Influence of High-Resolution Radar Rainfall Data Quality on Flash Flood Simulation Performance for Operational EWS in Mountainous Thailand

Mountain regions exhibit complex topography and climate patterns, leading to highly variable meteorological conditions that complicate the prediction of heavy rainfall, flash floods, and landslides. Gridded radar rainfall data are therefore essential for monitoring and forecasting intense storms in mountainous catchments where rain gauge observations are limited. The reliability of near-real-time radar rainfall products depends on individual radar data quality, radar compositing techniques, and bias correction procedures. Hydrological modelling is a key component of operational Early Warning Systems (EWS) for monitoring and forecasting runoff conditions; however, model accuracy remains sensitive to uncertainties in measurement instruments, rating curves, and rainfall inputs. This study aims to investigate the impact of rainfall input quality on the accuracy of flood simulations in a mountainous catchment in northern Thailand, namely the Klong Suan Mak basin. Radar compositing was performed using data from three weather radar stations: Omkoi (approximately 180 km northwest), Takhli (approximately 170 km southeast), and Chainat (approximately 167 km southeast) relative to the Klong Suan Mak basin, with a quality-index-based approach applied to rainfall estimation. A spatially distributed, physically based hydrological model for flash flood simulation was driven by three rainfall inputs: (i) rain gauge observations, (ii) an event-based bias-corrected radar composite, and (iii) an hourly Kalman filter–based bias-corrected radar composite. Model calibration was performed using the Flow Duration Curve (FDC) approach in logarithmic scale, based on discharge observations and spatial rainfall inputs during the 2022 flood events.

Results clearly indicate that the sparse rain gauge network in the mountainous region yields the poorest flood simulation performance. In contrast, radar-based rainfall products exhibit more complex behavior: although the hourly Kalman filter–based product provides the highest rainfall data quality, variability in its bias factor increases uncertainty in model parameter estimation. By comparison, the event-based bias-corrected radar composite with a single bias factor yields more stable model parameters, making it more suitable for both calibration and validation across multiple events. Consequently, the event-based radar rainfall product was adopted as the baseline input to stabilize model parameters and subsequently integrated with the dynamic Kalman filter–based product, leading to a significant enhancement in model performance and improving prediction accuracy by up to 32%, particularly during high-discharge periods where the dynamic Kalman filter–based radar rainfall data exhibited a significant improvement in efficiency. This integrated approach has strong potential to support multi-hazard mitigation, including landslides and soil erosion. When combined with short-term radar rainfall nowcasting, it could provide critical lead time for disaster preparedness and national early warning systems.