Abstract
Comprehensive spatially referenced soil data are a crucial input in predicting biophysical and hydrological landscape processes. In most developing countries, these detailed soil data are not yet available. The objective of this study was, therefore, to evaluate the detail needed in soil resource inventories to predict the hydrologic response of watersheds. Using three distinctively different digital soil inventories, the widely used and tested soil and water assessment tool (SWAT) was selected to predict the discharge in two watersheds in the headwaters of the Blue Nile: the 1316 km2 Rib watershed and the nested 3.59 km2 Gomit watershed. The soil digital soil inventories employed were in increasing specificity: the global Food and Agricultural Organization (FAO), the Africa Soil Information Service (AfSIS) and the Amhara Design and Supervision Works Enterprise (ADSWE). Hydrologic simulations before model calibration were poor for all three soil inventories used as input. After model calibration, the streamflow predictions improved with monthly Nash–Sutcliffe efficiencies greater than 0.68. Predictions were statistically similar for the three soil databases justifying the use of the global FAO soil map in data-scarce regions for watershed discharge predictions.
Highlights
Soil data are crucial for landscape and water-resource planning [1]
The surface runoff with the ADSWE inventory was the lower in the Rib watershed compared to the surface runoff predicted with the Africa Soil Information Service (AfSIS) and the Food and Agricultural Organization (FAO) inventories
The opposite was true for the Gomit watershed where simulated surface runoff was greater with the ADSWE inventory
Summary
Soil data are crucial for landscape and water-resource planning [1]. With the advancement of remote-sensing technologies, geostatistics and geographic information system (GIS) data integration, soil datasets have become available in digital form with ever increasing precision and utility.The outcome of hydrological models is strongly influenced by spatial variability of ecological and physical processes in the landscape, which are linked with soil genesis [1,2,3,4]. Soil data are crucial for landscape and water-resource planning [1]. With the advancement of remote-sensing technologies, geostatistics and geographic information system (GIS) data integration, soil datasets have become available in digital form with ever increasing precision and utility. The outcome of hydrological models is strongly influenced by spatial variability of ecological and physical processes in the landscape, which are linked with soil genesis [1,2,3,4]. Adequate representation of soil genesis underlying the soil classification digital soil inventories has become especially important in hydrological modeling [1]. In developing countries, many digital soil inventories are not and freely available [5]. Before investing in a digital soil inventory, there is a need to assess the accuracy required for the soil inventory for improved simulation results
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