Floods and Heavy Precipitation at the Global Scale: 100-Year Analysis and 180-Year Reconstruction

Abstract

Floods and heavy precipitation have disruptive impacts worldwide, but their historical variability remains only partially understood at the global scale. This article aims at reducing this knowledge gap by jointly analyzing seasonal maxima of streamflow and precipitation at more than 3,000 stations over a 100-year period. The analysis is based on Hidden Climate Indices (HCIs). Like standard climate indices (e.g., Nino 3.4, NAO), HCIs are used as covariates explaining the temporal variability of data, but unlike them, HCIs are estimated from the data. In this work, a distinction is made between common HCIs, that affect both heavy precipitation and floods, and specific HCIs, that exclusively affect one or the other. Overall, HCIs do not show noticeable autocorrelation, but some are affected by noticeable trends. In particular, strong and wide-ranging trends are identified in precipitation-specific HCIs, while trends affecting flood-specific HCIs are weaker and have more localized effects. A probabilistic model is then derived to link HCIs and large-scale atmospheric variables (pressure, wind, temperature) and to reconstruct HCIs since 1836 using the 20CRv3 reanalysis. In turn this allows estimating the probability of occurrence of floods and heavy precipitation at the global scale. This 180-year reconstruction highlights flood hot-spots and hot-moments in the distant past, well before the establishment of perennial monitoring networks. The approach presented in this study is generic and paves the way for an improved characterization of historical variability by making a better use of long but highly irregular station data sets.

Key Points

• We perform a joint analysis of station-based flood and heavy precipitation data, at the global scale and over a long 100-year period

• Results highlight wide-ranging increasing trends affecting heavy precipitation, whereas flood trends appear weaker and less consistent

• A 180-year reconstruction of flood and heavy precipitation probabilities is proposed, using atmospheric predictors from the 20CR reanalysis

Plain Language Summary

Floods and heavy precipitation events still hold some mystery despite their disruptive impacts. As an illustration, the latest IPCC report (recently released in 2021) indicates that “the frequency and intensity of heavy precipitation events have increased since the 1950s”, but that at the same time “confidence about peak flow trends over past decades on the global scale is low.” Why this apparent disconnect between floods and heavy precipitation? Beyond trends, do floods and heavy precipitation vary together at the global scale? How are they related to atmospheric variables such as winds, temperature, atmospheric pressure? This article describes a 100-year analysis of floods and heavy precipitation data at the global scale. This analysis is made possible by an original probabilistic model adapted to station data sets with highly variable data availability (https://vimeo.com/802751683). The analysis first highlights wide-ranging increasing trends affecting heavy precipitation, whereas flood trends appeared weaker and less consistent. It is then used to identify climate configurations associated with the occurrence of floods and heavy precipitation, and to build a 180-year (1836–2015) reconstruction of floods and heavy precipitation probabilities at the global scale. This contributes to a better understanding of the historical variability of hydrologic extremes in the distant past.