Flood hazard and change impact assessments may profit from rethinking model calibration strategies

Abstract

<jats:p>Abstract. Floods cause large damages, 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. However, uncertainties in simulated floods can be considerable and yield unreliable hazard and climate change impact assessments. A reliable hydrologic model ideally reproduces both local flood characteristics and spatial aspects of flooding, which is, however, not guaranteed especially when using standard model calibration metrics. In this paper 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. We compare how four well-known models (SAC, HBV, VIC, and 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 is challenging. They further show that changes in precipitation and temperature are not necessarily well translated to changes in flood flow, which makes local and regional flood hazard assessments even more difficult for future conditions. We conclude that models calibrated on integrated metrics such as the Kling–Gupta efficiency alone have limited reliability in flood hazard assessments, in particular in regional and future assessments, and suggest the development of alternative process-based and spatial evaluation metrics. </jats:p>

Comparing Flood Projection Approaches Across Hydro‐Climatologically Diverse United States River Basins

“…For hydrologic simulation, the added value appears mixed based on these results. Overall, bias is larger and more negative than for the other approaches, which has been observed in other studies (Brunner et al, 2020). However, hydrologic simulation may be more justified for Cluster 2 where out‐of‐sample bias is similar to that of the statistical approaches.…”

“…SACSMA is a well‐known model that has been used to generate a hydrologic model performance benchmark for basins across the United States as part of the CAMELS project (Newman et al, 2015). While a distributed version is used in this study, the performance of lumped SACSMA is average when compared to other hydrologic models for basins across the United States (Brunner et al, 2020; Kratzert et al, 2020). In SACSMA, peak flow is strongly related to precipitation (Brunner et al, 2020), which makes it suitable for comparison with the precipitation informed approach.…”

“…While a distributed version is used in this study, the performance of lumped SACSMA is average when compared to other hydrologic models for basins across the United States (Brunner et al, 2020;Kratzert et al, 2020). In SACSMA, peak flow is strongly related to precipitation (Brunner et al, 2020), which makes it suitable for comparison with the precipitation informed approach.…”