Estimating Monthly Runoff Using Reanalysis Precipitation Data for Ungauged Areas in Kanchanaburi Province, Thailand
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Abstract
Estimating runoff for small-scale water resource development projects requires rainfall data from nearby stations. However, rain gauge stations are often sparse in mountainous areas. To address this issue, reanalysis precipitation data has been introduced for data-scarce regions. This study evaluates the performance of the WorldClim and CHELSA data by comparing them with observations from nine rain gauges in Kanchanaburi Province from 2000 to 2018. Results indicate that both datasets exhibit a strong correlation with gauges data, with CHELSA showing a slightly higher correlation coefficient (r=0.84) than WorldClim (r=0.79). Regarding accuracy, CHELSA demonstrates very good performance with
a slight positive bias (PBIAS=+4.16%), while WorldClim shows satisfactory performance but with
a considerably higher positive bias (PBIAS=+22.64%). Runoff estimates for six small-scale water resource projects, calculated using these reanalysis precipitation datasets, differ from those derived from gauge observations, with relative differences ranging from -28.82% to +100.68% for WorldClim and from -12.64% to +87.39% for CHELSA. This study highlights the potential of reanalysis precipitation data for hydrological applications in ungauged areas, supporting the planning and design of reservoir capacity and irrigable areas.
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