New Method Boosts Rainfall Accuracy in Hydro Models

Researchers develop algorithm to improve precipitation representation in hydrological models.

Mar. 19, 2026 at 6:34am

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A new study describes a novel method to better represent precipitation uncertainty in hydrological models, thereby improving their performance. The researchers developed an algorithm that applies stepwise back correction to hydrological models to resolve discrepancies and improve the representation of precipitation. The method was tested on models in the U.S. and Brazil, with the Soil and Water Assessment Tool (SWAT) showing the most consistent results with up to 18% higher accuracy than existing approaches.

Why it matters

Hydrological models are critical for water resource planning and management, but they depend on reliable input data for weather factors like precipitation, which can be very difficult to measure and represent accurately. This new method helps address a common limitation in hydrological modeling by improving the representation of precipitation uncertainty.

The details

The researchers, including a team from the University of Illinois Urbana-Champaign and the Universidad Industrial de Santander in Colombia, were studying the impact of land use changes on the Tona watershed in Colombia. They found that collecting accurate precipitation data was a challenge, as many places rely on manual readings rather than sophisticated weather stations. To improve the model calibration, they developed an algorithm that applies stepwise back correction to hydrological models to resolve discrepancies and improve the representation of precipitation. The method was tested on models in the Sangamon River watershed in Illinois, as well as the Grande River watershed and the Jequitinhonha River watershed in Brazil.

  • The research was published in March 2026.

The players

Jorge Guzman

Research assistant professor in the Department of Agricultural and Biological Engineering at the University of Illinois Urbana-Champaign.

Dany Hernandez

Researcher at the Universidad Industrial de Santander in Colombia.

Sandra Villamizar

Researcher at the Universidad Industrial de Santander in Colombia.

Maria Chu

Associate professor in the Department of Agricultural and Biological Engineering at the University of Illinois Urbana-Champaign.

Camila Ribeiro

Researcher at the Federal University of Lavras in Brazil.

Carlos de Mello

Researcher at the Federal University of Lavras in Brazil.

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What they’re saying

“Precipitation is very variable in space and time. There may be a single weather station collecting data in a large area, but turbulent wind can change measurements very fast across space. If you enter that information in the hydrological model as a single value, it can distort the model representation of rainfall for that area.”

— Jorge Guzman, Research assistant professor

“Collecting precipitation data is a challenge if you do not have access to sophisticated weather stations. In Colombia, many places rely on manual readings, where a person goes out once or twice a day to collect the measurements, so precipitation data may not be very accurate.”

— Sandra Villamizar, Researcher

“The Colombian team had data from rainfall and streamflow in the Tona watershed. If there's a lot of precipitation, there should be a lot of discharge. We used that information to develop an algorithm that applies stepwise back correction to hydrological models. This helps to resolve discrepancies and improve the representation of precipitation.”

— Jorge Guzman, Research assistant professor

“This study addresses a common limitation in hydrological modeling by developing a structural analysis framework that integrates parameter calibration with dynamic precipitation correction, and the results show important improvements in performance metrics.”

— Dany Hernandez, Researcher

What’s next

The researchers have made their back-correction tool freely available to other researchers, who can access the software and application instructions online from the published paper.

The takeaway

This new method for improving the representation of precipitation uncertainty in hydrological models can lead to significant improvements in the performance of these critical tools for water resource planning and management, especially in areas with limited access to sophisticated weather data.