Abstract
Abstract. The hydro-stochastic interpolation method based on traditional block Kriging has often been used to predict mean annual runoff in river basins. A caveat in such a method is that the statistic technique provides little physical insight into relationships between the runoff and its external forcing, such as the climate and land cover. In this study, the spatial runoff is decomposed into a deterministic trend and deviations from it caused by stochastic fluctuations. The former is described by the Budyko method (Fu's equation) and the latter by stochastic interpolation. This coupled method is applied to spatially interpolate runoff in the Huaihe River basin of China. Results show that the coupled method significantly improves the prediction accuracy of the mean annual runoff. The error of the predicted runoff by the coupled method is much smaller than that from the Budyko method and the hydro-stochastic interpolation method alone. The determination coefficient for cross-validation, Rcv2, from the coupled method is 0.87, larger than 0.81 from the Budyko method and 0.71 from the hydro-stochastic interpolation. Further comparisons indicate that the coupled method has also reduced the error in overestimating low runoff and underestimating high runoff suffered by the other two methods. These results confirm that the coupled method offers an effective and more accurate way to predict the mean annual runoff in river basins.
Highlights
The runoff observed at the outlet of a basin is a crucial element for investigating the hydrological cycle of the basin
The aim of this study is to combine the stochastic interpolation with the semi-empirical Budyko method to further improve the spatial interpolation/prediction of the mean annual runoff in the Huaihe River basin (HRB), China
A deviation from the Budyko estimated runoff is used by the stochastic interpolation that assumes spatially autocorrelated error
Summary
The runoff observed at the outlet of a basin is a crucial element for investigating the hydrological cycle of the basin. In estimating and predicting runoff and regional water resources availability, we have often used regional or global runoff mapping and geostatistical interpolation methods. In these methods, the value of a regional variable at a given location is often estimated as the weighted average of observed values at neighboring locations. The value of a regional variable at a given location is often estimated as the weighted average of observed values at neighboring locations This interpolation of runoff, which is assumed as an auto-correlated generalized stochastic field (Jones, 2009), uses secondary information from more than one variable (Li and Heap, 2008). Because the spatial mean could be used as a trend or nonstationary variation in space, OK has been
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