• Energy Research

Trash racks are usually composed of an array of bars installed in a hydropower scheme to safeguard the turbines by collecting water-borne detritus. However, current design approaches for the design of trash racks focus on structural criteria. A little attention renders the proper evaluation of hydraulic criteria, which causes a significant hydraulic head loss in low head hydropower schemes with an integral intake. This study investigates the head loss through trash racks by employing computational fluid dynamics (CFD) for several design combinations. A three-dimensional model of trash racks using fractional area/volume obstacle representation (FAVOR) method in FLOW-3D is set up to define the effects of the meshing on the geometry and several simulations are carried out considering various approach velocities and different bar spacings, inclination angles, and blockage ratios. The results indicate that head loss increases with an increase in approach velocity, the inclination angle of the rack with channel bed, and blockage ratio. It is noticed that a clear spacing between vertical bars greater than or equal to 0.075 m has a minimum head loss before it becomes significantly high for lower spacing. In addition, the head loss coefficient increases for screen angles greater than 60°, which can be considered as an optimal parameter for design purpose.

Trash racks are usually composed of an array of bars installed in a hydropower scheme to safeguard the turbines by collecting water-borne detritus. However, current design approaches for the design of trash racks focus on structural criteria. A little attention renders the proper evaluation of hydraulic criteria, which causes a significant hydraulic head loss in low head hydropower schemes with an integral intake. This study investigates the head loss through trash racks by employing computational fluid dynamics (CFD) for several design combinations. A three-dimensional model of trash racks using fractional area/volume obstacle representation (FAVOR) method in FLOW-3D is set up to define the effects of the meshing on the geometry and several simulations are carried out considering various approach velocities and different bar spacings, inclination angles, and blockage ratios. The results indicate that head loss increases with an increase in approach velocity, the inclination angle of the rack with channel bed, and blockage ratio. It is noticed that a clear spacing between vertical bars greater than or equal to 0.075 m has a minimum head loss before it becomes significantly high for lower spacing. In addition, the head loss coefficient increases for screen angles greater than 60°, which can be considered as an optimal parameter for design purpose.

AbstractQuantifying water-saving potential (WSP) is crucial for sustainable water resource management in canal command areas and river basins. Previous studies have partially or fully ignored the importance of groundwater in WSP assessments, particularly in irrigated areas. This study is aimed at quantifying WSP in the Lower Chenab Canal (LCC) command area of the Indus River Basin, Pakistan, under various scenarios of future climate change and groundwater recharge. These quantifications are conducted using an empirical model based on the Budyko theory. The model was forced using observed, remote sensing, and CMIP6 future climate data for two Shared Socioeconomic Pathways (SSP245 and SSP585) and their ensembles (cold-dry, cold-wet, warm-dry, and warm-wet) for possible futures. The results showed that the average WSP in the LCC command area was 466 ± 48 mm/year during the historical period (2001–2020). The WSP is projected to decrease by – 68 ± 3% under the warm-dry ensemble scenario (SSP245 and SSP585) and – 48 ± 13% under the ensembled cold-wet scenario by 2100. The results also demonstrated that WSP could be increased by up to 70 ± 9% by artificially recharging 20% of the abstracted groundwater per year in the LCC command area by the late twenty-first century. Our findings highlight the importance of adopting artificial groundwater recharge to enhance the WSP and sustainably manage water resources in the LCC command area. Policymakers should consider these findings when deciding on water resource management in the Indus River Basin.

AbstractQuantifying water-saving potential (WSP) is crucial for sustainable water resource management in canal command areas and river basins. Previous studies have partially or fully ignored the importance of groundwater in WSP assessments, particularly in irrigated areas. This study is aimed at quantifying WSP in the Lower Chenab Canal (LCC) command area of the Indus River Basin, Pakistan, under various scenarios of future climate change and groundwater recharge. These quantifications are conducted using an empirical model based on the Budyko theory. The model was forced using observed, remote sensing, and CMIP6 future climate data for two Shared Socioeconomic Pathways (SSP245 and SSP585) and their ensembles (cold-dry, cold-wet, warm-dry, and warm-wet) for possible futures. The results showed that the average WSP in the LCC command area was 466 ± 48 mm/year during the historical period (2001–2020). The WSP is projected to decrease by – 68 ± 3% under the warm-dry ensemble scenario (SSP245 and SSP585) and – 48 ± 13% under the ensembled cold-wet scenario by 2100. The results also demonstrated that WSP could be increased by up to 70 ± 9% by artificially recharging 20% of the abstracted groundwater per year in the LCC command area by the late twenty-first century. Our findings highlight the importance of adopting artificial groundwater recharge to enhance the WSP and sustainably manage water resources in the LCC command area. Policymakers should consider these findings when deciding on water resource management in the Indus River Basin.

