Extreme flooding controlled by stream network organization and flow regime

Abstract

River floods are among the most common natural disasters worldwide, with substantial economic and humanitarian costs. Despite enormous efforts, gauging the risk of extreme floods with unprecedented magnitude is an outstanding challenge. Limited observational data from very high-magnitude flood events hinders prediction efforts and the identification of discharge thresholds marking the rise of progressively larger floods, termed flood divides. Combining long hydroclimatic records and a process-based model for flood hazard assessment, here we demonstrate that the spatial organization of stream networks and the river flow regime control the appearance of flood divides and extreme floods. In contrast with their ubiquitous attribution to extreme rainfall and anomalous antecedent conditions, we show 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, namely the hydrograph recession exponent and the coefficient of variation of daily flows. These results highlight the propensity of certain rivers for generating extreme floods and demonstrate the importance of using hazard mapping tools that, rather than solely relying on past flood records, identify regions susceptible to the occurrence of extreme floods from ordinary discharge dynamics.

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 analyzed data series range from 1951 to 2013, encompassing 36–62 (median: 59) hydrological years per basin (November to October). From this data set, we identified river basins exhibiting a sharp increase in the magnitude of rare floods (i.e., a flood divide in their empirical flood magnitude‐frequency curve, sensu Miniussi et al., 2023; Basso et al., 2023) by visually examining empirical flood‐frequency curves. In order to facilitate their identification from a large data set, we calculated the L‐skewness of the annual maxima sample (Hosking, 1990), which is a general and universal index for tail heaviness and robust to the presence of outliers (Vogel & Fennessey, 1993), we noticed that, for Germany, all the cases with L‐skewness exceeding 0.3 were also presenting this issue (Figure S2 in Supporting Information ).…”

“…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).…”

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.…”

The Atmospheric Dynamics Related to Extreme Rainfall and Flood Events during September-October-November in South Sulawesi

“…Regional factors such as monsoon activity that experience anomalies in the form of direction and speed, the emergence of tropical disturbances (tropical cyclones, lowpressure areas, eddies, or the presence of Inter Tropical Convergence Zone-ITCZ), also contribute. Simultaneously, several local factors for instance local topography, elevation, atmospheric lability and local convective also have an impact (Basso et al, 2023). In 2007, a study analysing data spanning 22 years from 1982 to 2003 identified the primary factor behind floods in Indonesia as the occurrence of extreme wet weather systems (Chan et al, 2023).…”