition. Detailed procedures to estimate K, and attached parameters are given by FAO paper 56 (Allen et al., 1998), Rallo et al., 2012) Runoff component of the water balance in lysimeter studies is often neglected since it is either minimal or controlled in such a way that there exists no run-on and run-off. If the top level of the lysimeter is constructed a slightly above the ground elevation, surface water inflow or outflow could be eliminated. However, in certain torrential storms it is advisable to consider runoff from a lysimeter since water could overflow the lysimeter rim. Run- on in our experimental site did not occur since the field surrounding is constructed of earthen bunds covered with plastic sheets. Therefore, surface runoff in our experimental field has been considered only when rainfall magnitude overflows above the lysimeter rim level according to the following algorithm:
ee Lad sore. Figure-1. L Lysimeter set up ) details (All dimensions are in mm) Where ET, for standard conditions assumes hypothetical conditions where there is no short of water, actively growing disease free crops in an extensive area. However, this imaginary condition seldom occurs in a practical field condition. Detailed procedures to estimate K, and attached parameters are given by FAO paper 56 (Allen et al., 1998), Rallo et al., 2012) Runoff component of the water balance in lysimeter studies is often neglected since it is either minimal or controlled in such a way that there exists no run-on and run-off. If the top level of the lysimeter is constructed a slightly above the ground elevation, surface water inflow or outflow could be eliminated. However, in certain torrential storms it is advisable to consider runoff from a lysimeter since water could overflow the lysimeter rim. Run- on in our experimental site did not occur since the field surrounding is constructed of earthen bunds covered with plastic sheets. Therefore, surface runoff in our experimental field has been considered only when rainfall magnitude overflows above the lysimeter rim level according to the following algorithm:
Figure 2. Deep percolation at lysimeter 1(L1) for day (broken) and night times (solid) lines
Figure 2. Deep percolation at lysimeter 1(L1) for day (broken) and night times (solid) lines
Figure 4. Input (Irrigation-IRR and Rainfall) water and deep percolation
Figure 4. Input (Irrigation-IRR and Rainfall) water and deep percolation
L1=Lysimeter 1; L2 Slysimeter 2; cum = cumulative; ETA =A ctual Evapotranspiration
L1=Lysimeter 1; L2 Slysimeter 2; cum = cumulative; ETA =A ctual Evapotranspiration
Table 2. Statistical parameters for measured and computed deep percolation
Table 2. Statistical parameters for measured and computed deep percolation
The statistics shows that there occurs a good agreement between model predicted and measured values of deep percolation when applied on extended time steps. Thus we deduce from these results that locally constructed drainage type lysimeters could provide detailed information in characterizing deep percolation phenomena in an _ irrigated farm. Deep _ percolation measurements could be undertaken in the time intervals of 5 to 10 days in further investigation of deep percolation researches unless drainage outlets are installed at very shallow depths which may, however, not be practicable.
The statistics shows that there occurs a good agreement between model predicted and measured values of deep percolation when applied on extended time steps. Thus we deduce from these results that locally constructed drainage type lysimeters could provide detailed information in characterizing deep percolation phenomena in an _ irrigated farm. Deep _ percolation measurements could be undertaken in the time intervals of 5 to 10 days in further investigation of deep percolation researches unless drainage outlets are installed at very shallow depths which may, however, not be practicable.
The overall share of deep percolation in the water balance is quite high. We observed that there occurred above 80% of the volume of water input goes as deep percolation. The total amount of input water during the growing season was 3078.1mm and the total measured deep percolation was 2506.5mm (fig. 5) while the model computed deep percolation was 2646.60mm.
The overall share of deep percolation in the water balance is quite high. We observed that there occurred above 80% of the volume of water input goes as deep percolation. The total amount of input water during the growing season was 3078.1mm and the total measured deep percolation was 2506.5mm (fig. 5) while the model computed deep percolation was 2646.60mm.
Simulation For determination of suitable sites for construction of reservoirs in study area, one location has been identified selected on the main river. To develop drought mitigation strategies through scientific planning of water resources and management, MIKE BASIN model has been developed. In the present study, the irrigation management in the command of Bearma basin has been carried out from reservoir releases; therefore, reservoir, irrigation nodes and transfer of water through channel have been specified.
Simulation For determination of suitable sites for construction of reservoirs in study area, one location has been identified selected on the main river. To develop drought mitigation strategies through scientific planning of water resources and management, MIKE BASIN model has been developed. In the present study, the irrigation management in the command of Bearma basin has been carried out from reservoir releases; therefore, reservoir, irrigation nodes and transfer of water through channel have been specified.
The cone formula (Murthy, 1968) has been used to compute the capacities between two successive levels, which in turn gave the cumulative capacities at different levels of reservoir. The area elevation capacity (AEC) table for reservoir RS has been given below in Table 3. The total command area of each user with respect to total command area in each sub-basin is given in table 4, The water demands for soybean crop for each of the water users on ten daily basis is given in Table 5. Table 2. Reservoir characteristics for reservoir RS
The cone formula (Murthy, 1968) has been used to compute the capacities between two successive levels, which in turn gave the cumulative capacities at different levels of reservoir. The area elevation capacity (AEC) table for reservoir RS has been given below in Table 3. The total command area of each user with respect to total command area in each sub-basin is given in table 4, The water demands for soybean crop for each of the water users on ten daily basis is given in Table 5. Table 2. Reservoir characteristics for reservoir RS
Table 3. Area Elevation Capacity Table for reservoir RS
Table 3. Area Elevation Capacity Table for reservoir RS
Table 4. Total command area of each water user with respect to total command area in each sub-basin Table 5. Water demands of all users for soybean crop in Bearma basin in different ten daily period
Table 4. Total command area of each water user with respect to total command area in each sub-basin Table 5. Water demands of all users for soybean crop in Bearma basin in different ten daily period
ble 7. Water used-demand deficit for user WU7 directly connected through the reservoir RS for drought year
ble 7. Water used-demand deficit for user WU7 directly connected through the reservoir RS for drought year
rrom the results obtained, 1t 1s concluded that the demands are not fully satisfied and there were demand deficit under both scenarios for drought year. In second scenario one reservoir was planned and water was drawn from the reservoir as well as from the river and the analysis performed. The model was run to see the performance of the model and its ability to cope up during droughts. The model run in Scenario-1 shows that the demand deficits have increased significantly in all of the sub-basins as the supply in the river was very less. The maximum deficit was observed in sub-basin SW7.This indicates that the gravity of the situation magnifies as seen by the abrupt increase in the demand deficit in a drought year. Subsequently, the planning was carried out with the provision of one reservoir and model run in a drought years. Here it can be observed that the demand deficit has reduced considerably to 42.41 MCM which was aiming to achieve under such study. This study clearly demonstrates that planning for drought mitigation can be carried out by constructing small reservoir in the sub-basin to cater the increased demand during periods of intermittent dry spells during drought years.
rrom the results obtained, 1t 1s concluded that the demands are not fully satisfied and there were demand deficit under both scenarios for drought year. In second scenario one reservoir was planned and water was drawn from the reservoir as well as from the river and the analysis performed. The model was run to see the performance of the model and its ability to cope up during droughts. The model run in Scenario-1 shows that the demand deficits have increased significantly in all of the sub-basins as the supply in the river was very less. The maximum deficit was observed in sub-basin SW7.This indicates that the gravity of the situation magnifies as seen by the abrupt increase in the demand deficit in a drought year. Subsequently, the planning was carried out with the provision of one reservoir and model run in a drought years. Here it can be observed that the demand deficit has reduced considerably to 42.41 MCM which was aiming to achieve under such study. This study clearly demonstrates that planning for drought mitigation can be carried out by constructing small reservoir in the sub-basin to cater the increased demand during periods of intermittent dry spells during drought years.
Sardar Sarovar Project (SSP) is one of the major irrigation projects of Gujarat state of India. Sardar Sarovar (Narmada) Project Phase -IIA covers Culturable Command Area (C.C.A of 20, 42, 39 Ha) between Mahi and Surashtra Branch Canal off— taking from Narmada Main Canal. The study area is selected in agro-climatic zone no.6 of SSP Command area which is situated at village Rampur.
Sardar Sarovar Project (SSP) is one of the major irrigation projects of Gujarat state of India. Sardar Sarovar (Narmada) Project Phase -IIA covers Culturable Command Area (C.C.A of 20, 42, 39 Ha) between Mahi and Surashtra Branch Canal off— taking from Narmada Main Canal. The study area is selected in agro-climatic zone no.6 of SSP Command area which is situated at village Rampur.
Table 1 Design of PINS Pipe (8hrs Power Supply)
Table 1 Design of PINS Pipe (8hrs Power Supply)
Figure 3: Schematic Diagram of PINS (24 hrs Power Supply) (Source: SSNNL, Gandhinagar) 6. CONCLUSIONS
Figure 3: Schematic Diagram of PINS (24 hrs Power Supply) (Source: SSNNL, Gandhinagar) 6. CONCLUSIONS
Figure 1. Erosion rates of Punegaon Catchment
Figure 1. Erosion rates of Punegaon Catchment
Figure 2. Erosion causing parameters R, K, L, S, C, P of Punegaon reservoir catchment
Figure 2. Erosion causing parameters R, K, L, S, C, P of Punegaon reservoir catchment
Himalayan ranges in the North-West Bhutan at an elevation of about 7000m and join at Punatsangchhu. The Punatsan about 320 km from its source with Brahmaputra in Assam. I about 250 km. The catchment Punakha to form the river gchhu River has a total length of in Bhutan to its confluence point ts course in Bhutan has a length of area of Punatsangchhu river upto dam site extends from latitude 27°15°N to 28°30’N and longitude 89°15’ E to 90°30’ the project site is 6390 km? o E. The total Catchment area upto ut of which 3115 km? is snowfed area and the remaining 3275 km’ is rainfed area. Figure 1. The location plan for Punatsangchhu-I H. E. Project is presented in.
Himalayan ranges in the North-West Bhutan at an elevation of about 7000m and join at Punatsangchhu. The Punatsan about 320 km from its source with Brahmaputra in Assam. I about 250 km. The catchment Punakha to form the river gchhu River has a total length of in Bhutan to its confluence point ts course in Bhutan has a length of area of Punatsangchhu river upto dam site extends from latitude 27°15°N to 28°30’N and longitude 89°15’ E to 90°30’ the project site is 6390 km? o E. The total Catchment area upto ut of which 3115 km? is snowfed area and the remaining 3275 km’ is rainfed area. Figure 1. The location plan for Punatsangchhu-I H. E. Project is presented in.
For hydraulic computations, the roughness coefficient was simulated by Manning’s ‘n’. In the present study, steady flow computations were carried out for calibrating the model by adjusting the ‘n’ value. Water levels observed during flood on 26th May 2009 and 3rd July 2010 were used for calibration of the model. Water levels along the river reach was matched with the model results. The results are presented in Fig. 3. Manning’s ‘n’ was assumed as 0.048 for the channel portion.
For hydraulic computations, the roughness coefficient was simulated by Manning’s ‘n’. In the present study, steady flow computations were carried out for calibrating the model by adjusting the ‘n’ value. Water levels observed during flood on 26th May 2009 and 3rd July 2010 were used for calibration of the model. Water levels along the river reach was matched with the model results. The results are presented in Fig. 3. Manning’s ‘n’ was assumed as 0.048 for the channel portion.
Figure 5. Sediment rating curve Suspended sediment concentration along with corresponding discharge observations were made at the W angdue rapid gauging site for the period from July 1992 to July 2009. Using the above data sediment rating curve was developed and the same is presented as Fig.5. The sediment rating curve was verified with the sediment data available from gauge site at 1.5 km upstream of dam axis established by M/s WAPCOS. Sediment data from January 2010 to June 2010 was available and used for verification.
Figure 5. Sediment rating curve Suspended sediment concentration along with corresponding discharge observations were made at the W angdue rapid gauging site for the period from July 1992 to July 2009. Using the above data sediment rating curve was developed and the same is presented as Fig.5. The sediment rating curve was verified with the sediment data available from gauge site at 1.5 km upstream of dam axis established by M/s WAPCOS. Sediment data from January 2010 to June 2010 was available and used for verification.
material gradation curves (Fig.6) at five locations upstream of dam axis were available and hence used in the simulations.
material gradation curves (Fig.6) at five locations upstream of dam axis were available and hence used in the simulations.
The following flow chart describes the methodology in brief.
The following flow chart describes the methodology in brief.
Interpolation of Water spread area (WSA) at Regular Interval
Interpolation of Water spread area (WSA) at Regular Interval
Ah LUV GUY 1 ULL (it? 1th) A, and A, are areas of reservoir water spread at elevation h, and hy.
Ah LUV GUY 1 ULL (it? 1th) A, and A, are areas of reservoir water spread at elevation h, and hy.
Figure -4Graph showing comparison of Live storage capacity as per different surveys Figure -3 Graph between R.L. and respective water spread areas (estimated from satell
Figure -4Graph showing comparison of Live storage capacity as per different surveys Figure -3 Graph between R.L. and respective water spread areas (estimated from satell
Figure -2 Satellite images of Nath Sagar Reservoir of different dates in order of reducing water levelimage
Figure -2 Satellite images of Nath Sagar Reservoir of different dates in order of reducing water levelimage
Revised content table for the live storage zone has been prepared at 0.1 m contour interval which can be of great use for the reservoir management authority during reservoir operation. Revised Live storage capacity of Nath Sagar reservoir between M.D.D.L. 455.524 m and FRL 463.906 m is estimated to be 1991.987 Mm’ for the year 2012-13 as against Original Live storage capacity of 2170.935 Mm° between these levels, with a loss of 178.948 Mm? (8.24 %). The average annual percent loss in live storage for the period of 36 years between 1976 and 2012 works out to 0.23% which is not severe. Sedimentation in the dead storage zone i.e. below M.D.D.L. 455.52 m could not be estimated by remote sensing method. For this, a hydrographic survey is necessary. 1. INTRODUCTION: Table -3 showing the comparison of original live storage capacity with that of year 1996 survey and year 2012-13 survey
Revised content table for the live storage zone has been prepared at 0.1 m contour interval which can be of great use for the reservoir management authority during reservoir operation. Revised Live storage capacity of Nath Sagar reservoir between M.D.D.L. 455.524 m and FRL 463.906 m is estimated to be 1991.987 Mm’ for the year 2012-13 as against Original Live storage capacity of 2170.935 Mm° between these levels, with a loss of 178.948 Mm? (8.24 %). The average annual percent loss in live storage for the period of 36 years between 1976 and 2012 works out to 0.23% which is not severe. Sedimentation in the dead storage zone i.e. below M.D.D.L. 455.52 m could not be estimated by remote sensing method. For this, a hydrographic survey is necessary. 1. INTRODUCTION: Table -3 showing the comparison of original live storage capacity with that of year 1996 survey and year 2012-13 survey
Figure 1. Daily Rainfall Data of the series with 1st difference
Figure 1. Daily Rainfall Data of the series with 1st difference
Figure 2. Decomposition of the D aily Rainfall Data
Figure 2. Decomposition of the D aily Rainfall Data
Figure 5. The forecast of the rainfall data using auto.arima R function Figure 4. The ACF and PACF of the Rainfall Data
Figure 5. The forecast of the rainfall data using auto.arima R function Figure 4. The ACF and PACF of the Rainfall Data
Figure 9. Training data and model output data Figure 7. Original data set is broken into wavelets
Figure 9. Training data and model output data Figure 7. Original data set is broken into wavelets
Figure 8. Forecast & correlation coeff.
Figure 8. Forecast & correlation coeff.
Table 1. Staistics of WNN and ANN for Calibration and Validation period
Table 1. Staistics of WNN and ANN for Calibration and Validation period
Table 2. Statistical moments of the observed and modeled inflow during validation period
Table 2. Statistical moments of the observed and modeled inflow during validation period
Figure 2. Wavelet based multilayer perceptron (MLP) neural network Figure 1. Diagram of multiresolution analysis of signal REFERENCES:
Figure 2. Wavelet based multilayer perceptron (MLP) neural network Figure 1. Diagram of multiresolution analysis of signal REFERENCES:
Figure 3. Distribution of error plots along the magnitude of flow during validation period i, Cannas, B., Fanni, A., Sias, G., Tronei, S., Zedda, M.K., 2005. River flow forecasting using neural networks and wavelet analysis. In: EGU 2005, European Geosciences Union, Vienna, Austria, 24-29 April, 2005.
Figure 3. Distribution of error plots along the magnitude of flow during validation period i, Cannas, B., Fanni, A., Sias, G., Tronei, S., Zedda, M.K., 2005. River flow forecasting using neural networks and wavelet analysis. In: EGU 2005, European Geosciences Union, Vienna, Austria, 24-29 April, 2005.
Where B provides the basis for desired shape of membership function (B=1 for linear; B >1 and B <1 for nonlinear) and Zy, Z, are maximum and minimum acceptable levels of the objective. Introducing a new variable A, the problem in multiobjective environment is stated as (Raju and Nagesh Kumar, 2014): The inertia factor is used for refining the swarm’s behavior towards the magnitude of the search domain. The velocity of the particles is directly proportional to inertia; larger values of inertia increases the search domain while smaller values of inertia narrow the scope of search. The velocity of all the particles significantly reduces as the iteration count increases resulting in initial rapid search for optima in the beginning followed by a convergence towards the end. Number of iterations was specified as termination criteria.
Where B provides the basis for desired shape of membership function (B=1 for linear; B >1 and B <1 for nonlinear) and Zy, Z, are maximum and minimum acceptable levels of the objective. Introducing a new variable A, the problem in multiobjective environment is stated as (Raju and Nagesh Kumar, 2014): The inertia factor is used for refining the swarm’s behavior towards the magnitude of the search domain. The velocity of the particles is directly proportional to inertia; larger values of inertia increases the search domain while smaller values of inertia narrow the scope of search. The velocity of all the particles significantly reduces as the iteration count increases resulting in initial rapid search for optima in the beginning followed by a convergence towards the end. Number of iterations was specified as termination criteria.
