Selection of Effective GCM Bias Correction Methods and Evaluation of Hydrological Response under Future Climate Scenarios

• Published in: Climate (Basel) Vol. 8; no. 10; p. 108
• Main Authors: Tan, Yaogeng, Guzman, Sandra M., Dong, Zengchuan, Tan, Liang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2020
• Subjects: Annual precipitation; Bias; bias correction methods; Climate change; Climate change scenarios; Climate effects; Climate models; Daily precipitation; Evapotranspiration; Floods; Future climates; GCM; Global climate; Global climate models; Hydrologic cycle; Hydrologic data; Hydrologic models; Hydrological cycle; hydrological simulation; Hydrology; Methods; Monthly distribution; Precipitation; Precipitation and runoff; Precipitation-temperature relationships; River basins; Runoff; Simulation; Soil water; Supply & demand; Temperature; Temperature effects; Variables; Water management; Water resources; Water resources management; Water shortages; Water supply; Watersheds; China
• ISSN: 2225-1154; 2225-1154
• DOI: 10.3390/cli8100108

Global climate change is presenting a variety of challenges to hydrology and water resources because it strongly affects the hydrologic cycle, runoff, and water supply and demand. In this study, we assessed the effects of climate change scenarios on hydrological variables (i.e., evapotranspiration and runoff) by linking the outputs from the global climate model (GCM) with the Soil and Water Assessment Tool (SWAT) for a case study in the Lijiang River Basin, China. We selected a variety of bias correction methods and their combinations to correct the lower resolution GCM outputs of both precipitation and temperature. Then, the SWAT model was calibrated and validated using the observed flow data and corrected historical GCM with the optimal correction method selected. Hydrological variables were simulated using the SWAT model under emission scenarios RCP2.6, RCP4.5, and RCP8.5. The results demonstrated that correcting methods have a positive effect on both daily precipitation and temperature, and a hybrid method of bias correction contributes to increased performance in most cases and scenarios. Based on the bias corrected scenarios, precipitation annual average, temperature, and evapotranspiration will increase. In the case of precipitation and runoff, projection scenarios show an increase compared with the historical trends, and the monthly distribution of precipitation, evapotranspiration, and runoff shows an uneven distribution compared with baseline. This study provides an insight on how to choose a proper GCM and bias correction method and a helpful guide for local water resources management.