Hydrometeorological Forecast of a Typical Watershed in an Arid Area Using Ensemble Kalman Filter

Abstract

The stationarity test and systematic prediction of hydrometeorological parameters are becoming increasingly important in water resources management. Based on the Ensemble Kalman Filter (EnKF) and wavelet analysis, this study selects precipitation, evaporation, temperature, and runoff as model variables, builds a model, tests and analyzes the stationarity of the hydrometeorological parameters of the Manas River, and forecasts the selected parameters for two years. The results of the study show that during the 2000–2020 study period, precipitation in the Manas River Basin on the northern slope of the Tianshan Mountains shows a significant downward trend from 2016 to 2020, with an annual average decline rate of 23.30 mm/a over five years. The proportion of runoff during the flood season also increases, with the statistical probability of an extremely low value of runoff increasing by 37.62% on average. After using wavelet decomposition to provide input to EnKF, the NSE of the model for the prediction of precipitation, evaporation, temperature, and runoff reached 0.86, 0.89, 0.96, and 0.9 respectively. At the same time, the K-S value increases from 0.28 to 0.40, which means that the wavelet analysis technique has great potential as a preprocessing of the Ensemble Kalman filter.