Extreme Rainfall Nonstationarity Investigation and Intensity–Frequency–Duration Relationship

Yilmaz, Abdullah Gokhan; Perera, B. J. C

doi:10.1061/(asce)he.1943-5584.0000878

Cited by 111 publications

(77 citation statements)

References 47 publications

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“…We argue that although it is crucial to recognize nonstationarity in precipitation extremes, the stationary form of IDFs can still represent the extreme rainfall statistics for the present-day climate over the Southern Ontario region. Our results are consistent with Yilmaz et al (2014) and Yilmaz and Perera (2013), in which authors found despite the presence of (statistically) significant trends in rainfall extremes; nonstationary GEV models did not show any additional advantages over the stationary models. As supported by the previous study (Singh et al, 2016), we attribute that the little or no changes in extreme rainfall statistics in the urbanized setting is due to the stabilization of urban development leading to no substantial variations in the land use pattern.…”

Section: Discussionsupporting

confidence: 92%

“…Nevertheless, the methodology to derive existing IDF curves has certain drawbacks, for example that the current IDF curves in Canada are based on the assumption of stationarity, which implies that statistical properties of hydroclimatic time series will remain the same over the period of time. However, the impact of urbanization and humaninduced climate change (IPCC SREX, 2012;Villarini et al, 2009a;Milly et al, 2009;Kunkel, 2003) raises the question of whether the stationarity assumption to derive IDF curves is still reliable for urban infrastructural planning (Sarhadi and Soulis, 2017;Cheng and AghaKouchak, 2014;Jakob, 2013;Yilmaz et al, 2014a;Yilmaz and Perera, 2013).…”

Section: Introductionmentioning

confidence: 99%

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

Ganguli

Coulibaly

2017

Hydrol. Earth Syst. Sci.

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Abstract. In Canada, risk of flooding due to heavy rainfall has risen in recent decades; the most notable recent examples include the July 2013 storm in the Greater Toronto region and the May 2017 flood of the Toronto Islands. We investigate nonstationarity and trends in the short-duration precipitation extremes in selected urbanized locations in Southern Ontario, Canada, and evaluate the potential of nonstationary intensity-duration-frequency (IDF) curves, which form an input to civil infrastructural design. Despite apparent signals of nonstationarity in precipitation extremes in all locations, the stationary vs. nonstationary models do not exhibit any significant differences in the design storm intensity, especially for short recurrence intervals (up to 10 years). The signatures of nonstationarity in rainfall extremes do not necessarily imply the use of nonstationary IDFs for design considerations. When comparing the proposed IDFs with current design standards, for return periods (10 years or less) typical for urban drainage design, current design standards require an update of up to 7 %, whereas for longer recurrence intervals (50-100 years), ideal for critical civil infrastructural design, updates ranging between ∼ 2 and 44 % are suggested. We further emphasize that the above findings need re-evaluation in the light of climate change projections since the intensity and frequency of extreme precipitation are expected to intensify due to global warming.

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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

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

Ganguli

Coulibaly

2017

Hydrol. Earth Syst. Sci.

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“…Jakob et al (2011a, b) investigated the potential effects of climate change and variability on rainfall intensityfrequency-duration (IFD) relationships in Australia, considering possible non-stationarity of extreme rainfall data in design rainfall estimates. Yilmaz and Perera (2014) developed stationary and non-stationary GEV models using a single station in Melbourne considering data for storm durations ranging from 6 min to 72 h to construct IFD curves through frequency analysis. They investigated the advantages of nonstationary models over stationary ones using graphical tests.…”

Section: A G Yilmaz Et Al: Climate Change and Variability On Rainfmentioning

confidence: 99%

“…Expected rainfall intensities for return periods of 2, 5, 10, 20, 50 and 100 years were derived and compared for two time slices : 1925-1966 (i.e. cooler period) and 1967-2010 (warmer period) after selecting 1967 as the change point based on the findings of Yilmaz and Perera (2014). Yilmaz and Perera (2014) conducted the change point analysis for extreme rainfall data for storm durations ranging from 6 min to 72 h in Melbourne, and stated that the year 1966 is the change point.…”

