Future Trends in the Interdependence Between Flood Peaks and Volumes: Hydro‐Climatological Drivers and Uncertainty

Abstract

Reliable flood estimates are needed for designing safe and cost-effective flood protection structures. Classical flood estimation methods applied for deriving such estimates focus on peak discharge and neglect other important flood characteristics such as flood volume and the interdependence among different flood characteristics. Furthermore, they do not account for potential nonstationarities in hydrological time series due to climate change. The consideration of both the interdependence between peak discharge and flood volume and its nonstationarity might help us derive more reliable flood estimates. A few studies have looked at changes in the general dependence of peak discharge and flood volume for small sets of catchments and explored ways of modeling such changes. However, spatial analyses of trends in this dependence or in their climatological drivers have not been carried out. The aim of this study was to help close this knowledge gap by first quantifying trends in the general dependence between peak discharge and flood volume as described by Kendall's tau on a spatially comprehensive data set of 307 catchments in Switzerland. Second, potential climatological drivers for changes in the dependence between peak discharge and flood volume were identified. Our results show that the dependence between peak discharge and flood volume and its trends are spatially heterogeneous. This pattern cannot be explained by one driver only but by an interplay of changes in precipitation, snowmelt, and soil moisture. Both the trends and the links between drivers and trends depend on the climate model chain considered and are therefore uncertain.

Future streamflow regime changes in the United States: assessment using functional classification

“…Regime changes are caused by changes in precipitation seasonality and intensity (Brönnimann et al, 2018) and seasonal shifts and decreases in melt contributions (Farinotti et al, 2016;Jenicek et al, 2018) related to reduced snow and glacier storage (Beniston et al, 2018). Predicted regime changes are relatively robust (Addor et al, 2014) compared to changes in high and low flows, which are highly uncertain (Brunner et al, 2019c;Madsen et al, 2014) because of diverse uncertainty sources introduced in various steps along the modeling chain (Clark et al, 2016). It has been shown that future regime changes can be linked to changes in flood and drought characteristics, e.g.…”

Origin and variability of statistical dependencies between peak, volume, and duration of rainfall-driven flood events

“…In literature there are several methods used to select a flood event (e.g., [51][52][53][54] ). Among these, here, we have chosen a simple method based on a discharge threshold, a fixed percentile of daily discharge, also adopted in 18,32,51,53,[55][56][57] . The beginning and end of a flood event are respectively the first and last value, when the discharge is greater or equal the threshold.…”

“…Vezzoli et al 29 used the pairwise dependence in order to assess the performance of climate-hydrology models. Some studies have investigated the possible non-stationarity of such dependencies 30 – 32 .…”

Probabilistic Projections of Multidimensional Flood Risks at a Convection‐Permitting Scale

“…Previous univariate and bivariate flood risk assessments assume the missing flood characteristic to be the long‐term, historical average and be independent of other characteristics considered, leading to overestimation or underestimation of flood risks (Alfieri et al., 2017; Balistrocchi et al., 2017). Thus, it is desired to explore the interdependence among flood characteristics, and multivariate approaches have received great attention in recent years (Brunner et al., 2019; Quinn et al., 2019; Santhosh & Srinivas, 2013). As one of the most popular multivariate approaches, copula has been extensively used to assess the dependence structure of flood characteristics and to estimate the return period of floods (Favre et al., 2004; Jeong et al., 2014; Mallakpour et al., 2019; Moftakhari et al., 2017).…”