Temporal Downscaling of Precipitation from Daily to Hourly Based on Nonparametric Approach: Assessment of the Climate Change Impacts on the Hourly Precipitation for the Gyeongnam Region

Abstract

Hourly hydro-meteorological data are vital to flood control system and to assess the hydrological effects of climate change on medium and small watersheds. However, finder than daily hydro-meteorological data are not easy to obtain. A temporal downscaling method to obtain finer time series, e.g. hourly, might be very useful for assessing the hydrological effects of the variations of precipitation by climate change. In the current study, a temporal downscaling model that combines a nonparametric stochastic simulation approach with Genetic Algorithm (GA) is tested. The tested model was applied to Busan-Gyeongnam station in South Korea for a historical time period to validate the model performance. The results revealed that the applied model preserves the key statistics (i.e., the mean, standard deviation, skewness, lag-1 correlation, and maximum) of the historical hourly precipitation data. Furthermore, the RCP 4.5 and RCP 8.5 climate scenarios for the Busan-Gyeongnam area were also analyzed. The results illustrated that the mean significantly increased while the standard deviation and maximum slightly increased in these scenarios. The magnitude of the increase was greater in RCP 8.5 than RCP 4.5. The downscaled hourly precipitation adequately reproduced the statistical behaviors of the historical hourly precipitation data for all durations considered. Overall, the results demonstrated that the applied temporal downscaling model is a good alternative method for downscaling simulated daily precipitation data to hourly especially for assessing the impacts of climate change especially from the variations of precipitation.