Critical rainfall statistics for predicting watershed flood responses: rethinking the design storm concept

Knighton, James; Walter, M. Todd

doi:10.1002/hyp.10888

Cited by 25 publications

(17 citation statements)

References 48 publications

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“…In this study, instead of dealing with individual rainfall event cycles, the stochastic representation as detailed in Table is used in the modeling of the long‐term performance of CSO tanks. It is recognized that the simplified way of characterizing individual rainfall events as used in the above‐described analytical probabilistic and stochastic approaches may result in inaccuracies in estimating the system responses (Knighton & Walter, ). That is why the analytical results obtained in this study will be compared with results from continuous simulations where no such simplification is made.…”

Section: Analytical Description Of Point Rainfall Seriesmentioning

confidence: 99%

An Analytical Stochastic Approach for Evaluating the Performance of Combined Sewer Overflow Tanks

Wang

Guo

2018

Water Resources Research

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Storm water detention tanks are widely used for the control of combined sewer overflows.Conventional continuous simulation and recently developed analytical probabilistic models have been used for analyzing the hydrologic operation of storm water detention tanks. These analyses are necessary in order to accurately estimate the runoff capture efficiency of a given control system or the required storage capacity for achieving a desired runoff capture efficiency. The analytical probabilistic models still have the shortcomings of making simplifying assumptions about the initial storage conditions of a detention tank. Developed in this study is a new stochastic analysis method which can provide similar results as provided by continuous simulations and overcome some of the shortcomings of the previously developed analytical probabilistic models. This stochastic analysis method uses closed-form analytical equations to estimate the runoff capture efficiency and required storage capacity. Results from these analytical equations are validated by comparing with continuous simulation results and close agreements are observed. These analytical equations are therefore proposed as a computationally efficient alternative for analyzing the hydrologic performance of combined sewer overflow tanks.Plain Language Summary Storm water detention tanks are widely used for the control of combined sewer overflows. Conventional continuous simulation and recently developed analytical probabilistic models can be used for analyzing their hydrologic operations. Continuous simulations are time consuming to perform and require a large amount of input data. Analytical probabilistic models still have the shortcoming of making simplifying assumptions about the initial storage conditions of a detention tank. A new analytical stochastic model is developed in this study which eliminates the shortcomings of both the continuous simulation and analytical probabilistic approaches. The new analytical stochastic model uses closed-form equations to directly estimate the system performance statistics. It can thus be used as a convenient tool for the planning, design, and operation of combined sewer overflow tanks.Storm water detention tanks primarily serve two purposes in the control of CSOs (Andr es-Dom enech et al., 2010). First, with the proper configuration of the tank size and its discharge capacity, the frequency and severity of CSOs can be greatly reduced. Second, storm tanks allow for the sedimentation and selfpurification of stored inflow, and therefore aid in mitigating water pollution from outflows.

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Section: Analytical Description Of Point Rainfall Seriesmentioning

confidence: 99%

An Analytical Stochastic Approach for Evaluating the Performance of Combined Sewer Overflow Tanks

Wang

Guo

2018

Water Resources Research

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“…A shallow confining layer (0.5 to 1.5 m) leads to a prevalence of saturation excess runoff (Easton et al, 2007). Regionally, surface runoff is primarily generated during the spring following extratropical rain-on-snow coincident with frozen or saturated soils and during the fall period of tropicalmoisture-derived precipitation (Knighton and Walter, 2016;J. O. Knighton et al, 2017).…”

Section: Study Region: Tompkins Countymentioning

confidence: 99%

“…Inter-seasonal predictions of future precipitation for Tompkins County show high variability, and potentially inaccurate estimates of seasonal extreme rainfall related to numerical and physical limitations of current general circulation models (GCMs) (Wobus et al, 2017;. The difficulty in predicting future extreme precipitation and strong influence of the land surface on flood runoff (Ivancic and Shaw, 2015;J. O. Knighton et al, 2017) has yielded projections of mild to no increase in future riverine flooding hazard in the Northeast USA, often accompanied by high uncertainty (e.g., Hirabayashi et al, 2013;Wing et al, 2018), or an average increase in risk, but with high spatial variability (e.g., Wobus et al, 2017).…”

Section: Study Region: Tompkins Countymentioning

confidence: 99%

Challenges to implementing bottom-up flood risk decision analysis frameworks: how strong are social networks of flooding professionals?

Knighton

Tsuda

Elliott

et al. 2018

Hydrol. Earth Syst. Sci.

