Uncertainty of the Assumptions Required for Estimating the Regulatory Flood: Red River of the North

Todhunter, Paul E.

doi:10.1061/(asce)he.1943-5584.0000560

Cited by 13 publications

(22 citation statements)

References 54 publications

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“…Todhunter [101] studied the peak flow data for the Grand Forks station highlighting the necessity of examining the assumptions required for the application of the conventional stationary frequency analysis based on LP3 distribution recommended by the US federal guidelines detailed in Bulletin 17B [112]. In particular, Todhunter [101] checked the presence of climatic trends, the temporal independence of the records, watershed changes and the flood generating mechanism.…”

Section: Little Sugar Creek: Modeling Resultsmentioning

confidence: 99%

“…In particular, Todhunter [101] checked the presence of climatic trends, the temporal independence of the records, watershed changes and the flood generating mechanism. However, in spite of the author's suggestion of quantifying an effectively communicate the various uncertainties in flood risk assessment, the sampling uncertainty of some of the methods used in that study is not assessed, thus affecting the conclusions.…”

Section: Little Sugar Creek: Modeling Resultsmentioning

confidence: 99%

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Stationarity is undead: Uncertainty dominates the distribution of extremes

Serinaldi

Kilsby

2015

Advances in Water Resources

359

257

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Keywords:Nonstationary flood frequency analysis Nonstationary return period Risk of failure Nonstationary confidence intervals Generalized linear models Generalized additive models a b s t r a c tThe increasing effort to develop and apply nonstationary models in hydrologic frequency analyses under changing environmental conditions can be frustrated when the additional uncertainty related to the model complexity is accounted for along with the sampling uncertainty. In order to show the practical implications and possible problems of using nonstationary models and provide critical guidelines, in this study we review the main tools developed in this field (such as nonstationary distribution functions, return periods, and risk of failure) highlighting advantages and disadvantages. The discussion is supported by three case studies that revise three illustrative examples reported in the scientific and technical literature referring to the Little Sugar Creek (at Charlotte, North Carolina), Red River of the North (North Dakota/Minnesota), and the Assunpink Creek (at Trenton, New Jersey). The uncertainty of the results is assessed by complementing point estimates with confidence intervals (CIs) and emphasizing critical aspects such as the subjectivity affecting the choice of the models' structure. Our results show that (1) nonstationary frequency analyses should not only be based on at-site time series but require additional information and detailed exploratory data analyses (EDA); (2) as nonstationary models imply that the time-varying model structure holds true for the entire future design life period, an appropriate modeling strategy requires that EDA identifies a well-defined deterministic mechanism leading the examined process; (3) when the model structure cannot be inferred in a deductive manner and nonstationary models are fitted by inductive inference, model structure introduces an additional source of uncertainty so that the resulting nonstationary models can provide no practical enhancement of the credibility and accuracy of the predicted extreme quantiles, whereas possible model misspecification can easily lead to physically inconsistent results; (4) when the model structure is uncertain, stationary models and a suitable assessment of the uncertainty accounting for possible temporal persistence should be retained as more theoretically coherent and reliable options for practical applications in real-world design and management problems; (5) a clear understanding of the actual probabilistic meaning of stationary and nonstationary return periods and risk of failure is required for a correct risk assessment and communication.

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Section: Little Sugar Creek: Modeling Resultsmentioning

confidence: 99%

Section: Little Sugar Creek: Modeling Resultsmentioning

confidence: 99%

Stationarity is undead: Uncertainty dominates the distribution of extremes

Serinaldi

Kilsby

2015

Advances in Water Resources

359

257

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“…Flood heterogeneity has been shown to invalidate the iid assumption in different catchments (e.g. Murphy, 2001;Todhunter, 2012). Further, flood clustering may introduce serial correlation in the time series and may invalidate the independence assumption.…”

Section: Flood Estimation: Process-neutral or Process-based?mentioning

confidence: 99%

Floods and climate: emerging perspectives for flood risk assessment and management

Merz

Aerts

Arnbjerg‐Nielsen

et al. 2014

Nat. Hazards Earth Syst. Sci.

