Observation‐Constrained Projection of Global Flood Magnitudes With Anthropogenic Warming. Issue 3 (25th March 2021)

• Record Type: Journal Article
• Authors: Liu, Wenbin; Yang, Tao; Sun, Fubao; Wang, Hong; Feng, Yao; Du, Muye
• Is Part Of: Water resources research. Volume 57:Issue 3(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-03-25
• Subjects: Hydrology -- Periodicals; 333.91
• DOI: 10.1029/2020WR028830
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: River flooding is among the costliest natural disasters with severe economic, societal, and environmental consequences. However, substantial uncertainties remain in global and regional projections of future flood conditions simulated by global climate models (GCMs) and/or global hydrological models (GHMs). Using physical models coupled with machine learning (ML), for the first time, we project changes in flood magnitudes of 2062 global river basins by constraining physical‐based streamflow simulations with observations under 1.5°C and 2°C warming scenarios identified for the Representative Concentration Pathway 8.5. We found that, during the validation period, the GHMs‐simulated flood magnitudes would improve with reduced uncertainty over the selected river basins after ML with a Long Short‐Term Memory network. Our estimation suggested that flood magnitudes would increase in many Northern Hemisphere mid‐ and high‐latitude rivers (e.g., Lena River, Amur River and Volga River) but decrease in some river basins in southern Finland and Eastern Europe in future periods (i.e., 1.5°C and 2°C warming levels). In 1.5°C and 2°C warmer worlds, the decreasing flood magnitudes in most South American rivers are associated with decreased soil moisture and increased evapotranspiration induced by warmer temperatures. Although the geographical pattern of changes in flood magnitudes for the +2°C experiment is close to that of the +1.5°C experiment, a 1.5°C warming target is more likely to reduce flood magnitudes of many river basins worldwide (e.g., in central and eastern Siberia, Alaska/Northwest Canada and South America). Key Points: Changes in global flood magnitudes are projected through physical‐based modeling pipelines constrained by observations Uncertainty in flood magnitude simulations has been reduced through LSTM‐based machine learning Fewer floods would occur in Siberia and low‐latitude Africa when keeping warming to 1.5°C instead of 2°C above pre‐industrial levels