Abstract
Accurate assessment of spatial and temporal precipitation is crucial for simulating hydrological processes in basins, but is challenging due to insufficient rain gauges. Our study aims to analyze different precipitation interpolation schemes and their performances in runoff simulation during light and heavy rain periods. In particular, combinations of different interpolation estimates are explored and their performances in runoff simulation are discussed. The study was carried out in the Pengxi River basin of the Three Gorges Basin. Precipitation data from 16 rain gauges were interpolated using the Thiessen Polygon (TP), Inverse Distance Weighted (IDW), and Co-Kriging (CK) methods. Results showed that streamflow predictions employing CK inputs demonstrated the best performance in the whole process, in terms of the Nash–Sutcliffe Coefficient (NSE), the coefficient of determination (R2), and the Root Mean Square Error (RMSE) indices. The TP, IDW, and CK methods showed good performance in the heavy rain period but poor performance in the light rain period compared with the default method (least sophisticated nearest neighbor technique) in Soil and Water Assessment Tool (SWAT). Furthermore, the correlation between the dynamic weight of one method and its performance during runoff simulation followed a parabolic function. The combination of CK and TP achieved a better performance in decreasing the largest and lowest absolute errors compared to any single method, but the IDW method outperformed all methods in terms of the median absolute error. However, it is clear from our findings that interpolation methods should be chosen depending on the amount of precipitation, adaptability of the method, and accuracy of the estimate in different rain periods.
Highlights
Precipitation is a major driving force of hydrological processes
Each polygon contains only one rain gauge, and the weights of the rain gauges are computed by their relative areas, which are estimated with the Thiessen polygon network
Where Z (s0 ) is the average precipitation in the center of sub-basin; Z represents measured precipitation at the rain gauge i; Fi is the area of Thiessen polygon associated with gauge i; F is the area of the sub-basin
Summary
Precipitation is a major driving force of hydrological processes. Spatial precipitation patterns are consistently affected by topography and wind direction, which influence the estimation of the volume of storm runoff, peak runoff, and time-to-peak simulated by hydrological models [1]. Woldemeskel et al used a combination of thin plate smoothed splines and the IDW method to merge satellite and station data on a monthly time scale They found that there was an improvement in rainfall estimation, in regions with a sparse station network [22]. Due to the scarcity of of accurate precipitation development of power water generation, resource studies in this area issupply limited aspects in data, China,the such as flood control, navigation, and water [38].[33,39]. Due to In a previous study of carried outprecipitation in the Daning basin of theofThree the scarcity accurate data,River the development waterGorges resourceBasin, studiesprecipitation in this area isinput was identified as a major source of error for runoff modeling [33].
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