Water resources are precious in arid and semi-arid areas such as the Wadis of Iran. To sustainably manage these limited water resources, the residents of the Iranian Wadis have been traditionally using several water u...Water resources are precious in arid and semi-arid areas such as the Wadis of Iran. To sustainably manage these limited water resources, the residents of the Iranian Wadis have been traditionally using several water use systems(WUSs) which affect natural hydrological processes. In this study, WUSs and soil and water conservation measures(SWCMs) were integrated in a hydrological model of the Halilrood Basin in Iran. The Soil and Water Assessment Tool(SWAT) model was used to simulate the hydrological processes between 1993 and 2009 at daily time scale. To assess the importance of WUSs and SWCMs, we compared a model setup without WUSs and SWCMs(Default model) with a model setup with WUSs and SWCMs(WUS-SWCM model). When compared to the observed daily stream flow, the number of acceptable calibration runs as defined by the performance thresholds(Nash-Sutcliffe efficiency(NSE)≥0.68, –25%≤percent bias(PBIAS)≤25% and ratio of standard deviation(RSR)≤0.56) is 177 for the Default model and 1945 for the WUS-SWCM model. Also, the average Kling–Gupta efficiency(KGE) of acceptable calibration runs for the WUS-SWCM model is higher in both calibration and validation periods. When WUSs and SWCMs are implemented, surface runoff(between 30% and 99%) and water yield(between 0 and 18%) decreased in all sub-basins. Moreover, SWCMs lead to a higher contribution of groundwater flow to the channel and compensate for the extracted water by WUSs from the shallow aquifer. In summary, implementing WUSs and SWCMs in the SWAT model enhances model plausibility significantly.展开更多
Land use patterns arise from interactive processes between the physical environment and anthropogenic activities.While land use patterns and the associated explanatory variables have often been analyzed on the large s...Land use patterns arise from interactive processes between the physical environment and anthropogenic activities.While land use patterns and the associated explanatory variables have often been analyzed on the large scale,this study aims to determine the most important variables for explaining land use patterns in the 50 km^2 catchment of the Kielstau,Germany,which is dominated by agricultural land use.A set of spatially distributed variables including topography,soil properties,socioeconomic variables,and landscape indices are exploited to set up logistic regression models for the land use map of 2017 with detailed agricultural classes.Spatial validation indicates a reasonable performance as the relative operating characteristic (ROC) ranges between 0.73 and 0.97 for all land use classes except for corn (ROC = 0.68).The robustness of the models in time is confirmed by the temporal validation for which the ROC values are on the same level (maximum deviation 0.1).Non-agricultural land use is generally better explained than agricultural land use.The most important variables are the share of drained area,distance to protected areas,population density,and patch fractal dimension.These variables can either be linked to agriculture or the river course of the Kielstau.展开更多
基金The German Academic Exchange Service (DAAD) provided funding for the first authorThe German Federal Ministry of Education and Research (BMBF) provided funding for the second author through the “GLANCE” project (Global Change Effects on River Ecosystems, 01LN1320A)。
文摘Water resources are precious in arid and semi-arid areas such as the Wadis of Iran. To sustainably manage these limited water resources, the residents of the Iranian Wadis have been traditionally using several water use systems(WUSs) which affect natural hydrological processes. In this study, WUSs and soil and water conservation measures(SWCMs) were integrated in a hydrological model of the Halilrood Basin in Iran. The Soil and Water Assessment Tool(SWAT) model was used to simulate the hydrological processes between 1993 and 2009 at daily time scale. To assess the importance of WUSs and SWCMs, we compared a model setup without WUSs and SWCMs(Default model) with a model setup with WUSs and SWCMs(WUS-SWCM model). When compared to the observed daily stream flow, the number of acceptable calibration runs as defined by the performance thresholds(Nash-Sutcliffe efficiency(NSE)≥0.68, –25%≤percent bias(PBIAS)≤25% and ratio of standard deviation(RSR)≤0.56) is 177 for the Default model and 1945 for the WUS-SWCM model. Also, the average Kling–Gupta efficiency(KGE) of acceptable calibration runs for the WUS-SWCM model is higher in both calibration and validation periods. When WUSs and SWCMs are implemented, surface runoff(between 30% and 99%) and water yield(between 0 and 18%) decreased in all sub-basins. Moreover, SWCMs lead to a higher contribution of groundwater flow to the channel and compensate for the extracted water by WUSs from the shallow aquifer. In summary, implementing WUSs and SWCMs in the SWAT model enhances model plausibility significantly.
基金the financial support from the China Scholarship Council(CSC)through a scholarship for the first author
文摘Land use patterns arise from interactive processes between the physical environment and anthropogenic activities.While land use patterns and the associated explanatory variables have often been analyzed on the large scale,this study aims to determine the most important variables for explaining land use patterns in the 50 km^2 catchment of the Kielstau,Germany,which is dominated by agricultural land use.A set of spatially distributed variables including topography,soil properties,socioeconomic variables,and landscape indices are exploited to set up logistic regression models for the land use map of 2017 with detailed agricultural classes.Spatial validation indicates a reasonable performance as the relative operating characteristic (ROC) ranges between 0.73 and 0.97 for all land use classes except for corn (ROC = 0.68).The robustness of the models in time is confirmed by the temporal validation for which the ROC values are on the same level (maximum deviation 0.1).Non-agricultural land use is generally better explained than agricultural land use.The most important variables are the share of drained area,distance to protected areas,population density,and patch fractal dimension.These variables can either be linked to agriculture or the river course of the Kielstau.
