Selection of Bias Correction Methods to Assess the Impact of Climate Change on Flood Frequency Curves

• Main Authors: Enrique Soriano, Luis Mediero, Carlos Garijo
• Format: Article
• Language: English
• Published: MDPI AG 2019-03-01
• Series: Proceedings
• Subjects: Bias Correction; Quantile Mapping; Climate Change; Floods; CORDEX
• Online Access: http://www.mdpi.com/2504-3900/7/1/14

Annual maximum daily rainfalls will change in the future because of climate change, according to climate projections provided by EURO-CORDEX. This study aims at understanding how the expected changes in precipitation extremes will affect the flood behavior in the future. Hydrological modeling is required to characterize the rainfall-runoff process adequately in a changing climate to estimate flood changes. Precipitation and temperature projections given by climate models in the control period usually do not fit the observations in the same period exactly from a statistical point of view. To correct such errors, bias correction methods are used. This paper aims at finding the most adequate bias correction method for both temperature and precipitation projections, minimizing the errors between observed and simulated precipitation and flood frequency curves. Four catchments located in central western Spain have been selected as case studies. The HBV hydrological model has been calibrated, using the observed precipitation, temperature, and streamflow data available at a daily scale. Expected changes in precipitation extremes are usually smoothed by the reduction of soil moisture content due to expected increases in temperatures and decreases in mean annual precipitation. Consequently, rainfall is the most significant input to the model and polynomial quantile mapping is the best bias correction method.