| Title |
Assessing the Sensitivity of Runoff Projections Under Precipitation and Temperature Variability Using IHACRES and GR4J Lumped Runoff-Rainfall Models |
| Authors |
우동국(Woo, Dong Kook) ; 조지현(Jo, Jihyeon) ; 강부식(Kang, Boosik) ; 이송희(Lee, Songhee) ; 이가림(Lee, Garim) ; 노성진(Noh, Seong Jin) |
| DOI |
https://doi.org/10.12652/Ksce.2023.43.1.0043 |
| Keywords |
기후 스트레스; 유출 변동성; 집중형 수문모형; IHACRES; GR4J Climate stress; Discharge variability; Lumped hydrological model; IHACRES; GR4J |
| Abstract |
Due to climate change, drought and flood occurrences have been increasing. Accurate projections of watershed discharges are imperative to effectively manage natural disasters caused by climate change. However, climate change and hydrological model uncertainty can lead to imprecise analysis. To address this issues, we used two lumped models, IHACRES and GR4J, to compare and analyze the changes in discharges under climate stress scenarios. The Hapcheon and Seomjingang dam basins were the study site, and the Nash-Sutcliffe efficiency (NSE) and the Kling-Gupta efficiency (KGE) were used for parameter optimizations. Twenty years of discharge, precipitation, and temperature (1995-2014) data were used and divided into training and testing data sets with a 70/30 split. The accuracies of the modeled results were relatively high during the training and testing periods (NSE>0.74, KGE>0.75), indicating that both models could reproduce the previously observed discharges. To explore the impacts of climate change on modeled discharges, we developed climate stress scenarios by changing precipitation from -50 % to +50 % by 1 % and temperature from 0 °C to 8 °C by 0.1 °C based on two decades of weather data, which resulted in 8,181 climate stress scenarios. We analyzed the yearly maximum, abundant, and ordinary discharges projected by the two lumped models. We found that the trends of the maximum and abundant discharges modeled by IHACRES and GR4J became pronounced as changes in precipitation and temperature increased. The opposite was true for the case of ordinary water levels. Our study demonstrated that the quantitative evaluations of the model uncertainty were important to reduce the impacts of climate change on water resources. |