Groundwater has a significant contribution to water storage and is considered to be one of the sources for agricultural irrigation; industrial; and domestic water use. The Gravity Recovery and Climate Experiment (GRACE) satellite provides a unique opportunity to evaluate terrestrial water storage (TWS) and groundwater storage (GWS) at a large spatial scale. However; the coarse resolution of GRACE limits its ability to investigate the water storage change at a small scale. It is; therefore; needed to improve the resolution of GRACE data at a spatial scale applicable for regional-level studies. In this study; a machine-learning-based downscaling random forest model (RFM) and artificial neural network (ANN) model were developed to downscale GRACE data (TWS and GWS) from 1° to a higher resolution (0.25°). The spatial maps of downscaled TWS and GWS were generated over the Indus basin irrigation system (IBIS). Variations in TWS of GRACE in combination with geospatial variables; including digital elevation model (DEM), slope; aspect; and hydrological variables; including soil moisture; evapotranspiration; rainfall; surface runoff; canopy water; and temperature; were used. The geospatial and hydrological variables could potentially contribute to; or correlate with; GRACE TWS. The RFM outperformed the ANN model and results show Pearson correlation coefficient (R) (0.97), root mean square error (RMSE) (11.83 mm), mean absolute error (MAE) (7.71 mm), and Nash–Sutcliffe efficiency (NSE) (0.94) while comparing with the training dataset from 2003 to 2016. These results indicate the suitability of RFM to downscale GRACE data at a regional scale. The downscaled GWS data were analyzed; and we observed that the region has lost GWS of about −9.54 ± 1.27 km3 at the rate of −0.68 ± 0.09 km3/year from 2003 to 2016. The validation results showed that R between downscaled GWS and observational wells GWS are 0.67 and 0.77 at seasonal and annual scales with a confidence level of 95%, respectively. It can; therefore; be concluded that the RFM has the potential to downscale GRACE data at a spatial scale suitable to predict GWS at regional scales.

Groundwater has a significant contribution to water storage and is considered to be one of the sources for agricultural irrigation; industrial; and domestic water use. The Gravity Recovery and Climate Experiment (GRACE) satellite provides a unique opportunity to evaluate terrestrial water storage (TWS) and groundwater storage (GWS) at a large spatial scale. However; the coarse resolution of GRACE limits its ability to investigate the water storage change at a small scale. It is; therefore; needed to improve the resolution of GRACE data at a spatial scale applicable for regional-level studies. In this study; a machine-learning-based downscaling random forest model (RFM) and artificial neural network (ANN) model were developed to downscale GRACE data (TWS and GWS) from 1° to a higher resolution (0.25°). The spatial maps of downscaled TWS and GWS were generated over the Indus basin irrigation system (IBIS). Variations in TWS of GRACE in combination with geospatial variables; including digital elevation model (DEM), slope; aspect; and hydrological variables; including soil moisture; evapotranspiration; rainfall; surface runoff; canopy water; and temperature; were used. The geospatial and hydrological variables could potentially contribute to; or correlate with; GRACE TWS. The RFM outperformed the ANN model and results show Pearson correlation coefficient (R) (0.97), root mean square error (RMSE) (11.83 mm), mean absolute error (MAE) (7.71 mm), and Nash–Sutcliffe efficiency (NSE) (0.94) while comparing with the training dataset from 2003 to 2016. These results indicate the suitability of RFM to downscale GRACE data at a regional scale. The downscaled GWS data were analyzed; and we observed that the region has lost GWS of about −9.54 ± 1.27 km3 at the rate of −0.68 ± 0.09 km3/year from 2003 to 2016. The validation results showed that R between downscaled GWS and observational wells GWS are 0.67 and 0.77 at seasonal and annual scales with a confidence level of 95%, respectively. It can; therefore; be concluded that the RFM has the potential to downscale GRACE data at a spatial scale suitable to predict GWS at regional scales.