Fang et al. (2025) Rainfall Characteristics and Effective Precipitation Analysis of Rice Growth Period in Typical Areas in East China

Identification

• Journal: Water
• Year: 2025
• Date: 2025-12-14
• Authors: Hongjie Fang, Zhan Weng, Miao Hu, Xingya Feng, Qiang Liu
• DOI: 10.3390/w17243542

Research Groups

• Department of Logistics and Infrastructure Management, Zhejiang Tongji Vocational College of Science and Technology, Hangzhou, China
• Zhejiang Institute of Hydraulic and Estuary, Hangzhou, China
• Zhejiang Guangchuan Engineering Consulting Co. Ltd., Hangzhou, China
• Key Laboratory of Disaster Prevention and Reduction of Zhejiang Province, Hangzhou, China
• Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, China

Short Summary

This study analyzed rainfall characteristics and effective precipitation during the rice growth period in East China using long-term data and a soil-water balance method. It then developed a Support Vector Regression (SVR) model to predict effective precipitation utilization coefficients, demonstrating its potential for optimizing irrigation scheduling and water-saving crop cultivation.

Objective

• To characterise the long-term rainfall pattern during the rice-growing season at three typical irrigation stations in East China.
• To quantify the effective precipitation and its utilisation coefficient using a soil–water balance approach.
• To develop a Support Vector Regression (SVR) model to predict daily effective precipitation utilisation coefficients based on rainfall characteristics and field-water conditions.

Study Configuration

• Spatial Scale: Three typical irrigation stations in East China: Pinghu (Hangjiahu Plain), Jinqing (coastal island zone of eastern Zhejiang), and Yongkang (hilly low-mountain region of southeastern Zhejiang). Soil bulk densities ranged from 1100 to 1500 kg/m³. An effective root zone depth of 0.20 m was assumed.
• Temporal Scale:
  • Rainfall characteristics analysis: June–November, 1986–2017, for Pinghu, Jinqing, and Yongkang stations.
  • Effective precipitation calculation (soil–water balance): Rice-growing seasons of 2018–2020 for Pinghu station.
  • SVR model development and validation: Daily data from 2018–2020 for Pinghu station.

Methodology and Data

• Models used:
  • Soil–water balance method (for effective precipitation estimation: Pe = Wt − W0 − D + ETt)
  • Penman–Monteith equation (for field evapotranspiration, ETt)
  • ε-insensitive Support Vector Regression (SVR) model with a Radial Basis Function (RBF) kernel (for predicting effective precipitation utilization coefficient)
• Data sources:
  • Long-term daily rainfall data (1986–2017) from routine meteorological observations at Pinghu, Jinqing, and Yongkang irrigation experimental stations.
  • Field-measured data (2018–2020) from Pinghu station: crop transpiration, deep percolation, irrigation depth, crop water requirement, and observed daily rainfall.
  • Input variables for SVR model: Daily rainfall (Pt), 7-day antecedent precipitation index (API), number of consecutive dry days (D), and extreme rainfall indicator (E, 1 if precipitation ≥ 50 mm, else 0).

Main Results

• Rainfall during the rice-growing season (June–November) generally accounted for 40–80% of the annual total rainfall, exceeding 50% in more than 60% of the study years.
• The long-term average seasonal rainfall (1986–2017) was highest at Jinqing (1092 mm), followed by Yongkang (817 mm), and lowest at Pinghu (734 mm). All three stations recorded their minimum seasonal rainfall in 2003.
• Pinghu and Yongkang stations showed upward trends in seasonal rainfall (slopes of 3.8 mm/year and 3.3 mm/year, respectively), while Jinqing station exhibited a downward trend (slope of -1.9 mm/year).
• Over 80% of the seasonal rainfall at all stations occurred between June and September, coinciding with critical rice growth stages.
• At Pinghu station (2018–2020), the overall effective precipitation utilization coefficients were 0.573 (2018), 0.644 (2019), and 0.764 (2020), confirming effective precipitation as a crucial water source for paddy fields.
• Effective precipitation utilization coefficients were generally high for both heavy rainfall events (≥30 mm, typically >0.67) and small showers (≤5 mm, often 1.000). However, moderate rainfall (5–30 mm) showed high variability (0.024–1.000), influenced by factors like antecedent soil moisture and irrigation practices.
• The optimized SVR model achieved a coefficient of determination (R²) of 0.904 and a root mean square error (RMSE) of 4.58 mm for daily effective precipitation on the testing dataset, effectively capturing nonlinear relationships.
• The SVR model showed good agreement for low-to-moderate effective precipitation values but underestimated high-value cases (>80 mm), indicating limitations in extrapolating to extreme rainfall events due to data scarcity.

Contributions

• Provides a systematic and integrated analysis of long-term rainfall characteristics, soil–water balance-based effective precipitation, and data-driven prediction for paddy fields in East China, addressing a research gap.
• Quantifies the non-linear responses of effective precipitation utilization to different rainfall magnitudes, highlighting the importance of field management for moderate rainfall events.
• Develops and validates a Support Vector Regression (SVR) model as a practical, alternative tool for predicting effective precipitation utilization, which can support irrigation scheduling optimization and water-saving rice cultivation.

Funding

• National Key Research and Development Program of China (2023YFB3711500)

Citation

@article{Fang2025Rainfall,
  author = {Fang, Hongjie and Weng, Zhan and Hu, Miao and Feng, Xingya and Liu, Qiang},
  title = {Rainfall Characteristics and Effective Precipitation Analysis of Rice Growth Period in Typical Areas in East China},
  journal = {Water},
  year = {2025},
  doi = {10.3390/w17243542},
  url = {https://doi.org/10.3390/w17243542}
}