Figure 1. Nonlinear Membership Function complex project has three storage reservoirs, Panshet, Warasgaon, and Temghar with the gross storage of 871Mm°, New Mutha Right Bank Canal (NMRBC) serving 62146 ha command area (of length 202 km), Janai-Sirsai Lift Irrigation Scheme (JSLIS) (14080 ha command area), Purandar Lift Irrigation Scheme (PLIS) (25100 ha command area) (refer Fig 2.) The Pune city generates around 451 Million Liter per Day (MLD) of sewage. Pimpri-Chinchwad city generates 287 MLD of sewage (Tirthakar et al., 2009). At present, 68% of the total sewage generated by PMC and 63% sewage generated by Pimpri Chinchwad Municipal Corporation (PCMC) is treated before being discharged into the rivers. This water is proposed to be used for irrigation through pumping in existing canal system and an independent lift irrigation scheme, PLIS. The model developed adopts conjunctive use concept; in addition it also uses treated waste water as a supplement to irrigation water. QAIMATURMATICAT MONET INC
Figure 1. Nonlinear Membership Function complex project has three storage reservoirs, Panshet, Warasgaon, and Temghar with the gross storage of 871Mm°, New Mutha Right Bank Canal (NMRBC) serving 62146 ha command area (of length 202 km), Janai-Sirsai Lift Irrigation Scheme (JSLIS) (14080 ha command area), Purandar Lift Irrigation Scheme (PLIS) (25100 ha command area) (refer Fig 2.) The Pune city generates around 451 Million Liter per Day (MLD) of sewage. Pimpri-Chinchwad city generates 287 MLD of sewage (Tirthakar et al., 2009). At present, 68% of the total sewage generated by PMC and 63% sewage generated by Pimpri Chinchwad Municipal Corporation (PCMC) is treated before being discharged into the rivers. This water is proposed to be used for irrigation through pumping in existing canal system and an independent lift irrigation scheme, PLIS. The model developed adopts conjunctive use concept; in addition it also uses treated waste water as a supplement to irrigation water. QAIMATURMATICAT MONET INC
Figure.5. PLIS Cropping Pattern
Figure.5. PLIS Cropping Pattern
Figure 6. Monthly Irrigation Water Use Policy Figure 7. Monthly Groundwater Use Policy Figure 8.Monthly Treated W aste W ater Use Policy
Figure 6. Monthly Irrigation Water Use Policy Figure 7. Monthly Groundwater Use Policy Figure 8.Monthly Treated W aste W ater Use Policy
. Chokka Rao Godavari Lift Irrigation scheme has been envisaged to lift 14m°/s of Godavari water to EL. 308 m & partly up to EL. 540 m to irrigate approximately 2.85 Lacks Acres of Command area. Project Envisages Lifting of water from Godavari River at Gangaram village, Eturnagaram, Warangal District in Telangana state in 7 stages with for water conductor system 200.340 Kms approximately, long steel pipelines connecting 8 Nos. of existing tanks (i) intake to Dharmasagar via. Bhimghanpur, Salivagu (ii) from R.S. Ghanpur to Chittakodur via. Aswaraopalli, (iii) from Dharmasagar to Tapashpally via., Gandiramaram, Bommakur. The hydraulic details of the lift irrigation project are provided in tablel. The transmission line alignment and steady state hydraulic gradient line for the pumping discharge of 14m*/s is shown in Fig.2.
. Chokka Rao Godavari Lift Irrigation scheme has been envisaged to lift 14m°/s of Godavari water to EL. 308 m & partly up to EL. 540 m to irrigate approximately 2.85 Lacks Acres of Command area. Project Envisages Lifting of water from Godavari River at Gangaram village, Eturnagaram, Warangal District in Telangana state in 7 stages with for water conductor system 200.340 Kms approximately, long steel pipelines connecting 8 Nos. of existing tanks (i) intake to Dharmasagar via. Bhimghanpur, Salivagu (ii) from R.S. Ghanpur to Chittakodur via. Aswaraopalli, (iii) from Dharmasagar to Tapashpally via., Gandiramaram, Bommakur. The hydraulic details of the lift irrigation project are provided in tablel. The transmission line alignment and steady state hydraulic gradient line for the pumping discharge of 14m*/s is shown in Fig.2.
Fig.1. Schematic sketch of Air Vessel (Source: Stephension, 2002) Where, Ar ~ neaad drop i Mm, VY ~ HOW fate ih M/s. Lille resistance (R) is correspond to the orifice sizes (and pipe diameters) that are provided for inflow and outflow from pipe system to air vessel. Inflow resistance governs the rate of flow into the pipe system where as outflow resistance governs the rate of flow into the air vessel. Value of polytrophic exponent or the gas expansion constant has significant influence on the required air vessel size. The air vessel design problem can be stated as a constrained optimization problem in which objective is to find total volume of air vessel and initial air volume and the constraints to be considered as range of negative and positive water hammer pressures. For a_ typical cylindrical and vertically mounted vessel the design variables are vesse volume C =hS, and initial air volume, which can be given by the initial height of air into the vessel(Zp). The typical sketch of air vessel is shown in Fig.1. The following physical, functional and fluid parameters dictate the size (volume) of air vessel for a given problem. (i) Physical parameters of the pipe: static or geometric elevation to overcome (H),Length(L), diameter(D), friction factor(f); (ii) Fluid-pipe mixed parameters: celerity of the pressure waves (a); (iii) Parameters related to the steady- state: velocity(V ,); (iv)Functional parameters that represent the extreme piezometric heads or pressures desirable at the upstream without loss of generality) end: H,,,, and H,;,. There is no explicit and direct relationship between these parameters and the size of air vessel required. Sever approaches based on different tests and heuristic criteria can be found in the iterature. However, professionals find that air vessel optimization is usually a trial and error process, generally performed the transient simulation using surge software that, eventually, provides the minimum air vessel volume required so that the maximum and minimum developed pressures at the pumping station do not exceed Hy» and Hin respectively. A neural network that encapsulates this unknown trial and error process from a relevant number of cases, already solved, that allows to directly obtaining the minimum air vessel volume
Fig.1. Schematic sketch of Air Vessel (Source: Stephension, 2002) Where, Ar ~ neaad drop i Mm, VY ~ HOW fate ih M/s. Lille resistance (R) is correspond to the orifice sizes (and pipe diameters) that are provided for inflow and outflow from pipe system to air vessel. Inflow resistance governs the rate of flow into the pipe system where as outflow resistance governs the rate of flow into the air vessel. Value of polytrophic exponent or the gas expansion constant has significant influence on the required air vessel size. The air vessel design problem can be stated as a constrained optimization problem in which objective is to find total volume of air vessel and initial air volume and the constraints to be considered as range of negative and positive water hammer pressures. For a_ typical cylindrical and vertically mounted vessel the design variables are vesse volume C =hS, and initial air volume, which can be given by the initial height of air into the vessel(Zp). The typical sketch of air vessel is shown in Fig.1. The following physical, functional and fluid parameters dictate the size (volume) of air vessel for a given problem. (i) Physical parameters of the pipe: static or geometric elevation to overcome (H),Length(L), diameter(D), friction factor(f); (ii) Fluid-pipe mixed parameters: celerity of the pressure waves (a); (iii) Parameters related to the steady- state: velocity(V ,); (iv)Functional parameters that represent the extreme piezometric heads or pressures desirable at the upstream without loss of generality) end: H,,,, and H,;,. There is no explicit and direct relationship between these parameters and the size of air vessel required. Sever approaches based on different tests and heuristic criteria can be found in the iterature. However, professionals find that air vessel optimization is usually a trial and error process, generally performed the transient simulation using surge software that, eventually, provides the minimum air vessel volume required so that the maximum and minimum developed pressures at the pumping station do not exceed Hy» and Hin respectively. A neural network that encapsulates this unknown trial and error process from a relevant number of cases, already solved, that allows to directly obtaining the minimum air vessel volume
Table2. Transient Pressures and size of Air Vessel from results of SAP2.0
Table2. Transient Pressures and size of Air Vessel from results of SAP2.0
Fig 2. Longitudinal alignment of pipeline and HGL 3.0 NEURAL NETWORKS The multilayer perceptron (MLP) is one of the most widely used feed forward artificial neural networks. This network consists of a layer (input layer) of inputs, x;, another layer (output layer) of outputs, zx, and one or more intermediate layers (hidden layers). Figure 2 which takes into consideration only one hidden layer with outputs noted by yj, and only one unit (neuron) in the output layer. Each unit of the hidden and output layers has a function assigned, f, (which may be
Fig 2. Longitudinal alignment of pipeline and HGL 3.0 NEURAL NETWORKS The multilayer perceptron (MLP) is one of the most widely used feed forward artificial neural networks. This network consists of a layer (input layer) of inputs, x;, another layer (output layer) of outputs, zx, and one or more intermediate layers (hidden layers). Figure 2 which takes into consideration only one hidden layer with outputs noted by yj, and only one unit (neuron) in the output layer. Each unit of the hidden and output layers has a function assigned, f, (which may be
Surge protection devices like Air vessels are necessary for the pumping mains. Mathematical formulation of water hammer equations and boundary conditions of air vessel are presented. A regression based ANN model is demonstrated for sizing the economical air vesse in Phase-I of JCR Devadual Lift irrigation project of Telangana state is considered. The information on ranges of water hammer pressures occurs whi using SAP2.0. Thes neural network model. The neural network model predictions were compared witl software, SAP2 (S . A case study of pumping main of Reach- e tripping of power to pumps was obtained e system parameters are used to train the h the sizes obtained from application o urge Analysis Package version2.0) and observed that ANN models provides economical sizes.
Surge protection devices like Air vessels are necessary for the pumping mains. Mathematical formulation of water hammer equations and boundary conditions of air vessel are presented. A regression based ANN model is demonstrated for sizing the economical air vesse in Phase-I of JCR Devadual Lift irrigation project of Telangana state is considered. The information on ranges of water hammer pressures occurs whi using SAP2.0. Thes neural network model. The neural network model predictions were compared witl software, SAP2 (S . A case study of pumping main of Reach- e tripping of power to pumps was obtained e system parameters are used to train the h the sizes obtained from application o urge Analysis Package version2.0) and observed that ANN models provides economical sizes.
Figure-1; Feed Forward Neural Network neurons. The neural network maps input layer to output layer with the help of connecting weights between nodes. For ANN model identification of number of factors such as model structure, training algorithm, training data set, data standardization, number of training iterations, play an important role.
Figure-1; Feed Forward Neural Network neurons. The neural network maps input layer to output layer with the help of connecting weights between nodes. For ANN model identification of number of factors such as model structure, training algorithm, training data set, data standardization, number of training iterations, play an important role.
There are many algorithms used for discrete wavelet transform In present study Mallat algorithm is used. In discrete wavelet analysis the signal is passed through high pass and low pass filters to analyze high frequency and low frequency without losing information. Mother wavelet giving the detail coefficients represents low scale and high frequency components. Father wavelet giving approximation coefficients represents high scale and low frequency components. In general the resolution level of decomposition is decided by formulae INT (log n), where n is length if time series and INT is integer number, log is normal Logarithm (Wang and Ding 2003). Where t is integer time step, j and k are integers that control scale and time respectively, Wj, is wavelet coefficient for scale factor 2) and time factor t = 2/&
There are many algorithms used for discrete wavelet transform In present study Mallat algorithm is used. In discrete wavelet analysis the signal is passed through high pass and low pass filters to analyze high frequency and low frequency without losing information. Mother wavelet giving the detail coefficients represents low scale and high frequency components. Father wavelet giving approximation coefficients represents high scale and low frequency components. In general the resolution level of decomposition is decided by formulae INT (log n), where n is length if time series and INT is integer number, log is normal Logarithm (Wang and Ding 2003). Where t is integer time step, j and k are integers that control scale and time respectively, Wj, is wavelet coefficient for scale factor 2) and time factor t = 2/&
Figure- 4: Expected and Predicted Inflow for ANN Model for Testing Period Figure- 5: Expected and Predicted Inflow for WNN Model for Testing Period
Figure- 4: Expected and Predicted Inflow for ANN Model for Testing Period Figure- 5: Expected and Predicted Inflow for WNN Model for Testing Period
Present study is done at Pondicherry Station (11°56' N 79°53' E) in Tamil Nadu, India which is owned and maintained by Indian ational Centre for Ocean Information Services (INCOIS) of ndia. For this, 24hr and 48 hr ahead numerically forecasted values of significant wave heights (provided by INCOIS) and the previously measured significant wave heights at the same time steps of 24hr and 48hr for 3 years from 2011 to 2013 at Pondicherry station were used. The difference between measured and forecasted wave heights is the ‘error’ and that of only to be minimized. A time series of this ‘error’ is used as input to calibrate and test the models for forecasting the error at 24hr and 48hr in advance at the same location. Readers are referred to http://www. incois.gov.in for more details. Figure 1: Location Map of Pondicherry Station.
Present study is done at Pondicherry Station (11°56' N 79°53' E) in Tamil Nadu, India which is owned and maintained by Indian ational Centre for Ocean Information Services (INCOIS) of ndia. For this, 24hr and 48 hr ahead numerically forecasted values of significant wave heights (provided by INCOIS) and the previously measured significant wave heights at the same time steps of 24hr and 48hr for 3 years from 2011 to 2013 at Pondicherry station were used. The difference between measured and forecasted wave heights is the ‘error’ and that of only to be minimized. A time series of this ‘error’ is used as input to calibrate and test the models for forecasting the error at 24hr and 48hr in advance at the same location. Readers are referred to http://www. incois.gov.in for more details. Figure 1: Location Map of Pondicherry Station.
These predicted errors were then added or subtracted from the numerical model forecasts of respective lead times and forecasts done by numerical models for 24hr and 48hr ahead lead times were improvised. Subsequently these improvised forecasts were compared with the observed wave heights to perceive the model competency and the results of which are discussed in next section. Table 1: Model details for prediction of errors
These predicted errors were then added or subtracted from the numerical model forecasts of respective lead times and forecasts done by numerical models for 24hr and 48hr ahead lead times were improvised. Subsequently these improvised forecasts were compared with the observed wave heights to perceive the model competency and the results of which are discussed in next section. Table 1: Model details for prediction of errors
Development of these models to predict the errors at 24hr, 48hr ahead lead times, the current and previous errors at current time step and previous time steps upto 24hr, 48hr back respectively were used as inputs. 70% of the data from the total data set of errors was used to train the model while remaining 30% data was used for validation (15%) and testing (15%) to develop each model. Separate models were developed to predict error at 24 hr & A8hr ahead lead time. Table 1 presents the input- output and architectural details of these models.
Development of these models to predict the errors at 24hr, 48hr ahead lead times, the current and previous errors at current time step and previous time steps upto 24hr, 48hr back respectively were used as inputs. 70% of the data from the total data set of errors was used to train the model while remaining 30% data was used for validation (15%) and testing (15%) to develop each model. Separate models were developed to predict error at 24 hr & A8hr ahead lead time. Table 1 presents the input- output and architectural details of these models.
Table 3: Model Assessment
Table 3: Model Assessment
Table 2: Correlation coefficients
Table 2: Correlation coefficients
Figure 3: Wave plot of 24 hr ahead forecast Figure 4: Wave plot of 48 hr ahead forecast
Figure 3: Wave plot of 24 hr ahead forecast Figure 4: Wave plot of 48 hr ahead forecast
Figure 5: Scatter plot for 48 hr forecast
Figure 5: Scatter plot for 48 hr forecast
n the present work only significant wave height (Hs) of previous time steps were used as predictors. Here, wave height values up to previous 12hour were taken into consideration as predictor variables to predict Hs (ttn) where Hs (t+n) is the future significant wave height and ‘n’ denotes the lead times in hours with t as current significant wave height. Only 3hr lead time datasets were used.
n the present work only significant wave height (Hs) of previous time steps were used as predictors. Here, wave height values up to previous 12hour were taken into consideration as predictor variables to predict Hs (ttn) where Hs (t+n) is the future significant wave height and ‘n’ denotes the lead times in hours with t as current significant wave height. Only 3hr lead time datasets were used.
The results obtained from the both single WSVM model and single SVM model are presented in the form of various performance indices like R, RMSE, Scatter, Bias etc. through tables, and various graphs. Figure 2 shows the representative decomposition of signal of significant wave height for mother wavelet Daubechies of order 2 with level of decomposition 5.The subseries of decomposed time series in the shape of approximations and detail coefficients are with single series “s” can be clearly understood from the figure 2. Fig .2 Decomposition of signal using mother wavelet db2 and L5
The results obtained from the both single WSVM model and single SVM model are presented in the form of various performance indices like R, RMSE, Scatter, Bias etc. through tables, and various graphs. Figure 2 shows the representative decomposition of signal of significant wave height for mother wavelet Daubechies of order 2 with level of decomposition 5.The subseries of decomposed time series in the shape of approximations and detail coefficients are with single series “s” can be clearly understood from the figure 2. Fig .2 Decomposition of signal using mother wavelet db2 and L5
4, CONCLUSIONS The proposed hybrid WSVM model outperformed single SVM model for a 3hr lead time prediction. The improvement of results in WSVM model is due to dividing the dataset into multi- frequency bands using DWT to make data as a stationary data. SVM is good at handling non-stationary data, but it has shown excellence in handling stationary data and hence the proposed model performed very well. It was noticed that as the decomposition level for different wavelets was increased, the performance of the hybrid WSVM model also increased. This enhancement was minute for some wavelets but was noticeable for all the different wavelets. Also, Level 7 of decomposition had less error than level 5 and 6. While Db4 of Levels 5, 6& 7 giving similar results R=0.9969. Selection of proper mother wavelet was also carried out in the present study. Db3 wavelet performed better at decomposition level 7 suggesting that at higher decomposition level it can perform better. The conjunction model of WSVM can also be tried with various lead times such 6hr, 12hr, 24hr and 48hr lead time as a future scope of study. Also, Conjunction of SVM with other mother wavelets ike Haar ,Sym, Coif etc can also be tried.