Section: A G Yilmaz Et Al: Climate Change and Variability On Rainfmentioning

confidence: 99%

“…cooler period) and 1967-2010 (warmer period) after selecting 1967 as the change point based on the findings of Yilmaz and Perera (2014). Yilmaz and Perera (2014) conducted the change point analysis for extreme rainfall data for storm durations ranging from 6 min to 72 h in Melbourne, and stated that the year 1966 is the change point. Moreover, Jones (2012) listed the period 1910-1967 as stationary and 1968-2010 as nonstationary according to the observed minimum and maximum temperature and rainfall data in south-eastern Australia (which includes the Melbourne region).…”

Section: A G Yilmaz Et Al: Climate Change and Variability On Rainfmentioning

confidence: 99%

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Effect of climate change and variability on extreme rainfall intensity–frequency–duration relationships: a case study of Melbourne

Yilmaz

Hossain

Perera

2014

Hydrol. Earth Syst. Sci.

Self Cite

115

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Abstract. The increased frequency and magnitude of extreme rainfall events due to anthropogenic climate change, and decadal and multi-decadal climate variability question the stationary climate assumption. The possible violation of stationarity in climate can cause erroneous estimation of design rainfalls derived from extreme rainfall frequency analysis. This may result in significant consequences for infrastructure and flood protection projects since design rainfalls are essential input for design of these projects. Therefore, there is a need to conduct frequency analysis of extreme rainfall events in the context of non-stationarity, when nonstationarity is present in extreme rainfall events. A methodology consisting of threshold selection, extreme rainfall data (peaks over threshold data) construction, trend and nonstationarity analysis, and stationary and non-stationary generalised Pareto distribution (GPD) models was developed in this paper to investigate trends and non-stationarity in extreme rainfall events, and potential impacts of climate change and variability on intensity-frequency-duration (IFD) relationships. The methodology developed was successfully implemented using rainfall data from an observation station in Melbourne (Australia) for storm durations ranging from 6 min to 72 h. Although statistically significant trends were detected in extreme rainfall data for storm durations of 30 min, 3 h and 48 h, statistical non-stationarity tests and non-stationary GPD models did not indicate non-stationarity for these storm durations and other storm durations. It was also found that the stationary GPD models were capable of fitting extreme rainfall data for all storm durations. Furthermore, the IFD analysis showed that urban flash flood producing hourly rainfall intensities have increased over time.

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Quantifying Changes in Future Intensity‐Duration‐Frequency Curves Using Multimodel Ensemble Simulations

Ragno

AghaKouchak

Love

et al. 2018

Water Resources Research

116

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During the last century, we have observed a warming climate with more intense precipitation extremes in some regions, likely due to increases in the atmosphere's water holding capacity. Traditionally, infrastructure design and rainfall‐triggered landslide models rely on the notion of stationarity, which assumes that the statistics of extremes do not change significantly over time. However, in a warming climate, infrastructures and natural slopes will likely face more severe climatic conditions, with potential human and socioeconomical consequences. Here we outline a framework for quantifying climate change impacts based on the magnitude and frequency of extreme rainfall events using bias corrected historical and multimodel projected precipitation extremes. The approach evaluates changes in rainfall Intensity‐Duration‐Frequency (IDF) curves and their uncertainty bounds using a nonstationary model based on Bayesian inference. We show that highly populated areas across the United States may experience extreme precipitation events up to 20% more intense and twice as frequent, relative to historical records, despite the expectation of unchanged annual mean precipitation. Since IDF curves are widely used for infrastructure design and risk assessment, the proposed framework offers an avenue for assessing resilience of infrastructure and landslide hazard in a warming climate.

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Extreme Rainfall Nonstationarity Investigation and Intensity–Frequency–Duration Relationship

Cited by 111 publications

References 47 publications

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

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

Effect of climate change and variability on extreme rainfall intensity–frequency–duration relationships: a case study of Melbourne

Quantifying Changes in Future Intensity‐Duration‐Frequency Curves Using Multimodel Ensemble Simulations

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