Non-stationary flood frequency analysis in continental Spanish rivers, using climate and reservoir indices as external covariates

Cruz, Jesús López de la; Francés, Félix

doi:10.5194/hess-17-3189-2013

Cited by 240 publications

(158 citation statements)

References 64 publications

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“…presented this method using the generalized additive models for location, scale and shape parameters (GAMLSS; Rigby and Stasinopoulos, 2005), a flexible framework to assess nonstationary time series. The time-varying parameter method can be extended to the physical covariate analysis by replacing time with any other physical covariates (Jiang et al, 2015b;Kwon et al, 2008;López and Francés, 2013;Liu et al, 2015;Villarini and Strong, 2014). For example, Jiang et al (2015b) used reservoir index as an explanatory variable based on the time-varying copula method for bivariate frequency analysis of nonstationary low-flow series in Hanjiang River, China.…”

Section: Introductionmentioning

confidence: 99%

Multiple causes of nonstationarity in the Weihe annual low-flow series

Xiong

Chen

et al. 2018

Hydrol. Earth Syst. Sci.

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Abstract. Under the background of global climate change and local anthropogenic activities, multiple driving forces have introduced various nonstationary components into lowflow series. This has led to a high demand on low-flow frequency analysis that considers nonstationary conditions for modeling. In this study, through a nonstationary frequency analysis framework with the generalized linear model (GLM) to consider time-varying distribution parameters, the multiple explanatory variables were incorporated to explain the variation in low-flow distribution parameters. These variables are comprised of the three indices of human activities (HAs; i.e., population, POP; irrigation area, IAR; and gross domestic product, GDP) and the eight measuring indices of the climate and catchment conditions (i.e., total precipitation P , mean frequency of precipitation events λ, temperature T , potential evapotranspiration (EP), climate aridity index AI EP , base-flow index (BFI), recession constant K and the recession-related aridity index AI K ). This framework was applied to model the annual minimum flow series of both Huaxian and Xianyang gauging stations in the Weihe River, China (also known as the Wei He River). The results from stepwise regression for the optimal explanatory variables show that the variables related to irrigation, recession, temperature and precipitation play an important role in modeling. Specifically, analysis of annual minimum 30-day flow in Huaxian shows that the nonstationary distribution model with any one of all explanatory variables is better than the one without explanatory variables, the nonstationary gamma distribution model with four optimal variables is the best model and AI K is of the highest relative importance among these four variables, followed by IAR, BFI and AI EP . We conclude that the incorporation of multiple indices related to low-flow generation permits tracing various driving forces. The established link in nonstationary analysis will be beneficial to analyze future occurrences of low-flow extremes in similar areas.

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Section: Introductionmentioning

confidence: 99%

Multiple causes of nonstationarity in the Weihe annual low-flow series

Xiong

Chen

et al. 2018

Hydrol. Earth Syst. Sci.

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“…1) to climate (Tramblay et al, 2013;López and Francés, 2013), in order to derive flood projections under climate change. Large-scale monsoon intensity is used to explain the scale parameter of the non-stationary flood frequency distribution representing the variability of floods in the lower Mekong Basin.…”

Section: Introductionmentioning

confidence: 99%

Projecting flood hazard under climate change: an alternative approach to model chains

Delgado

Merz

Apel

2014

Nat. Hazards Earth Syst. Sci.

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Abstract. Flood hazard projections under climate change are typically derived by applying model chains consisting of the following elements: "emission scenario -global climate model -downscaling, possibly including bias correctionhydrological model -flood frequency analysis". To date, this approach yields very uncertain results, due to the difficulties of global and regional climate models to represent precipitation. The implementation of such model chains requires major efforts, and their complexity is high.We propose for the Mekong River an alternative approach which is based on a shortened model chain: "emission scenario -global climate model -non-stationary flood frequency model". The underlying idea is to use a link between the Western Pacific monsoon and local flood characteristics: the variance of the monsoon drives a non-stationary flood frequency model, yielding a direct estimate of flood probabilities. This approach bypasses the uncertain precipitation, since the monsoon variance is derived from large-scale wind fields which are better represented by climate models. The simplicity of the monsoon-flood link allows deriving large ensembles of flood projections under climate change. We conclude that this is a worthwhile, complementary approach to the typical model chains in catchments where a substantial link between climate and floods is found.

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“…The statistical pa-rameters may show increasing/decreasing changes that can be modelled (as a trend or smooth function) using time as covariate (Villarini et al, 2009), or they can be related to hydro-climatic covariates such as circulation indices (e.g. Pacific Decadal Oscillation -PDO, North Atlantic Oscillation -NAO, Arctic Oscillation -AO) characterising this lowfrequency climatic variability (López and Francés, 2013). The application of these non-stationary models to historical and palaeoflood hydrology requires a numerical characterisation of the occurrence rate (covariate) during the recorded period.…”

Section: Historical Floods In a Non-stationary Hydrologymentioning

confidence: 99%

“…"100-year flood") using the North Atlantic Oscillation index and a reservoir index as external covariates (Machado et al, 2015). This non-stationary modelling was based on Generalized Additive Models for Location, Scale and Shape parameters (GAMLSS; Rigby and Stasinopoulos, 2005) that described the temporal variation of statistical parameters (mean, variance) in probability distribution functions (Villarini et al, 2010;López and Francés, 2013). In this example, the non-stationary models show that the peak flood associated with a "100-year" flood (0.01 annual exceedance probability) may range between 4180 and 560 m 3 s −1 , whereas the same model under stationary conditions provided the best fitting results to a log-normal distribution, with a discharge of 1450 m 3 s −1 (Fig.…”

Section: Historical Floods In a Non-stationary Hydrologymentioning

confidence: 99%

Quantitative historical hydrology in Europe

Benito

Brázdil

Herget

et al. 2015

Hydrol. Earth Syst. Sci.

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Abstract. In recent decades, the quantification of flood hydrological characteristics (peak discharge, hydrograph shape, and runoff volume) from documentary evidence has gained scientific recognition as a method to lengthen flood records of rare and extreme events. This paper describes the methodological evolution of quantitative historical hydrology under the influence of developments in hydraulics and statistics. In the 19th century, discharge calculations based on flood marks were the only source of hydrological data for engineering design, but were later left aside in favour of systematic gauge records and conventional hydrological procedures. In the last two decades, there has been growing scientific and public interest in understanding long-term patterns of rare floods, in maintaining the flood heritage and memory of extremes, and developing methods for deterministic and statistical application to different scientific and engineering problems. A compilation of 46 case studies across Europe with reconstructed discharges demonstrates that (1) in most cases present flood magnitudes are not unusual within the context of the last millennium, although recent floods may exceed past floods in some temperate European rivers (e.g. the Vltava and Po rivers); (2) the frequency of extreme floods has decreased since the 1950s, although some rivers (e.g. the Gardon and Ouse rivers) show a reactivation of rare events over the last two decades. There is a great potential for gaining understanding of individual extreme events based on a combined multiproxy approach (palaeoflood and documentary records) providing high-resolution time flood series and their environmental and climatic changes; and for developing non-systematic and non-stationary statistical models based on relations of past floods with external and internal covariates under natural low-frequency climate variability.

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Non-stationary flood frequency analysis in continental Spanish rivers, using climate and reservoir indices as external covariates

Cited by 240 publications

References 64 publications

Multiple causes of nonstationarity in the Weihe annual low-flow series

Multiple causes of nonstationarity in the Weihe annual low-flow series

Projecting flood hazard under climate change: an alternative approach to model chains

Quantitative historical hydrology in Europe

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