Does nonstationarity in rainfall require nonstationary intensity–duration–frequency curves?

Ganguli, Poulomi; Coulibaly, Paulin

doi:10.5194/hess-21-6461-2017

Cited by 92 publications

(80 citation statements)

References 135 publications

(169 reference statements)

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“…A station is considered as nonstationary for a given duration category if consistent significant trends are obtained for all analysed series across study periods or significance levels (α). Change point detection in the rainfall series is also analysed using Pettitt's (1979) test (e.g., Ganguli & Coulibaly, 2017) with the aim of identifying stations with potential data irregularities. All rainfall series with at least 20 years of data (e.g., Figure 2a was not to perform a comparison of trends across stations, for which a common study period with few missing data for all analysed series would be ideally considered (e.g., Khaliq et al, 2009).…”

Section: Nonstationary Stationsmentioning

confidence: 99%

Pooled frequency analysis for intensity–duration–frequency curve estimation

Requena

Burn

Coulibaly

2019

Hydrological Processes

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Rainfall intensity–duration–frequency (IDF) curves are used in the design of urban infrastructure. Their estimation is based on rainfall frequency analysis, usually performed on rainfall records from a single gauged station. However, available at‐site record length is often too short to provide accurate estimates for long return periods. In the present study, a general framework for pooled rainfall frequency analysis based on the index‐event model is proposed for IDF estimation at gauged stations. Pooling group formation is defined by the region of influence approach on the basis of the geographical distance similarity measure. Several pooled approaches are defined and evaluated by a procedure through which quantile estimation and uncertainty are assessed. Alternate approaches for the definition of a pooling group are based on different criteria regarding initial pooling group size (and the relationship between size and return period), approaches for assessing pooling group homogeneity, and the use of macroregions in pooling group formation. The proposed framework is applied to identify the preferred approach for pooled rainfall intensity frequency analysis in Canada. Pooled approaches are found to provide more precise estimates than the at‐site approach, especially for long return periods. Pooled parent distribution selection supported the use of the generalized extreme value distribution across the country. Recommendations for pooling group formation include increasing the pooling group size with increases in return period and identifying an appropriate trade‐off between pooling group homogeneity and size for long return periods.

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Section: Nonstationary Stationsmentioning

confidence: 99%

Pooled frequency analysis for intensity–duration–frequency curve estimation

Requena

Burn

Coulibaly

2019

Hydrological Processes

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“…In these cases, the use of the more complex nonstationary model may not be justified as the predictions of both models are similar. Similar conclusions were also obtained in Ganguli and Coulibaly (2017), where despite the presence of nonstationary signals in short-duration rainfall extremes, statistically indistinguishable differences were obtained between stationary and nonstationary return level estimates.…”

Section: Discussionmentioning

confidence: 99%

“…They stressed out the importance of a fair comparison of the models through the assessment of sampling uncertainties. Ganguli and Coulibaly () investigated nonstationarities and trends in short‐duration precipitation extremes in selected urbanized locations in Southern Ontario, Canada, and indicated that the nonstationarity signature in rainfall extremes does not necessarily imply the use of nonstationary IDFs for design purposes. Ganguli and Coulibaly () used RCP8.5 scenario projections for the same study area and found a significant increase in shorter return levels using both stationary and nonstationary frequency analysis methods and a detectable trend was noted for longer return period estimates.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty of stationary and nonstationary models for rainfall frequency analysis

