长江流域水储量异常变化的NARNN-SA预测模型研究OA

Accurate and reliable predictions of Terrestrial Water Storage Anomaly(TWSA)changes over the Changjiang River Basin are crucial for achieving efficient and sustainable water resource management.This study utilizes a method combining sea-sonal adjustment and a nonlinear autoregressive neural network(NARNN-SA),leveraging the autocorrelation of TWSA changes in the Changjiang River Basin for the first prediction.Two tests are designed to evaluate the effectiveness of the NARNN-SA model by comparing it against three models:AutoRegression(AR),Seasonal AutoRegressive Integrated Moving Average(SARI-MA),and a nonlinear autoregressive neural network without seasonal adjustment(NARNN).Results demonstrated that the intro-duction of seasonal adjustment significantly improved the predictive performance of the NARNN-SA model,which achieved high consistency with GRACE observations,with correlation coefficient(CC)of 0.894,Nash-Sutcliffe Efficiency(NSE)of 0.769,and root mean square error(RMSE)of 1.425 cm during the testing phase.Additionally,NARNN-SA successfully predicted TWSA changes in the source,eastern,and western regions of the Changjiang River Basin and bridged the data gap between GRACE and its successor,GRACE-FO missions.