Assessment of Weather Research and Forecasting (WRF) Physical Schemes Parameterization to Predict Moderate to Extreme Rainfall in Poorly Gauged Basin

Abstract

Incomplete hydro-meteorological data and insufficient rainfall gauges have caused difficulties in establishing a reliable flood forecasting system. This study attempted to adopt the remotely sensed hydro-meteorological data as an alternative to the incomplete observed rainfall data in the poorly gauged Kuantan River Basin (KRB), the main city at the east coast of Peninsula Malaysia. Performance of Weather Research and Forecasting (WRF) schemes’ combinations, including eight microphysics (MP) and six cumulus, were evaluated to determine the most suitable combination of WRF MPCU in simulating rainfall over KRB. All the obtained results were validated against observed moderate to extreme rainfall events. Among all, the combination scheme Stony Brook University and Betts–Miller–Janjic (SBUBMJ) was found to be the most suitable to capture both spatial and temporal rainfall, with average percentage error of about ±17.5% to ±25.2% for heavy and moderate rainfall. However, the estimated PE ranges of −58.1% to 68.2% resulted in uncertainty while simulating extreme rainfall events, requiring more simulation tests for the schemes’ combinations using different boundary layer conditions and domain configurations. Findings also indicate that for the region where hydro-meteorological data are limited, WRF, as an alternative approach, can be used to achieve more sustainable water resource management and reliable hydrological forecasting.

Assessment of the WRF model in reproducing a flash-flood heavy rainfall event over Kosovo

“…High-resolution WRF forecasts, as demonstrated by Song and Sohn (2018), Gao et al (2017), and Lekhadiya et al (2018), exhibit keen responsiveness to the quality of initial data, boundary conditions, atmospheric boundary layer schemes, and various thermodynamic and dynamic factors. Zaldi et al (2022) investigated the suitability of Weather Research and Forecasting (WRF) microphysical schemes for predicting moderate to extreme rainfall in the poorly gauged Kuantan River Basin. Various combinations of eight microphysics (MP) and six cumulus schemes were evaluated, demonstrating effectiveness in capturing both spatial and temporal rainfall patterns, making WRF a promising alternative for regions with limited hydro-meteorological data, and enhancing water resource management and hydrological forecasting reliability.…”