Comparative Evaluation of Radar–Gauge Fusion and Rain Gauge Rainfall Inputs for Flood Simulation in a Small Pumped-Storage Hydropower Catchment

Rainfall input, as the primary driver of distributed hydrological models, can be derived from rain gauge observations or weather radar quantitative precipitation estimates (QPEs). While radar–gauge fusion products offer high-resolution spatial rainfall information, their comparative performance against traditional gauge-interpolated rainfall in small regulated catchments remains insufficiently understood. This study conducted a systematic comparison of radar–gauge fusion and gauge-interpolated rainfall inputs for flood simulation in the Yongtai pumped-storage hydropower catchment (∼60.5 km2) in Fujian Province, China. Using the physically based Liuxihe distributed hydrological model, six typical flood events (2023–2025) encompassing various magnitudes and hydrograph patterns were simulated. Model parameters were optimized via particle swarm optimization, and simulation accuracy was evaluated using NSE, KGE, peak relative error (PRE), and absolute peak time error (APTE). Rainfall spatial variability was quantified using the coefficient of variation (CV) and information entropy (H). Results showed that both rainfall inputs achieved generally acceptable simulation accuracy for most events after parameter optimization, with APTE within 1 h and PRE mostly below 15%, although event-to-event differences remained evident. Radar–gauge rainfall exhibited consistently higher CV (+34.1%) and H (+84.4%) than gauge-interpolated rainfall across all events, indicating greater spatial variability and entropy-derived spatial information diversity. Under the independent-calibration framework, the radar–gauge rainfall-driven configuration showed better metrics for the investigated multi-peak and localized intense-rainfall events; however, it systematically underestimated rainfall magnitude under weak rainfall conditions, leading to degraded peak simulation (PRE of 54.1% vs. 14.3%). These differences reflect the combined effects of rainfall input and separately optimized parameter sets and should not be interpreted as evidence of the intrinsic superiority of one rainfall product. Based on these event-limited findings, a “radar-primary, gauge-auxiliary” multi-source strategy is suggested as a potential operational option for small pumped-storage catchments to balance spatial representativeness and observational reliability.

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Journal
Water
Published
2026-07-27
DOI
https://doi.org/10.3390/w18151818
Primary Topic
Precipitation Measurement and Analysis
Type
article
Field-Weighted Citation Impact
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article

Comparative Evaluation of Radar–Gauge Fusion and Rain Gauge Rainfall Inputs for Flood Simulation in a Small Pumped-Storage Hydropower Catchment

梁纪泽, Yangbo Chen, Zhihui Lin, Xiaoqi Yu et al.
Water
Precipitation Measurement and Analysis
article

Comparative Evaluation of Radar–Gauge Fusion and Rain Gauge Rainfall Inputs for Flood Simulation in a Small Pumped-Storage Hydropower Catchment

梁纪泽, Yangbo Chen, Zhihui Lin, Xiaoqi Yu, Z Huang
article en

Abstract

Rainfall input, as the primary driver of distributed hydrological models, can be derived from rain gauge observations or weather radar quantitative precipitation estimates (QPEs). While radar–gauge fusion products offer high-resolution spatial rainfall information, their comparative performance against traditional gauge-interpolated rainfall in small regulated catchments remains insufficiently understood. This study conducted a systematic comparison of radar–gauge fusion and gauge-interpolated rainfall inputs for flood simulation in the Yongtai pumped-storage hydropower catchment (∼60.5 km2) in Fujian Province, China. Using the physically based Liuxihe distributed hydrological model, six typical flood events (2023–2025) encompassing various magnitudes and hydrograph patterns were simulated. Model parameters were optimized via particle swarm optimization, and simulation accuracy was evaluated using NSE, KGE, peak relative error (PRE), and absolute peak time error (APTE). Rainfall spatial variability was quantified using the coefficient of variation (CV) and information entropy (H). Results showed that both rainfall inputs achieved generally acceptable simulation accuracy for most events after parameter optimization, with APTE within 1 h and PRE mostly below 15%, although event-to-event differences remained evident. Radar–gauge rainfall exhibited consistently higher CV (+34.1%) and H (+84.4%) than gauge-interpolated rainfall across all events, indicating greater spatial variability and entropy-derived spatial information diversity. Under the independent-calibration framework, the radar–gauge rainfall-driven configuration showed better metrics for the investigated multi-peak and localized intense-rainfall events; however, it systematically underestimated rainfall magnitude under weak rainfall conditions, leading to degraded peak simulation (PRE of 54.1% vs. 14.3%). These differences reflect the combined effects of rainfall input and separately optimized parameter sets and should not be interpreted as evidence of the intrinsic superiority of one rainfall product. Based on these event-limited findings, a “radar-primary, gauge-auxiliary” multi-source strategy is suggested as a potential operational option for small pumped-storage catchments to balance spatial representativeness and observational reliability.

WaterVol. 18(15)
Sun Yat-sen University (CN), Minjiang University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 12%
Precipitation Measurement and Analysis
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