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Abstract. Recent developments in bottom-up vulnerability-based decision analysis frameworks present promising opportunities for flood practitioners to simplify complex decisions regarding risk mitigation and climate adaptation. This family of methodologies relies on strong social networks among flood practitioners and the public to support careful definition of stakeholder-relevant thresholds and vulnerabilities to hazards. In parallel, flood researchers are directly considering distinct atmospheric mechanisms that induce flooding to readily incorporate information on future climate projections. We perform a case study of flood professionals actively engaged in flood risk mitigation within Tompkins County, New York, USA, a community dealing with moderate flooding, to gage how much variance exists among professionals from the perspective of establishing a bottom-up flood mitigation study from an atmospheric perspective. Results of this case study indicate disagreement among flooding professionals as to which socioeconomic losses constitute a flood, disagreement on anticipated community needs, weak understanding of climate–weather–flood linkages, and some disagreement on community perceptions of climate adaptation. In aggregate, the knowledge base of the Tompkins County flood practitioners provides a well-defined picture of community vulnerability and perceptions. Our research supports the growing evidence that collaborative interdisciplinary flood mitigation work could reduce risk, and potentially better support the implementation of emerging bottom-up decision analysis frameworks for flood mitigation and climate adaptation.

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“…A site in Java, Indonesia, was selected to demonstrate the application of IDF curves from 6-h rainfall to derive IDF curves for three sites where IDF curves exist [30]. In the United States, the National Oceanic and Atmospheric Administration (NOAA) Atlas #14 also provides IDF curves information and is usually used to construct design storms in the country [31]. Latin America is not an exception since the study of IDF curves is a frequent practice.…”

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confidence: 99%

Physical Parameterization of IDF Curves Based on Short-Duration Storms

Gutiérrez-López

Hernandez

Sandoval

2019

Water

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Intensity-duration-frequency (IDF) curves are empirical mathematical formulations that have been used for years in engineering for planning, design, and operation of hydraulic projects. The expression proposed by Sherman (1931) has been validated and used largely by many researchers. In all cases, the four parameters of this formulation are obtained through a numerical procedure. Although these parameters are obtained from historical rainfall observations, the optimization of these parameters implies an infinite combination between them and all those solutions would be valid. Of the four parameters, only one of them (C) has units, and for this reason, a physical sense of parameter C is searched for. Having certainty that some of them can be measured in situ would represent a great advance for modern hydrology. With data from 523 storms monitored every minute, a parametric adjustment was made to the Sherman equation and the typical duration of storms at each site was also obtained. To demonstrate how rainfall intensities vary with the change in C value, rainfall intensities calculations for of 5, 10, 15, and 20 min rainfall duration are used to validate the proposed methodology. The results show that typical storm duration is correlated with the additive parameter of Sherman's formula. the rainfall depths of 5, 10, 15, and 30 min are inversely correlated with the rainfall depth of one hour, establishing empirical factors 0.29, 0.45, 0.57, and 0.79 respectively. Using data from nearly 200 rain gauges, Hershfield found values for non-dimensional factors that allowed the transformation of daily rainfall data to calculate IDF curves. In this research, frequency analysis was performed by fitting the Gumbel and Lognormal probability functions, using the maximum rainfall depth accumulated in 24 h to provide functional mathematical relationships for desegregation of rainfall in minutes. Even today, the same distributions are used for the analysis of the maximum annual precipitation associated with duration of 1, 2, 3, 6, 12, 18, 24, 48, and 72 h [4]. There are different methods or approaches to obtain IDF curves. A considerable literature on IDF curves has been published. The reference studies for IDF curves for Australia were conducted by [5][6][7]. In Belgium, the reference IDF curves were presented by [8,9]. In Canada, a number of investigations in the field of IDF curves were published by [10][11][12][13][14][15][16]. In Denmark [17,18] used Sherman's formulation to define IDF curves. In Sweden, detailed studies have also been formalized to define IDF curves [19][20][21]. To date, various methods have been developed and introduced in Spain to compute IDF curves [22][23][24]. In Taiwan, IDF curves were prepared according to the procedure used by [25][26][27]. Reference [28] have recently developed a methodology for select IDF curves in China. In Peninsular Malaysia, a software tool for estimated IDF curves has been developed as an Excel add-in by using Visual Basics for Applications [29]. A site in Java, Indonesia, wa...

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Critical rainfall statistics for predicting watershed flood responses: rethinking the design storm concept

Cited by 25 publications

References 48 publications

An Analytical Stochastic Approach for Evaluating the Performance of Combined Sewer Overflow Tanks

An Analytical Stochastic Approach for Evaluating the Performance of Combined Sewer Overflow Tanks

Challenges to implementing bottom-up flood risk decision analysis frameworks: how strong are social networks of flooding professionals?

Physical Parameterization of IDF Curves Based on Short-Duration Storms

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