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Reliability and Performance of Fifth-Generation Reanalysis and National Centers for Environmental Prediction-Climate Forecast System Reanalysis Rainfall for Engineering Hydrology in the Upper Ping River Basin, Thailand

Year: 2026
Type: article
Abstract: Reliable precipitation forcing is essential for engineering hydrology, yet in-situ gauge records are often spatially sparse in topographically complex regions.This study evaluates two global reanalysis products over 38-year (1985-2022): The European centre for medium-range weather forecasts (ECMWF) Fifth-Generation reanalysis (ERA5) and the National centers for environmental prediction (NCEP) Climate forecast system reanalysis (CFSR) and Climate forecast system version 2 (CFSv2), using observations from 46-station rain-gauge in the Upper Ping River Basin, Northern Thailand.A multi-metric framework assessed rainfall performance at daily, monthly, and annual temporal scales.Raw reanalysis datasets exhibit a pronounced "drizzle effect," with excessive rainfall frequency and underestimated extreme intensity.ERA5 shows better spatial consistency and monthly temporal correlation (r ≈ 0.80) than CFSR, particularly in mountainous areas.Empirical Quantile mapping (QM) substantially reduces volumetric bias, improves rainfall extremes and upper-tail rainfall characteristics, and enhances overall agreement with observations.Bias-corrected ERA5 provides the most consistent overall performance across spatial and temporal scales.By integrating multiscale, spatial, continuous, categorical, and distributional diagnostics, this study systematically assesses raw and bias-corrected reanalysis rainfall in a tropical mountainous basin.The findings provide practical guidance for selecting and bias-correcting reanalysis rainfall for hydrological and water-resources applications where gauge observations are sparse or incomplete.
DOI: 10.30919/es2490
Language: English
Open Access status: diamond
OpenAlex ID: W7203970525
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