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A Dynamic Downscaling Method to Estimate Climate Change for Vulnerable Infrastructure Identification

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2019, Doctor of Philosophy, University of Akron, Civil Engineering.
Climate Change has been a wildly issue in recent years across the United States and all over the World. The increases of the temperature, extreme precipitation and intensity have been observed and is projected to be worse in the future. The damage caused by the extreme weather and changing climate is irreversibly. Climate researchers use Global Climate Models (GCMs) to project the future climate, which has lower spatial resolution. For engineering practice, higher-resolution of the climate data is the key to identify the risk of infrastructure in order to minimize the damage caused by the weather disasters. In this dissertation, a framework with specific steps is developed to gain more reliable and higher resolution future climate situation in a regional level. WRF (Weather Research and Forecasting Model) model is used to dynamically downscale the climate data generated by GCMs, and considered using multiple cases simulations to decrease the uncertainties of the regional climate. Three types of climate systematic bias correction methods, “RQUANT”, “QUANT” and “SSPLN” are proposed to minimize the bias. then, the corrected future climate data is able to be used for risk analysis of the engineering infrastructure. Afterwards that a case study at the State of Ohio, USA is conducted to identify the vulnerable transportation infrastructure, bridges and culverts by using the bias-corrected projected future precipitation as one of the input factors along with the condition of infrastructure. The specific precipitation data generated in this case study is ready to use in the State of Ohio for further analysis. Meanwhile, the results that identifies the locations of the high-risk transportation infrastructure in the study area in near future, which is a reference for decision makers.
Ping Yi (Advisor)
Richard L. Einsporn (Committee Member)
Xiaosheng Gao (Committee Member)
Qindan Huang (Committee Member)
Anil K. Patnaik (Committee Member)
154 p.

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Citations

  • Luo, W. (2019). A Dynamic Downscaling Method to Estimate Climate Change for Vulnerable Infrastructure Identification [Doctoral dissertation, University of Akron]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=akron1589465412871131

    APA Style (7th edition)

  • Luo, Wen. A Dynamic Downscaling Method to Estimate Climate Change for Vulnerable Infrastructure Identification . 2019. University of Akron, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=akron1589465412871131.

    MLA Style (8th edition)

  • Luo, Wen. "A Dynamic Downscaling Method to Estimate Climate Change for Vulnerable Infrastructure Identification ." Doctoral dissertation, University of Akron, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=akron1589465412871131

    Chicago Manual of Style (17th edition)