Abstract
Sufficient rainfall data are required to estimate the probable rainfall for a range of different return periods; however, for various reasons, many areas lack rainfall data. In this study, the Weather Research Forecasting (WRF) model was applied to obtain rainfall information based on varying parameter sets representing physical atmospheric conditions. Generated rainfall data were considered historical rainfall information for a region plagued by insufficient rainfall information. To analyze the applicability of the generated rainfall data, at-site/regional frequency analyses were conducted with various compositions of generated rainfall data. Prior to frequency analysis, comparisons of annual maximum rainfall for specific periods highlighted the need for bias correction when utilizing WRF generated rainfall data (e.g. underestimation of WRF around 22.53–79.12%). For at-site/regional frequency analyses, generated rainfall data after bias correction (GRA) provided a more accurate estimate in terms of relative root mean square error (RRMSE) with the Monte Carlo simulation. The increased accuracy using observations (OBS) with GRA was 87.5% for at-site frequency during the none-exceedance probability at 1. For regional frequency analysis, the estimates of rainfall for higher return periods (> 100 year) from generated rainfall data by WRF without bias correction (GRW) had a similar median to the observation. The maximum of RRMSEs of GRW, GRA, and OBS were 0.218, 0.181, and 0.175, respectively. In terms of possible combinations of regional frequency analysis, the findings show that physical location or diverse parameter set methods are both feasible for use in frequency analysis.
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This study was supported by Wonkwang University in 2022.
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Choi, G., Jung, Y. Applicability of physically generated rainfall data using a regional weather model. Stoch Environ Res Risk Assess 36, 2979–2994 (2022). https://doi.org/10.1007/s00477-022-02173-7
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DOI: https://doi.org/10.1007/s00477-022-02173-7