Equidistance Quantile Matching Method for Updating IDFCurves under Climate Change

Srivastav, Roshan; Schardong, André; Simonović́, Slobodan P.

doi:10.1007/s11269-014-0626-y

Cited by 98 publications

(63 citation statements)

References 16 publications

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“…These climate variables are extracted from four GCMs ( Table 1) The ANUSPLIN and GCM data sets used in this study have different spatial resolutions. For climate change impact assessment at the catchment scale, all the data sets are spatially interpolated to the ten locations of interest (Table 2) using the inverse distance square method [24].…”

Section: Study Area and Data Usedmentioning

confidence: 99%

Uncertainty in Precipitation Projection under Changing Climate Conditions: A Regional Case Study

Mandal¹,

Breach²,

Simonović́³

2016

AJCC

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This study investigates different sources of uncertainty in the assessment of the climate change impacts on total monthly precipitation in the Campbell River basin, British Columbia, Canada. Four global climate models (GCMs), three greenhouse gas emission scenarios (RCPs) and six downscaling methods (DSMs) are used in the assessment. These sources of uncertainty are analyzed separately for two future time periods (2036 to 2065 and 2066 to 2095). An uncertainty metric is calculated based on the variation in simulated precipitation due to choice of GCMs, emission scenarios and downscaling models. The results show that the selection of a downscaling method provides the largest amount of uncertainty when compared to the choice of GCM and/or emission scenario. However, the choice of GCM provides a significant amount of uncertainty if downscaling methods are not considered. This assessment work is conducted at ten different locations in the Campbell River basin.

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Section: Study Area and Data Usedmentioning

confidence: 99%

Uncertainty in Precipitation Projection under Changing Climate Conditions: A Regional Case Study

Mandal¹,

Breach²,

Simonović́³

2016

AJCC

Self Cite

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“…Alam and Elshorbagy (2015) used the K-nearest neighbour technique to disaggregate daily precipitation generated with a stochastic rainfall generator to hourly and sub-hourly scales, and thus evaluate the climate induced changes on DDF curves. Srivastav, Schardong, and Simonovic (2014) proposed the use of the Equidistance Quantile Matching methodology (also known as quantile-quantile mapping) as a downscaling method for GCM data (Lehmann, Phatak, Stephenson, & Lau, 2016;Simonovic, Schardong, Sandink, & Srivastav, 2016;Singh, Arya, Taxak, & Vojinovic, 2016). The idea of this method is to apply a bias correction derived from the differences between observed data and GCM/RCM outputs for a baseline period (quantile mapping functions), which are then used to modify the GCM/RCM outputs in future periods, from which DDF curves are then calculated.…”

Section: Introductionmentioning

confidence: 99%

The impact of climate change on extreme precipitation in Sicily, Italy

Forestieri

Arnone

Blenkinsop

et al. 2018

Hydrological Processes

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Increasing precipitation extremes are one of the possible consequences of a warmer climate. These may exceed the capacity of urban drainage systems, and thus impact the urban environment. Because short‐duration precipitation events are primarily responsible for flooding in urban systems, it is important to assess the response of extreme precipitation at hourly (or sub‐hourly) scales to a warming climate. This study aims to evaluate the projected changes in extreme rainfall events across the region of Sicily (Italy) and, for two urban areas, to assess possible changes in Depth‐Duration‐Frequency (DDF) curves. We used Regional Climate Model outputs from Coordinated Regional Climate Downscaling Experiment for Europe area ensemble simulations at a ~12 km spatial resolution, for the current period and 2 future horizons under the Representative Concentration Pathways 8.5 scenario. Extreme events at the daily scale were first investigated by comparing the quantiles estimated from rain gauge observations and Regional Climate Model outputs. Second, we implemented a temporal downscaling approach to estimate rainfall for sub‐daily durations from the modelled daily precipitation, and, lastly, we analysed future projections at daily and sub‐daily scales. A frequency distribution was fitted to annual maxima time series for the sub‐daily durations to derive the DDF curves for 2 future time horizons and the 2 urban areas. The overall results showed a raising of the growth curves for the future horizons, indicating an increase in the intensity of extreme precipitation, especially for the shortest durations. The DDF curves highlight a general increase of extreme quantiles for the 2 urban areas, thus underlining the risk of failure of the existing urban drainage systems under more severe events.

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“…Apart from trend analysis methods, which develop IDF curves by modeling historical rainfall time series data, researchers have incorporated outputs from General Circulation Models (GCMs) or Regional Circulation Models (RCMs) using bias correction and downscaling to simulate non-stationary IDF curves [10,13,14]. Lehmann [10] integrated RCM outputs into spatial Bayesian hierarchical models to investigate the characteristics of future extreme rainfall events stemming from numerous climate change scenarios.…”

Section: Introductionmentioning

confidence: 99%

“…Lehmann [10] integrated RCM outputs into spatial Bayesian hierarchical models to investigate the characteristics of future extreme rainfall events stemming from numerous climate change scenarios. Srivastav [14] updated the IDF curves using spatial and temporal downscaling, combining the changes in the distributional characteristics of the GCM/RCM between the baseline period and future period to assess the impact of climate change on future extreme precipitation. Lima [13] proposed a Bayesian beta distribution model to estimate IDF curves based on observed rainfall series and daily rainfall outputs under climate change scenarios provided by the GCM/RCM.…”

Section: Introductionmentioning

confidence: 99%

An Uncertainty Investigation of RCM Downscaling Ratios in Nonstationary Extreme Rainfall IDF Curves

et al. 2018

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Designed for rainstorms and flooding, hydrosystems are largely based on local rainfall Intensity-Duration-Frequency (IDF) curves which include nonstationary components accounting for climate variability. IDF curves are commonly calculated using downscaling outputs from General Circulation Models (GCMs) or Regional Circulation Models (RCMs). However, the downscaling procedures used in most studies are based on one specific time scale (e.g., 1 h) and generally ignore scale-driven uncertainty. This study analyzes the uncertainties in IDF curves stemming from RCM downscaling ratios for four representative weather stations in the United Kingdom. We constructed a series of IDF curves using distribution-based scaling bias-correction technology and a statistical downscaling method to explore the scale-driven uncertainty of IDF curves. The results revealed considerable scale-induced uncertainty of IDF curves for short durations and long return periods; however, there was no clear correlation with the mean storm intensity of the IDF curves of different RCM ensemble members for each duration and return period. The scale-driven uncertainty of IDF curves, which may be propagated or enhanced through hydrometeorological applications, is critical and cannot be ignored in the hydrosystem design process; therefore, a multi-scale method to derive IDF curves must be developed.

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Equidistance Quantile Matching Method for Updating IDFCurves under Climate Change

Cited by 98 publications

References 16 publications

Uncertainty in Precipitation Projection under Changing Climate Conditions: A Regional Case Study

Uncertainty in Precipitation Projection under Changing Climate Conditions: A Regional Case Study

The impact of climate change on extreme precipitation in Sicily, Italy

An Uncertainty Investigation of RCM Downscaling Ratios in Nonstationary Extreme Rainfall IDF Curves

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