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

Department of Civil Engineering: Hydraulic, Energy and Environment, Universidad Politécnica de Madrid, 3, 28040 Madrid, Spain
Author to whom correspondence should be addressed.
Water 2019, 11(11), 2266;
Received: 5 September 2019 / Revised: 7 October 2019 / Accepted: 21 October 2019 / Published: 29 October 2019
(This article belongs to the Special Issue Influence of Climate Change on Floods)
Climate projections provided by EURO-CORDEX predict changes in annual maximum series of daily rainfall in the future in some areas of Spain because of climate change. Precipitation and temperature projections supplied by climate models do not usually fit exactly the statistical properties of the observed time series in the control period. Bias correction methods are used to reduce such errors. This paper seeks to find the most adequate bias correction techniques for temperature and precipitation projections that minimizes the errors between observations and climate model simulations in the control period. Errors in flood quantiles are considered to identify the best bias correction techniques, as flood quantiles are used for hydraulic infrastructure design and safety assessment. In addition, this study aims to understand how the expected changes in precipitation extremes and temperature will affect the catchment response in flood events in the future. Hydrological modelling is required to characterize rainfall-runoff processes adequately in a changing climate, in order to estimate flood changes expected in the future. Four catchments located in the central-western part of Spain have been selected as case studies. The HBV hydrological model has been calibrated in the four catchments by using the observed precipitation, temperature and streamflow data available on a daily scale. Rainfall has been identified as the most significant input to the model, in terms of its influence on flood response. The quantile mapping polynomial correction has been found to be the best bias correction method for precipitation. A general reduction in flood quantiles is expected in the future, smoothing the increases identified in precipitation quantiles by the reduction of soil moisture content in catchments, due to the expected increase in temperature and decrease in mean annual precipitations. View Full-Text
Keywords: bias correction; quantile mapping; climate change; floods; CORDEX bias correction; quantile mapping; climate change; floods; CORDEX
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Soriano, E.; Mediero, L.; Garijo, C. Selection of Bias Correction Methods to Assess the Impact of Climate Change on Flood Frequency Curves. Water 2019, 11, 2266.

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