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ECHO Post-Doctoral Fellowship Solicitation Request
Project Title: Coastal Compound Flooding Probabilistic Hazard Assessment Advancements
Research Advisor: Dr. Meredith L. Carr, PhD, PE, Meredith.L.Carr@erdc.dren.mil is a Research Civil Engineer in the Harbors, Entrances, & Structures Branch at CHL. Dr. Carr is the Team Lead for the Coastal Compound Flood Team in the Coastal Hazards Group, working on using probabilistic hazard analysis to model compound inland flooding and coastal storm surge from tropical cyclones using cutting edge statistical methods. As a member of the Coastal Hazards Group, Dr. Carr’s experience in inland and coastal flooding will be complemented by team members with backgrounds in statistical analysis, hazard assessment, coastal hazards, hydro-climatology, and coastal structures including Dr. Norberto.C.Nadal-Caraballo@erdc.dren.mil, Luke.A.Aucoin@erdc.dren.mil and Madison.C.Yawn@erdc.dren.mil
Proposed Length of Time: 2 years with option to extend 1 year
USACE and its local partners require a technically robust method for probabilistic assessment of coastal storm hazards to analyze risk (Nadal-Caraballo et al 2022). Traditionally, coastal probabilistic flood hazard studies mainly focused on surge and wave as drivers of the flood hazard. Uncertainty in such analyses have been attributed to neglecting compound hazards and can results in underestimating water levels (e.g., Bevacqua et al., 2017; Moftakhari et al., 2019). To properly describe the full probability space of coastal hazards is difficult due to the limited Tropical Cyclone (TC) occurrence, combined with limited record lengths and low geospatial data resolution. That issue is magnified for coastal compound hazards, with even less data due to the lower frequency of compound events.
In coastal hazard analysis, Joint Probability Method (JPM) approaches were developed and optimized, with CHL leading the way with regional studies and the Probabilistic Coastal Hazard Analysis (PCHA) framework (Nadal-Caraballo et al., 2020). PCHA is a comprehensive statistical and probabilistic framework for characterization of regional storm climatology, joint probability analysis of atmospheric forcing and storm responses, high-resolution numerical modeling, machine learning, and quantification of associated aleatory and epistemic uncertainties. Using copula theory for a more complete representation of the joint probability and Gaussian Process Metamodeling (GPM) machine learning (Kyprioti et al, 2022) to expand the storm set, these advancements expand compound PCHA to better define the physical and probabilistic hazard space. Extending the PCHA approach to model compound hazards provides an existing suite of storms and their driving atmospheric parameters, and an expandable framework for the parameterization of the multi-forcing compound flooding problem. In framework development and testing, CHL has been applying the same statistical set of storm parameters developed for modeling storm surge and coastal hazards as input to TC Rainfall models (e.g. TCRM, Lu et al. 2018) which then drive inland flood models. Currently, two large efforts are beginning for the Compound Flood Hazard Team, with collaboration among team members. The first involves a large collaboration with academics, researchers and the Galveston District to test JPM methods for compound flooding on several basins in Texas. Additionally, as part of FEMA’s risk studies, we will be applying the compound model to a test region and assessing its sensitivity to various approaches and ability to appropriately represent the physics of compound flooding.
Project Goals: This opportunity will involve the review, assessment and development of approaches to address elements of the extension of PCHA to compound flooding that are considered gaps, particularly involving TC rainfall modeling and probabilities and temporal issues. The candidate will review and assess the sensitivity of the framework to any differences between probabilities of compound events and TC rainfall driven by TC atmospheric parameters, the inclusion of stochastic elements in the TC rainfall response, flood peak arrival timing and hurricane stalling. Approaches the candidate may develop to addressing these gaps will be probabilistic or statistical in nature, to support the framework in remaining location agnostic for use across the coast. Methods such as improving representation of correlation between parameters through copulas or stochastic methods as well as integrating information through the machine learning model are likely approaches.
PhD in civil engineering, coastal/ocean engineering, statistics and data science, natural hazards/flood risk, atmospheric science, or related disciplines. The successful candidate should have strong skills in applying foundation concepts of probability and statistics to assess frequency, severity, and uncertainty of natural hazards. Experience and interest in coastal hazard analyses including probabilistic analyses, statistical techniques, joint probability methods and machine learning techniques for civil, coastal, and risk engineering applications are preferred, as well as strong skills in MATLAB and/or Python. Experience or interest in crossing interdisciplinary specialties in the natural’s hazards, such as inland flooding, coastal flooding, storms and precipitation would be highly valued.
Bevacqua, E., Maraun, D., Hobæk Haff, I., Widmann, M., and Vrac, M. 2017. Multivariate statistical modelling of compound events via pair-copula constructions: analysis of floods in Ravenna (Italy), Hydrol. Earth Syst. Sci., 21, 2701–2723, https://doi.org/10.5194/hess-21-2701-2017
Kyprioti, A., Taflanidis, A.A., Nadal Caraballo, N. C., Yawn, M. and Aucoin, L. (2022). Integration of Node Classification in Storm Surge Surrogate Modeling. Journal of Marine Science and Engineering. 10. 551. 10.3390/jmse10040551.
Lu, P., N. Lin, K. Emanuel, D. Chavis, and J. Smith. 2018. Assessing Hurricane Rainfall Mechanisms Using a Physics-Based Model: Hurricanes Isabel (2003) and Irene (2011). Journal of Atmospheric Science 75: 2337–2358. https://doi.org/10.1175/JAS-D-17-0264.1
Nadal-Caraballo, N.C. Campbell, M.O., Gonzalez, V.M., Torres, M.J., Melby, J.A., , Taflanidis, A.A. 2020. Coastal Hazards System: A Probabilistic Coastal Hazard Analysis Framework. Journal of Coastal Research; 95 (SI): 1211–1216. https://doi.org/10.2112/SI95-235.1
Nadal-Caraballo, N. C., Yawn, M.C., Aucoin, L.A., Carr, M.L., Melby, J.A., Ramos-Santiago, E., Gonzalez, V.M., Taflanidis, A.A., Kyprioti, A ., Cobell, Z., and Cox, A.T. (2022) Coastal Hazards System-Louisiana (CHS-LA), ERDC/CHL TR-22-16 Engineer Research and Development Center, Vicksburg, MS. https://hdl.handle.net/11681/45286