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

Yousef, Latifa A.; Ouarda, Taha B. M. J.

doi:10.7763/ijesd.2015.v6.641

Cited by 14 publications

(7 citation statements)

References 34 publications

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“…Nonstationarity in TDF curves, IDF curves or similar tools have not been introduced until recently 14 , 27 . The general procedure presented in this work can be applied to other variables such as precipitations (IDF curves) and floods (QDF curves) for which the influence of climate variability is relatively well documented 28 – 30 .…”

Section: Discussionmentioning

confidence: 99%

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Nonstationary Temperature-Duration-Frequency curves

Ouarda¹,

Charron²

2018

Sci Rep

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Persistent extreme heat events are of growing concern in a climate change context. An increase in the intensity, frequency and duration of heat waves is observed in several regions. Temperature extremes are also influenced by global-scale modes of climate variability. Temperature-Duration-Frequency (TDF) curves, which relate the intensity of heat events of different durations to their frequencies, can be useful tools for the analysis of heat extremes. To account for climate external forcings, we develop a nonstationary approach to the TDF curves by introducing indices that account for the temporal trend and teleconnections. Nonstationary TDF modeling can find applications in adaptive management in the fields of health care, public safety and energy production. We present a one-step method, based on the maximization of the composite likelihood of observed heat extremes, to build the nonstationary TDF curves. We show the importance of integrating the information concerning climate change and climate oscillations. In an application to the province of Quebec, Canada, the influence of Atlantic Multidecadal Oscillations (AMO) on heat events is shown to be more important than the temporal trend.

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Section: Discussionmentioning

confidence: 99%

“…Rainfall Intensity-Duration-Frequency (IDF) curves are widely used tools for the planning, design and operation of water resources infrastructure 14 , 15 . IDF curves relate rainfall intensities corresponding to different durations to a series of return periods.…”

Section: Introductionmentioning

confidence: 99%

Nonstationary Temperature-Duration-Frequency curves

Ouarda¹,

Charron²

2018

Sci Rep

Self Cite

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“…However, discrepancies are largely reported when forecasting precipitation using numerical weather prediction (NWP) models. The inaccurate predictions are then magnified in flood forecasts when simulated rainfall is used to drive a hydrological model (Wang and Seaman, 1997;Nielsen-Gammon et al, 2005;Yousef and Ouarda, 2015). Standard hydrological models are often driven by precipitation products inferred from radar, rain gauge, and remote sensing observations, or a combination of them.…”

Section: Introductionmentioning

confidence: 99%

Analysis of an extreme weather event in a hyper-arid region using WRF-Hydro coupling, station, and satellite data

Wehbe

Temimi

Weston

et al. 2019

Nat. Hazards Earth Syst. Sci.

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Abstract. This study investigates an extreme weather event that impacted the United Arab Emirates (UAE) in March 2016, using the Weather Research and Forecasting (WRF) model version 3.7.1 coupled with its hydrological modeling extension package (WRF-Hydro). Six-hourly forecasted forcing records at 0.5∘ spatial resolution, obtained from the National Center for Environmental Prediction (NCEP) Global Forecast System (GFS), are used to drive the three nested downscaling domains of both standalone WRF and coupled WRF–WRF-Hydro configurations for the recent flood-triggering storm. Ground and satellite observations over the UAE are employed to validate the model results. The model performance was assessed using precipitation from the Global Precipitation Measurement (GPM) mission (30 min, 0.1∘ product), soil moisture from the Advanced Microwave Scanning Radiometer 2 (AMSR2; daily, 0.1∘ product) and the NOAA Soil Moisture Operational Products System (SMOPS; 6-hourly, 0.25∘ product), and cloud fraction retrievals from the Moderate Resolution Imaging Spectroradiometer Atmosphere product (MODATM; daily, 5 km product). The Pearson correlation coefficient (PCC), relative bias (rBIAS), and root-mean-square error (RMSE) are used as performance measures. Results show reductions of 24 % and 13 % in RMSE and rBIAS measures, respectively, in precipitation forecasts from the coupled WRF–WRF-Hydro model configuration, when compared to standalone WRF. The coupled system also shows improvements in global radiation forecasts, with reductions of 45 % and 12 % for RMSE and rBIAS, respectively. Moreover, WRF-Hydro was able to simulate the spatial distribution of soil moisture reasonably well across the study domain when compared to AMSR2-derived soil moisture estimates, despite a noticeable dry and wet bias in areas where soil moisture is high and low. Temporal and spatial variabilities of simulated soil moisture compare well to estimates from the NOAA SMOPS product, which indicates the model's capability to simulate surface drainage. Finally, the coupled model showed a shallower planetary boundary layer (PBL) compared to the standalone WRF simulation, which is attributed to the effect of soil moisture feedback. The demonstrated improvement, at the local scale, implies that WRF-Hydro coupling may enhance hydrological and meteorological forecasts in hyper-arid environments.

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“…In order to produce reliable flashflood warnings, accurate predictions of precipitation timings and amounts, along with its impact of resulting runoff are needed. However, discrepancies are always reported when forecasting precipitation using numerical weather prediction (NWP) models, which are then magnified in flashflood forecasts when simulated rainfall is used to drive a hydrological model [1][2][3]. Standard hydrological models are usually driven by outputs from radar, rain gauge, and remote sensing or NWP-simulated precipitation.…”

Section: Introductionmentioning

confidence: 99%