Reduction of the uncertainties in the hydrological projections in Korean river basins using dynamically downscaled climate projections

Abstract

How the added value of dynamically downscaled climate variables can be transferable to the hydrological impact assessment has been a long standing issue. This study investigates the potential benefit of highresolution climate data locally tailored over South Korea in terms of the reduction of uncertainties in hydrological projections. For this purpose, a large ensemble consisting of three Global Climate Model (GCM) projections and their downscaling products with different resolutions (i.e., 20 and 5 km), and four bias correction (BC) methods is fed into a semi-distributed hydrological model (HM) customized over Korean river basins. The in-depth comparison among the 45-members hydrological simulations proves that the added value of RCM would not be erased by the application of BC. While this study acknowledges the necessity of BC to remove the systematic bias in climate simulations, it is found that the high-resolution dynamical downscaling can significantly narrow the spread brought with different BC methods, thus reducing the uncertainty in the projected hydrological change. The projected streamflow changes for both the mean of wet season and the high flows indicate that there will be an intensified runoff, especially for the extremes, over South Korea under the warming. Altogether, this study provides a valuable exploration of uncertainty reduction in hydrological projections from the perspective of resolution effect of dynamical downscaling, which is meaningful for hydroclimate studies and climate change impact assessment.

Statistical downscaling of maximum temperature under CMIP6 global climate models and evaluation of heat wave events using deep learning methods for Indo‐Gangetic Plain

“…[Colour figure can be viewed at wileyonlinelibrary.com] and 3.5 C. In this process, climate models AWI-CM-1-1-MR and MPI-ESM1-2-LR exhibit considerable uncertainty in DNN and RNN processes; however, LSTM shows less uncertainty in these models. Furthermore, earlier studies have suggested that uncertainty is reduced after applying ANN and statistical downscaling in climate models (Qiu et al, 2022;Okkan et al, 2023).…”

“…As precipitation is a fundamental component in the water cycle, a comprehensive outlook is essential for managing water systems under changing climate (Sachindra & Perera, 2016). Studies has shown that the downscale high‐resolution data will significantly improve in the simulation of meteorological and hydrological variables (Ghosh and Mujumdar, 2008; Qiu et al, 2022; Singh & Mall, 2023). According to Okkan et al (2023) downscale GCM outputs through the ANN structures will provide sufficient simulation accuracy for climate uncertainty and climate impact study.…”

Direct and Indirect Application of Univariate and Multivariate Bias Corrections on Heat-stress Indices based on Multi-RCM Simulations

“…They cannot adjust the inter-variable dependencies, which are important for representing physical processes and estimating compound hazards. It was not until quite recently that the multivariate BC technique was considered and proposed (e.g., Bárdossy & Pegram, 2012;Cannon, 2018;Mehrotra & Sharma, 2015, 2016Robin et al, 2019;Vrac, 2018), and they have been applied to various climate change impact studies (Dieng et al, 2022;Meyer et al, 2019;Qiu et al, 2022;Zscheischler et al, 2019). Although it is intuitively recognized that multivariate BC could be more suitable for dealing with climate variables characterized by a strong physical linkage in nature, an unambiguous assessment of univariate and multivariate BC methods is essential to understand the potential limitations of individual methods and to avoid misleading application.…”