Uncertainty assessment of future projections on water resources according to climate downscaling and hydrological models

• Published in: Journal of hydroinformatics Vol. 20; no. 3; pp. 597 - 607
• Main Authors: Lee, Moon-Hwan, Bae, Deg-Hyo
• Format: Journal Article
• Language: English
• Published: London IWA Publishing 01.05.2018
• Subjects: Bias; Climate change; Climate models; Environmental impact; General circulation models; Greenhouse effect; High flow; Hydrologic models; Hydrology; Impact assessment; Inflow; Low flow; Mathematical models; Methods; Modelling; Post-production processing; Runoff; Simulation; Statistical analysis; Statistical methods; Studies; Uncertainty; Uncertainty analysis; Water resources; Water shortages; Winter; United Kingdom > UK; England; East Asia
• ISSN: 1464-7141; 1465-1734
• DOI: 10.2166/hydro.2018.132

Abstract Quantifying the uncertainty of future projection is important to assess the reliable climate change impact. In this sense, this study is aimed at investigating the uncertainty sources of various water variables (seasonal dam inflow, 1-day maximum dam inflow, and 30-day minimum dam inflow) according to downscaling methods and hydrological modeling. Five regional climate models (RCMs), five statistical post-processing methods and two hydrological models were applied for the uncertainty analysis. The changes for seasonal dam inflow are 0.1, 58.8, 5.1, and 1.1 mm for the SWAT model and 2.1, 76.1, −8.5, and −2.9 mm for the VIC model in spring, summer, autumn, and winter, respectively. The effects of the hydrological model is smaller than that of RCM for future projections of the seasonal dam inflow. The changes of annual 1-day maximum dam inflow vary according to the selection of RCM whereas the changes of annual 30-day minimum dam inflow are sensitive to the selection of hydrological model. The RCM is the dominant source of uncertainty of all seasonal dam inflow (except for winter) and high flow, whereas the hydrological model is the dominant source of uncertainty in winter dam inflow and low flow. Considering these results, the appropriate multi-model ensemble chain according to target variable will be necessary for reliable climate change impact assessment.