In Environmental science and pollution research international
Pan evaporation (EVP) is an important element of the hydrological cycle and exhibits a close relationship with climate change. In this study, the generalized regression neural network (GRNN) model and extreme gradient boosting (Xgboost) model were applied to estimate the monthly EVP. The spatiotemporal distributions of EVP and influencing factors in China and eight subregions from 1961 to 2017 were analyzed. The root mean square error (RMSE) of all GRNN models was approximately 10%, and the Nash-Sutcliffe efficiency (NSE) coefficient was larger than 0.94 in different subregions. The annual mean EVP in all subregions and throughout China showed decreasing trends before 1993, while EVP increasing trends occurred in East China (EC), South China (SC), Southwest China (SWC), west of Northwest China (WNC), and throughout China after 1994. Subsequently, the variable importance in projection (VIP) between EVP and climatic factors obtained by partial least squares (PLS) regression and the relative contribution calculated by Xgboost stepwise regression analysis (SRA) were used to investigate the climatic parameter sensitivity to EVP. The results indicated that the combined effects of the vapor pressure deficit (VPD), sunshine duration (SSD), and wind speed (WIN) were the main reasons for the variations in EVP across China. At the seasonal scale, SSD, WIN, relative humidity (RHU), and VPD were the most sensitive climatic factors to EVP in different seasons. In addition, the Pacific decadal oscillation (PDO) index showed a significant negative correlation with EVP, and the El Niño 3.4 (N3.4) and East Atlantic/Western Russia (EA/WR) indices revealed positive correlations in most regions from 1961 to 1993, while the North Atlantic oscillation (NAO) was negatively correlated with EVP. Moreover, N3.4 and Atlantic multidecadal oscillation (AMO) were positively correlated with EVP from 1994 to 2017. Finally, the yearly number of heatwave events (HWN) was highly correlated with EVP because of the high VPD and SSD levels during the heatwave event periods.
Niu Zigeng, Wang Lunche, Chen Xinxin, Yang Liu, Feng Lan
Heatwave event, Machine learning, Meteorological factors, Pan evaporation, Spatiotemporal distributions