A Stepwise Correction Model for Operational Forecasting of Surface Soil Moisture During the Spring Sowing Period

Abstract

Accurately predicting soil moisture conditions and determining the optimal sowing timing during the spring sowing period play a crucial role in ensuring high and stable grain yields. To address the limitations of existing soil moisture prediction models, i.e., the complexity and parameterization challenges of hydrological models, the weak theoretical foundations of statistical models, and the data dependence, lack of interpretability, and poor generalization of machine learning approaches, a prediction model for surface soil water content (SWC) was developed in this study. The model is based on the water balance principle and uses a stepwise correction approach with normalized functions of key influencing factors. The results are as follows: (1) The model requires readily available parameters from public databases and is driven by daily scale meteorological variables (precipitation, temperature, wind speed, and vapor pressure deficit), facilitating its integration into existing operational weather forecasts. (2) After parameterization, only the moisture exchange between surface and deep soil layers needs optimization, resulting in low computational demand. (3) A trial in Shenyang region showed that the model explains 82.1% of the SWC variance, with an RMSE of 1.7% for 1–7 day lead predictions. (4) When applied to regions without initial soil moisture observations, the model achieves satisfactory accuracy after an initial condition sensitivity period of approximately 40 days. These results provide a methodological reference for soil moisture prediction studies and offer technical support for meteorological services to integrate soil moisture forecasting into their operational frameworks.