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    Communicating the Impacts of Projected Climate Change on Heavy Rainfall Using a Weighted Ensemble Approach

    Source: Journal of Hydrologic Engineering:;2018:;Volume ( 023 ):;issue: 004
    Author:
    Markus Momcilo;Angel James;Byard Gregory;McConkey Sally;Zhang Chen;Cai Ximing;Notaro Michael;Ashfaq Moetasim
    DOI: 10.1061/(ASCE)HE.1943-5584.0001614
    Publisher: American Society of Civil Engineers
    Abstract: Urban flood risks are often determined by the frequency analysis of observed rainfall data, communicated using isohyetal maps showing rainfall totals for a range of durations and recurrence intervals. However, to assess future changes in heavy rainfall, it is necessary to study the future projected rainfall time series. Impacts of climate change are typically assessed using climate projections based on global climate model (GCM) outputs and downscaled to finer temporal and spatial scales. The projected data, however, are not generated in a format that urban planners and engineers can easily use to design for future conditions. This research presents a method to analyze and express climate data in a format that can be readily used in hydrologic models to assess the effects of future extreme rainfall events. Future conditions’ climate data were analyzed using a weighted ensemble approach, which resulted in projected rainfall frequency estimates and their confidence limits. Two multimodel data sets were selected to illustrate this approach in Cook County, Illinois, which belongs to the Chicago metropolitan area. The first data set included statistical downscaling data based on the Intergovernmental Panel for Climate Change’s (IPCC) Coupled Model Intercomparison Project Phase 3 (CMIP3) data. The weighted ensemble analysis applied to this data set produced results that indicated significant increases in projected heavy rainfall. For example, for CMIP3 Scenario A2, for the late 21st century, the 1-year, 24-h rainfall in the northern parts of the county was 29% larger than the model-generated rainfall for the present time. For the same time horizon and scenario, the confidence interval based on projected data was 87% wider compared with that of the published source (NOAA Atlas 14), calculated using the past observed data. Also, equal-weight delta-corrected IPCC CMIP5-based dynamical downscaling data were applied to the same region for the mid-21st century, producing increases in heavy rainfall fairly similar to those of CMIP3.
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      Communicating the Impacts of Projected Climate Change on Heavy Rainfall Using a Weighted Ensemble Approach

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    contributor authorMarkus Momcilo;Angel James;Byard Gregory;McConkey Sally;Zhang Chen;Cai Ximing;Notaro Michael;Ashfaq Moetasim
    date accessioned2019-02-26T07:59:42Z
    date available2019-02-26T07:59:42Z
    date issued2018
    identifier other%28ASCE%29HE.1943-5584.0001614.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4250745
    description abstractUrban flood risks are often determined by the frequency analysis of observed rainfall data, communicated using isohyetal maps showing rainfall totals for a range of durations and recurrence intervals. However, to assess future changes in heavy rainfall, it is necessary to study the future projected rainfall time series. Impacts of climate change are typically assessed using climate projections based on global climate model (GCM) outputs and downscaled to finer temporal and spatial scales. The projected data, however, are not generated in a format that urban planners and engineers can easily use to design for future conditions. This research presents a method to analyze and express climate data in a format that can be readily used in hydrologic models to assess the effects of future extreme rainfall events. Future conditions’ climate data were analyzed using a weighted ensemble approach, which resulted in projected rainfall frequency estimates and their confidence limits. Two multimodel data sets were selected to illustrate this approach in Cook County, Illinois, which belongs to the Chicago metropolitan area. The first data set included statistical downscaling data based on the Intergovernmental Panel for Climate Change’s (IPCC) Coupled Model Intercomparison Project Phase 3 (CMIP3) data. The weighted ensemble analysis applied to this data set produced results that indicated significant increases in projected heavy rainfall. For example, for CMIP3 Scenario A2, for the late 21st century, the 1-year, 24-h rainfall in the northern parts of the county was 29% larger than the model-generated rainfall for the present time. For the same time horizon and scenario, the confidence interval based on projected data was 87% wider compared with that of the published source (NOAA Atlas 14), calculated using the past observed data. Also, equal-weight delta-corrected IPCC CMIP5-based dynamical downscaling data were applied to the same region for the mid-21st century, producing increases in heavy rainfall fairly similar to those of CMIP3.
    publisherAmerican Society of Civil Engineers
    titleCommunicating the Impacts of Projected Climate Change on Heavy Rainfall Using a Weighted Ensemble Approach
    typeJournal Paper
    journal volume23
    journal issue4
    journal titleJournal of Hydrologic Engineering
    identifier doi10.1061/(ASCE)HE.1943-5584.0001614
    page4018004
    treeJournal of Hydrologic Engineering:;2018:;Volume ( 023 ):;issue: 004
    contenttypeFulltext
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