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Updating intensity–duration–frequency curves for urban infrastructure design under a changing environment

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Abstract The intensity and frequency of extreme precipitation have increased in many regions in the past century due to climate change. Many studies have revealed that short‐duration extreme precipitations are likely to become more and more severe in many areas, thus raising a question on whether our urban infrastructures have been designed adequately to cope with these changes. Currently, Intensity–duration–frequency (IDF) curves, which summarize the relationships between the intensity and frequency of extreme precipitation for different durations, are recommended as a criterion for urban infrastructure design and stormwater management. However, climate change is thought to have invalidated the stationary assumption in deriving IDF curves, that is, current IDF curves could misevaluate future extreme precipitation in many cases. Therefore, it is necessary to update the current IDF curves by considering possible changes of extreme precipitation. In this review, we first summarize observed changes in urban short‐duration extreme precipitation and explore the physical mechanisms associated with changes. Then, we introduce two major approaches for updating IDF curves, namely the covariate‐based nonstationary IDF curves and climate‐model‐based IDF curves. Advances in these two updating approaches for IDF curves are the focus of this review. These include the investigation of physically‐based covariates with nonstationary modeling of extreme precipitation; nonstationary precipitation design strategies; and the statistical downscaling and dynamic downscaling methods for projecting future short‐duration precipitation. Finally, we summarize some future research challenges and opportunities on providing reliable projections of future short‐duration extreme precipitation and better characterize the probabilistic behavior of short‐duration extreme precipitation for IDF design. This article is categorized under: Engineering Water > Engineering Water
Variations in (a) 1 hr, (b) 6 hr, and (c) 24 hr duration annual maximum rainfall of the Hyderabad city and linear best fit of the variation (dashed lines; Agilan & Umamahesh, 2017)
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Ten‐year return period stationary, nonstationary and future intensity–duration curves of (a) RCP 2.6, (b) RCP 4.5, (c) RCP 6.0, and (d) RCP 8.5 scenario
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2015–2056 reliability ensemble average technique based change factors for (a) RCP 2.6, (b) RCP 4.5, (c) RCP 6.0, and (d) RCP 8.5 scenario
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Return level diagrams for WRB (a) using different design approaches (ENE, DLL, ER, and ADLL) with 95% bootstrapped confidence intervals under the covariate scenario 2. The covariate scenario 2 refers to the projected precipitation under RCP4.5 and population. The solid lines are the design floods while the dashed lines are the upper and lower limits of the 95% confidence intervals
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Non‐stationary intensity–duration curves of Hyderabad city for (a) 10 year and (b) 100 year return periods (Agilan & Umamahesh, 2017)
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Schematic diagram depicting the evolution of probability distributions of extreme events from T1 year to T2 year. pt is the time‐varying exceedance probability in case of an increase of the intense rainfall frequencies or intensities
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Schematic diagram showing the relative contribution of the global process and local process to long‐duration and short‐duration rainfall, respectively (Agilan & Umamahesh, 2017)
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The global process and local process that are supposed to destroy the stationary assumption (Agilan & Umamahesh, 2015)
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Rainfall depths annual maximums series fitted to a generalized extreme value distribution. (a,b) For the Case A (15 min–24 hr) and (c,d) for Case B (60 min–24 hr). Crosses for observed values. Thin lines: M0 model. Thick lines: M1 model. Dashed lines: 90% credibility intervals (Boukhelifa et al., 2018)
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