Ouarda¹,

Charron²,

St‐Hilaire³

2019

Intl Journal of Climatology

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The development of nonstationary frequency analysis models is gaining popularity in the field of hydro‐climatology. Such models account for nonstationarities related to climate change and climate variability but at the price of added complexity. It has been debated if such models are worth developing considering the increase in uncertainty inherent to more complex models. However, the uncertainty associated to nonstationary models is rarely studied. The objective of this article is to compare the uncertainties in stationary and nonstationary models based on objective criteria. The study is based on observed rainfall data in the United Arab Emirates (UAE) where strong nonstationarities were observed. In this study, a nonstationary frequency analysis introducing covariates into the distribution parameters was carried out for total and maximum annual rainfalls observed in the UAE. The generalized extreme value (GEV) distribution was used to model annual maximum rainfalls and the gamma (G) distribution was used to model total annual rainfalls. A number of nonstationary models, using time and climate indices as covariates, were developed and compared to classical stationary frequency analysis models. Two climate oscillation patterns having strong impacts on precipitation in the UAE were selected: the Oceanic Niño Index and the Northern Oscillation Index. Results indicate that the inclusion of a climate oscillation index generally improves the fit of the models to the observed data and the inclusion of two covariates generally provides the overall best fits. Uncertainties of estimated quantiles were assessed with confidence intervals (CIs) computed with the parametric bootstrap method. Results show that for the small sample sizes in this study, the width of the CIs can be very large for extreme nonexceedance probabilities and for the most extreme values of the climate index covariates. The weaknesses of nonstationary models revealed by the bootstrap uncertainties are discussed and words of caution are formulated.

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“…There is no general consensus about the influence of global warming on local precipitation regimes. For some authors, the physical consequence of temperature rise is the general increase in precipitation extremes due to the increase in atmospheric water retention (Bao, Sherwood, Alexander, & Evans, ; Barbero, Fowler, Lenderink, & Blenkinsop, ; Ganguli & Coulibaly, ; Tsanis, Koutroulis, Daliakopoulos, & Jacob, ); for others, such a correlation could be deeply influenced by local features or counter‐feedbacks (e.g., soil–water availability; Panthou et al, ). However, several studies in the Mediterranean area, which is considered a “hot spot” for climate change (Giorgi, ; Giorgi & Lionello, ), showed that an increase in the frequency of extreme events should be expected, often associated with a reduction in rainfall total volumes (Bucchignani, Montesarchio, Zollo, & Mercogliano, ; Chiew et al, ; Ntegeka, Baguis, Roulin, & Willems, ; Zollo, Rillo, Bucchignani, Montesarchio, & Mercogliano, ).…”

Section: Introductionmentioning

confidence: 99%

“…In the framework of climate change, extreme value analysis is usually undertaken with two different approaches. One approach consists in the statistical analysis of historical data, looking for trends in significant indicators such as mean value, moments, and probability distributions (Ganguli & Coulibaly, 2017;Markonis, Batelis, Dimakos, Moschou, & Koutsoyiannis, 2017). The second approach relies on the availability of climate projections providing estimates of future trends under different GHGs' concentration scenarios (DeGaetano & Castellano, 2017;Fadhel, Rico-Ramirez, & Han, 2017;Rodriguez et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

An ensemble approach for the analysis of extreme rainfall under climate change in Naples (Italy)

Padulano

Reder

Rianna

2019

Hydrological Processes

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In the present paper, an ensemble approach is proposed to estimate possible modifications caused by climate changes in the extreme precipitation regime, with the rain gauge Napoli Servizio Idrografico (Naples, Italy) chosen as test case. The proposed research, focused on the analysis of extremes on the basis of climate model simulations and rainfall observations, is structured in several consecutive steps. In the first step, all the dynamically downscaled EURO‐CORDEX simulations at about 12 km horizontal resolution are collected for the current period 1971–2000 and the future period 2071–2100, for the RCP4.5 and the RCP8.5 concentration scenarios. In the second step, the significance of climate change effects on extreme precipitation is statistically tested by comparing current and future simulated data and bias‐correction is performed by means of a novel approach based on a combination of simple delta change and quantile delta mapping, in compliance with the storm index method. In the third step, two different ensemble models are proposed, accounting for the variabilities given by the use of different climate models and for their hindcast performances. Finally, the ensemble models are used to build novel intensity–duration–frequency curves, and their effects on the early warning system thresholds for the area of interest are evaluated.

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Does nonstationarity in rainfall require nonstationary intensity–duration–frequency curves?

Cited by 92 publications

References 135 publications

Pooled frequency analysis for intensity–duration–frequency curve estimation

Pooled frequency analysis for intensity–duration–frequency curve estimation

Uncertainty of stationary and nonstationary models for rainfall frequency analysis

An ensemble approach for the analysis of extreme rainfall under climate change in Naples (Italy)

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