Stochastic generation of spatially coherent river discharge peaks for continental event-based flood risk assessment

Diederen, Dirk; Liu, Ye; Gouldby, Ben; Diermanse, Ferdinand; Vorogushyn, Sergiy

doi:10.5194/nhess-19-1041-2019

Cited by 37 publications

(38 citation statements)

References 31 publications

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“…CC BY 4.0 License. the individual catchments using the 25 th percentile of the corresponding time series of annual maxima as a threshold (Schlef et al, 2019) and by prescribing a minimum time lag of 10 days between events (Diederen et al, 2019). In Step 2, a data set consisting of the dates of flood occurrences across all catchments is compiled.…”

Section: Flood Event Identificationmentioning

confidence: 99%

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

Brunner

Melsen

Wood

et al. 2020

Preprint

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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.

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Section: Flood Event Identificationmentioning

confidence: 99%

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

Brunner

Melsen

Wood

et al. 2020

Preprint

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show abstract

“…In the first step, independent POT events are identified in the daily time series of the individual catchments using the u th percentile of the corresponding time series of annual maxima as a threshold, which is varied in a sensitivity analysis described in more detail below (section 2.6). A minimum time lag of 10 days between events is prescribed to ensure independence (Brunner, Gilleland, et al, 2020; Diederen et al, 2019). Using an annual maxima based threshold of u = 20th percentile as used in the main analysis resulted in 57 extracted events per catchment, on average.…”

Section: Methodsmentioning

confidence: 99%

How Probable Is Widespread Flooding in the United States?

Brunner

Papalexiou

Clark

et al. 2020

Water Resources Research

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Widespread flooding can cause major damages and substantial recovery costs. Still, estimates of how susceptible a region is to widespread flooding are largely missing mainly because of the sparseness of widespread flood events in records. The aim of this study is to assess the seasonal susceptibility of regions in the United States to widespread flooding using a stochastic streamflow generator, which enables simulating a large number of spatially consistent flood events. Furthermore, we ask which factors influence the strength of regional flood susceptibilities. We show that susceptibilities to widespread flooding vary regionally and seasonally. They are highest in regions where catchments show regimes with a strong seasonality, that is, the Pacific Northwest, the Rocky Mountains, and the Northeast. In contrast, they are low in regions where catchments are characterized by a weak seasonality and intermittent regimes such as the Great Plains. Furthermore, susceptibility is found to be the highest in winter and spring when spatial flood dependencies are strongest because of snowmelt contributions and high soil moisture availability. We conclude that regional flood susceptibilities emerge in river basins with catchments sharing similar streamflow and climatic regimes.

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“…Similarly, multi-site, single variable applications are common in the literature (e.g., Lamb et al, 2010;Diederen et al, 2019).…”

Section: Reject Sample Unless Is a Maximummentioning

confidence: 99%

Multivariate statistical modelling of the drivers of compound flood events in South Florida

Jane¹,

Cadavid²,

Obeysekera³

et al. 2020

Preprint

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Abstract. Miami-Dade County (south-east Florida) is among the most vulnerable regions to sea-level rise in the United States, due to a variety of natural and human factors. The co-occurrence of multiple, often statistically dependent flooding drivers – termed compound events – typically exacerbates impacts compared with their isolated occurrence. Ignoring dependencies between the drivers will potentially lead to underestimation of flood risk and under-design of flood defence structures. At present, design assessments of flood defence structures in Miami-Dade County assume rainfall and Ocean-side Water Level (O-sWL) are fully dependent, a conservative assumption inducing large safety factors. Here, an analysis of the dependence between the principal flooding drivers over a range of lags at three locations across the county is carried out. The conservative nature of the existing structural design assessment is subsequently explored, by combining a two-dimensional analysis of rainfall and O-sWL with regional sea-level rise projections. Finally, the vine copula and Heffernan and Tawn (2004) models are shown to outperform five standard higher dimensional copulas in capturing the dependence between the principal drivers of compound flooding: rainfall, O-sWL, and groundwater level. This leads to recommendations for revised future design frameworks able to capture and represent dependencies between different flood drivers.

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Stochastic generation of spatially coherent river discharge peaks for continental event-based flood risk assessment

Cited by 37 publications

References 31 publications

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

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

How Probable Is Widespread Flooding in the United States?

Multivariate statistical modelling of the drivers of compound flood events in South Florida

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