4, CONCLUSIONS The proposed hybrid WSVM model outperformed single SVM model for a 3hr lead time prediction. The improvement of results in WSVM model is due to dividing the dataset into multi- frequency bands using DWT to make data as a stationary data. SVM is good at handling non-stationary data, but it has shown excellence in handling stationary data and hence the proposed model performed very well. It was noticed that as the decomposition level for different wavelets was increased, the performance of the hybrid WSVM model also increased. This enhancement was minute for some wavelets but was noticeable for all the different wavelets. Also, Level 7 of decomposition had less error than level 5 and 6. While Db4 of Levels 5, 6& 7 giving similar results R=0.9969. Selection of proper mother wavelet was also carried out in the present study. Db3 wavelet performed better at decomposition level 7 suggesting that at higher decomposition level it can perform better. The conjunction model of WSVM can also be tried with various lead times such 6hr, 12hr, 24hr and 48hr lead time as a future scope of study. Also, Conjunction of SVM with other mother wavelets ike Haar ,Sym, Coif etc can also be tried.
Table 1. Typologies of classification of wetland systems Table 2. Mechanisms and process in pollutant removals in CWs
Table 1. Typologies of classification of wetland systems Table 2. Mechanisms and process in pollutant removals in CWs
Table 3: Factors effecting wetland performance of constructed wetland systems
Table 3: Factors effecting wetland performance of constructed wetland systems
The degree of clogging may be assessed by the traditional methods which comprise of tracer testing and chemical analysis of composition of clog matter. Now a day’s the recent methods are being employed for assessing the degree of clogging are the constant-head permeameter tests and falling-head permeametet tests. These in-situ tests provide valuable insight on the assessment and evaluation of the extent of clogging (Nivala et al., 2011). Further, accuracy in the results may be achieved by adopting even more sophisticated methods like the finite element analysis models (Knowles and Davis, 2011). Being non- cohesive, gravel samples cannot be extracted non-destructively to use for analysis by standard laboratory tests (Ranieri, 2003). Hence the need for advanced non-conventional methods arises. The quantity of TCOD and BOD; indirectly account fot clogging as this will result in solids accumulation through microbial growth. Wastewater containing soluble organic biodegradable constituents account for most of the TCOD and BODs. nr o4 . . a nm nm
The degree of clogging may be assessed by the traditional methods which comprise of tracer testing and chemical analysis of composition of clog matter. Now a day’s the recent methods are being employed for assessing the degree of clogging are the constant-head permeameter tests and falling-head permeametet tests. These in-situ tests provide valuable insight on the assessment and evaluation of the extent of clogging (Nivala et al., 2011). Further, accuracy in the results may be achieved by adopting even more sophisticated methods like the finite element analysis models (Knowles and Davis, 2011). Being non- cohesive, gravel samples cannot be extracted non-destructively to use for analysis by standard laboratory tests (Ranieri, 2003). Hence the need for advanced non-conventional methods arises. The quantity of TCOD and BOD; indirectly account fot clogging as this will result in solids accumulation through microbial growth. Wastewater containing soluble organic biodegradable constituents account for most of the TCOD and BODs. nr o4 . . a nm nm
Table 5. Potential interventions in deprived performance of CW systems
Table 5. Potential interventions in deprived performance of CW systems
The potential interventions which found mainly responsible ir depriving the systems have been listed in Table 5. Table 4: Indicative parameters and their effect on clogging of CW systems
The potential interventions which found mainly responsible ir depriving the systems have been listed in Table 5. Table 4: Indicative parameters and their effect on clogging of CW systems
Strategies for minimization of clogging
Strategies for minimization of clogging
Table 6.Strategies in minimization of clogging of CW systems
Table 6.Strategies in minimization of clogging of CW systems
RBC consist single or multiple stage (shown in Fig. 1). There are many parameters affecting RBCs performance like organic loading, hydraulic loading, biomass, rotational speed, wastewater temperature, staging, RBC media, Dissolved oxygen levels, medium submergence.
RBC consist single or multiple stage (shown in Fig. 1). There are many parameters affecting RBCs performance like organic loading, hydraulic loading, biomass, rotational speed, wastewater temperature, staging, RBC media, Dissolved oxygen levels, medium submergence.
Figure 2. Effect of phenol concentration on rotating biological reactor (Pradeep et al. 2011).
Figure 2. Effect of phenol concentration on rotating biological reactor (Pradeep et al. 2011).
Hmerensniya et al. (2ZU11) reported that treatment of distillery wastewater using RBCs. They found that dissolved oxygen (DO) level is nearly enhanced 40% which is contradictory of normal treatment that DO concentrations drop during the experiment. This indicated that treatment with RBC reduces the organic load after secondary treatment. The COD values showed nearly 60% reduction after treating with RBC’s and the effluents was suitable to be reused. Guimaraes et al. (2005) investigated that rotating biological contactor (RBC) containing P. chrysosporium immobilized on PUF disks with optimized decolourization medium (basal medium without both thiamine and exogenous nitrogen) in continuous mode with a residence time of 3 days. The RBC reactor was monitored to determine the active life of the biocatalyst (Fig. 3). During the initial 17 days an average decolourization of 54% and an average total phenols reduction of 62% were observed. From the 17" day of continuous operation, a progressive decrease in colour removal was observed while the reduction of total phenols was reasonably stable. Minimum values of 27 and 56% were recorded on the 24” day, for colour and total phenols reduction, respectively. Guimaraes et al. (2011) suggested that, the decrease in efficiency with the increase in the treatment period recorded was probably due to the loss of mycelial activity, primarily in the first stage, caused by diffusion imitations. Figure 3. Colour and total phenols removal performance of continuous RBC reactor operated i in one way feeding mode rRRARFA
Hmerensniya et al. (2ZU11) reported that treatment of distillery wastewater using RBCs. They found that dissolved oxygen (DO) level is nearly enhanced 40% which is contradictory of normal treatment that DO concentrations drop during the experiment. This indicated that treatment with RBC reduces the organic load after secondary treatment. The COD values showed nearly 60% reduction after treating with RBC’s and the effluents was suitable to be reused. Guimaraes et al. (2005) investigated that rotating biological contactor (RBC) containing P. chrysosporium immobilized on PUF disks with optimized decolourization medium (basal medium without both thiamine and exogenous nitrogen) in continuous mode with a residence time of 3 days. The RBC reactor was monitored to determine the active life of the biocatalyst (Fig. 3). During the initial 17 days an average decolourization of 54% and an average total phenols reduction of 62% were observed. From the 17" day of continuous operation, a progressive decrease in colour removal was observed while the reduction of total phenols was reasonably stable. Minimum values of 27 and 56% were recorded on the 24” day, for colour and total phenols reduction, respectively. Guimaraes et al. (2011) suggested that, the decrease in efficiency with the increase in the treatment period recorded was probably due to the loss of mycelial activity, primarily in the first stage, caused by diffusion imitations. Figure 3. Colour and total phenols removal performance of continuous RBC reactor operated i in one way feeding mode rRRARFA
Figure 5.. Removal efficiency of VOC (MEK, MIBK, Toulene mixture) in the rotating biological contactor (Datta and Philip, 2014).
Figure 5.. Removal efficiency of VOC (MEK, MIBK, Toulene mixture) in the rotating biological contactor (Datta and Philip, 2014).
Gas production from the mixture of P. hyserophorus with cattle dung shown in Fig.1. Gas production was started only from the sixth week. Maximum production was found to be 2.4 litre per week. During the five week fermentation period, the reductions of total solid and organic carbon were 15.4% and 18.4% respectively. Figure 1. Total gas production during anaerobic digestion of P. hyserophorus (adopted from Gunaseelan (1987)
Gas production from the mixture of P. hyserophorus with cattle dung shown in Fig.1. Gas production was started only from the sixth week. Maximum production was found to be 2.4 litre per week. During the five week fermentation period, the reductions of total solid and organic carbon were 15.4% and 18.4% respectively. Figure 1. Total gas production during anaerobic digestion of P. hyserophorus (adopted from Gunaseelan (1987)
Figure 2. Effect of time on the sorption of Ni (II) on P. hysterophorus ash (Singh et al., 2009) and Cd (II) removal onto P. hysterophorus (Ajmal et al., 2006). Gunaseelam (1999) investigated that errect of Inoculam size and pretreatments on methane prod uction from parthenium weed in 2L fermentes at 26+1 °C temperature. Maximum yield of methane was observed (152+ oml/gVS). Fresh parthenium at 1000ml cattle manure slurry (inoculums) for 21 days conditions and 140+8ml of methane per g The methane yields from HC of V, as dried parthenium weed. and NaOH treated parthenium were 45 and 69% respectively, higher than untreated parthenium. Gunaseelam (1994) reported that 23% of volatile solid in terms of lignin content and should be pre-treated before use as feed stock for methane production. Abbasi et al (1990) investigated eight common aquatic weeds such as Salvinia molesta, Hydrilla verticillates, Nymphala stellata, Azolla pinnata, Ceratopteris sp. Scirpus sp. Cyperus sp. and Utricularia reticulate for production of energy. They found methane yield in the order of 10° Kcal per ha per year as in Salvinia weed. Abbasi and Nipaney (1991) studied biogas production from Pistia stratiotes weed and found 58-68 % average methane content in the 10 days period. They also observed different chemicals such as propionic acid, butyric, isobutyric, valeric acid along with biogas production. Gunaseelan (1987) investigated methane yield on the parthenium hysterophorus weeds mixed with cattle manure (10% v/v) at 30 +1°C in 3L batch digesters. They found methane content of the gas varied between 60% and 70%. Nizani et.al (2009) reviewed grass biomethane process with multiple stages. One ton of volatile solid produces 300m? of methane when mass of volatile solids in the grass as a feedstock. Fig. 2 shows effect of time on the adsorption of Ni(II) maximum value at 50 min attained ( 97.54 %) (Singh et al., 2009). The effect of contact time on the adsorption of Cd(II) at 50 mg/l initial Cd(II) concentration is shown in Fig. 2. The rate of adsorption is very fast initially and maximum removal of Cd(II) occurs with 20 min (Ajmal et al. 2006). P. hysteophorous ash has shown great potential for the removal of Ni (II)/Cd (II) from aqueous solution. P. hysteophorous is a problem creating weed. Instead of burning, they may be dried
Figure 2. Effect of time on the sorption of Ni (II) on P. hysterophorus ash (Singh et al., 2009) and Cd (II) removal onto P. hysterophorus (Ajmal et al., 2006). Gunaseelam (1999) investigated that errect of Inoculam size and pretreatments on methane prod uction from parthenium weed in 2L fermentes at 26+1 °C temperature. Maximum yield of methane was observed (152+ oml/gVS). Fresh parthenium at 1000ml cattle manure slurry (inoculums) for 21 days conditions and 140+8ml of methane per g The methane yields from HC of V, as dried parthenium weed. and NaOH treated parthenium were 45 and 69% respectively, higher than untreated parthenium. Gunaseelam (1994) reported that 23% of volatile solid in terms of lignin content and should be pre-treated before use as feed stock for methane production. Abbasi et al (1990) investigated eight common aquatic weeds such as Salvinia molesta, Hydrilla verticillates, Nymphala stellata, Azolla pinnata, Ceratopteris sp. Scirpus sp. Cyperus sp. and Utricularia reticulate for production of energy. They found methane yield in the order of 10° Kcal per ha per year as in Salvinia weed. Abbasi and Nipaney (1991) studied biogas production from Pistia stratiotes weed and found 58-68 % average methane content in the 10 days period. They also observed different chemicals such as propionic acid, butyric, isobutyric, valeric acid along with biogas production. Gunaseelan (1987) investigated methane yield on the parthenium hysterophorus weeds mixed with cattle manure (10% v/v) at 30 +1°C in 3L batch digesters. They found methane content of the gas varied between 60% and 70%. Nizani et.al (2009) reviewed grass biomethane process with multiple stages. One ton of volatile solid produces 300m? of methane when mass of volatile solids in the grass as a feedstock. Fig. 2 shows effect of time on the adsorption of Ni(II) maximum value at 50 min attained ( 97.54 %) (Singh et al., 2009). The effect of contact time on the adsorption of Cd(II) at 50 mg/l initial Cd(II) concentration is shown in Fig. 2. The rate of adsorption is very fast initially and maximum removal of Cd(II) occurs with 20 min (Ajmal et al. 2006). P. hysteophorous ash has shown great potential for the removal of Ni (II)/Cd (II) from aqueous solution. P. hysteophorous is a problem creating weed. Instead of burning, they may be dried
Implicit finite difference scheme is used for solving the Saint- Venant equation. The Preissmann Scheme, which has been used extensively since early 1960’s will be used here. It’s a four point weighted implicit difference approximation which is used to transform the nonlinear partial differential equations of Saint- Venant equation into a nonlinear algebraic equations. The partial derivatives and other coefficients are approximated as follows.
Implicit finite difference scheme is used for solving the Saint- Venant equation. The Preissmann Scheme, which has been used extensively since early 1960’s will be used here. It’s a four point weighted implicit difference approximation which is used to transform the nonlinear partial differential equations of Saint- Venant equation into a nonlinear algebraic equations. The partial derivatives and other coefficients are approximated as follows.
The finite difference form of unsteady state Saint Venant equations is derived from equations 2 & 3 using four point weighted finite difference Preismenn scheme.
The finite difference form of unsteady state Saint Venant equations is derived from equations 2 & 3 using four point weighted finite difference Preismenn scheme.
Finite Difference formulation of dissolved equation for steady state and unsteady state condition is derived from equation () by four point finite difference scheme as: Steady state: Finite Difference formulation of BOD for steady state anc unsteady state condition is derived from equation () by fou point finite difference scheme as: Steady state:
Finite Difference formulation of dissolved equation for steady state and unsteady state condition is derived from equation () by four point finite difference scheme as: Steady state: Finite Difference formulation of BOD for steady state anc unsteady state condition is derived from equation () by fou point finite difference scheme as: Steady state:
Boundary conditions:
Boundary conditions:
For verification of the results hypothetical problem solved by finite difference method and determined the depth and discharge at various nodes by using the Saint Venant equation. The problem solved by numerical method and compares it by analytical solution. We have no data for unsteady state problem, so compared unsteady state solution to the steady state solution.
For verification of the results hypothetical problem solved by finite difference method and determined the depth and discharge at various nodes by using the Saint Venant equation. The problem solved by numerical method and compares it by analytical solution. We have no data for unsteady state problem, so compared unsteady state solution to the steady state solution.
Figure 3. DO Vs Distance
Figure 3. DO Vs Distance
Table 1. List of panchayats of study area 2.3 Analytical methodology:
Table 1. List of panchayats of study area 2.3 Analytical methodology:
Table 2b.Physico-chemical characteristics of groundwater of Station I. Table 2a.Physico-chemical characteristics of groundwater of Station I.
Table 2b.Physico-chemical characteristics of groundwater of Station I. Table 2a.Physico-chemical characteristics of groundwater of Station I.
Table 2c.Physico-chemical characteristics of groundwater of Station III.
Table 2c.Physico-chemical characteristics of groundwater of Station III.
Table 2e.Physico-chemical characteristics of groundwater of Station V.
Table 2e.Physico-chemical characteristics of groundwater of Station V.
Table 2f.Physico-chemical characteristics of groundwater of Station VI.
Table 2f.Physico-chemical characteristics of groundwater of Station VI.
F-Fluoride, Fe-Iron, N- Nitrate, TH-Total hardness, TA -Total alkalinity, TDS-Total dissolve solid, Cl-Chloride, D.O.-Dissolve oxygen, EC-Electrical conductivity.
F-Fluoride, Fe-Iron, N- Nitrate, TH-Total hardness, TA -Total alkalinity, TDS-Total dissolve solid, Cl-Chloride, D.O.-Dissolve oxygen, EC-Electrical conductivity.
Table 4b.Statistical data of physico-chemical parameters of Station II.
Table 4b.Statistical data of physico-chemical parameters of Station II.
Table 4a.Statistical data of physico-chemical parameters of Station I.
Table 4a.Statistical data of physico-chemical parameters of Station I.
Table 4e.Statistical data of physico-chemical parameters o| Station V.
Table 4e.Statistical data of physico-chemical parameters o| Station V.
Table 5b. Pearson correlationmatrixfor physico-chemical parameters of Station II.
Table 5b. Pearson correlationmatrixfor physico-chemical parameters of Station II.
Table 5a. Pearson correlationmatrix for physico-chemical parameters of Station I.
Table 5a. Pearson correlationmatrix for physico-chemical parameters of Station I.
Table 5e. Pearson correlationmatrix for physico-chemical parameters of Station V.
Table 5e. Pearson correlationmatrix for physico-chemical parameters of Station V.
