Hydrological Drought Forecasting Incorporating Climatic and Human-Induced Indices

Abstract

Abstract Many drought forecasting methods have been proposed, but only a few have considered the changing environment. The main purpose of this study is to improve the accuracy of drought forecasting models under changing environments by considering the influence of large-scale climate patterns and human activities on hydrological drought. To select the most significant large-scale climatic index that influences drought events in the Luanhe River basin, Spearman’s rho correlation test was applied to detect the relationship between large-scale oceanic–atmospheric circulation patterns and the standardized runoff index (SRI). We also proposed a human activity index (HI) to represent the effect of human activities on hydrological drought. Based on a multivariate normal distribution, we included the above indices in a probabilistic forecasting model, which forecasted the probabilities of transition from the current to a future SRI value. Using the Liying hydrological station as an example, the impacts of a controlled large-scale climatic index (Niño-3.4) and the HI on the transition probabilities were illustrated, and the results showed that the turning point of the Niño-3.4 effect on the transition probabilities occurred within the range from 25.91 to 26.90. Finally, a scoring method was applied to compare the forecasting model performances. The results showed that the inclusion of the large-scale climatic index and HI improved the forecasting accuracy.