The impact of climate change on rainfall Intensity–Duration–Frequency (IDF) curves in Alabama

Mirhosseini, Golbahar; Srivastava, Puneet; Stefanova, Lydia

doi:10.1007/s10113-012-0375-5

Cited by 112 publications

(73 citation statements)

References 19 publications

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“…Several distributions can be used to fit each series; for example the Gumbel distribution has traditionally been used (Kite, 1977), but the GEV distribution is also widely used (Gellens, 2002;Overeem, Buishand, & Holleman, 2008;Mirhosseini, Srivastava, & Stefanova, 2013). Figure 2 shows an example of the probability of extreme rainfall, both measured and fitted series, for several rainfall durations at the Agustin Melgar station.…”

Section: Resultsmentioning

confidence: 99%

Extreme rainfall relationship in Mexico

et al. 2014

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Precipitation statistics are inherent to the design of water resource systems by the prediction of two aspects of hydrological processes: the extremes and the averages. Extreme rainfall with high temporal resolution (i.e. an hour or less) is necessary for the design of urban drainage systems, as urban areas are generally characterized by their fast response. However, most weather stations only register daily rainfall. The relationship between the hourly intensity of rain and the daily rain intensity is called parameter K. Thus, if extreme daily rainfall data is available, the parameter K allows the estimation of extreme short-duration rainfall intensities. This study offers a map with the regionalization of the relationship between the maximum intensities of precipitation occurring over intervals 1 and 24 hours (parameter K) for the whole country of Mexico, facilitating the description of the geographical variability of precipitation. In this country a high spatial variability of the parameter K was observed, according to the large area studied.

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Section: Resultsmentioning

confidence: 99%

Extreme rainfall relationship in Mexico

et al. 2014

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“…This would have severe implications on water structures and management strategies. With rainfall characteristics being used to design water management infrastructures, reviewing and updating rainfall characteristics (i.e., IDF curves) for future climate scenarios is necessary [8]. In order to represent hydro-meteorological variables appropriately, the reality of the impact of climate change on rainfall extremes must be properly addressed.…”

Section: Climate Change and Extreme Precipitation Eventsmentioning

confidence: 99%

Adaptation of Water Resources Management to Changing Climate: The Role of Intensity-Duration-Frequency Curves

Yousef¹,

Ouarda²

2015

IJESD

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Abstract-Climate change influences the intensity and frequency of precipitation events, causing the need for new strategies in water resources management. This paper presents the role of hydrology in helping to establish these new strategies, focusing specifically on rainfall intensity-duration-frequency (IDF) curves. A methodology is proposed for developing IDF curves using covariates of climate indices. A case study is presented for the station of Riviere Heva in Quebec, Canada. The IDF models incorporating climate indices exhibit significant improvements in terms of goodness of fit over the traditional models, forming the conclusion that design standards for water-related structures must be updated to incorporate climate change.

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“…To this end, we present a fresh empirical statistical approach and case study from New England region. In answering this question, this work complements the emerging research literature that focuses on nonstationarity from the standpoint of extreme event magnitude and intensity [Khaliq et al, 2006;Mirhosseini et al, 2013;Salas and Obeysekera, 2013;Cheng and AghaKouchak, 2014].…”

Section: Introductionmentioning

confidence: 96%

Nonstationarity in seasonality of extreme precipitation: A nonparametric circular statistical approach and its application

Dhakal

Jain

Gray

et al. 2015

Water Resources Research

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Changes in seasonality of extreme storms have important implications for public safety, storm water infrastructure, and, in general, adaptation strategies in a changing climate. While past research on this topic offers some approaches to characterize seasonality, the methods are somewhat limited in their ability to discern the diversity of distributional types for extreme precipitation dates. Herein, we present a comprehensive approach for assessment of temporal changes in the calendar dates for extreme precipitation within a circular statistics framework which entails: (a) three measures to summarize circular random variables (traditional approach), (b) four nonparametric statistical tests, and (c) a new nonparametric circular density method to provide a robust assessment of the nature of probability distribution and changes. Two 30 year blocks (1951-1980 and 1981-2010) of annual maximum daily precipitation from 10 stations across the state of Maine were used for our analysis. Assessment of seasonality based on nonparametric approach indicated nonstationarity; some stations exhibited shifts in significant mode toward Spring season for the recent time period while some other stations exhibited multimodal seasonal pattern for both the time periods. Nonparametric circular density method, used in this study, allows for an adaptive estimation of seasonal density. Despite the limitation of being sensitive to the smoothing parameter, this method can accurately characterize one or more modes of seasonal peaks, as well as pave the way toward assessment of changes in seasonality over time.

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The impact of climate change on rainfall Intensity–Duration–Frequency (IDF) curves in Alabama

Cited by 112 publications

References 19 publications

Extreme rainfall relationship in Mexico

Extreme rainfall relationship in Mexico

Adaptation of Water Resources Management to Changing Climate: The Role of Intensity-Duration-Frequency Curves

Nonstationarity in seasonality of extreme precipitation: A nonparametric circular statistical approach and its application

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