Return Period and Risk of Hydrologic Events. II: Applications

Fernández, Bonifacio; Salas, José D.

doi:10.1061/(asce)1084-0699(1999)4:4(308)

Cited by 50 publications

(26 citation statements)

References 15 publications

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“…In the conventional stationary analyses for designing hydraulic structures, the terms probability of exceedance, return period, and risk of failure during the design life are widely used. Referring to Chow et al [6], Loaiciga and Mariño [49], Fernández and Salas [23,24], Douglas et al [13], Cooley [9] and references therein for formal introductions, here, we would like to recall and slightly expand the critical discussion reported by Serinaldi [83].…”

Section: Setting the Frameworkmentioning

confidence: 97%

Stationarity is undead: Uncertainty dominates the distribution of extremes

Serinaldi

Kilsby

2015

Advances in Water Resources

388

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: Setting the Frameworkmentioning

confidence: 97%

Stationarity is undead: Uncertainty dominates the distribution of extremes

Serinaldi

Kilsby

2015

Advances in Water Resources

388

257

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“…It can also be understood as the average time until the level x T is exceeded (Stedinger et al, 1993;Reiss and Thomas, 1997) or as the average number of time intervals between occurrence of an event xOx T (for the righthand tail of a distribution; Tallaksen, 2000). For independent and identically distributed (iid) events, these formulations are equivalent (Fernández and Salas, 1999). In that case, the return periods for droughts derived using the threshold level approach can be calculated as (Stedinger et al, 1993;Cunnane, 1979)…”

Section: Return Periodsmentioning

confidence: 99%

Drought in groundwater—drought distribution and performance indicators

Peters

Lanen

Torfs

et al. 2005

Journal of Hydrology

104

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“…Lohani and Loganathan (1997) forecast drought conditions for future months, based on the current drought class described by the Palmer drought index. Fernández and Salas (1999) presented a method for estimating the return period of droughts when underlying hydrological data series (annual streamflow) are autocorrelated. They assumed that the binary process consisting of dry years (D: X t < x 0 ) and wet years (W: X t ≥ x 0 ) follows a simple (first-order) Markov chain with two states (dry and wet).…”

Section: Preliminaries On Markov Chainsmentioning

confidence: 99%

“…Various authors investigating return periods of droughts have found different levels of persistence in precipitation as well as in streamflow data. The identified order of Markov chain depended on the applied thresholds to distinguish between wet and dry conditions, the analysed time interval and the type of the indicator (Chin 1977, Roldan and Woolhiser 1982, Şen 1990, Fernández and Salas 1999, Cancelliere and Salas 2004, Bayazit and Önöz 2005, Schoof and Pryor 2008, Sharma and Panu 2008, Akyuz et al 2012. Time series of SPI-SRI indicator for the 1966-2006 period were analysed as a discrete-state, discrete-time variable with five possible states S = {0,1,2,3,4}.…”

Section: Identification Of the Order Of The Markov Chainmentioning

confidence: 99%

Combined analysis of precipitation and water deficit for drought hazard assessment

Tokarczyk

Szalińska

2014

Hydrological Sciences Journal

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The combined analysis of precipitation and water scarcity was done with the use of the Standardized Precipitation Index (SPI) and the Standardized Runoff Index (SRI), developed as a monthly, two-variable SPI-SRI indicator to identify different classes of hydrometeorological conditions. Stochastic analysis of a long-term time series of monthly SPI-SRI indicator values was performed using a first-order Markov chain model. This provided characteristics of regional features of drought formation, evolution and persistence, as well as tools for statistical longterm drought hazard prediction. The study was carried out on two subbasins of the Odra River (Poland) of different orography and land use: the mountainous Nysa Kłodzka basin and the lowland, agricultural Prosna basin. Classification obtained with the SPI-SRI indicator was compared with the output from the NIZOWKA model that provided identification of hydrological drought events including drought duration and deficit volume. Severe and long-duration droughts corresponded to SPI-SRI Class 3 (dry meteorological and dry hydrological), while severe but short-term droughts (lasting less than 30 days) corresponded to SPI-SRI Class 4 (wet meteorological and dry hydrological). The results confirm that, in Poland, meteorologically dry conditions often shift to hydrologically dry conditions within the same month, droughts rarely last longer than 2 months and two separate drought events can be observed within the same year.

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Return Period and Risk of Hydrologic Events. II: Applications

Cited by 50 publications

References 15 publications

Stationarity is undead: Uncertainty dominates the distribution of extremes

Stationarity is undead: Uncertainty dominates the distribution of extremes

Drought in groundwater—drought distribution and performance indicators

Combined analysis of precipitation and water deficit for drought hazard assessment

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