Rainfall Characteristics and Effective Precipitation Analysis of Rice Growth Period in Typical Areas in East China

Rainfall characteristics during the rice growth period were analyzed using long-term data (1986–2017) from three typical irrigation stations, namely, Pinghu, Jinqing, and Yongkang in East China. Annual rainfall, concentration indices, and monthly distributions were examined, and the soil–water balance method was applied to estimate effective rainfall (precipitation) from 2018 to 2020. Results showed that rainfall during the growth period generally accounted for 40–80% of the annual total rainfall, with more than 60% of the years exceeding half of the annual rainfall. The effective precipitation utilization coefficients at Pinghu Station reached 0.573, 0.644, and 0.764 in 2018, 2019 and 2020, respectively, indicating relatively high utilization efficiency and confirming effective precipitation as a major water source during rice growth. High utilization was observed for extreme rainfall events (≥30 mm or ≤5 mm), whereas moderate rainfall (5–30 mm) showed larger variability due to soil and management factors. To further improve quantitative assessment, a Support Vector Regression (SVR) model was employed to predict daily effective precipitation using rainfall, antecedent precipitation index (API), drought days, and extreme rainfall indicators as inputs. The effective precipitation utilization coefficient was then derived as the ratio of effective to total precipitation. The optimized SVR model achieved a coefficient of determination of R2=0.904 and a root mean square error (RMSE) of 4.58 mm for daily effective precipitation, effectively capturing the nonlinear relationship between rainfall characteristics and effective precipitation. These findings highlight that the machine learning method can complement existing estimation models, offering an alternative tool for irrigation scheduling and water-saving crop cultivation.