Nonstationarity in Flood Time Series

Gül, Gülay Onuşluel; Aşıkoğlu, Ömer Levend; Gül, Ali; Yaşoğlu, F. Gülçem; Benzeden, Ertuğrul

doi:10.1061/(asce)he.1943-5584.0000923

Cited by 14 publications

(11 citation statements)

References 63 publications

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“…The significance indicator of the t-test is the p-value with the chosen significance level of 0.05. The non-stationary GEV model is a commonly accepted model to describe non-stationarity in flood data series due to the skewed character of annual maximum flows and the flexibility of the distribution in terms of the inclusion of covariates in the parameters (Delgado et al, 2010;Gül et al, 2014;Katz et al, 2002;Prosdocimi et al, 2015;Salas and Obeysekera, 2014;Singh et al, 2013). The cumulative distribution function of the GEV distribution is defined as:…”

Section: Methodsmentioning

confidence: 99%

“…The parameter estimation is usually performed using the maximum likelihood method (MLE) (Katz et al, 2002;Obeysekera and Salas, 2014;Prosdocimi et al, 2015) or the generalized maximum likelihood method (GMLE) (El Adlouni et al, 2007;Gül et al, 2014) and sometimes the Bayesian method through the Markov chain Monte Carlo (MCMC) approach (Cheng et al, 2014). However, the study of Cheng et al (2014) was dedicated to the annual temperature frequencies rather than floods.…”

Section: Methodsmentioning

confidence: 99%

“…a quantile-quantile plot, a probability plot, etc. In order to determine the best fitting model, the Akaike Information Criterion (AIC) (Akaike, 1974) was performed which is commonly used for model selection among various nested models in the case that model parameters are estimated using the MLE method (Gül et al, 2014;Salas and Obeysekera, 2014). The model associated with the smallest AIC value indicates the best model performance.…”

Section: Methodsmentioning

confidence: 99%

“…They computed recurrence reduction factors for streamflow data across the US and reported that today's 100-year flood may become more frequent in the future in many watersheds. Singh et al (2013) applied nonstationary flood frequency analysis based on generalized extreme value (GEV) and lognormal distributions and reported significant variation in return periods, and Gül et al (2014) conducted an analysis with a GEV-CDN (GEV conditional density estimation network) where the GEV parameters were allowed to vary with time.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

The influence of non-stationarity in extreme hydrological events on flood frequency estimation

Šraj

Viglione

Parajka

et al. 2016

Journal of Hydrology and Hydromechanics

108

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Substantial evidence shows that the frequency of hydrological extremes has been changing and is likely to continue to change in the near future. Non-stationary models for flood frequency analyses are one method of accounting for these changes in estimating design values. The objective of the present study is to compare four models in terms of goodness of fit, their uncertainties, the parameter estimation methods and the implications for estimating flood quantiles. Stationary and non-stationary models using the GEV distribution were considered, with parameters dependent on time and on annual precipitation. Furthermore, in order to study the influence of the parameter estimation approach on the results, the maximum likelihood (MLE) and Bayesian Monte Carlo Markov chain (MCMC) methods were compared. The methods were tested for two gauging stations in Slovenia that exhibit significantly increasing trends in annual maximum (AM) discharge series. The comparison of the models suggests that the stationary model tends to underestimate flood quantiles relative to the non-stationary models in recent years. The model with annual precipitation as a covariate exhibits the best goodness-of-fit performance. For a 10% increase in annual precipitation, the 10-year flood increases by 8%. Use of the model for design purposes requires scenarios of future annual precipitation. It is argued that these may be obtained more reliably than scenarios of extreme event precipitation which makes the proposed model more practically useful than alternative models.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The influence of non-stationarity in extreme hydrological events on flood frequency estimation

