Research on low flow forecast based on a distributed hydrological model and autoregressive error correction
With the increasing impact of climate change and human activities,drought events occur frequently,and water supply-demand conflicts during dry seasons become more prominent.Therefore,accurate low flow fore-casting becomes increasingly important.In this paper,the distributed hydrological model(GBEHM)and autore-gressive(AR)error correction method were used to correct the simulated runoff,and then,combined with predic-ted precipitation,a low flow forecast method was established and applied to the watershed above the Shigu hydro-logical station of the Yangtze River,and the runoff simulation and prediction research were carried out at five-day,ten-day,and monthly scales from 2000 to 2012.The results show that the GBEHM model has good simulation per-formance on daily runoff with Nash-Sutcliffe efficiency coefficient(NSE)of 0.94 and 0.91,and the relative water balance error(WBE)of 0.98％and 3.9％in the calibration and validation periods,respectively.However,the simulated runoff during dry seasons is lower than the observed.After the AR error correction,the simulation pass rate has increased to 81％to 96％in the calibration and validation periods,respectively.The forecasting pass rate during dry seasons and severe droughts are less than 80％and 85％,respectively.After AR error correction,the forecasting pass rates have been improved into 91％and 97％,respectively.This study has achieved high precision forecast of low flow at five-day,ten-day and monthly scales,significantly improving the forecasting accuracy dur-ing droughts and dry seasons.These results have promising applications in engineering.
runoff forecastinglow flowdistributed hydrological modelreal-time correctionthe Upper Yangtze River
万方数据
维普
