Future projection of low flows in the Chungju basin, Korea and their uncertainty decomposition

Abstract

Low flow projections in response to global warming and the uncertainty decomposition into different components are performed with the focus on the Chungju basin, South Korea. Four downscaled climate simulations, two bias correction methods, and two hydrological models with two different potential evapotranspiration computation methods are applied to constitute the 32 ensemble members (4 × 2 × 4) of low flows projections. An analysis of the variance is used to decompose the total variance in the low flow projections into the contribution from three different sources. The result shows that hydrological models, particularly their structures, are the key factor that affects the low flow projections. They contribute more than 85% of the total uncertainty, and the projected change of low flow using different hydrological models can be of opposite signs, even under the same climate input data. On the other hand, the uncertainties related to climate simulation and bias correction are much smaller, comprising around 10 and 2%, respectively. The possible mechanisms behind the large uncertainty of projected low flow are investigated by comparing the relevant hydrological variables obtained from different hydrological simulations. The different representations in the hydrological models for the freezing of the soil layer during winter, which will undergo large changes as temperature increases, are likely to affect the projected change in low flow greatly. The difference in potential evapotranspiration methods also affects the low flow projections, but its contribution is relatively weaker than the effects of the hydrological model structure.

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

“…The uncertainty sources of hydrological projections have been quantified in the previous studies for different regions around the world. These studies generally reach a similar conclusion that for most regions, CMs act as the leading source of uncertainty for the wet season or high flow projections (e.g., Aryal et al 2019;Zhang et al 2021), whereas HMs may contribute more to the uncertainty of dry season or low flow projections (e.g., Vetter et al 2017;Lee et al 2021). Also, the interactions among the uncertainty factors are non-negligible and may even play a larger role than the individual sources (Bosshard et al 2013;Vetter et al 2015;Zhang et al 2021).…”

“…9b), ENSGCM-REF_RAW again barely captures either the magnitude or spatial distribution. The downscaling effectively demonstrates the effect of resolution in improving the reproduction of spatial variability, but it also inherits the problem of overestimation from the precipitation representation (Qiu and Im 2021). However, as mentioned in Section 2.4, it should also be noted that the hydrological simulation driven by interpolated station data (i.e., the OBS run) may have the problem of underestimated high flow, especially for the extremely heavy case.…”

“…Thus, for each CMIP5 model, we have climate simulations in the three resolutions (i.e., the original GCM, WRF20, and WRF05). The overall performance of the downscaling climate simulations and the added value of WRF05 were comprehensively evaluated in the previous studies (Qiu et al 2020;Qiu and Im 2021).…”

“…In this study, we conduct a large ensemble of hydrological projections driven by long-term climate change simulations over South Korea from three different resolutions, i.e., three GCMs and their dynamical downscaling products in 20 and 5 km. As a follow-up study of Qiu and Im (2021) 1 , this study aims to evaluate and explore how the demonstrated advantage of very-high-resolution (i.e., 5 km) climate simulation benefits the hydrological studies. In this regard, we use four BC methods to process the climate projections of the three resolutions and feed the original and bias-corrected climate data into a designated HM for streamflow projections.…”

“…The Korean Peninsula is a typical region needing highresolution climate modeling since its hydroclimate characteristics are strongly influenced by the varying geographic features with sharp topographical gradients encompassed by long coastlines. Also, it has been demonstrated the river flows of South Korea will witness substantial changes (Ryu et al 2011;Jung et al 2013;Seo et al 2016;Ahn and Kim 2016;Lee et al 2018aLee et al , 2021Kim et al 2020) given the condition that the regional climate has been projected to experience a non-trivial change in the future (Seo et al 2013;Kim et al 2018;Oh and Suh 2018). Though the simulations covering the whole country, we target over the Han River basin (HRB) and Nakdong River basin (NRB) for the streamflow analyses, which are the two largest river basins in South Korea taking up more than two-thirds of the area and a large portion of the population (Lee et al 2019b).…”