Flood spatial coherence, triggers, and performance in hydrological simulations: large-sample evaluation of four streamflow-calibrated models

Abstract

Abstract. Floods cause extensive damage, especially if they affect large regions. Assessments of current, local, and regional flood hazards and their future changes often involve the use of hydrologic models. A reliable hydrologic model ideally reproduces both local flood characteristics and spatial aspects of flooding under current and future climate conditions. However, uncertainties in simulated floods can be considerable and yield unreliable hazard and climate change impact assessments. This study evaluates the extent to which models calibrated according to standard model calibration metrics such as the widely used Kling–Gupta efficiency are able to capture flood spatial coherence and triggering mechanisms. To highlight challenges related to flood simulations, we investigate how flood timing, magnitude, and spatial variability are represented by an ensemble of hydrological models when calibrated on streamflow using the Kling–Gupta efficiency metric, an increasingly common metric of hydrologic model performance also in flood-related studies. Specifically, we compare how four well-known models (the Sacramento Soil Moisture Accounting model, SAC; the Hydrologiska Byråns Vattenbalansavdelning model, HBV; the variable infiltration capacity model, VIC; and the mesoscale hydrologic model, mHM) represent (1) flood characteristics and their spatial patterns and (2) how they translate changes in meteorologic variables that trigger floods into changes in flood magnitudes. Our results show that both the modeling of local and spatial flood characteristics are challenging as models underestimate flood magnitude, and flood timing is not necessarily well captured. They further show that changes in precipitation and temperature are not always well translated to changes in flood flow, which makes local and regional flood hazard assessments even more difficult for future conditions. From a large sample of catchments and with multiple models, we conclude that calibration on the integrated Kling–Gupta metric alone is likely to yield models that have limited reliability in flood hazard assessments, undermining their utility for regional and future change assessments. We underscore that such assessments can be improved by developing flood-focused, multi-objective, and spatial calibration metrics, by improving flood generating process representation through model structure comparisons and by considering uncertainty in precipitation input.

Challenges in modeling and predicting floods and droughts: A review

“…6. Dependencies representation: Spatial flood dependencies have been shown to be underestimated by hydrological models (Brunner et al, 2021;Prudhomme et al, 2011), which means that simulated flood events are less spatially coherent than observed ones, that is, it is less likely to find widespread flood events in the simulations than the observations. The misrepresentation of spatial dependencies is likely related to input uncertainty.…”

“… Model calibration : Different calibration metrics usually focus either on low or high flows and focusing on one type of extreme may result in performance decreases for the other type of extreme (Kollat, Reed, & Wagener, 2012; Pool, Vis, Knight, & Seibert, 2017) and for the representation of event transitions. In addition, the use of calibration metrics said to be ideal for certain applications (e.g., Nash‐Sutcliffe‐efficiency‐based metrics for floods) does not guarantee a reliable reproduction of extremes (Brunner et al, 2021; Lane et al, 2019; Mizukami et al, 2019). While model calibration for extremes is already challenging at the local scale, it gets even more demanding in a regional context.…”

“…On the other hand, land cover and subsurface properties also influence spatial dependency, which may not be well reproduced if such characteristics are not well represented in models. In the case of calibrated models, a misrepresentation of spatial dependencies may also be related to the lack of spatial calibration strategies (Brunner et al, 2021) and uncertainty of streamflow observations (McMillan, Freer, Pappenberger, Krueger, & Clark, 2010) used for model calibration. In addition to spatial dependencies, dependencies between different flood/drought characteristics are not necessarily well represented by models as illustrated by Brunner and Sikorska (2018) for flood peaks and volumes. Ungauged catchments : In catchments that lack streamflow observations, flood and drought prediction either relies on statistical methods such as regional frequency analysis or hydrological modeling by inferring model parameters using data from similar catchments for which observations are available (Hrachowitz et al, 2013).…”

On the links between sub-seasonal clustering of extreme precipitation and high discharge in Switzerland and Europe

“…Quantifying the relevance of TCEP for high discharge levels is therefore important to properly characterise flood risk, improve forecasts, support process-based calibration of rainfall-runoff models (Cullmann et al, 2008;Brunner et al, 2021) and develop informed storylines for impact assessment (Sillmann et al, 2021). The impact of sub-seasonal TCEP on discharge has not been explicitly addressed, to our knowledge, except briefly in the case of Switzerland by Tuel and Martius (2021b).…”

Reply on RC1

“…a reliable representation of hydrological processes and their drivers. Further improvement in model representation of high flows may be needed to reduce bias and improve process representation (Mizukami et al, 2019;Brunner et al, 2021).…”