2026-03-18 イリノイ大学アーバナ・シャンペーン校
＜関連情報＞
- https://aces.illinois.edu/news/new-approach-improves-precipitation-accuracy-hydrological-models
- https://www.sciencedirect.com/science/article/pii/S1364815226000551
水文モデルにおける降水表現のための段階的逆補正関数 A stepwise back-correction function for precipitation representation in hydrologic models
Dany A. Hernandez, Jorge A. Guzman, Sandra R. Villamizar, Maria L. Chu, Camila Ribeiro, Carlos R. de Mello
Environmental Modelling & Software Available online: 10 February 2026
DOI:https://doi.org/10.1016/j.envsoft.2026.106908
Graphical abstract
Highlights
- A stepwise back correction improves model performance.
- Precipitation correction was sensitive to the model structure.
- The reanalysis preserved the water balance within acceptable ranges.
- Accurate mean areal precipitation is essential to model parameterization.
- Areal precipitation helps reduce uncertainty in the predicted model output.
Abstract
This study addresses how spatial and temporal uncertainties in precipitation limit calibration of hydrological models. Adjusting model parameters alone cannot compensate for poorly represented precipitation at the model’s lower resolution. A reanalysis framework that integrates traditional calibration with a stepwise precipitation back correction approach was introduced. Using a composite exponential error function, the method derives precipitation correction factors from mismatches between observed and simulated streamflow. The approach was tested with three hydrological models—SWAT, MIKE-SHE, and MHD—across watersheds in the United States and Brazil. The workflow involved an initial standard calibration, followed by iterative precipitation correction without altering model parameters, and a final recalibration incorporating the corrected precipitation. Results showed 10–18% improvements in KGE while maintaining PBIAS below 10% at most stations. The study highlights the value of constraining water balance to avoid unrealistic corrections and demonstrates how addressing precipitation uncertainties enhances model performance across diverse hydrological settings.