The study area is located in state of Tamil Nadu, India and is a part of Nagari watershed. It spreads out in Pallipattu block of Thiruvallur district. The study area comprises four non-system tanks in a cascade namely Athimanjeri, Konasamudram, Podatturpet and Pandravedu. There are nine villages benefiting from this tank cascade. In addition to agriculture most of the villagers largely depend on non-farm activity like weaving and dyeing. The area is generally hilly and sloppy with hard rock formations overlain by top sandy soil. Figure-1 shows the index map of the study area. Figure-1.Index map of study area Water quality analysis
The study area is located in state of Tamil Nadu, India and is a part of Nagari watershed. It spreads out in Pallipattu block of Thiruvallur district. The study area comprises four non-system tanks in a cascade namely Athimanjeri, Konasamudram, Podatturpet and Pandravedu. There are nine villages benefiting from this tank cascade. In addition to agriculture most of the villagers largely depend on non-farm activity like weaving and dyeing. The area is generally hilly and sloppy with hard rock formations overlain by top sandy soil. Figure-1 shows the index map of the study area. Figure-1.Index map of study area Water quality analysis
Table-1.A bbreviations of Sampling Stations 3. RESULTS AND DISCUSSION bacteriological parameters for two seasons namely rainy and summer. During water sample collection it was observed that the Pandravedu tank receives untreated wastewater generated from the dyeing processes along with untreated domestic wastewater from the Podatturpet households through a lined channel. The community expressed that in the recent years the water quality of the Pandravedu tank has deteriorated which in turn affect their ivelihoods including environment. Therefore in addition to water quality analysis the community perception on changing water quality and its implication on economic uses of water, ecological functions for healthy environment as well as socio- cultural uses was ascertained.The samples are coded as given in Table-1: Water quality index Water quality index is calculated for the four tanks using equations 1 and 2 as it is a useful tool to assess the present drinking water quality status andto compare with the BIS standards (Y ogendra et al, 2007).
Table-1.A bbreviations of Sampling Stations 3. RESULTS AND DISCUSSION bacteriological parameters for two seasons namely rainy and summer. During water sample collection it was observed that the Pandravedu tank receives untreated wastewater generated from the dyeing processes along with untreated domestic wastewater from the Podatturpet households through a lined channel. The community expressed that in the recent years the water quality of the Pandravedu tank has deteriorated which in turn affect their ivelihoods including environment. Therefore in addition to water quality analysis the community perception on changing water quality and its implication on economic uses of water, ecological functions for healthy environment as well as socio- cultural uses was ascertained.The samples are coded as given in Table-1: Water quality index Water quality index is calculated for the four tanks using equations 1 and 2 as it is a useful tool to assess the present drinking water quality status andto compare with the BIS standards (Y ogendra et al, 2007).
Water quality analysis of water samples confirm that at certain locations the values exceeded the permissible limits of drinking standards. The presence of E coli in tank water and in groundwater at certain places indicates that the water is polluted with waste water. The higher values of TDS ranging between 188 mg/l to 1133 mg/l prove that water is unfit for drinking. The total hardness and presence of chlorine is very high in the Pandravedu tank which made unfit for domestic use and cattle drinking. The BOD, COD and DO also exceeded the permissible values at certain locations. Table-2. Seasonal Variation of Drinking water quality parameters The water quality index obtained for the four tanks A thimanjeri, Konasamudram, Podatturpet and Pandravedu are 178, 1148, 151 and 261 respectively. Comparison of Drinking water quality parameters are expressed in Table-2.
Water quality analysis of water samples confirm that at certain locations the values exceeded the permissible limits of drinking standards. The presence of E coli in tank water and in groundwater at certain places indicates that the water is polluted with waste water. The higher values of TDS ranging between 188 mg/l to 1133 mg/l prove that water is unfit for drinking. The total hardness and presence of chlorine is very high in the Pandravedu tank which made unfit for domestic use and cattle drinking. The BOD, COD and DO also exceeded the permissible values at certain locations. Table-2. Seasonal Variation of Drinking water quality parameters The water quality index obtained for the four tanks A thimanjeri, Konasamudram, Podatturpet and Pandravedu are 178, 1148, 151 and 261 respectively. Comparison of Drinking water quality parameters are expressed in Table-2.
Qualitative research method PL LLYOUU IL WaALGL YUCLLLY 1HULUGO LU1 LGtiy Glu OUILLILIOL OUGOU Tio. Table-3. Seasonal Variation of Irrigation W ater Quality Indices The SAR for all the tanks and wells fall within the range of 26 for both the seasons. All the four tanks exceeded the standard permissible value of 40 for SSP whereas the bore wells and open wells are found within the limit. The MAR values for all the ocations fall within the standard permissible limit of 50%. The Kelly’s Ratio is found to be greater than 1 in all the tanks whereas the well samples are within the permissible range. Ramesh et al, 2010). The Table-3gives the seasonal variation of irrigation water quality | indices for rainy and summer seasons.
Qualitative research method PL LLYOUU IL WaALGL YUCLLLY 1HULUGO LU1 LGtiy Glu OUILLILIOL OUGOU Tio. Table-3. Seasonal Variation of Irrigation W ater Quality Indices The SAR for all the tanks and wells fall within the range of 26 for both the seasons. All the four tanks exceeded the standard permissible value of 40 for SSP whereas the bore wells and open wells are found within the limit. The MAR values for all the ocations fall within the standard permissible limit of 50%. The Kelly’s Ratio is found to be greater than 1 in all the tanks whereas the well samples are within the permissible range. Ramesh et al, 2010). The Table-3gives the seasonal variation of irrigation water quality | indices for rainy and summer seasons.
Figure-2. Comparison of Irrigation water quality indices
Figure-2. Comparison of Irrigation water quality indices
Table 2. Objective functions used for the optimization methods
Table 2. Objective functions used for the optimization methods
Table 3. Major Findings of Various researchers by applicatior of Booster Chlorination After reviewing the work of most of the researchers it is found that coupled water quality modeling tool with advanced optimization methods can serve as important decision making tool for the operation of booster chlorination station to manage
Table 3. Major Findings of Various researchers by applicatior of Booster Chlorination After reviewing the work of most of the researchers it is found that coupled water quality modeling tool with advanced optimization methods can serve as important decision making tool for the operation of booster chlorination station to manage
4,2 Mechanism C ontrolling the Groundwater Chemistry Table 1. Hydro-chemical characteristics of the Groundwater during Pre-monsoon 2008
4,2 Mechanism C ontrolling the Groundwater Chemistry Table 1. Hydro-chemical characteristics of the Groundwater during Pre-monsoon 2008
Figure 1. Map showing location of sampling sites
Figure 1. Map showing location of sampling sites
responsible ror controiling the chemical COMposiuon OF varlous bodies of water on the surface of the earth. The major cations that characterize the end-members of the world surface waters are Ca for freshwater bodies and Na for high-saline water bodies. Gibbs plotted the weight ratio Na/(Na+Ca) on the x-axis and the variation in total salinity on the y-axis (Fig. 2). This ordered arrangement can serve as a basis for discussion of the several mechanisms that control world water chemistry. The first of these mechanisms is the atmospheric precipitation. The chemical compositions of low-salinity waters are controlled by the amount of dissolved salts furnished by precipitation. These waters consist mainly of the rivers having sources in thoroughly leached areas of low relief in which the rate of supply of dissolved salts to the rivers is very low and the amount of rainfall is high — much greater in proportion to the low amount of dissolved salts supplied from the rocks. In addition, the composition of this precipitation differs from that of rock- derived dissolved salts. The second mechanism is the rock dominance controlling world water chemistry. The waters of this rock-dominated end-members are more or less in partial equilibrium with the materials in their basins. Their positions within this grouping are dependent on the relief and climate of each basin and the composition of each basin. The third major mechanism that controls the chemical composition of the earth’s surface waters is the evaporation-fractional crystallization process. This mechanism produces a series extending from the Ca-rich, medium-salinity (freshwater), ‘rock source’ end- member grouping to the opposite, Na-rich, high-salinity end- member.
responsible ror controiling the chemical COMposiuon OF varlous bodies of water on the surface of the earth. The major cations that characterize the end-members of the world surface waters are Ca for freshwater bodies and Na for high-saline water bodies. Gibbs plotted the weight ratio Na/(Na+Ca) on the x-axis and the variation in total salinity on the y-axis (Fig. 2). This ordered arrangement can serve as a basis for discussion of the several mechanisms that control world water chemistry. The first of these mechanisms is the atmospheric precipitation. The chemical compositions of low-salinity waters are controlled by the amount of dissolved salts furnished by precipitation. These waters consist mainly of the rivers having sources in thoroughly leached areas of low relief in which the rate of supply of dissolved salts to the rivers is very low and the amount of rainfall is high — much greater in proportion to the low amount of dissolved salts supplied from the rocks. In addition, the composition of this precipitation differs from that of rock- derived dissolved salts. The second mechanism is the rock dominance controlling world water chemistry. The waters of this rock-dominated end-members are more or less in partial equilibrium with the materials in their basins. Their positions within this grouping are dependent on the relief and climate of each basin and the composition of each basin. The third major mechanism that controls the chemical composition of the earth’s surface waters is the evaporation-fractional crystallization process. This mechanism produces a series extending from the Ca-rich, medium-salinity (freshwater), ‘rock source’ end- member grouping to the opposite, Na-rich, high-salinity end- member.
Figure 3. Scatter plot of (CatMg) vs TZ* and (Ca+Mg) vs (Na+K) (Pre- and Post-monsoon) Figure 4. Scatter plot of (Na+K) vs TZ* and Na vs Cl (Pre- and Post-monsoon)
Figure 3. Scatter plot of (CatMg) vs TZ* and (Ca+Mg) vs (Na+K) (Pre- and Post-monsoon) Figure 4. Scatter plot of (Na+K) vs TZ* and Na vs Cl (Pre- and Post-monsoon)
5. CONCLUSION ———————E—— TOO The plot of (Ca+Mg) vs HCO34S80, is a major indicator to identify the ion exchange process activated in the study area. If ion exchange is the process, the points shift to right side of the plot due to excess of HCO3+SO,. If reverse ions exchange is the process, points shift left due to excess Cat+Mg. Plot of (Ca+Mg) vs HCO3+SO, shows that most of the plotted points clusters around the 1:1 equiline and fall in HCO3+SO, indicating the ion exchange process which may be due to excess bicarbonate (Fig. 5).
5. CONCLUSION ———————E—— TOO The plot of (Ca+Mg) vs HCO34S80, is a major indicator to identify the ion exchange process activated in the study area. If ion exchange is the process, the points shift to right side of the plot due to excess of HCO3+SO,. If reverse ions exchange is the process, points shift left due to excess Cat+Mg. Plot of (Ca+Mg) vs HCO3+SO, shows that most of the plotted points clusters around the 1:1 equiline and fall in HCO3+SO, indicating the ion exchange process which may be due to excess bicarbonate (Fig. 5).
Figure 5. Scatter plot of (Cat+Mg) vs HCO3 and (Ca+Mg) vs (HCO3+S0,) (Pre- and Post-monsoon)
Figure 5. Scatter plot of (Cat+Mg) vs HCO3 and (Ca+Mg) vs (HCO3+S0,) (Pre- and Post-monsoon)
A single source WDN (Kessler et al. 1998) as shown in Figure. 1 is considered for the evaluation of various objectives for a set of known sensor locations. The network has 12 pipes and 8 consumer locations- the number of (given in parenthesis) is shown in Figure tank and a pump. The average nodal demands are given in Lps. The pipe diameters are given in Ta ble 1. T (ratio of actual demand to average demand in 24 hours are given in Table 2. Total length of the pipe in the network is 19364 meters and total consumers of the network are 7600. The pipe 10 is 3209 m long, while al m in length. Hazen-Williams coefficient for all pipes is 100. Other details can be obtained from the Kessler et al. (1998). consumers at each location , a source, a storage he demand multiplier for different periods other pipes are 1609
A single source WDN (Kessler et al. 1998) as shown in Figure. 1 is considered for the evaluation of various objectives for a set of known sensor locations. The network has 12 pipes and 8 consumer locations- the number of (given in parenthesis) is shown in Figure tank and a pump. The average nodal demands are given in Lps. The pipe diameters are given in Ta ble 1. T (ratio of actual demand to average demand in 24 hours are given in Table 2. Total length of the pipe in the network is 19364 meters and total consumers of the network are 7600. The pipe 10 is 3209 m long, while al m in length. Hazen-Williams coefficient for all pipes is 100. Other details can be obtained from the Kessler et al. (1998). consumers at each location , a source, a storage he demand multiplier for different periods other pipes are 1609
5.1.4 Evaluation of VC: Table 4. Calculation of objectives for Sensor location at node: 32, 23
5.1.4 Evaluation of VC: Table 4. Calculation of objectives for Sensor location at node: 32, 23
5.1.3 Evaluation of PE, EC, DL and NFD:
5.1.3 Evaluation of PE, EC, DL and NFD:
Performance objectives evaluated through water quality simulations provides more realistic results as they are obtained by considering variation in demands over time and concentration of pollutant. However, water quality simulation require more efforts and computation time. From Table 5 and 6 it can be observed that the difference between the various objective values under hydraulic simulation and water quality simulation are not much. Table 7. Evaluation of Risk objective * indicated detection time for detected events only; indicates calculated objective values for detected events only andundetected events are ignored, indicates detection time for detected and undetected events both
Performance objectives evaluated through water quality simulations provides more realistic results as they are obtained by considering variation in demands over time and concentration of pollutant. However, water quality simulation require more efforts and computation time. From Table 5 and 6 it can be observed that the difference between the various objective values under hydraulic simulation and water quality simulation are not much. Table 7. Evaluation of Risk objective * indicated detection time for detected events only; indicates calculated objective values for detected events only andundetected events are ignored, indicates detection time for detected and undetected events both
Dal Lake has been the centre of Kashmir civilization and is one of the most beautiful spots of tourist attraction. This shallow-post glacial freshwater body is bounded on Southwest and West by Srinagar city, and its remaining sides are surrounded by gentle terraced slopes at the base of precipitous mountains. Dal Lake is unique because of: Habitation within the lake.
Dal Lake has been the centre of Kashmir civilization and is one of the most beautiful spots of tourist attraction. This shallow-post glacial freshwater body is bounded on Southwest and West by Srinagar city, and its remaining sides are surrounded by gentle terraced slopes at the base of precipitous mountains. Dal Lake is unique because of: Habitation within the lake.
al he Dal Lake lies in the flood plains of river Jhelum whose broad meanders have cut swampy low lands out of the Karewa terraces. The inflow Telbal nallah channel enters the lake from the North bringing water from the high altitude Mansar Lake. During its downward journey the inflow stream collects large quantities of silt from the denuded catchment and deposits it in the lake. Numbers of ephemeral water channels, surface drains enter the lake from the human settlements discharging large quantities of wastes. An estimated load of 12.30 x10°m’ of liquid waste with 18.17 tons and 25 tons of phosphorus and inorganic nitrogen is enriching the lake annually. Within Lake Basin itself a number of freshwater springs (mostly choked at present) act as permanent source of water to the lake. Towards the South-west
al he Dal Lake lies in the flood plains of river Jhelum whose broad meanders have cut swampy low lands out of the Karewa terraces. The inflow Telbal nallah channel enters the lake from the North bringing water from the high altitude Mansar Lake. During its downward journey the inflow stream collects large quantities of silt from the denuded catchment and deposits it in the lake. Numbers of ephemeral water channels, surface drains enter the lake from the human settlements discharging large quantities of wastes. An estimated load of 12.30 x10°m’ of liquid waste with 18.17 tons and 25 tons of phosphorus and inorganic nitrogen is enriching the lake annually. Within Lake Basin itself a number of freshwater springs (mostly choked at present) act as permanent source of water to the lake. Towards the South-west
FAB technology consists of screening, grit removal, biological treatment (bioreactors), tertiary treatment of clarifloculator (with alum), centrifuge and chlorination. The six units were proposed of which five have been made operational. The treated effluent of STP 1 (c) and 3 (a) is discharged in channels leaving Dal Lake via Amir Khan. Dalgate exit and Brari-Nambal cut). Thus only 40% of the total of 36.7 mld finds its way into the Dal Lake.
FAB technology consists of screening, grit removal, biological treatment (bioreactors), tertiary treatment of clarifloculator (with alum), centrifuge and chlorination. The six units were proposed of which five have been made operational. The treated effluent of STP 1 (c) and 3 (a) is discharged in channels leaving Dal Lake via Amir Khan. Dalgate exit and Brari-Nambal cut). Thus only 40% of the total of 36.7 mld finds its way into the Dal Lake.
Table 4a: Water Quality changes in Hazratbal Basin of Dal Lake over a period of time
Table 4a: Water Quality changes in Hazratbal Basin of Dal Lake over a period of time
In their studies during April 2008 (Table 3a) regarding the functioning of FAB based STP reported 44% increase in nitrate- nitrogen content of the treated sewage indicating the malfunctioning of the STP’s installed. Table 3(a) Efficiency of nutrient removal through FAB — STP (April 2008)
In their studies during April 2008 (Table 3a) regarding the functioning of FAB based STP reported 44% increase in nitrate- nitrogen content of the treated sewage indicating the malfunctioning of the STP’s installed. Table 3(a) Efficiency of nutrient removal through FAB — STP (April 2008)
Table 4d: Water Quality changes in Nigeen Lake over a period of time Table 4c: Water Quality changes in Nehru Park basin of Dal Lake over a period of time
Table 4d: Water Quality changes in Nigeen Lake over a period of time Table 4c: Water Quality changes in Nehru Park basin of Dal Lake over a period of time
Sample collection points and location of study area Fig 2: Sample Location map study area.
Sample collection points and location of study area Fig 2: Sample Location map study area.
Chemical Analysis of Ground water: Groundwater is the main source of water that meets the agricultural, industrial and household requirements. Population growth, socioeconomic development, technological and climate changes has increased the demand for potable water manifolds in the past few years (Alcamo et al. 2007).One of the internationally accepted human rights is the access to safe drinking water which is the basic need for human health and development (WHO 2001). The general health and life expectancy of the people is reported to be adversely affected due to lack of the availability of clean drinking water in many developing countries of the world (Nash and McCall 1995). In irrigation, the poor water quality not only affects the crop yield but also affects the physical conditions of the soil (Ayers and West cot 1994). Since the dependence on groundwater has increased tremendously in India due to vagaries of monsoon and scarcity of surface water in recent years, therefore groundwater quality and surface water needs to be monitored and managed. The water sample is analysed by using BIS 1983 permissible limits which is shown in table no 2.
