On future flood magnitudes and estimation uncertainty across 151 catchments in mainland China

• Published in: International journal of climatology 2021-01, Vol.41 (S1), p.E779-E800
• Main Authors: Gu, Lei, Yin, Jiabo, Zhang, Hongbo, Wang, Hui‐Min, Yang, Guang, Wu, Xushu
• Format: Article
• Language: English
• Subjects: Atmospheric models; Atmospheric moisture; Catchment area; Catchments; China; Climate; Climate change; Climate models; Climatic evolution; Early warning systems; Environmental risk; Extreme weather; flood quantiles; Flood risk; Flooding; Floods; Global climate; Global climate models; Global warming; Hazard mitigation; Hydrologic models; hydrological modelling; Hydrology; Mitigation; Quantiles; Sampling; Seasonal variations; Seasonality; Spatial variations; statistical downscaling; Storms; Stream discharge; Stream flow; Uncertainty; Warning systems; Weather
• ISSN: 0899-8418; 1097-0088
• DOI: 10.1002/joc.6725

As atmospheric moisture holding capacity is positively dependent on temperatures, a large intensification of precipitation extremes is projected under foreseeable climate warming. Flooding that is mainly attributed to extreme storms usually accounts for an ambitious target in weather‐related hazard mitigation over China. Previous works seldom focused on flooding evolution patterns under climate change at a national scale, and fewer flooding projections considered the estimation uncertainty sourced from limited samples. This study systematically projected changes in flood quantiles based on annual maximum series and seasonality and also evaluated the variations of sampling uncertainty for 151 catchments over mainland China under the emission scenario of representative concentration pathway (RCP) 8.5. In order to project future streamflow series, the bias‐corrected outputs of six global climate models (GCMs) were input into a best‐performing hydrological model, which was selected from four calibrated hydrological models based on the KGE criteria. The Pearson type‐III (P‐III) distribution and L‐moments (L‐M) method were employed to derive the flood quantiles for different return periods during historical (1961–2005) and future (2056–2100) periods, and the bootstrapping method was applied to estimate the sampling uncertainty. A regression trend method was used to track the variations of flood seasonality in the context of climate warming. Our results project earlier flood timing and larger flood quantiles for most catchments in future period than those in the historical period, despite being accompanied by substantial spatial variations. We also project that the sampling uncertainty in estimating flood quantiles is increased in a warming future. Many catchments are exposed to dramatic changes in both flood quantile and estimation uncertainty by over 50%, while only a few catchments are projected to have decreasing flood risks. These results suggest an urgent need to improve the functionality of early warning systems and increase societal resilience to warming climates over China. Projected changes in flood dynamics and estimation uncertainty are evaluated for 151 catchments over mainland China. The bias‐corrected multi‐model climate ensemble outputs are combined with four hydrological models to project hydrological scenarios. The floods will occur earlier under future climate warming in most catchments. The flood quantiles and sampling uncertainty are both pro