Extreme flooding foretold by stream network organization and flow regime

Abstract

River floods are the commonest natural disaster worldwide causing substantial monetary losses and death tolls yearly. Despite enormous efforts, gauging the peril of extreme floods is an outstanding challenge for varied economic sectors and the society at large. Appraisal of the flood hazard is especially difficult when the magnitude of the rarer floods strongly increases. Limited data records in fact hinder prediction of these instances and the identification of flood divides marking the rise of progressively larger floods. Here we demonstrate that the spatial organization of stream networks and the river flow regime control the appearance of flood divides and extreme floods. Unlike their ubiquitous attribution to anomalous hydrometeorological conditions, we show for the first time that the propensity to generate extreme floods is well predicted by intrinsic properties of river basins. Most importantly it can be assessed prior to the occurrence of catastrophes through measurable metrics of these properties derived from commonly available discharge data, with far-reaching implications for flood hazard assessment and preparedness.

Prediction of Extraordinarily High Floods Emerging From Heterogeneous Flow Generation Processes

“…Catchments in wetter climates experience sustained water supply which determines unvaried runoff‐generation processes. Conversely, river basins in drier areas, where longer lag times between rainfall events allow for the catchment to dry, undergo transient conditions leading to varied runoff‐generation processes (Basso et al., 2023). Our results confirm that explicitly accounting for the existence of different runoff‐generation processes enables us to adequately model the tail of flood distributions in catchments exhibiting flood divides.…”

“…The appearance of flood divides has been often related to data scarcity preventing a good characterization of the tail of flood distributions (e.g., Miniussi et al., 2023), an issue possibly exacerbated by non‐stationarity of hydrologic processes caused by climate change (Yu et al., 2022). However, previous studies also linked them to non‐linearities in the catchment response (Rogger et al., 2012) arising from distinct physioclimatic characteristics of river basins (Basso et al., 2023) and to the existence of different runoff‐generation processes in a catchment (Merz et al., 2022). Runoff events and floods may indeed be triggered by different processes (e.g., rainfall on wet or dry soils, snowmelt, a combination of both; Hirschboeck, 1987b; Villarini & Smith, 2010; Sikorska et al., 2015) and thus be categorized into different types based on the inducing mechanisms (Stein et al., 2020; Tarasova, Basso, & Merz, 2020).…”

Constructing a geography of heavy-tailed flood distributions: insights from common streamflow dynamics

“…We excluded gauges located downstream of large dams in all four regions (Lehner et al, 2011;Wang et al, 2022). Consistent with previous studies (e.g., Botter et al, 2007a;Botter et al, 2010;Ceola et al, 2010;Doulatyari et al, 2015;Basso et al, 2021;Basso et al, 2023), we chose case studies in Germany, the UK, and the US characterized by limited snowfall, which minimizes the potential transfer of water across seasons due to strong snow accumulation and melting.…”

“…Specifically, the interplay between characteristic flood generation (Bernardara et al, 2008;Thorarinsdottir et al, 2018), the presence of mixed flood types (Morrison and Smith, 2002;Villarini and Smith, 2010), the tail heaviness of rainfall distributions (Gaume, 2006), catchment aridity (Molnar et al, 2006;Merz and Blöschl, 2009;Guo et al, 2014), and catchment area (Pallard et al, 2009;Villarini and Smith, 2010) are proposed as contributing factors to the nonlinearity of catchment responses. This nonlinearity is increasingly recognized as a plausible driver of heavytailed flood behavior (Fiorentino et al, 2007;Struthers and Sivapalan, 2007;Gioia et al, 2008;Rogger et al, 2012;Basso et al, 2015;Merz et al, 2022;Basso et al, 2023;Wang et al, 2023).…”

Stochastic in Space and Time: 1. Characterizing Orographic Gradients in Mean Runoff and Daily Runoff Variability

“…In these cases, nonlinear relationships between river morphology and long‐term erosion rates arise because erosional thresholds are exceeded more frequently as erosion rate and relief increase. The climate driver on river profile evolution is not the annual precipitation itself but how the soil water balance (Deal et al., 2018) and the hydrologic structure of watersheds (Basso et al., 2023) mediate flood frequency. These concepts place the central focus on water storage‐discharge relationships (Botter et al., 2009; Kirchner, 2009) to condition how rainfall events are converted to runoff ones.…”