Chemical Analysis of Ground water: Groundwater is the main source of water that meets the agricultural, industrial and household requirements. Population growth, socioeconomic development, technological and climate changes has increased the demand for potable water manifolds in the past few years (Alcamo et al. 2007).One of the internationally accepted human rights is the access to safe drinking water which is the basic need for human health and development (WHO 2001). The general health and life expectancy of the people is reported to be adversely affected due to lack of the availability of clean drinking water in many developing countries of the world (Nash and McCall 1995). In irrigation, the poor water quality not only affects the crop yield but also affects the physical conditions of the soil (Ayers and West cot 1994). Since the dependence on groundwater has increased tremendously in India due to vagaries of monsoon and scarcity of surface water in recent years, therefore groundwater quality and surface water needs to be monitored and managed. The water sample is analysed by using BIS 1983 permissible limits which is shown in table no 2.
For studying the chemical quality of groundwater 25 groundwater samples were collected and the sample locations are shown in fig 2. Water samples collected from bore wells in use and samples collected in the one litre pre-washed polythene bottles, were analysed in the chemical labaroatoty of the Department of Civil Engineering, SIT, Tumkur and the results are given in table no 3. The above methodology is used to find the chemical contamination of water samples of 25 location in Nagalamadike watershed of the Pavagoda taluk of Tumkur district karanataka state india.
For studying the chemical quality of groundwater 25 groundwater samples were collected and the sample locations are shown in fig 2. Water samples collected from bore wells in use and samples collected in the one litre pre-washed polythene bottles, were analysed in the chemical labaroatoty of the Department of Civil Engineering, SIT, Tumkur and the results are given in table no 3. The above methodology is used to find the chemical contamination of water samples of 25 location in Nagalamadike watershed of the Pavagoda taluk of Tumkur district karanataka state india.
Lithology map of study area: In the Lithology map the study area consists of Granite and PGC.is shown in fig 5
Lithology map of study area: In the Lithology map the study area consists of Granite and PGC.is shown in fig 5
Fig: 5 Lithology map of the study area
Fig: 5 Lithology map of the study area
Fig:6 Lu/Lc map and overlay of Iso concentration map of Fluoride. ISSN:2319-6890)(online),2347-5013(print) 18-19, Dec. 2014 Table 2 : Permissible limits (BIS-1983) of potable water in the study area
Fig:6 Lu/Lc map and overlay of Iso concentration map of Fluoride. ISSN:2319-6890)(online),2347-5013(print) 18-19, Dec. 2014 Table 2 : Permissible limits (BIS-1983) of potable water in the study area
samples. Fig:7 Chemical concentration of water sample Table 3: Chemical analysis of water Iso Concentration map of fluoride: Spatial distribution of fluoride and Iso contour maps is prepared using of Arc GIS tool. Fluoride content is more in most of the water samples out of 25 samples the fluoride present in 23 samples is more than the Results and Discussions: Chemical concentration analysis of water samples is collected from the study area. In the study area 25 water samples are water collected from various locations and analyzed in chemical lab the following details is shown in table 2
samples. Fig:7 Chemical concentration of water sample Table 3: Chemical analysis of water Iso Concentration map of fluoride: Spatial distribution of fluoride and Iso contour maps is prepared using of Arc GIS tool. Fluoride content is more in most of the water samples out of 25 samples the fluoride present in 23 samples is more than the Results and Discussions: Chemical concentration analysis of water samples is collected from the study area. In the study area 25 water samples are water collected from various locations and analyzed in chemical lab the following details is shown in table 2
permissible limit as shown in table 2, and fluoride iso- concentration map is shown in fig 8. Fluoride is more in south west region and remaining zone is less. Fig 8. Spatial distribution map of fluoride (Iso contour map of fluoride) Iso concentration map of Nitrate: Spatial distribution of Nitrate and Iso contour map is prepared using of Arc GIS tool. Nitrate content is more in most of the water samples out of 25 samples the Nitrate present in 15 samples is more than the permissible limit as shown in table 2, Nitrate concentration is shown in figure no 9. In North east and south east ,the nitrate contamination is more due to more application of artificial manure (NPK) in agriculture.
permissible limit as shown in table 2, and fluoride iso- concentration map is shown in fig 8. Fluoride is more in south west region and remaining zone is less. Fig 8. Spatial distribution map of fluoride (Iso contour map of fluoride) Iso concentration map of Nitrate: Spatial distribution of Nitrate and Iso contour map is prepared using of Arc GIS tool. Nitrate content is more in most of the water samples out of 25 samples the Nitrate present in 15 samples is more than the permissible limit as shown in table 2, Nitrate concentration is shown in figure no 9. In North east and south east ,the nitrate contamination is more due to more application of artificial manure (NPK) in agriculture.
Iso concentration map of Iron: Spatial distribution of the iron is prepared using Arc GIS and concentration of Iron is ranges from 0 to 0.4 is within the permissible limit the Iron concentration map is shown in fig 10.the iron concentration is distributed almost equal in all places.
Iso concentration map of Iron: Spatial distribution of the iron is prepared using Arc GIS and concentration of Iron is ranges from 0 to 0.4 is within the permissible limit the Iron concentration map is shown in fig 10.the iron concentration is distributed almost equal in all places.
Iso concentration map of pH scale: Spatial distribution mat pH is developed by using Arc GIS tool and pH is ranges from 6.28 to 8.26. All the samples in the study area falls within the permissible range. Fig: 11 Spatial distribution map of pH (Iso
Iso concentration map of pH scale: Spatial distribution mat pH is developed by using Arc GIS tool and pH is ranges from 6.28 to 8.26. All the samples in the study area falls within the permissible range. Fig: 11 Spatial distribution map of pH (Iso
Iso concentration map of Total Hardness: Spatial distribution of Iso counter map is developed by using the Arc GIS tool is shown in fig 12 .TDS is more in the south central and north central part of the study area and remaining area is less. Fig 12: Spatial Distribution map of Total Hardness (Iso contour map of Total Hardness)
Iso concentration map of Total Hardness: Spatial distribution of Iso counter map is developed by using the Arc GIS tool is shown in fig 12 .TDS is more in the south central and north central part of the study area and remaining area is less. Fig 12: Spatial Distribution map of Total Hardness (Iso contour map of Total Hardness)
Iso concentration map of Electrical Conductivity: Spatial distribution and Iso counter map is developed by using the Arc GIS tool and, Electrical Conductivity shown in fig 13. The electrical conductivity is more in Northern part of the study area where as remaining part the electrical conductivity is less. Fig 13: Spatial distribution map of Electrica Conductivity (Iso contour map of Electrical Conductivity)
Iso concentration map of Electrical Conductivity: Spatial distribution and Iso counter map is developed by using the Arc GIS tool and, Electrical Conductivity shown in fig 13. The electrical conductivity is more in Northern part of the study area where as remaining part the electrical conductivity is less. Fig 13: Spatial distribution map of Electrica Conductivity (Iso contour map of Electrical Conductivity)
Fig 15: Spatial Distribution map of sodium (Iso contour map of sodium)
Fig 15: Spatial Distribution map of sodium (Iso contour map of sodium)
north central part and south part of the study area and remaining area is less. Fig 14: Spatial distribution map of chloride (Iso contour map of chloride)
north central part and south part of the study area and remaining area is less. Fig 14: Spatial distribution map of chloride (Iso contour map of chloride)
Fig 16: Spatial distribution map of Total Dissolved Solids (Iso contour map of TDS)
Fig 16: Spatial distribution map of Total Dissolved Solids (Iso contour map of TDS)
2. MATERIAL AND METHODS The Ganga river carries the highest silt load of any river in the world and the deposition of this material in the delta region results in the largest river delta in the world (400 km from north to south and 320 km from east to west). The rich mangrove forests of the Gangetic delta contain very rare and valuable species of plants and animals and are unparalleled among many forest ecosystems.
2. MATERIAL AND METHODS The Ganga river carries the highest silt load of any river in the world and the deposition of this material in the delta region results in the largest river delta in the world (400 km from north to south and 320 km from east to west). The rich mangrove forests of the Gangetic delta contain very rare and valuable species of plants and animals and are unparalleled among many forest ecosystems.
Table 2. Formulations related to random variable Y in case of
Table 2. Formulations related to random variable Y in case of
Table 2. Formulations related to random variable Y in case of PE3 frequency distribution. F, (y) is cumulative distribution
Table 2. Formulations related to random variable Y in case of PE3 frequency distribution. F, (y) is cumulative distribution
nformation on nature, areal extent and spatial distribution of soils in the study region was extracted from soil map obtained from National Bureau of Soil Survey and Land Use Planning NBSS&LUP). Further, information pertaining to land-use/land- cover was extracted from Earth Science Data Interface (ESDI) at the Global Land Cover Facility (GLCF) available at web site: http://glcfapp.umiacs.umd.edu. The extracted information includes areas classified as built-up, agricultural, forest, water bodies and waste lands. Figure 1.Location of gauges considered for thepresent study in Godavari river basin
nformation on nature, areal extent and spatial distribution of soils in the study region was extracted from soil map obtained from National Bureau of Soil Survey and Land Use Planning NBSS&LUP). Further, information pertaining to land-use/land- cover was extracted from Earth Science Data Interface (ESDI) at the Global Land Cover Facility (GLCF) available at web site: http://glcfapp.umiacs.umd.edu. The extracted information includes areas classified as built-up, agricultural, forest, water bodies and waste lands. Figure 1.Location of gauges considered for thepresent study in Godavari river basin
Figure 11. Comparison of at-site (true) quantile estimates with regional quantile estimates for ungauged sites based on new ee ey ee ss OT Oe OD oo Wo We Table 3.Performance measures R-bias, AR-bias and R-RMSE computed based on errors in flood quantiles estimated corresponding to 50ungauged sites. mathematical approach and CIF methods for 100-year return period. The solid 1:1 line corresponds to the case where at-site and regional estimates are equal. A method is considered to be effective if points corresponding to the method are closer to the REFERENCES:
Figure 11. Comparison of at-site (true) quantile estimates with regional quantile estimates for ungauged sites based on new ee ey ee ss OT Oe OD oo Wo We Table 3.Performance measures R-bias, AR-bias and R-RMSE computed based on errors in flood quantiles estimated corresponding to 50ungauged sites. mathematical approach and CIF methods for 100-year return period. The solid 1:1 line corresponds to the case where at-site and regional estimates are equal. A method is considered to be effective if points corresponding to the method are closer to the REFERENCES:
Table 1.Wavelet selected from respective wavelet family.
Table 1.Wavelet selected from respective wavelet family.
not of same characteristics. This results in errors in terms of unacceptable depth readings. T the echo reflection differs from different objects found water. CWPRS and many states are using a particular a limit as below the type of However filters and Echo sounder pro Gaussian noises, which could the interpretation techniques provided in the software supports these system. As explained by M. Selva Balan, et all 2013) for large reservoir the preplanning is essential, which is possible with the image processing techniques applied on an his could not be corrected beyond vided under hydrology project. the data is collected it comes with various spiky non not been eliminated fully by the satellite imagery as the one shown in fig 1 below. Fig 1. Contour extraction of the reservoir for pre survey planning
not of same characteristics. This results in errors in terms of unacceptable depth readings. T the echo reflection differs from different objects found water. CWPRS and many states are using a particular a limit as below the type of However filters and Echo sounder pro Gaussian noises, which could the interpretation techniques provided in the software supports these system. As explained by M. Selva Balan, et all 2013) for large reservoir the preplanning is essential, which is possible with the image processing techniques applied on an his could not be corrected beyond vided under hydrology project. the data is collected it comes with various spiky non not been eliminated fully by the satellite imagery as the one shown in fig 1 below. Fig 1. Contour extraction of the reservoir for pre survey planning
In Savitzky-Golay filter, the odd-indexed coefficients of the impulse response design polynomial are all zero. The nominal normalized cut off (3 dB down) frequency depends on both the implicit polynomial order and the length of the impulse response. The impulse response of filter is symmetric, so the frequency response is purely real. These filters have very flat frequency response in their pass bands and fair attenuation characteristics in their stop band regions.
In Savitzky-Golay filter, the odd-indexed coefficients of the impulse response design polynomial are all zero. The nominal normalized cut off (3 dB down) frequency depends on both the implicit polynomial order and the length of the impulse response. The impulse response of filter is symmetric, so the frequency response is purely real. These filters have very flat frequency response in their pass bands and fair attenuation characteristics in their stop band regions.
The results presented in Table 2 show PSNR values for different wavelets. It can be seen that Haar wavelet is giving better result than other wavelets in this case. Table 2. Values of PSNR for different types of wavelets.
The results presented in Table 2 show PSNR values for different wavelets. It can be seen that Haar wavelet is giving better result than other wavelets in this case. Table 2. Values of PSNR for different types of wavelets.
The 3D profiles shows that wavelet and Savitzky-Golay filters have smoothened the noisy data and hence improves the accuracy of sedimentation volume calculations. Fig 7 shows the capacity loss calculated with one survey using two frequencies.
The 3D profiles shows that wavelet and Savitzky-Golay filters have smoothened the noisy data and hence improves the accuracy of sedimentation volume calculations. Fig 7 shows the capacity loss calculated with one survey using two frequencies.
4, CONCLUSION The analysis of ultrasonic depth data received through sediment and water using two techniques: HARR wavelet Transform and Savitzky-Golay filter. It is found that out of all wavelet transforms, HARR wavelet is most suitable for noise reduction in ultrasonic signal based on high PSNR value. In Savitzky- Golay Filter analysis, higher order of polynomial with lesser frame size increases the PSNR.
4, CONCLUSION The analysis of ultrasonic depth data received through sediment and water using two techniques: HARR wavelet Transform and Savitzky-Golay filter. It is found that out of all wavelet transforms, HARR wavelet is most suitable for noise reduction in ultrasonic signal based on high PSNR value. In Savitzky- Golay Filter analysis, higher order of polynomial with lesser frame size increases the PSNR.
A typical flat rooitop household rainwater harvesting system having a collection tank capacity of S,,.... was considered for carrying out the simulation study. The yield from the rainwater harvesting system was drawn according to the water demand. Simulation of the operation of the system was carried out using Standard Operating Policy (SOP) given by Equations (1) to (3). n simulation whenever the demand is not satisfied associated failure occurs, computed in terms of deficit volume defined by Equation (4). where Y, is the yield from the collection system at period t (m’); Q; inflow to the collection tank in period t (m°); D, is the demand during the period t (m*); S, is the storage in the time period t and Spill, the spill occurring (m*) if any when the collection tank is full and Snax the maximum design capacity of the collection tank. D represents the deficit occurring (m?) in period t.
A typical flat rooitop household rainwater harvesting system having a collection tank capacity of S,,.... was considered for carrying out the simulation study. The yield from the rainwater harvesting system was drawn according to the water demand. Simulation of the operation of the system was carried out using Standard Operating Policy (SOP) given by Equations (1) to (3). n simulation whenever the demand is not satisfied associated failure occurs, computed in terms of deficit volume defined by Equation (4). where Y, is the yield from the collection system at period t (m’); Q; inflow to the collection tank in period t (m°); D, is the demand during the period t (m*); S, is the storage in the time period t and Spill, the spill occurring (m*) if any when the collection tank is full and Snax the maximum design capacity of the collection tank. D represents the deficit occurring (m?) in period t.
where N! and N*“° are the total number of periods in the study and number of periods in which failure occurs, d(j) represents the duration of j” failure event, v(j) is the deficit occurred during j failure event and M is the number of failure events.
where N! and N*“° are the total number of periods in the study and number of periods in which failure occurs, d(j) represents the duration of j” failure event, v(j) is the deficit occurred during j failure event and M is the number of failure events.
Table 1: Reliability of the rainwater collection system fo1 average daily rainfall
Table 1: Reliability of the rainwater collection system fo1 average daily rainfall
5. ACKNOWLEDGEMENT
5. ACKNOWLEDGEMENT
Figure 1. Location Map of study area
Figure 1. Location Map of study area
Rooftop rainwater harvesting is the technique through which rainwater is captured from the roof catchments and stored in reservoirs. Harvested rainwater can be stored in sub-surface ground water reservoir by adopting artificial recharge techniques to meet the household needs through storage in tanks. (C.G.W.B., 2007) In saline areas, rainwater provides good quality water and when recharged to groundwater, it reduces salinity and also helps in maintaining balance between the fresh- water interfaces.
Rooftop rainwater harvesting is the technique through which rainwater is captured from the roof catchments and stored in reservoirs. Harvested rainwater can be stored in sub-surface ground water reservoir by adopting artificial recharge techniques to meet the household needs through storage in tanks. (C.G.W.B., 2007) In saline areas, rainwater provides good quality water and when recharged to groundwater, it reduces salinity and also helps in maintaining balance between the fresh- water interfaces.
Figure 4, Major catchments 3.3 Rooftop Rainwater Harvesting
Figure 4, Major catchments 3.3 Rooftop Rainwater Harvesting
Paired ‘t’ test considering two samples having same mean was performed to verify whether the estimated and measured values are significantly different. It was found that calculated ‘t’ values (-1.59, -0.91) is less than critical t value of 1.83, 1.83 (Table 2). This means, Null hypothesis (that the means of two samples are equal) can not be rejected at 5% significance level. Table 2: Descriptive statistics and paired t-test
Paired ‘t’ test considering two samples having same mean was performed to verify whether the estimated and measured values are significantly different. It was found that calculated ‘t’ values (-1.59, -0.91) is less than critical t value of 1.83, 1.83 (Table 2). This means, Null hypothesis (that the means of two samples are equal) can not be rejected at 5% significance level. Table 2: Descriptive statistics and paired t-test
Figure 1: Scatter plot of measured W,, and fitted model parameters
Figure 1: Scatter plot of measured W,, and fitted model parameters
Coefficient of variation (CV) of estimated values also showed some improvements over measured values. Analyses of skewness revealed that the positively skewed distribution has been improved by the model estimation but with flatter curve (kurtosis). The goodness of fit criteria was used for comparing estimated W;, with measured Wy. The R? value of 0.85 shows model estimation is appropriate. Similarly for W pwp, R’ value is 0.81 indicating reasonable model estimation.
