Abstract
Precipitation provides the most crucial input for hydrological modeling. However, rain gauge networks, the most common precipitation measurement mechanisms, are sometimes sparse and inadequately distributed in practice, resulting in an imperfect representation of rainfall spatial variability. The objective of this study is to analyze the sensitivity of different model structures to the different density and distribution of rain gauges and evaluate their reliability and robustness. Based on a rain gauge network of 20 gauges in the Jinjiang River Basin, south-eastern China, this study compared the performance of two conceptual models (the hydrologic model (HYMOD) and Xinanjiang) and one process-based distributed model (the water and energy transfer between soil, plants and atmosphere model (WetSpa)) with different rain gauge distributions. The results show that the average accuracy for the three models is generally stable as the number of rain gauges decreases but is sensitive to changes in the network distribution. HYMOD has the highest calibration uncertainty, followed by Xinanjiang and WetSpa. Differing model responses are consistent with changes in network distribution, while calibration uncertainties are more related to model structures.
Highlights
Precipitation is one of the most crucial inputs in catchment runoff modeling and measuring rainfall is essential for determining hydrological catchment response [1,2]
One explanation is that former studies used lumped hydrological models that applied average surface rainfall as input while the SWAT used in Chaplot et al [26] is a distributed model that takes advantage of rainfall data interpolated into each sub-catchment
The results for all three models showed that a reduction in the number of rain gauges only resulted in worse performance when the rain gauge distribution was inhomogeneous
Summary
Precipitation is one of the most crucial inputs in catchment runoff modeling and measuring rainfall is essential for determining hydrological catchment response [1,2]. Because precipitation is generated by extremely complicated, non-linear, and sensitive atmospheric physical process [3], it shows highly spatial and temporal variability at the basin scale [4,5,6,7]. Singh [9] provided a detailed literature review on the influence of spatial–temporal variability in hydrological factors on rainfall runoff modeling. Sun et al [10] found that runoff prediction errors at the catchment scale were significantly related to the representation of rainfall data spatial variability. Shen et al [11] showed that noticeable uncertainty in stream flow and non-point source pollution modeling was caused by spatial rainfall uncertainty obtained from different precipitation interpolation methods
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