Flow forecasting is used in activities requiring stream flow data such as irrigation development, water supply, and flood control and hydropower development. Real time flow forecasting with special interest to floodin...Flow forecasting is used in activities requiring stream flow data such as irrigation development, water supply, and flood control and hydropower development. Real time flow forecasting with special interest to flooding is one of the most important applications of hydrology for decision making in water resources. In order to meet flood and flow forecasts using hydrological models may be used and subsequently be updated in accordance with residuals. Therefore in this study, different flood forecasting methods are evaluated for their potential of stream flow forecasting using Galway River Flow Forecasting and Modeling System (GFFMS) in Lake Tana basin, upper Blue Nile basin, Ethiopia. The areal rainfall and temperature data was used for the model input. Three forecast updating methods, i.e., autoregressive (AR), linear transfer function (LTF) and neuron network updating (NNU) methods were compared for stream flow forecasting, at one to six days lead time. The most sensitive parameters were fine-tuned first and modeled for a calibration period of 1994-2004 for three selected watersheds of the Tana basin. The results indicate that with the exception of the simple linear model, an acceptable result could be obtained using models embedded in the software. Artificial neural network model performed well for Gilgel Abay (NSE = 0.87) and Gumara (NSE = 0.9) watersheds but for Megech watershed, SMAR model (NSE = 0.78) gave a better forecast result. In capturing the peak flows LTF and NNU in forecast updating mode performed better for Gilgel Abay and Megech watersheds, respectively. The results of this study implied that GFFMS can be used as a useful tool to forecast peak stream flows for flood early warning in the upper Blue Nile basin.展开更多
Agricultural intensification to meet the food needs of the rapidly growing population in developing countries is negatively affecting the water quality. In most of these countries such as Ethiopia, information on surf...Agricultural intensification to meet the food needs of the rapidly growing population in developing countries is negatively affecting the water quality. In most of these countries such as Ethiopia, information on surface and especially groundwater quality is lacking. This limits the measure that can be taken to stop pollution. We, therefore, investigated the spatial and temporal variation of groundwater quality in the upland watershed. Tikur-Wuha watershed was selected because it is located in the Lake Tana watershed, which is seeing the first signs of eutrophication. Groundwater samples were collected from July 2014 to June 2015 from 19 shallow wells located throughout the watershed. Collected water samples were analyzed both in situ and in the laboratory to determine pH, electric conductivity (EC) and total dissolved solid (TDS), concentration of chemicals (nitrate, dissolved phosphorus, calcium, magnesium, aluminum and iron) and Escherichia coli (E. coli). We found that shallow groundwater had greater chemical concentrations and E. coli level in the monsoon rain phase than in the dry phase. Wells located down slope exhibited greater concentrations than mid- and upper-slope positions, with the exception of the nitrate concentration that was less down slope, due to denitrification in the shallow groundwater. Only E. coli level was above the WHO drinking water quality standards. Further studies on groundwater quality should be carried out to understand the extent of groundwater contamination.展开更多
文摘Flow forecasting is used in activities requiring stream flow data such as irrigation development, water supply, and flood control and hydropower development. Real time flow forecasting with special interest to flooding is one of the most important applications of hydrology for decision making in water resources. In order to meet flood and flow forecasts using hydrological models may be used and subsequently be updated in accordance with residuals. Therefore in this study, different flood forecasting methods are evaluated for their potential of stream flow forecasting using Galway River Flow Forecasting and Modeling System (GFFMS) in Lake Tana basin, upper Blue Nile basin, Ethiopia. The areal rainfall and temperature data was used for the model input. Three forecast updating methods, i.e., autoregressive (AR), linear transfer function (LTF) and neuron network updating (NNU) methods were compared for stream flow forecasting, at one to six days lead time. The most sensitive parameters were fine-tuned first and modeled for a calibration period of 1994-2004 for three selected watersheds of the Tana basin. The results indicate that with the exception of the simple linear model, an acceptable result could be obtained using models embedded in the software. Artificial neural network model performed well for Gilgel Abay (NSE = 0.87) and Gumara (NSE = 0.9) watersheds but for Megech watershed, SMAR model (NSE = 0.78) gave a better forecast result. In capturing the peak flows LTF and NNU in forecast updating mode performed better for Gilgel Abay and Megech watersheds, respectively. The results of this study implied that GFFMS can be used as a useful tool to forecast peak stream flows for flood early warning in the upper Blue Nile basin.
文摘Agricultural intensification to meet the food needs of the rapidly growing population in developing countries is negatively affecting the water quality. In most of these countries such as Ethiopia, information on surface and especially groundwater quality is lacking. This limits the measure that can be taken to stop pollution. We, therefore, investigated the spatial and temporal variation of groundwater quality in the upland watershed. Tikur-Wuha watershed was selected because it is located in the Lake Tana watershed, which is seeing the first signs of eutrophication. Groundwater samples were collected from July 2014 to June 2015 from 19 shallow wells located throughout the watershed. Collected water samples were analyzed both in situ and in the laboratory to determine pH, electric conductivity (EC) and total dissolved solid (TDS), concentration of chemicals (nitrate, dissolved phosphorus, calcium, magnesium, aluminum and iron) and Escherichia coli (E. coli). We found that shallow groundwater had greater chemical concentrations and E. coli level in the monsoon rain phase than in the dry phase. Wells located down slope exhibited greater concentrations than mid- and upper-slope positions, with the exception of the nitrate concentration that was less down slope, due to denitrification in the shallow groundwater. Only E. coli level was above the WHO drinking water quality standards. Further studies on groundwater quality should be carried out to understand the extent of groundwater contamination.