Šraj

Viglione

Parajka

et al. 2016

Journal of Hydrology and Hydromechanics

108

View full text Add to dashboard Cite

show abstract

“…Other studies, such as Kharin and Zwiers [59] and Mailhot et al [30], also used the GEV distribution method to calculate extreme-rainfall storms by using climate model datasets. The GEV distribution method is a commonly accepted approach in the nonstationarity study of extreme flows owing to the skewed nature of annual flow maxima and the ability to include covariates in the parameters of distribution [60][61][62][63][64]. The shape, location, and scale parameters required to fit the GEV distribution to each standardized pool of data were estimated using L-moments [51].…”

Section: Methodsmentioning

confidence: 99%

Understanding the Effects of Climate Change on Urban Stormwater Infrastructures in the Las Vegas Valley

2016

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Abstract:The intensification of the hydrological cycle due to climate change entails more frequent and intense rainfall. As a result, urban water systems will be disproportionately affected by the climate change, especially in such urban areas as Las Vegas, which concentrates its population, infrastructure, and economic activity. Proper design and management of stormwater facilities are needed to attenuate the severe effects of extreme rainfall events. The North American Regional Climate Change Assessment Program is developing multiple high-resolution projected-climate data from different combinations of regional climate models and global climate models. The objective of this study was to evaluate existing stormwater facilities of a watershed within the Las Vegas Valley in southern Nevada by using a robust design method for the projected climate. The projected climate change was incorporated into the model at the 100 year return period with 6 h duration depths, using a statistical regionalization analysis method. Projection from different sets of climate model combinations varied substantially. Gridded reanalysis data were used to assess the performance of the climate models. An existing Hydrologic Engineering Center's Hydrological Modeling System (HEC-HMS) model was implemented using the projected change in standard design storm. Hydrological simulation using HEC-HMS showed exceedances of existing stormwater facilities that were designed under the assumption of stationarity design depth. Recognizing climate change and taking an immediate approach in assessing the city's vulnerability by using proper strategic planning would benefit the urban sector and improve the quality of life.

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Decreasing flood hazard evaluated in Turkey using nonstationary models

Tosunoğlu

Slater

2022

River Research & Apps

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An assessment of changing flood hazard in the river basins of Turkey is currently lacking. This study evaluates the drivers of flood flows using distributional regression models. The generalized additive models for location, scale, and shape (GAMLSS), a flexible framework well‐suited to probabilistic nonstationary modeling, is applied to annual instantaneous maximum flows (AIMF) from 12 gauging stations located in the upper, middle and lower parts of the Euphrates basin, the most important river basin in the Middle East. First, the Pettitt test was used to detect abrupt changes in the AIMF data series, and the Mann–Kendall test to evaluate temporal trends. Significant change points for two stations and significant decreasing temporal changes for seven stations were observed. Three strictly positive, continuous distributions (namely, Gamma, Lognormal, and Weibull) were then fitted to the AIMF data series and the distribution parameters were specified as functions of both time and physically based covariates (precipitation, temperature, and climate oscillation indices). Among the candidate distributions, the Lognormal was found to be the most appropriate for describing flood flows across the basin. Annual precipitation, seasonal temperature and annual oscillation indices were the most relevant covariates. Among the climate indices, the East Atlantic Oscillation was most relevant in the lower part of the basin, the Southern and North Atlantic Oscillations in the middle, and the East Atlantic‐West Russia pattern in the upper part. The models were then employed to estimate and compare historical stationary flood quantiles with nonstationary quantiles (identified with physically based covariates) under various return periods (10, 20, 50, 100, and 200 years). Results indicate that flood processes can be better described by employing distributional regression models with physical covariates and this study can serve as a reference for regional water resources management in the basin and possibly for other river basins.

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Nonstationarity in Flood Time Series

Cited by 14 publications

References 63 publications

The influence of non-stationarity in extreme hydrological events on flood frequency estimation

The influence of non-stationarity in extreme hydrological events on flood frequency estimation

Understanding the Effects of Climate Change on Urban Stormwater Infrastructures in the Las Vegas Valley

Decreasing flood hazard evaluated in Turkey using nonstationary models

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