271

165

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Abstract. Flood estimation and flood management have traditionally been the domain of hydrologists, water resources engineers and statisticians, and disciplinary approaches abound. Dominant views have been shaped; one example is the catchment perspective: floods are formed and influenced by the interaction of local, catchment-specific characteristics, such as meteorology, topography and geology. These traditional views have been beneficial, but they have a narrow framing. In this paper we contrast traditional views with broader perspectives that are emerging from an improved understanding of the climatic context of floods. We come to the following conclusions: (1) extending the traditional system boundaries (local catchment, recent decades, hydrological/hydraulic processes) opens up exciting possibilities for better understanding and improved tools for flood risk assessment and management. (2) Statistical approaches in flood estimation need to be complemented by the search for the causal mechanisms and dominant processes in the Published by Copernicus Publications on behalf of the European Geosciences Union. B. Merz et al.: Floods and climate: emerging perspectives for flood risk assessment and managementatmosphere, catchment and river system that leave their fingerprints on flood characteristics. (3) Natural climate variability leads to time-varying flood characteristics, and this variation may be partially quantifiable and predictable, with the perspective of dynamic, climate-informed flood risk management. (4) Efforts are needed to fully account for factors that contribute to changes in all three risk components (hazard, exposure, vulnerability) and to better understand the interactions between society and floods. (5) Given the global scale and societal importance, we call for the organization of an international multidisciplinary collaboration and datasharing initiative to further understand the links between climate and flooding and to advance flood research.

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“…Outcalt et al (1997) present a method of Hurst rescaling that can be used to identify distinct physical regimes in geophysical time series. The method has been used to identify regime transitions and changes in the homogeneity of time series (Runnalls and Oke 2006;Todhunter 2012). The mean of the original geophysical time series is subtracted from each of the n observations in the series.…”

Section: Water Balance Modelmentioning

confidence: 99%

“…Although regional aspects of the Devils Lake flood hazard have been investigated (Todhunter andRundquist 2004, 2008), the hydroclimatological basis of the more recent flooding has received less intensive examination (Vecchia 2008;Hoerling et al 2010;Todhunter and Knish 2014). Bluemle (1995) argues that natural hydroclimatological forcing of lake-level variation at Devils Lake during the Holocene should be assumed until proven otherwise, but notes that, since the early 1980s, many local stakeholders have attributed the lake rise to local agricultural and wetland drainage.…”

Section: Introductionmentioning

confidence: 99%

Natural hydroclimatic forcing of historical lake volume fluctuations at Devils Lake, North Dakota (USA)

Todhunter

Fietzek-DeVries

2016

Nat Hazards

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Devils Lake, a terminal saline lake in eastern North Dakota, has experienced catastrophic flooding over the past two decades producing direct damages in excess of $1 billion ($USD). We use three long-term datasets to examine the temporal coherence between historical lake fluctuations and basic hydroclimatic drivers. Monthly precipitation and mean monthly air temperature data are used to characterize long-term precipitation delivery and evaporative demand. Monthly water balance data for a representative location are used to assess basin soil moisture conditions. A lake volume time series documents lake volume fluctuation in response to long-term precipitation and regional soil moisture conditions. Three variables are derived from the datasets, each characterizing a different aspect of the region's hydroclimatology. A rescaling technique is applied to each variable to examine the temporal coherence and relative patterns of the variables and to identify distinct homogeneous hydroclimatic regimes during the historical period. The three rescaled variables show strong temporal coherence and confirm 1980 as an abrupt transition year between two distinct long-term hydroclimatic modes. Mode 1, a longer and drier phase, runs from 1907 to 1980, while mode 2, a shorter and wetter phase, extends from 1981 to the present. Multi-decadal and century-scale fluctuations between these two modes are the key drivers of long-term lake volume fluctuations, upon which interannualand interdecadal-scale climatic variability are superimposed. The similar rates of change among the rescaled variables provides evidence in support of the conclusion that long-term natural hydroclimatological variability is the primary driver of observed lake volume changes at Devils Lake during the Twentieth Century and provides a foundation upon which to evaluate the potential contributing effects of anthropogenic climate change, and human alterations of the land use hydrology.

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Uncertainty of the Assumptions Required for Estimating the Regulatory Flood: Red River of the North

Cited by 13 publications

References 54 publications

Stationarity is undead: Uncertainty dominates the distribution of extremes

Stationarity is undead: Uncertainty dominates the distribution of extremes

Floods and climate: emerging perspectives for flood risk assessment and management

Natural hydroclimatic forcing of historical lake volume fluctuations at Devils Lake, North Dakota (USA)

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