Impact of Rain Gauge Density on Flood Forecasting Performance: A PBDHM’s Perspective

The structures and parameters of physically-based distributed hydrological models (PBDHMs) can now be established and derived from remote-sensing data with relative ease. When engineers apply PBDHMs for flood forecasting in mesoscale catchments, they encounter varying rain gauge infrastructure conditions. Understanding model performance expectations under varying rain gauge density conditions is crucial for wide PDBHM construction. This study presents a case study of a PBDHM called the Liuxihe Model and examines six rain gauge density scenarios designed based on real-world data to assess the impact of rain gauge density on model flood forecasting performance. The study focuses on a mesoscale catchment in Jiangxi Province, China, covering an area of 2364 km2 with 62 rain gauges. The results indicate that models optimized under an adequate rain gauge density condition are less affected by gauge density changes, maintaining accuracy within a range of change. Compared to Kling–Gupta Efficiency (KGE) and Nash–Sutcliffe Efficiency (NSE), the indicators absolute peak time error (APTE) and peak relative error (PRE) are less sensitive to variation in rain gauge density. The study further discusses how rain gauge density changes related to the interpolated rainfall surfaces and parameter optimization, hoping to facilitate the broader application of PBDHMs and offer insights for future practices.