Abstract
AbstractHydrometeorological flood generating processes (excess rain, short rain, long rain, snowmelt, and rain‐on‐snow) underpin our understanding of flood behavior. Knowledge about flood generating processes improves hydrological models, flood frequency analysis, estimation of climate change impact on floods, etc. Yet, not much is known about how climate and catchment attributes influence the spatial distribution of flood generating processes. This study aims to offer a comprehensive and structured approach to close this knowledge gap. We employ a large sample approach (671 catchments across the contiguous United States) and evaluate how catchment attributes and climate attributes influence the distribution of flood processes. We use two complementary approaches: A statistics‐based approach which compares attribute frequency distributions of different flood processes; and a random forest model in combination with an interpretable machine learning approach (accumulated local effects [ALE]). The ALE method has not been used often in hydrology, and it overcomes a significant obstacle in many statistical methods, the confounding effect of correlated catchment attributes. As expected, we find climate attributes (fraction of snow, aridity, precipitation seasonality, and mean precipitation) to be most influential on flood process distribution. However, the influence of catchment attributes varies both with flood generating process and climate type. We also find flood processes can be predicted for ungauged catchments with relatively high accuracy (R2 between 0.45 and 0.9). The implication of these findings is flood processes should be considered for future climate change impact studies, as the effect of changes in climate on flood characteristics varies between flood processes.
Highlights
Flood processes influence flood behavior (Fischer et al, 2016; Gaál et al, 2012; Keller et al, 2018; Merz & Blöschl, 2005; Tarasova et al, 2019)
We employed a statistics-based approach and a machine learning approach to evaluate which catchment attributes influence flood generating processes
accumulated local effects (ALE) have only recently be introduced to the field of hydrology (Konapala et al, 2020)
Summary
Flood processes influence flood behavior (Fischer et al, 2016; Gaál et al, 2012; Keller et al, 2018; Merz & Blöschl, 2005; Tarasova et al, 2019). The need to classify these processes has long been recognized and several studies have developed flood classification approaches (e.g., Berghuijs et al, 2016, 2019; Blöschl et al, 2017; Diezig & Weingartner, 2007; Merz & Blöschl, 2003; Sikorska et al, 2015; Stein et al, 2019; Tarasova et al, 2020). Very few of those studies evaluate how catchment and climate attributes influence flood generating processes (Merz & Blöschl, 2003; Stein et al, 2019). Knowing the temporal and spatial distribution of processes can potentially inform or explain changes in flood characteristics
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