Coefficient of variation (CV) of estimated values also showed some improvements over measured values. Analyses of skewness revealed that the positively skewed distribution has been improved by the model estimation but with flatter curve (kurtosis). The goodness of fit criteria was used for comparing estimated W;, with measured Wy. The R? value of 0.85 shows model estimation is appropriate. Similarly for W pwp, R’ value is 0.81 indicating reasonable model estimation.
For model validation trend line analysis of estimated value with the observed one has been done (Figures. 5&6). The higher R’ value at FC (84.4%) and at PWP (81.4%) is indicative of model fitting well (Table 3). Linear trend analysis was performed and the best fit model close to 1:1 line was evaluated using the SDiff criteria. The best model should be close to 1:1 line with SDiff value lower or closer to zero. From table 3 it is evident that SDiff of the fitted model at FC and PWP are 16.6% and 14.8% respectively which is closer to zero thus model is behaving well. viii. Rawls, W. J.& Brakensiek, D.L. (1985) Estimating soil water retention from soil properties. In Watershed management In the eighties. (ed. By E.B Jones. & TJ. Ward.) (Proc. Irrig. Drain. div., ASCE, Aprl 30 - May 1,1985), Denver, CO., 293-299 Further Nash-Sutcliffe efficiency (NSE) was also used for performance evaluation of the models. If NSE value is positive (0 to 1), the model is acceptable. NSE (< 0) indicates that the mean value of the observed series would have been a better predictor than the model, and thus the model is not acceptable. In table 3 it is clearly shown that at FC and PWP the NSE is 0.801 and 0.795 respectively means model is efficient. A similar criteria d-index at FC (0.944) and PWP (0.943) indicates that the model estimation is satisfactory. REFERENCES the presence of coarse fragments and organic carbon contents in the soil. The model thus uses the basic soil data which is easily available in soil survey reports. The model is useful in water balance simulation, irrigation scheduling, and watershed management. The field engineers, water managers and agronomists may use this empirical equations for estimation of water content at FC and PWP for the soils of Chattisgarh state or similar types of soils available in other states. ix. Rawls, W. J., Ahuja, L.R. Brakensiek, D.L. & Sirmohammadi, A. (1992) Infiltration and soil water movement. In: Handbook of Hydrology (ed. by David R. Maidment), p5.15, Mc Graw-Hill
For model validation trend line analysis of estimated value with the observed one has been done (Figures. 5&6). The higher R’ value at FC (84.4%) and at PWP (81.4%) is indicative of model fitting well (Table 3). Linear trend analysis was performed and the best fit model close to 1:1 line was evaluated using the SDiff criteria. The best model should be close to 1:1 line with SDiff value lower or closer to zero. From table 3 it is evident that SDiff of the fitted model at FC and PWP are 16.6% and 14.8% respectively which is closer to zero thus model is behaving well. viii. Rawls, W. J.& Brakensiek, D.L. (1985) Estimating soil water retention from soil properties. In Watershed management In the eighties. (ed. By E.B Jones. & TJ. Ward.) (Proc. Irrig. Drain. div., ASCE, Aprl 30 - May 1,1985), Denver, CO., 293-299 Further Nash-Sutcliffe efficiency (NSE) was also used for performance evaluation of the models. If NSE value is positive (0 to 1), the model is acceptable. NSE (< 0) indicates that the mean value of the observed series would have been a better predictor than the model, and thus the model is not acceptable. In table 3 it is clearly shown that at FC and PWP the NSE is 0.801 and 0.795 respectively means model is efficient. A similar criteria d-index at FC (0.944) and PWP (0.943) indicates that the model estimation is satisfactory. REFERENCES the presence of coarse fragments and organic carbon contents in the soil. The model thus uses the basic soil data which is easily available in soil survey reports. The model is useful in water balance simulation, irrigation scheduling, and watershed management. The field engineers, water managers and agronomists may use this empirical equations for estimation of water content at FC and PWP for the soils of Chattisgarh state or similar types of soils available in other states. ix. Rawls, W. J., Ahuja, L.R. Brakensiek, D.L. & Sirmohammadi, A. (1992) Infiltration and soil water movement. In: Handbook of Hydrology (ed. by David R. Maidment), p5.15, Mc Graw-Hill
Figure 1. Methodology Adopted
Figure 1. Methodology Adopted
Table 1. Area Covered by each class LS-SVM is employed by collection of 285 samples. The training sample is about 70 percent of the total sample collection and testing sample is 30 percent. The parameters gamma and sigma of LS-SVM Kernel function (Radial basis) is optimized to 7223.5463 and 343.37529 respectively. The accuracy obtained after prediction of classes by LS-SVM model is almost 100 percent which is comparatively better than MLC classifier.
Table 1. Area Covered by each class LS-SVM is employed by collection of 285 samples. The training sample is about 70 percent of the total sample collection and testing sample is 30 percent. The parameters gamma and sigma of LS-SVM Kernel function (Radial basis) is optimized to 7223.5463 and 343.37529 respectively. The accuracy obtained after prediction of classes by LS-SVM model is almost 100 percent which is comparatively better than MLC classifier.
The methodology presented is employed for land cover classification of Allahabad and adjoining districts (2471 to 25.67 ° N, 80.78 to 82.03° E). The classified image (MLC) is shown in Figure 2. Figure 2. Maximum Likelihood Classified LANDSAT 8 Image The classified image of study area is classified in seven broad categories namely water body, urban settlement, stoney waste, sandy area, low vegetation, dense vegetation and barren land. The areas covered by all these classes are shown in Table 1.
The methodology presented is employed for land cover classification of Allahabad and adjoining districts (2471 to 25.67 ° N, 80.78 to 82.03° E). The classified image (MLC) is shown in Figure 2. Figure 2. Maximum Likelihood Classified LANDSAT 8 Image The classified image of study area is classified in seven broad categories namely water body, urban settlement, stoney waste, sandy area, low vegetation, dense vegetation and barren land. The areas covered by all these classes are shown in Table 1.
Figure la. Satellite images from Landsat - 7 ETM+ bands 4, 3 and 2 (BGR) showing the study area. 1b. Digital elevation model of study area.
Figure la. Satellite images from Landsat - 7 ETM+ bands 4, 3 and 2 (BGR) showing the study area. 1b. Digital elevation model of study area.
Figure 2a and 2b. The different soil and slope categories of Kadalundi River basin respectively.
Figure 2a and 2b. The different soil and slope categories of Kadalundi River basin respectively.
Fig 3. The 41 Sub-basin and 751 different HRUs of Kadalundi River basin.
Fig 3. The 41 Sub-basin and 751 different HRUs of Kadalundi River basin.
Table 1: Descriptions and sources of hydro-meteorological database of study area. 3.3 Model calibration and validation
Table 1: Descriptions and sources of hydro-meteorological database of study area. 3.3 Model calibration and validation
Figure 4: Land use /land cover classification of 1988 and 2000 of Kadalundi River Basin.
Figure 4: Land use /land cover classification of 1988 and 2000 of Kadalundi River Basin.
Table 2. Area (Km’) and overall amount of change (%) in LULC of study area over the period of 1988-2000. The simulation and calibration/validation results of LULC change impacts on surface runoff at the sub-basinal scale have been estimated using SWAT model and how the runoff at the outlet of the River basin changes for the rainfall by 1988 to 2000. The comparison between simulated and observed monthly stream-flow values in the periods of January 1988 to December 2000 based on LULC 1988 is shows in the figure 5a, similarly simulated monthly stream flow for the period of 1988 to 2000 for 2000 LULC given in figure 6a. Further the results and calibration from January 1988 to December 1995 and validation for January 1996 to December 2000.
Table 2. Area (Km’) and overall amount of change (%) in LULC of study area over the period of 1988-2000. The simulation and calibration/validation results of LULC change impacts on surface runoff at the sub-basinal scale have been estimated using SWAT model and how the runoff at the outlet of the River basin changes for the rainfall by 1988 to 2000. The comparison between simulated and observed monthly stream-flow values in the periods of January 1988 to December 2000 based on LULC 1988 is shows in the figure 5a, similarly simulated monthly stream flow for the period of 1988 to 2000 for 2000 LULC given in figure 6a. Further the results and calibration from January 1988 to December 1995 and validation for January 1996 to December 2000.
Figure 5(a) Precipitation, observed discharge, simulated discharge of Kadalundi River basin during the period of 1988 to 2000 with their trend imposed by using 1988 LULC and (b) correlation between observed and calibrated/validated discharge.
Figure 5(a) Precipitation, observed discharge, simulated discharge of Kadalundi River basin during the period of 1988 to 2000 with their trend imposed by using 1988 LULC and (b) correlation between observed and calibrated/validated discharge.
Figure 6(a) Precipitation, observed discharge, simulatec discharge of Kadalundi River basin during the period of 1988 tc 2000 with their trend impose by using 2000 LULC and (b) correlation between observed and calibrated/validated discharge.
Figure 6(a) Precipitation, observed discharge, simulatec discharge of Kadalundi River basin during the period of 1988 tc 2000 with their trend impose by using 2000 LULC and (b) correlation between observed and calibrated/validated discharge.
The study area considered for assessing LULC change on hydrological response from catchments is located in northern zone (Yelahanka) of Bangalore, India, (Figure 1). Length of storm water drain network in the study area is about 18 km. The network is divided into 19 conduits that are shown in Figure 1. Area of watershed contributing flow to the network is about 70 km?. The watershed is divided into 34 sub-watersheds (Figure1).
The study area considered for assessing LULC change on hydrological response from catchments is located in northern zone (Yelahanka) of Bangalore, India, (Figure 1). Length of storm water drain network in the study area is about 18 km. The network is divided into 19 conduits that are shown in Figure 1. Area of watershed contributing flow to the network is about 70 km?. The watershed is divided into 34 sub-watersheds (Figure1).
ca fas amet we Sica [a fii bi UViwvy The LULC maps corresponding to the years 1996, 2002, 2006 and 2012 were merged with the HSG map to derive WCN corresponding to each of the 34 sub-watersheds in the study area. Figure 4 depicts WCN for 34 sub-watersheds in the study area corresponding to each of the years. On comparing the WCN across the years 1996 to 2012, it can be observed that WCN for all the sub-watersheds have increased. The sub-watersheds numbered 16, 18, 22 to 26 and 32 (Figure 1) have shown significant change (>10) in the value of WCN as compared to
ca fas amet we Sica [a fii bi UViwvy The LULC maps corresponding to the years 1996, 2002, 2006 and 2012 were merged with the HSG map to derive WCN corresponding to each of the 34 sub-watersheds in the study area. Figure 4 depicts WCN for 34 sub-watersheds in the study area corresponding to each of the years. On comparing the WCN across the years 1996 to 2012, it can be observed that WCN for all the sub-watersheds have increased. The sub-watersheds numbered 16, 18, 22 to 26 and 32 (Figure 1) have shown significant change (>10) in the value of WCN as compared to
other sub-watersheds. This increase in WCN can be attributed to transition from crop land to urban built-up area, which was observed from LULC maps. Changes in LULC were relatively minimal for the remaining sub-watersheds. Interesting LULC changes in sub-watershednumbered 14 were insignificant. It is to be noted that the WCN has increased significantly between the years 2006 to 2012 when compared with the earlier years.
other sub-watersheds. This increase in WCN can be attributed to transition from crop land to urban built-up area, which was observed from LULC maps. Changes in LULC were relatively minimal for the remaining sub-watersheds. Interesting LULC changes in sub-watershednumbered 14 were insignificant. It is to be noted that the WCN has increased significantly between the years 2006 to 2012 when compared with the earlier years.
Duration 1-hr of 5-year return period Figure 5: Water balance componentsquantified (in depth units) corresponding to 3h duration 5-year return period design hyetograph.
Duration 1-hr of 5-year return period Figure 5: Water balance componentsquantified (in depth units) corresponding to 3h duration 5-year return period design hyetograph.
Figure 6 shows effect of LULC change on runoff generated from each sub-watershed in the study area by SWMM model corresponding to a few typical design hyetographs. It can be observed from the figure that rainfall with higher return period generatesmore runoff, as expected. Comparison across the years 1996, 2002, 2006 and 2012 showed that the volume of runoff has increased consistently from the year1996 to the year 2012 across all sub-watersheds. The increase is significant during 2006-2012 for sub-watersheds numbered 16, 18, 22 and 26 across all hypothetical rain events (design hyetographs). These sub-watersheds were earlier found to have marked increase in their WCN (Figures4 and 6). Similarly, no significant changes were noted in runoff from sub-watershed numbered 14 for which changes in LULC’ were previously found to be insignificant.(Figures 4 and 6).
Figure 6 shows effect of LULC change on runoff generated from each sub-watershed in the study area by SWMM model corresponding to a few typical design hyetographs. It can be observed from the figure that rainfall with higher return period generatesmore runoff, as expected. Comparison across the years 1996, 2002, 2006 and 2012 showed that the volume of runoff has increased consistently from the year1996 to the year 2012 across all sub-watersheds. The increase is significant during 2006-2012 for sub-watersheds numbered 16, 18, 22 and 26 across all hypothetical rain events (design hyetographs). These sub-watersheds were earlier found to have marked increase in their WCN (Figures4 and 6). Similarly, no significant changes were noted in runoff from sub-watershed numbered 14 for which changes in LULC’ were previously found to be insignificant.(Figures 4 and 6).
Figure 6: Volume of runoff generated corresponding to 1h and 3h duration rainfall of 2year and 5year return period for each of the 34 sub-watershed.
Figure 6: Volume of runoff generated corresponding to 1h and 3h duration rainfall of 2year and 5year return period for each of the 34 sub-watershed.
Figure 1: Location Map of Study Area
Figure 1: Location Map of Study Area
approach i. e. remote sensing and GIS in conjunction with secondary data has been adopted. The remote sensing and GIS data were handled with the help of Erdas Imagine 9.3 and Arc GIS Desktop 9.3 respectively. The detailed methodology of the study is given below:
approach i. e. remote sensing and GIS in conjunction with secondary data has been adopted. The remote sensing and GIS data were handled with the help of Erdas Imagine 9.3 and Arc GIS Desktop 9.3 respectively. The detailed methodology of the study is given below:
Table 2: Land use/land cover changes during year 2003-04, and 2011-12
Table 2: Land use/land cover changes during year 2003-04, and 2011-12
Figure 3: Land use/land cover-2003-04 Figure 4: Land use/land cover-2011-12 kharrif crop and double crop has been at the expense of land with or without scrub/ waste land which had a decrease of 70.4%. The cropping pattern in the study area which had the practice of Rabi cultivation only, has now changed with more farmers adopting the soybean crop in the kharrif season along with wheat in the Rabi season. Even though there has been slight change in the acreage under forest, the area under fellow land has increased from 21.54 to 69.65 sq. km, a 223 % increase.
Figure 3: Land use/land cover-2003-04 Figure 4: Land use/land cover-2011-12 kharrif crop and double crop has been at the expense of land with or without scrub/ waste land which had a decrease of 70.4%. The cropping pattern in the study area which had the practice of Rabi cultivation only, has now changed with more farmers adopting the soybean crop in the kharrif season along with wheat in the Rabi season. Even though there has been slight change in the acreage under forest, the area under fellow land has increased from 21.54 to 69.65 sq. km, a 223 % increase.
Figure 5: Statistics of land use/land cover during year 2003 & 2011.
Figure 5: Statistics of land use/land cover during year 2003 & 2011.
