Satellite-derived precipitation can be a potential source of forcing data for assessing water availability and managing water supply in mountainous regions of East Asia. This study investigates the hydrological utility of satellite-derived precipitation and uncertainties attributed to error propagation of satellite products in hydrological modeling. To this end, four satellite precipitation products (tropical rainfall measuring mission (TRMM) multi-satellite precipitation analysis (TMPA) version 6 (TMPAv6) and version 7 (TMPAv7), the global satellite mapping of precipitation (GSMaP), and the climate prediction center (CPC) morphing technique (CMORPH)) were integrated into a physically-based hydrologic model for the mountainous region of South Korea. The satellite precipitation products displayed different levels of accuracy when compared to the intra- and inter-annual variations of ground-gauged precipitation. As compared to the GSMaP and CMORPH products, superior performances were seen when the TMPA products were used within streamflow simulations. Significant dry (negative) biases in the GSMaP and CMORPH products led to large underestimates of streamflow during wet-summer seasons. Although the TMPA products displayed a good level of performance for hydrologic modeling, there were some over/underestimates of precipitation by satellites during the winter season that were induced by snow accumulation and snowmelt processes. These differences resulted in streamflow simulation uncertainties during the winter and spring seasons. This study highlights the crucial need to understand hydrological uncertainties from satellite-derived precipitation for improved water resource management and planning in mountainous basins. Furthermore, it is suggested that a reliable snowfall detection algorithm is necessary for the new global precipitation measurement (GPM) mission.

Reliable long-range reservoir inflow forecast is essential to successfully manage water supply from reservoirs. This study aims to develop statistical reservoir inflow forecast models for a reservoir watershed, based on hydroclimatic teleconnection between monthly reservoir inflow and climatic variables. Predictability of such a direct relationship has not been assessed yet at the monthly time scale using the statistical ensemble approach that employs multiple data-driven models as an ensemble. For this purpose, three popular data-driven models, namely multiple linear regression (MLR), support vector machines (SVM) and artificial neural networks (ANN) were used to develop monthly reservoir inflow forecasting models. These models have been verified using leave-one-out cross-validation with expected error S as a measure of forecast skill. The S values of the MLR model ranged from 0.21 to 0.55, the ANN model ranged from 0.20 to 0.52 and the SVM from 0.21 to 0.56 for different months. When used as an ensemble, Bayesian model averaging was more accurate than simple model averaging and naïve forecast for four target years tested. These were considered to be decent prediction skills, indicating that teleconnection-based models have the potential to be used as a tool to make a decision for reservoir operation in preparing for droughts.

Identifying spatially-explicit risk is essential for efficient flood management with limited resources and budgets. We investigated national-scale relative flood risk for 139 sub-basins in South Korea under current and future climate conditions. A non-parametric index method was employed to calculate relative flood risk based on a sensitivity index, an exposure index, and an adaptive capacity index for each sub-basin. A dynamically downscaled climate simulation based on the A2 Greenhouse Gas (GHG) emission scenario, provided by the Korean Meteorological Research Institute (METRI), was used to examine possible change in flood risk. The estimated flood risk generally agreed with historical flood damage. The highly vulnerable sub-basins had high exposure indices, reflecting frequent heavy rainfall, as well as high sensitivity indices due to dense population at low elevation. Although rainfall intensity and frequency is likely to increase under the A2 GHG emission scenario, the spatial pattern of relative flood risk did not change remarkably. Our results indicate that reducing flood sensitivity levels and enhancing the adaptive capacity in vulnerable regions will be critical aspects of climate change preparation in South Korea.