Hydrological drought forecasting under a changing environment in the Luanhe River basin

Forecasting the occurrence of hydrological drought according to a forecasting system is an important disaster reduction strategy. In this paper, a new drought prediction model adapted to changing environments was constructed. Taking the Luanhe River basin in China as an example, first, nonstationarity analysis of hydrological sequences in the basin was carried out. Then, conditional distribution models with the human activity factor as an exogenous variable were constructed to forecast hydrological drought based on meteorological drought, and the results were compared with the traditional normal distribution model and conditional distribution model. Finally, a scoring mechanism was applied to evaluate the performance of the three drought forecasting models. The results showed that the runoff series of the Luanhe River basin from 1961 to 2010 were nonstationary; moreover, when human activities were not considered, the hydrological drought class tended to be the same as the meteorological drought class. The calculation results of the models involving HI as an exogenous variable were significantly different from the models that did not consider human activities. When the current drought class tended towards less severe or normal, the meteorological drought tended to turn into more severe hydrological drought with the increase in human index values. According to the scores of the three drought forecasting models, the conditional distribution models involving the human index can further improve the forecasting accuracy of drought in the Luanhe River basin.