Table-1 Basic description of Soraon command area
Table-1 Basic description of Soraon command area
Table -2 Area, and production of major crops cultivated in the Allahabad
Table -2 Area, and production of major crops cultivated in the Allahabad
Figure -1 Map of study area (a) Location map of India (b) Location map of Uttar Pradesh in India (c) Location map of soraon canal command in UP
Figure -1 Map of study area (a) Location map of India (b) Location map of Uttar Pradesh in India (c) Location map of soraon canal command in UP
Table-4 T otal water available from all resource After estimation of total water availability from various resource (Groundwater, Canal water, Surface water) in Soraon command area different scenarios are made with possible water availability condition that can occur. Detail of various scenarios given in Table-5
Table-4 T otal water available from all resource After estimation of total water availability from various resource (Groundwater, Canal water, Surface water) in Soraon command area different scenarios are made with possible water availability condition that can occur. Detail of various scenarios given in Table-5
Table -5 Water availability in crop seasons (Kharif & Rabi)
Table -5 Water availability in crop seasons (Kharif & Rabi)
[Zp=Value (1000) 104 Rs ] [Zp= 10’Value in Quintal] Table -7 Objective Function value for benefit and production
[Zp=Value (1000) 104 Rs ] [Zp= 10’Value in Quintal] Table -7 Objective Function value for benefit and production
Table -6 Economics of Kharif/Rabi Season Crop Equation for maximization of net benefit is obtained by multiplying benefit coefficient of each crop to crop variable from the Table 6 in Equation 1. Similarly maximization of production is obtained by multiplying production coefficient of each crop area as shown in Equation 2. Equation 1 and 2 is subjected to different water available scenario and constraint as discussed in section 2.1. Result obtained for maximization of benefit and production is shown in Table-7. Single objective based planning has certain limitation because of maximization or minimization of one objective. If one objective is optimized, other objectives obtained from optimal variable of optimized objective may not be optimal. To overcome this situation multi- objective approach is adopted. Multi-objective approach gives optimal value for more than o: one obj ective. miititbsennTKns_f ee so erm ii4ten. lll mn
Table -6 Economics of Kharif/Rabi Season Crop Equation for maximization of net benefit is obtained by multiplying benefit coefficient of each crop to crop variable from the Table 6 in Equation 1. Similarly maximization of production is obtained by multiplying production coefficient of each crop area as shown in Equation 2. Equation 1 and 2 is subjected to different water available scenario and constraint as discussed in section 2.1. Result obtained for maximization of benefit and production is shown in Table-7. Single objective based planning has certain limitation because of maximization or minimization of one objective. If one objective is optimized, other objectives obtained from optimal variable of optimized objective may not be optimal. To overcome this situation multi- objective approach is adopted. Multi-objective approach gives optimal value for more than o: one obj ective. miititbsennTKns_f ee so erm ii4ten. lll mn
Table 8(B) Objective function value for single objective and Multi-objective for Production
Table 8(B) Objective function value for single objective and Multi-objective for Production
Table 8 (A) Objective function value for single objective, and Multi-objective for Benefit
Table 8 (A) Objective function value for single objective, and Multi-objective for Benefit
demonstrates the signal identity. The high-frequency component (detail, D) is nuance. The decomposition process can be iterated, with successive approximations being decomposed in turn, so that one signal is broken down into many lower resolution components (Fig. 1). Figure 1. Diagram of multiresolution analysis of signal.
demonstrates the signal identity. The high-frequency component (detail, D) is nuance. The decomposition process can be iterated, with successive approximations being decomposed in turn, so that one signal is broken down into many lower resolution components (Fig. 1). Figure 1. Diagram of multiresolution analysis of signal.
its generalization properties. Figure 2 shows the multilayer perceptron (MLP) neural network architecture when the original signal taken as input of the neural network architecture. method of combining wavelet analysis with ANN: The decomposed details (D) and approximation (A) were taken as inputs to neural network structure as shown in Figure 3, where ‘i’ is the level of decomposition varying from 1 to I and j is the number of antecedent values varying from 0 to J and N is the length of the time series. To obtain the optimal weights (parameters) of the neural network structure, LM back propagation algorithm was used to train the network. A standard MLP with a logarithmic tangent sigmoid transfer function for the hidden layer and log sigmoid transfer function for the output layer were used in the analysis. The number of hidden nodes was determined by trial and error procedure. The output node will be the original value at one step ahead. Performance criteria: The performance of various models during calibration and validation were evaluated by using the statistical indices: the Root Mean Squared Error (RMSE), Correlation Coefficient (R) and Coefficient of Efficiency (COE).
its generalization properties. Figure 2 shows the multilayer perceptron (MLP) neural network architecture when the original signal taken as input of the neural network architecture. method of combining wavelet analysis with ANN: The decomposed details (D) and approximation (A) were taken as inputs to neural network structure as shown in Figure 3, where ‘i’ is the level of decomposition varying from 1 to I and j is the number of antecedent values varying from 0 to J and N is the length of the time series. To obtain the optimal weights (parameters) of the neural network structure, LM back propagation algorithm was used to train the network. A standard MLP with a logarithmic tangent sigmoid transfer function for the hidden layer and log sigmoid transfer function for the output layer were used in the analysis. The number of hidden nodes was determined by trial and error procedure. The output node will be the original value at one step ahead. Performance criteria: The performance of various models during calibration and validation were evaluated by using the statistical indices: the Root Mean Squared Error (RMSE), Correlation Coefficient (R) and Coefficient of Efficiency (COE).
The original time series was decomposed into Details and Approximations to certain number of sub-time series {D; Dy, ... D,, Ap} by wavelet transform algorithm. These play different role in the original time series and the behavior of each sub-time series is distinct (Wang and Ding, 2003). So the contribution to original time series varies from each successive approximations being decomposed in turn, so that one signal is broken down into many lower resolution components, tested using different scales from 1 to 10 with different sliding window amplitudes. In this context, dealing with a very irregular signal shape, an irregular wavelet, the Daubechies wavelet of order 5 (DB5), has been used at level 3. Consequently, D,, D2, D3 were detail time series, and A3 was the approximation time series. An ANN was constructed in which the sub-series {D,, D2, D3, A3} at time t are input of ANN and the original time series at t + T time are output of ANN, where T is the length of time to forecast. The input nodes are the antecedent values of the time series and were presented in Table 1. The Wavelet Neural Network mode (WNN) was formed in which the weights are learned with Feed forward neural network with Back Propagation algorithm. The number of hidden neurons for BPNN was deter-mined by trial and error procedure.
The original time series was decomposed into Details and Approximations to certain number of sub-time series {D; Dy, ... D,, Ap} by wavelet transform algorithm. These play different role in the original time series and the behavior of each sub-time series is distinct (Wang and Ding, 2003). So the contribution to original time series varies from each successive approximations being decomposed in turn, so that one signal is broken down into many lower resolution components, tested using different scales from 1 to 10 with different sliding window amplitudes. In this context, dealing with a very irregular signal shape, an irregular wavelet, the Daubechies wavelet of order 5 (DB5), has been used at level 3. Consequently, D,, D2, D3 were detail time series, and A3 was the approximation time series. An ANN was constructed in which the sub-series {D,, D2, D3, A3} at time t are input of ANN and the original time series at t + T time are output of ANN, where T is the length of time to forecast. The input nodes are the antecedent values of the time series and were presented in Table 1. The Wavelet Neural Network mode (WNN) was formed in which the weights are learned with Feed forward neural network with Back Propagation algorithm. The number of hidden neurons for BPNN was deter-mined by trial and error procedure.
Figure 4. Location of IMD Nunganbakkam rain gauge station in the Chennai City
Figure 4. Location of IMD Nunganbakkam rain gauge station in the Chennai City
Table 2. Goodness of fit statistics of the calibration and validation for the forecasted rainfall.
Table 2. Goodness of fit statistics of the calibration and validation for the forecasted rainfall.
Figure 2. Average share of different crops in virtual water during the period. The data for the last decade has been analysed and the average share of virtual water of soybean has been the highest which is followed by wheat, gram and cotton. The overall share of different crops selected under the study has been shown in figure 2.
Figure 2. Average share of different crops in virtual water during the period. The data for the last decade has been analysed and the average share of virtual water of soybean has been the highest which is followed by wheat, gram and cotton. The overall share of different crops selected under the study has been shown in figure 2.
Figure 3. Y earwise Virtual water content of major crops
Figure 3. Y earwise Virtual water content of major crops
Figure 4. Y earwise virtual water volume of different agroclimatic zones The yearwise variation of total virtual water volume for different agroclimatic zones in crops considered under the study is shown in Figure 4. It is seen that Malwa Plateau has highest virtual water content and thus is highest consumer of water in agriculture which is followed by Vindhya Plateau, Khymore and Satpura Hillls, Gird Region and Central Narmada Valley. However, when considering the virtual water volume as a percentage of total rainfall volume over the respective agroclimatic zone, Malwa Plateau tops in the consumption followed by Central Narmada Valley, Vindhya Plateau, Gird Region, Nimar Plains and Satpura Plateau as evident in Figure 5.
Figure 4. Y earwise virtual water volume of different agroclimatic zones The yearwise variation of total virtual water volume for different agroclimatic zones in crops considered under the study is shown in Figure 4. It is seen that Malwa Plateau has highest virtual water content and thus is highest consumer of water in agriculture which is followed by Vindhya Plateau, Khymore and Satpura Hillls, Gird Region and Central Narmada Valley. However, when considering the virtual water volume as a percentage of total rainfall volume over the respective agroclimatic zone, Malwa Plateau tops in the consumption followed by Central Narmada Valley, Vindhya Plateau, Gird Region, Nimar Plains and Satpura Plateau as evident in Figure 5.
A comparison of virtual water content in different agro-climatic zones for year 2000 and year 2010 is shown the Figure 5. There has been a substantial increase virtual water content in the period except for Bundelkhand region. The % increase in virtual water content in the period is given in Table 1.
A comparison of virtual water content in different agro-climatic zones for year 2000 and year 2010 is shown the Figure 5. There has been a substantial increase virtual water content in the period except for Bundelkhand region. The % increase in virtual water content in the period is given in Table 1.
Table-1:; Characteristics and summary statistics of daily FAO-56 PM ET, for the stuqly locations
Table-1:; Characteristics and summary statistics of daily FAO-56 PM ET, for the stuqly locations
where T; and O; = target (FAO-56 PM ET 0) and output (ET, resulted from GQSN models) values at the i” step, respectively. In order to evaluate the performance of the GQSN models, two statistical indices, namely, the root mean squared error (RMSE, mm day”) and coefficient of determination (R’, dimensionless) were considered. The expressions for the aforementioned statistical indices are given below.
where T; and O; = target (FAO-56 PM ET 0) and output (ET, resulted from GQSN models) values at the i” step, respectively. In order to evaluate the performance of the GQSN models, two statistical indices, namely, the root mean squared error (RMSE, mm day”) and coefficient of determination (R’, dimensionless) were considered. The expressions for the aforementioned statistical indices are given below.
Table-3: Performance statistics of models with model development locations
Table-3: Performance statistics of models with model development locations
Table-2: Performance statistics of HG, GLSN and GQSN models during model development Figure-1:; Scatter plots of HG method with respect to the FAO- 56 PM under (a) Semi-arid, (b) Arid, (c) Sub-humid, and (d) Humid regions Figure-1:; Scatter plots of HG method with respect to the FAO- 56 PM under (a) Semi-arid, (b) Arid, (c) Sub-humid, and (d) Humid regions
Table-2: Performance statistics of HG, GLSN and GQSN models during model development Figure-1:; Scatter plots of HG method with respect to the FAO- 56 PM under (a) Semi-arid, (b) Arid, (c) Sub-humid, and (d) Humid regions Figure-1:; Scatter plots of HG method with respect to the FAO- 56 PM under (a) Semi-arid, (b) Arid, (c) Sub-humid, and (d) Humid regions
Figure-2: Scatter plots of GQSN models with respect to FAO- 56 PM ET, under (a) Semi-arid; (b) Arid; (c) Sub-humid; and (d) Humid regions
Figure-2: Scatter plots of GQSN models with respect to FAO- 56 PM ET, under (a) Semi-arid; (b) Arid; (c) Sub-humid; and (d) Humid regions
Table-4: Performance statistics of models with model testing locations
Table-4: Performance statistics of models with model testing locations
Figure-1: Location Map of Kosi Basin
Figure-1: Location Map of Kosi Basin
Figure-3: Representation of terms in the Energy equation (Source:-HEC-RAS User Manual, 2010)
Figure-3: Representation of terms in the Energy equation (Source:-HEC-RAS User Manual, 2010)
The land-use/land-cover map delineated using satellite data (Figure 5) was used to generate the Manning’s n values for the main channel and the floodplain. After creating and digitizing all the required layers, GIS data are exported to HEC-RAS. Figure-5: Landuse/Landcover for Kosi Basin (Source: NRSC)
The land-use/land-cover map delineated using satellite data (Figure 5) was used to generate the Manning’s n values for the main channel and the floodplain. After creating and digitizing all the required layers, GIS data are exported to HEC-RAS. Figure-5: Landuse/Landcover for Kosi Basin (Source: NRSC)
It has been observed that the flood inundated area obtained using the model is confined to the main channel and flood plain. However, the model is not able to estimated flood inundation in low lying area beyond the flood plain.
It has been observed that the flood inundated area obtained using the model is confined to the main channel and flood plain. However, the model is not able to estimated flood inundation in low lying area beyond the flood plain.
algorithms used in equations (4 relationship has been developed rainfall, soil moisture and inunda observed that for the input values evel, soil moisture, TRMM rainfall were used as input to develop models for flood inundation mapping. Different to 9), were applied to obtain flood inundated area in Kosi Basin. Linear and non-linear for the year 2006, 2007 and 2009. There was Kosi breach during the year-2008, so this year has not been considered for analysis. Full cubic model shows best result for all possible combination of water level, discharge, ted area (Figure 7). It has been as Water Level and Discharge of Baltara, the results obtained using Full Cubic Method shows best result with r value equal combination of Water level of B | to 0.93. Further, the input altara, soil moisture and mean rainfall of Kosi basin shows r° va input as Discharge and water level of Barahkshetra and Bhimnagar shows very poor relationship with inundated area. Other combination of water level, discharge, rainfall, soi moisture also indicates poor results for Barahkshetra and Bhimnagar stations. The Full cub equations (10) and (11). ue equal to 0.98. However, the ic model developed is given in Figure-7: Full Cubic Model for flood inundated area Kosi Basin (a) Water Level, Rainfall and Inundated area (b) Water Level, Soil Moisture and Inundated area
algorithms used in equations (4 relationship has been developed rainfall, soil moisture and inunda observed that for the input values evel, soil moisture, TRMM rainfall were used as input to develop models for flood inundation mapping. Different to 9), were applied to obtain flood inundated area in Kosi Basin. Linear and non-linear for the year 2006, 2007 and 2009. There was Kosi breach during the year-2008, so this year has not been considered for analysis. Full cubic model shows best result for all possible combination of water level, discharge, ted area (Figure 7). It has been as Water Level and Discharge of Baltara, the results obtained using Full Cubic Method shows best result with r value equal combination of Water level of B | to 0.93. Further, the input altara, soil moisture and mean rainfall of Kosi basin shows r° va input as Discharge and water level of Barahkshetra and Bhimnagar shows very poor relationship with inundated area. Other combination of water level, discharge, rainfall, soi moisture also indicates poor results for Barahkshetra and Bhimnagar stations. The Full cub equations (10) and (11). ue equal to 0.98. However, the ic model developed is given in Figure-7: Full Cubic Model for flood inundated area Kosi Basin (a) Water Level, Rainfall and Inundated area (b) Water Level, Soil Moisture and Inundated area
ae eee ee a ee ee ee a, a One of the earliest methods of estimating RET involved the use of air temperature. The temperature methods are empirical equations that rely on air temperature as a substitute for the amount of energy that is available to the crop or watershed for Evapotranspiration. The formulae may be expressed as
ae eee ee a ee ee ee a, a One of the earliest methods of estimating RET involved the use of air temperature. The temperature methods are empirical equations that rely on air temperature as a substitute for the amount of energy that is available to the crop or watershed for Evapotranspiration. The formulae may be expressed as
Table — 2: Daily RET from different equations for the month of January-2003 in mm. Of all the above methods, Turc equation shows good correlation with R? = 0.77, 0.843, 0.813 for daily, monthly and yearly RET values. RET estimated from Turc method resulted in 91.08% and 93.97 % for Nash Co-efficiency of Error (NCE) when compared to Penman - Monteith method which was found to have highest closeness to predictor among all other methods in the present study. Hence, it is concluded that, for the Dharmaram watershed, Turc equation is best suitable among all the methods.
Table — 2: Daily RET from different equations for the month of January-2003 in mm. Of all the above methods, Turc equation shows good correlation with R? = 0.77, 0.843, 0.813 for daily, monthly and yearly RET values. RET estimated from Turc method resulted in 91.08% and 93.97 % for Nash Co-efficiency of Error (NCE) when compared to Penman - Monteith method which was found to have highest closeness to predictor among all other methods in the present study. Hence, it is concluded that, for the Dharmaram watershed, Turc equation is best suitable among all the methods.
The study has been carried in the Alaknanda and Ramganga Rivers which are the major tributaries of River Ganga. Both rivers originate from the Western Himalayas. River Alaknanda arises from the Satopanth and the Bhagirathi Kharak Glaciers from the Garhwal Himalayas. On the other hand, the Ramganga River has its origin from the Namik Glacier in the Kumaon Himalayas. The map of Figure 1. Geological map of the study area (Valdiya, 1980).
The study has been carried in the Alaknanda and Ramganga Rivers which are the major tributaries of River Ganga. Both rivers originate from the Western Himalayas. River Alaknanda arises from the Satopanth and the Bhagirathi Kharak Glaciers from the Garhwal Himalayas. On the other hand, the Ramganga River has its origin from the Namik Glacier in the Kumaon Himalayas. The map of Figure 1. Geological map of the study area (Valdiya, 1980).
The locations of sediment collection are shown in Table 1. Grain size distribution was estimated by the Particle Size Analyzer (Model Coulter LS230), which works on the principle of laser diffraction pattern to calculate the diameter of the individual grain. The pre-possessing of the sediment before analysis includes removal of organic matter by adding 30% H,O, and removal of carbonate by adding 10% HCl solution (Cuven et al. 2010). Using the approach followed by Folk and ward (1957), grain size characteristics such as Mean size (d50), sorting, skewness and kurtosis were calculated. The samples that were correlated approximately having the same altitude. Table 1. Sample locations of the study area with Coordinates and Elevations
The locations of sediment collection are shown in Table 1. Grain size distribution was estimated by the Particle Size Analyzer (Model Coulter LS230), which works on the principle of laser diffraction pattern to calculate the diameter of the individual grain. The pre-possessing of the sediment before analysis includes removal of organic matter by adding 30% H,O, and removal of carbonate by adding 10% HCl solution (Cuven et al. 2010). Using the approach followed by Folk and ward (1957), grain size characteristics such as Mean size (d50), sorting, skewness and kurtosis were calculated. The samples that were correlated approximately having the same altitude. Table 1. Sample locations of the study area with Coordinates and Elevations
two tributaries of River Ganga shows that different factors control the grain size variability that vary from basin to basin. The mean grain size in the Alaknanda and Ramganga Rivers varies from 138.4 to 539.9 pm and 308.7 to 504.9 pm respectively. Samples are varying from fine sand to coarse sand. At Govindghat, no tributary is added to the River Alaknanda resulting in low flow conditions that hampers river from carrying the larger grain size. The larger grain size at Devprayag is because of the steep slope of 40m/K™m that increases river energy to carry bigger grain size. The larger grain size at Devprayag is also attributed to the lithology change. But, if we see River Ramganga, the average grain size carried by the river is 420 pm and mostly medium. The reason for Ramganga to show this behaviour is the constant water flow; no big tributary is added to the river in the upstream. The sample RG5 (sample collected after Kalagarh dam) is coarse sand which is due to the gentle slope at Kalagarh which lead river to deposit the sediment load.
two tributaries of River Ganga shows that different factors control the grain size variability that vary from basin to basin. The mean grain size in the Alaknanda and Ramganga Rivers varies from 138.4 to 539.9 pm and 308.7 to 504.9 pm respectively. Samples are varying from fine sand to coarse sand. At Govindghat, no tributary is added to the River Alaknanda resulting in low flow conditions that hampers river from carrying the larger grain size. The larger grain size at Devprayag is because of the steep slope of 40m/K™m that increases river energy to carry bigger grain size. The larger grain size at Devprayag is also attributed to the lithology change. But, if we see River Ramganga, the average grain size carried by the river is 420 pm and mostly medium. The reason for Ramganga to show this behaviour is the constant water flow; no big tributary is added to the river in the upstream. The sample RG5 (sample collected after Kalagarh dam) is coarse sand which is due to the gentle slope at Kalagarh which lead river to deposit the sediment load.
