Event‐based classification for global study of river flood generating processes

Abstract

Better understanding of which processes generate floods in a catchment can improve flood frequency analysis and potentially climate change impacts assessment. However, current flood classification methods are either not transferable across locations or do not provide event‐based information. We therefore developed a location‐independent, event‐based flood classification methodology that is applicable in different climates and returns a classification of all flood events, including extreme ones. We use precipitation time series and very simply modelled soil moisture and snowmelt as inputs for a decision tree. A total of 113,635 events in 4155 catchments worldwide were classified into one of five hydro‐climatological flood generating processes: short rain, long rain, excess rainfall, snowmelt and a combination of rain and snow. The new classification was tested for its robustness and evaluated with available information; these two tests are often lacking in current flood classification approaches. According to the evaluation, the classification is mostly successful and indicates excess rainfall as the most common dominant process. However, the dominant process is not very informative in most catchments, as there is a high at‐site variability in flood generating processes. This is particularly relevant for the estimation of extreme floods which diverge from their usual flood generation pattern, especially in the United Kingdom, Northern France, Southeastern United States, and India.

Toward Global Stochastic River Flood Modeling

“…Table S2b illustrates higher model performance on the plains and lower performance in mountainous areas, consistent with results from Figure 6 and known inaccuracies in rainfall products in mountainous areas (Beck et al, 2019). Skill on plateaus lies somewhere in between that of plains and mountains, perhaps due to the diversity of flood drivers in such regions (e.g., snowmelt and ice‐jam floods as well as rainfall‐driven events; Stein et al, 2019). In Table S2c, model performance appears relatively insensitive to the permeability of the bedrock but improves as soils become more impermeable.…”

Trends in Global Flood and Streamflow Timing Based on Local Water Year

“…Given recent attention to the role of changing antecedent conditions affecting flooding in a nonstationary climate (Sharma et al, 2018; Wasko & Nathan, 2019) and the attribution of changes in flood seasonality to changes in the hydroclimatic drivers of flooding (Black & Werritty, 1997; Blöschl et al, 2017), we expect studies of nonstationarity in flood seasonality to continue to be of high importance. Moreover, with the continual improvement of technological resources, we expect studies using large‐scale hydrologic data sets, to be at the fore of understanding climate change impacts in hydrology (e.g., Do et al, 2020; Gudmundsson et al, 2019; Stein et al, 2020). But where center timing is calculated on calendar year over large diverse geographical areas (e.g., Do et al, 2018; Gudmundsson et al, 2018), we urge caution.…”

A Process‐Based Framework to Characterize and Classify Runoff Events: The Event Typology of Germany

“…The quality of data‐based characterization and classification schemes largely depends on the quality of the available data and the robustness of the applied classification methods with respect to several aspects of uncertainty (Tarasova et al, 2019). In fact, classification results are sensitive to the uncertainty of input data from different sources (e.g., different observed temperature or precipitation data (Kampf & Lefsky, 2016), various soil moisture, and snowmelt simulations (Stein et al, 2019), to the choice of indicators used to characterize event types and to the values of thresholds applied to attribute events to different classes (Sikorska et al, 2015; Stein et al, 2019). All these aspects must be examined to provide a robust characterization framework and guidelines for selection of input data, indicators, and their corresponding thresholds.…”