Publication Date:
2013-11-06
Description:
Numerous previous studies have constructed models to estimate baseflow characteristics from climatic and physiographic characteristics of catch- ments and applied these to ungauged regions. However, these studies generally used streamflow observations from a relatively small number of catchments ( 〈 200) located in small, homogeneous study areas, which may have led to less reliable models with limited applicability elsewhere. Here, we use streamflow observations from a highly heterogeneous set of 3394 catchments ( 〈 10000 km2) worldwide to construct reliable, widely applicable models based on 18 climatic and physiographic characteristics to estimate two important baseflow characteristics: (1) the baseflow index (BFI), defined as the ratio of long-term mean baseflow to total streamflow; and (2) the baseflow recession constant ( k ), defined as the rate of baseflow decay. Regression analysis results revealed that BFI and k were related to several climatic and physiographic characteristics, notably mean annual potential evaporation, mean snow-water equivalent depth, and abundance of surface-water bodies. Ensembles of Artificial Neural Networks (ANNs; obtained by sub-sampling the original set of catchments) were trained to estimate the baseflow characteristics from climatic and physiographic data. The catchment-scale estimation of the baseflow characteristics demonstrated encouraging performance with R 2 values of 0.82 for BFI and 0.72 for k . The connection weights of the trained ANNs indicated that climatic characteristics were more important for estimating k than BFI. Global maps of estimated BFI and k were obtained using global climatic and physiographic data as input to the derived mod- The resulting global maps are available for free download at http://www.hydrology-amsterdam.nl .
Print ISSN:
0043-1397
Electronic ISSN:
1944-7973
Topics:
Architecture, Civil Engineering, Surveying
,
Geography
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