Table 2. Grain size parameters of the study area. 3.1.3. Skewness and Kurtosis
Table 2. Grain size parameters of the study area. 3.1.3. Skewness and Kurtosis
Figure 2. Grain size variability of the Alaknanda and Ramganga Rivers sediments
Figure 2. Grain size variability of the Alaknanda and Ramganga Rivers sediments
The Double ring infiltrometer is inexpensive to construct and operate. One person can set up and run several tests simultaneously. The test, however, requires the most water of all three methods. The simplicity of the infiltrometer’s design allows for ease in replication and operation. Lateral flow is the most serious limitation in using of ring infiltrometers (Hills, 971). Another serious limitation is the method of placement. Hammering or jacking the rings into the ground can result in destruction of soil structure or compression influencing the true value of the saturated hydraulic conductivity. The practicality of the instrument is reduced by the fact the ring is relatively heavy. A flat undisturbed surface is required which sometimes may not be feasible. Edge effects may include flow along the edge of the cylinder causing the infiltration rate to be overestimate.
The Double ring infiltrometer is inexpensive to construct and operate. One person can set up and run several tests simultaneously. The test, however, requires the most water of all three methods. The simplicity of the infiltrometer’s design allows for ease in replication and operation. Lateral flow is the most serious limitation in using of ring infiltrometers (Hills, 971). Another serious limitation is the method of placement. Hammering or jacking the rings into the ground can result in destruction of soil structure or compression influencing the true value of the saturated hydraulic conductivity. The practicality of the instrument is reduced by the fact the ring is relatively heavy. A flat undisturbed surface is required which sometimes may not be feasible. Edge effects may include flow along the edge of the cylinder causing the infiltration rate to be overestimate.
Figure 1. (a)Comparison of Infiltration Rate (mm/min) (b)Cumulative Infiltration (cm) using TI and DI for station I
Figure 1. (a)Comparison of Infiltration Rate (mm/min) (b)Cumulative Infiltration (cm) using TI and DI for station I
Table 2.Comparison of Hydraulic conductivity(cm/min) using Tl and DI
Table 2.Comparison of Hydraulic conductivity(cm/min) using Tl and DI
Figure 2. (a) Comparison of Infiltration Rate (mm/min) (b)Cumulative Infiltration (cm)using TI and DI for station II
Figure 2. (a) Comparison of Infiltration Rate (mm/min) (b)Cumulative Infiltration (cm)using TI and DI for station II
Jalandhar, Kapurthala, Ludhiana, Moga, Nawanshehar and Rupnagar (Fig 2.1). Climate of the study area is semi-arid, hot and subtropical monsoon type with cold winter and hot summer. It is mainly influenced be the Himalayas in the North and the ‘Thar’ desert of Rajasthan in the south and south-west. It receives about 456 mmof average annual rainfall with huge variability i space as well as in time. About 75% of average annual rainfall i received during monsoon months. July and August are raini month.A gricultural in the major industry in the region, hence in the majority of land use belong to agriculture land.The study area is about 13,229 km? up to Harike Barrage which is the confluence of the Sutlej and Beas rivers, located at downstream of Bhakra dam. Monthly rainfall data from 1901-2010 for mine district headquarters stations across the Bist-doab region of the Punjab state of India have been analyzed to describe any changes in annual, seasonal and monthly rainfall amount.The district-wise rainfall data was obtained by simple linear averaging from the gridded data of the CRU dataset. The data was downloaded from the India water portal (http://www.indiawaterportal.org/) which has been originally publicly available at the website of Centre of Environmental Data Archival Centre for Climate Change Research, School of Environmental Sciences, University of East Anglia in Norwich, UK (http://badc.nerc.ac.uk).The box plot given blow (Fig 2.2) represents the major statistical characteristics such as a data set’s lowest value, highest value, median value, and the size of the first and third quartile of the rainfall data () from1901-2010 for the concerned districts of the study area.It is deceptive from Fig. 2.2 that the rainfall is decreasing from northern part of the region towards the south- western part for the said study period. Vicinity of the northern parts to the Shiwaliks foot hills could be one of the reason for this. The box plots of Seasonal analysis of the entire monthly rainfall data (1900-2010) based on the three prominent and distinct seasons viz. pre-monsoon or spring season (March — May), southwest monsoon season (June — October), northeast monsoon season or winter season (from November of the last
Jalandhar, Kapurthala, Ludhiana, Moga, Nawanshehar and Rupnagar (Fig 2.1). Climate of the study area is semi-arid, hot and subtropical monsoon type with cold winter and hot summer. It is mainly influenced be the Himalayas in the North and the ‘Thar’ desert of Rajasthan in the south and south-west. It receives about 456 mmof average annual rainfall with huge variability i space as well as in time. About 75% of average annual rainfall i received during monsoon months. July and August are raini month.A gricultural in the major industry in the region, hence in the majority of land use belong to agriculture land.The study area is about 13,229 km? up to Harike Barrage which is the confluence of the Sutlej and Beas rivers, located at downstream of Bhakra dam. Monthly rainfall data from 1901-2010 for mine district headquarters stations across the Bist-doab region of the Punjab state of India have been analyzed to describe any changes in annual, seasonal and monthly rainfall amount.The district-wise rainfall data was obtained by simple linear averaging from the gridded data of the CRU dataset. The data was downloaded from the India water portal (http://www.indiawaterportal.org/) which has been originally publicly available at the website of Centre of Environmental Data Archival Centre for Climate Change Research, School of Environmental Sciences, University of East Anglia in Norwich, UK (http://badc.nerc.ac.uk).The box plot given blow (Fig 2.2) represents the major statistical characteristics such as a data set’s lowest value, highest value, median value, and the size of the first and third quartile of the rainfall data () from1901-2010 for the concerned districts of the study area.It is deceptive from Fig. 2.2 that the rainfall is decreasing from northern part of the region towards the south- western part for the said study period. Vicinity of the northern parts to the Shiwaliks foot hills could be one of the reason for this. The box plots of Seasonal analysis of the entire monthly rainfall data (1900-2010) based on the three prominent and distinct seasons viz. pre-monsoon or spring season (March — May), southwest monsoon season (June — October), northeast monsoon season or winter season (from November of the last
Gurdaspur Hoshiarpur Rupnagar Nawanshahar Jalandhar Kapurthala Amritsar Ludhiana Moga Regional AV 2.3 Trend detection test Fig 2.1 Box plot of the rainfall data (1901-2010) for districts (North to South) of study area.
Gurdaspur Hoshiarpur Rupnagar Nawanshahar Jalandhar Kapurthala Amritsar Ludhiana Moga Regional AV 2.3 Trend detection test Fig 2.1 Box plot of the rainfall data (1901-2010) for districts (North to South) of study area.
Table 3.2: Z-V alue of MK Test for Annual and Seasonal series of rainfall In order to get the study area’s response the Z-values of annua and seasonal time series also have been interpolated using spline method. The Z-value maps of the study area for annual as wel seasonal rainfall trends are presented in the Fig. 3.1. Z-value maps of the area are useful to determine the Z statistics of Mann- Kendall test at any point location in the study area. The annua average rainfall of the lower Sutlej is supporting the rising trend with more than 95% significance level in most of the area. The analysis of the rainfall data by parametric and non-parametric statistical test and the magnitude of trends supports the rising trend over the study area. While analysing the results presented in the various tables and figures one can easily conclude that the only winter season is not supporting any trend but the monsoon and the annual rainfall in the region has been increased over the last century.
Table 3.2: Z-V alue of MK Test for Annual and Seasonal series of rainfall In order to get the study area’s response the Z-values of annua and seasonal time series also have been interpolated using spline method. The Z-value maps of the study area for annual as wel seasonal rainfall trends are presented in the Fig. 3.1. Z-value maps of the area are useful to determine the Z statistics of Mann- Kendall test at any point location in the study area. The annua average rainfall of the lower Sutlej is supporting the rising trend with more than 95% significance level in most of the area. The analysis of the rainfall data by parametric and non-parametric statistical test and the magnitude of trends supports the rising trend over the study area. While analysing the results presented in the various tables and figures one can easily conclude that the only winter season is not supporting any trend but the monsoon and the annual rainfall in the region has been increased over the last century.
Table 3.3Sen Estimator of slope (mm/year) for monthly rainfall. On analyzing the estimates of the slope (mm/year) for annual and seasonal rainfall series of all the districts (Table 3.4), it is implicit that three districts experienced decreasing rainfall in the winter season. The maximum reduction was found for Kapurthala (-0.08 mm/year). The maximum increase out of 9 districts was experienced by Gurdaspur in rainfall (1.95 mm/year annually and 1.56 mm/year Monsoon season) followed by Rupnagar (1.72 mm/year annually and 1.31 mm/year Monsoon season) and Amritsar (1.53 mm/year annually and 1.31 mm/year Monsoon season). Considering the study area as a whole, the
Table 3.3Sen Estimator of slope (mm/year) for monthly rainfall. On analyzing the estimates of the slope (mm/year) for annual and seasonal rainfall series of all the districts (Table 3.4), it is implicit that three districts experienced decreasing rainfall in the winter season. The maximum reduction was found for Kapurthala (-0.08 mm/year). The maximum increase out of 9 districts was experienced by Gurdaspur in rainfall (1.95 mm/year annually and 1.56 mm/year Monsoon season) followed by Rupnagar (1.72 mm/year annually and 1.31 mm/year Monsoon season) and Amritsar (1.53 mm/year annually and 1.31 mm/year Monsoon season). Considering the study area as a whole, the
Table 3.4: Sen Estimator of slope (mm/year) for Annual and seasonal rainfall.
Table 3.4: Sen Estimator of slope (mm/year) for Annual and seasonal rainfall.
The dT map is an important input for the determination of sensible heat. The value of dT varied from 3.157K to 14.753K for the study region. Since Sensible Heat Flux is an implicit function of components that contains it therefore an iterative process is run to derive the final value. The iteration process is carried with initial value of y, (correction factor for momentum transport) and wy (correction factor for heat transport) as zero then subsequently computing the maps of Friction Velocity U:), Aerodynamic resistance for heat transport (R.,), Sensible Heat (SH), Monin Obukhov Length (L), Xm, Wm Xn Wh in sequential order. The value of wand y, at the end of the first iteration is taken for the next iteration. This iterative process is run till a constant value of sensible heat is reached. Sensible heat flux ranged from 0.0066 to 141.0801 Wm” for the command area. Histogram of sensible heat flux shows a normal distribution of values. Table 1. Derived components of dry and wet pixel for the study region
The dT map is an important input for the determination of sensible heat. The value of dT varied from 3.157K to 14.753K for the study region. Since Sensible Heat Flux is an implicit function of components that contains it therefore an iterative process is run to derive the final value. The iteration process is carried with initial value of y, (correction factor for momentum transport) and wy (correction factor for heat transport) as zero then subsequently computing the maps of Friction Velocity U:), Aerodynamic resistance for heat transport (R.,), Sensible Heat (SH), Monin Obukhov Length (L), Xm, Wm Xn Wh in sequential order. The value of wand y, at the end of the first iteration is taken for the next iteration. This iterative process is run till a constant value of sensible heat is reached. Sensible heat flux ranged from 0.0066 to 141.0801 Wm” for the command area. Histogram of sensible heat flux shows a normal distribution of values. Table 1. Derived components of dry and wet pixel for the study region
y area is shown in Figure 1.
y area is shown in Figure 1.
Figure 2: Locations of raingauge stations and gauge-discharge site in Bina river
Figure 2: Locations of raingauge stations and gauge-discharge site in Bina river
Table 1: Derived truncation level at 75% dependability levels at site Rahatgarh (m*/s) The departure of daily stream flow from its truncation level demonstrating low flow conditions persisting for more than ten days periodis shown in Figure 4. From the analysis it can be seen that the Bina river had experienced 6 low flow period of 9 years from 1990 to 1998 indicating events over the or 2 low flow events every year. Analysis was also carried out to obtain deficitflow volume and severity of low flow eve Table 2. The low flow events in the basin usual July to September and terminated during December. The severity volume of low flow in mts as shown in y began during November to the river varied from 2.88 to 287.37 MCM and duration of low fl ow rangedfrom 10 to 22 days. The maximum severity volume of 287.37 MCM was observed for22 days during September, 1994. The years 1991 and 1995 experienced twolow flow events each, which were highest in any year. In the year 1991 two low flow events of total 23 days experienced a total severity of 39.27 MCM. In
Table 1: Derived truncation level at 75% dependability levels at site Rahatgarh (m*/s) The departure of daily stream flow from its truncation level demonstrating low flow conditions persisting for more than ten days periodis shown in Figure 4. From the analysis it can be seen that the Bina river had experienced 6 low flow period of 9 years from 1990 to 1998 indicating events over the or 2 low flow events every year. Analysis was also carried out to obtain deficitflow volume and severity of low flow eve Table 2. The low flow events in the basin usual July to September and terminated during December. The severity volume of low flow in mts as shown in y began during November to the river varied from 2.88 to 287.37 MCM and duration of low fl ow rangedfrom 10 to 22 days. The maximum severity volume of 287.37 MCM was observed for22 days during September, 1994. The years 1991 and 1995 experienced twolow flow events each, which were highest in any year. In the year 1991 two low flow events of total 23 days experienced a total severity of 39.27 MCM. In
‘Figure 3: Flow Duration Curves for August at Rahatgarh
‘Figure 3: Flow Duration Curves for August at Rahatgarh
Drought studies can provide an important input to water resources planners for water conservation and _ water management purposes. Flow Duration Curves technique helps to determine the probability of occurrence of particular flow at the site and dependable flow at various probability levels, which is helpful for planning of water resources projects and deriving truncation levels for specific purposes. Low flow analysis technique helps in assessing the hydrological drought frequency, its duration and severity in the river basin using daily stream flow data. The Bina river in Madhya Pradesh is of an intermittent nature which generally experiences 1 or 2 low flow condition every year. The low flow events in this basin usually begin during July to September and terminate during October to
Drought studies can provide an important input to water resources planners for water conservation and _ water management purposes. Flow Duration Curves technique helps to determine the probability of occurrence of particular flow at the site and dependable flow at various probability levels, which is helpful for planning of water resources projects and deriving truncation levels for specific purposes. Low flow analysis technique helps in assessing the hydrological drought frequency, its duration and severity in the river basin using daily stream flow data. The Bina river in Madhya Pradesh is of an intermittent nature which generally experiences 1 or 2 low flow condition every year. The low flow events in this basin usually begin during July to September and terminate during October to
Figure 4: Low Flow events at Rahatgarh the year 1995 two low flow events of total 29 days experienced total severity volume of 95.69 MCM. In the year 1992 one low flow event of 16 days experienced total severity of 17.03 MCM. Therefore, on the basis of above analysis, it can be concluded that years 1991 and 1995 were the drought years of severe deficit runoff volume in Bina river at Rahatgarh. The information on frequency of occurrence of low flow and runoff volume deficit in the river is useful in improvement of existing backup practices and to undertake water resources management and development of river basin in systematic manner to meet the various water demands. Table 2: Duration and severity of low flow
Figure 4: Low Flow events at Rahatgarh the year 1995 two low flow events of total 29 days experienced total severity volume of 95.69 MCM. In the year 1992 one low flow event of 16 days experienced total severity of 17.03 MCM. Therefore, on the basis of above analysis, it can be concluded that years 1991 and 1995 were the drought years of severe deficit runoff volume in Bina river at Rahatgarh. The information on frequency of occurrence of low flow and runoff volume deficit in the river is useful in improvement of existing backup practices and to undertake water resources management and development of river basin in systematic manner to meet the various water demands. Table 2: Duration and severity of low flow

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Grinson George

Marine Geodesy

An attempt has been made to develop a holistic understanding of upwelling and downwelling along the southwest coast of India. The main objective was to elucidate the roles of different forcings involved in the vertical motion along this coast. The southwest coast of India was characterized by upwelling during the southwest monsoon (May to September) and by downwelling during the northeast monsoon and winter (November to February). The average vertical velocity calculated along the southwest coast from the vertical shift of the 26 C isotherm is 0.57 m/day during upwelling and 0.698 m/ day during downwelling. It was concluded that upwelling along the southwest coast of India is driven by offshore Ekman transport due to the alongshore wind, Ekman pumping, horizontal divergence of currents and by the propagation of coastally trapped waves. Whereas downwelling along the coast is driven only by convergence of currents and the propagation of coastally trapped Kelvin waves. Along the west coast of India, the downwelling-favorable Kelvin waves come from the equator and upwelling-favorable waves come from the Gulf of Mannar region.

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