Spatiotemporal Characteristics and Statistical Model Prediction of Potential Evaporation during the Growing Season in Ningxia

Abstract

Based on the daily data of 24 meteorological warfare points in Ningxia, the potential evaporation (ET0), the Mann–Kendall test (MK), and inverse distance weight interpolation were used to analyze the temporal and spatial changes in the potential evaporation at different scales, and the Pearson correlation coefficient was used to determine the growth rate in Ningxia. The atmospheric circulation influencing factors of seasonal potential evaporation were established, and the prediction model of potential evaporation in the growing season in Ningxia was established by stepwise linear regression, using mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE). The error test was carried out, and the results show the following: (1) The areas with larger potential evaporation during the growing season are mainly the central area of Ningxia and the northern side of the northern area. The presence on the north side tends to decrease. (2) On the monthly scale, the potential evaporation continued to increase from April to June, reached the maximum in June, continued to decrease from July to October, and was the smallest in October. (3) During the MK test in the growing season, the change in potential evaporation in the growing season showed an “increase–decrease” trend. The results of the monthly MK test show that the potential evaporation in April, May, and June has shown an increasing trend over the past 30 years, and the UF value in July, August, and October has been relatively high. The change trend of potential evaporation has been relatively stable, and the potential evaporation in September has shown a downward trend over the past 20 years. (4) The prediction model of potential evaporation in the growing season established by the circulation factor has shown a good effect in Ningxia and can be used to establish the scheduling prediction of irrigation water.