Appropriate quantification and identification of the groundwater distribution in a hydrological basin may provide necessary information for effective management,planning and development of groundwater resources.Ground...Appropriate quantification and identification of the groundwater distribution in a hydrological basin may provide necessary information for effective management,planning and development of groundwater resources.Groundwater potential assessment and delineation in a highly heterogeneous environment with limited Spatiotemporal data derived from Gelana watershed of Abaya Chamo lake basin is performed,using integrated multi-criteria decision analysis(MCDA),water and energy transfer between soil and plant and atmosphere under quasi-steady state(WetSpass)models.The outputs of the WetSpass model reveal a favorable structure of water balance in the basin studied,mainly using surface runoff.The simulated total flow and groundwater recharge are validated using river measurements and estimated baseflow at two gauging stations located in the study area,which yields a good agreement.The WetSpass model effectively integrates a water balance assessment in a geographical information system(GIS)environment.The WetSpass model is shown to be computationally reputable for such a remote complex setting as the African rift,with a correlation coefficient of 0.99 and 0.99 for total flow and baseflow at a significant level of p-value<0.05,respectively.The simulated annual water budget reveals that 77.22%of annual precipitation loses through evapotranspiration,of which 16.54%is lost via surface runoff while 6.24%is recharged to the groundwater.The calibrated groundwater recharge from the WetSpass model is then considered when determining the controlling factors of groundwater occurrence and formation,together with other multi-thematic layers such as lithology,geomorphology,lineament density and drainage density.The selected five thematic layers through MCDA are incorporated by employing the analytical hierarchy process(AHP)method to identify the relative dominance in groundwater potential zoning.The weighted factors in the AHP are procedurally aggregated,based on weighted linear combinations to provide the groundwater potential index.Based on the potential indexes,the area then is demarcated into low,moderate,and high groundwater potential zones(GWPZ).The identified GWPZs are finally examined using the existing groundwater inventory data(static water level and springs)in the region.About 70.7%of groundwater inventory points are coinciding with the delineated GWPZs.The weighting comparison shows that lithology,geomorphology,and groundwater recharge appear to be the dominant factors influence on the resources potential.The assessment of groundwater potential index values identify 45.88%as high,39.38%moderate,and 14.73%as low groundwater potential zones.WetSpass model analysis is more preferable in the area like Gelana watershed when the topography is rugged,inaccessible and having limited gauging stations.展开更多
Hydrological forecasting plays an important role in water resource management, supporting socio-economic development and managing water-related risks in river basins. There are many flow forecasting techniques that ha...Hydrological forecasting plays an important role in water resource management, supporting socio-economic development and managing water-related risks in river basins. There are many flow forecasting techniques that have been developed several centuries ago, ranging from physical models, physics-based models, conceptual models, and data-driven models. Recently, Artificial Intelligence (AI) has become an advanced technique applied as an effective data-driven model in hydrological forecasting. The main advantage of these models is that they give results with compatible accuracy, and require short computation time, thus increasing forecasting time and reducing human and financial effort. This study evaluates the applicability of machine learning and deep learning in Hanoi water level forecasting where it is controlled for flood management and water supply in the Red River Delta, Vietnam. Accordingly, SANN (machine learning algorithm) and LSTM (deep learning algorithm) were tested and compared with a Physics-Based Model (PBM) for the Red River Delta. The results show that SANN and LSTM give high accuracy. The R-squared coefficient is greater than 0.8, the mean squared error (MSE) is less than 20 cm, the correlation coefficient of the forecast hydrology is greater than 0.9 and the level of assurance of the forecast plan ranges from 80% to 90% in both cases. In addition, the calculation time is much reduced compared to the requirement of PBM, which is its limitation in hydrological forecasting for large river basins such as the Red River in Vietnam. Therefore, SANN and LSTM are expected to help increase lead time, thereby supporting water resource management for sustainable development and management of water-related risks in the Red River Delta.展开更多
文摘Appropriate quantification and identification of the groundwater distribution in a hydrological basin may provide necessary information for effective management,planning and development of groundwater resources.Groundwater potential assessment and delineation in a highly heterogeneous environment with limited Spatiotemporal data derived from Gelana watershed of Abaya Chamo lake basin is performed,using integrated multi-criteria decision analysis(MCDA),water and energy transfer between soil and plant and atmosphere under quasi-steady state(WetSpass)models.The outputs of the WetSpass model reveal a favorable structure of water balance in the basin studied,mainly using surface runoff.The simulated total flow and groundwater recharge are validated using river measurements and estimated baseflow at two gauging stations located in the study area,which yields a good agreement.The WetSpass model effectively integrates a water balance assessment in a geographical information system(GIS)environment.The WetSpass model is shown to be computationally reputable for such a remote complex setting as the African rift,with a correlation coefficient of 0.99 and 0.99 for total flow and baseflow at a significant level of p-value<0.05,respectively.The simulated annual water budget reveals that 77.22%of annual precipitation loses through evapotranspiration,of which 16.54%is lost via surface runoff while 6.24%is recharged to the groundwater.The calibrated groundwater recharge from the WetSpass model is then considered when determining the controlling factors of groundwater occurrence and formation,together with other multi-thematic layers such as lithology,geomorphology,lineament density and drainage density.The selected five thematic layers through MCDA are incorporated by employing the analytical hierarchy process(AHP)method to identify the relative dominance in groundwater potential zoning.The weighted factors in the AHP are procedurally aggregated,based on weighted linear combinations to provide the groundwater potential index.Based on the potential indexes,the area then is demarcated into low,moderate,and high groundwater potential zones(GWPZ).The identified GWPZs are finally examined using the existing groundwater inventory data(static water level and springs)in the region.About 70.7%of groundwater inventory points are coinciding with the delineated GWPZs.The weighting comparison shows that lithology,geomorphology,and groundwater recharge appear to be the dominant factors influence on the resources potential.The assessment of groundwater potential index values identify 45.88%as high,39.38%moderate,and 14.73%as low groundwater potential zones.WetSpass model analysis is more preferable in the area like Gelana watershed when the topography is rugged,inaccessible and having limited gauging stations.
文摘Hydrological forecasting plays an important role in water resource management, supporting socio-economic development and managing water-related risks in river basins. There are many flow forecasting techniques that have been developed several centuries ago, ranging from physical models, physics-based models, conceptual models, and data-driven models. Recently, Artificial Intelligence (AI) has become an advanced technique applied as an effective data-driven model in hydrological forecasting. The main advantage of these models is that they give results with compatible accuracy, and require short computation time, thus increasing forecasting time and reducing human and financial effort. This study evaluates the applicability of machine learning and deep learning in Hanoi water level forecasting where it is controlled for flood management and water supply in the Red River Delta, Vietnam. Accordingly, SANN (machine learning algorithm) and LSTM (deep learning algorithm) were tested and compared with a Physics-Based Model (PBM) for the Red River Delta. The results show that SANN and LSTM give high accuracy. The R-squared coefficient is greater than 0.8, the mean squared error (MSE) is less than 20 cm, the correlation coefficient of the forecast hydrology is greater than 0.9 and the level of assurance of the forecast plan ranges from 80% to 90% in both cases. In addition, the calculation time is much reduced compared to the requirement of PBM, which is its limitation in hydrological forecasting for large river basins such as the Red River in Vietnam. Therefore, SANN and LSTM are expected to help increase lead time, thereby supporting water resource management for sustainable development and management of water-related risks in the Red River Delta.
