Funded Research Projects

Long-term recovery assessment of infrastructure systems and communities following Hurricane Harvey: Case study for the city of Port Aransas

     PI: Maria Koliou

In this proposal, a typical coastal community in Texas heavily impacted by Hurricane Harvey will be monitored over a long period of time in order to collect data in real-time associated with the infrastructure systems repair as well as the community recovery. An important advantage of the real-time long-term monitoring of this coastal community will be the opportunity to also assess the cumulative damage and associated recovery trajectories of infrastructure systems in case of future wind, storm surge or flooding events in the duration of the monitoring. The goal of this RAPID project is to collect damage and socio-economic data, which will be the basis of developing, validating, and calibrating infrastructure and community modeling frameworks. This will be a longitudinal field study, with visits of four month frequency after the initial visit for a period of 1 year (and maybe continued for longer based on the project direction and funding). Due to the geographic proximity, Dr. Koliou and her PhD student were able to perform visits within the first few weeks and conduct damage assessments in neighborhoods of varying socio-economic characteristics as well as business areas, which is of great importance to the performance of this study and respective data collection protocol. By including multiple visits to the impacted area in this research project, the homeowner decision factors over time will be accounted for in the community scale simulations to accurately predict the post-disaster community functionality. With the long-term access to this community, the overall framework as well as the individual components will be validated continually and updated where necessary. Furthermore, cumulative effects on future climate-related events that may impact the coastal community will be monitored and accounted for in the functionality and recovery simulations.


Community Resilience Collaborative: A Systems-Oriented Post-Event Assessment of Community Resilience

     PI: Burak Güneralp

Objectives of this research are to: (1) assess the impacts of interactions among prevailing socio-political and biophysical factors during and immediately after Hurricane Harvey, (2) create scientific knowledge to enable city officials, and private and social sector urban actors to understand, not only the risks they face within their own sector, but also how risks are transmitted across sectors through interdependencies between existing infrastructures and institutions (the knowledge thus created will serve as a basis to explore opportunities for building greater community resilience in the study area), develop a systemic framework that facilitates shared understanding among experts and stakeholders to develop an adaptation-focused strategy to future extreme events to increase community resilience, and (4) transfer the framework to other communities across the Texas Coast –and then beyond.

We will conduct a post-event assessment with explicit attention afforded to feedbacks among the physical (water, energy, transportation, and dwelling infrastructure), economic, social, and institutional structures. Our post-event assessment will be based on an hybrid approach that meshes i) the post event review capability (PERC), a novel framework recently implemented in floods in North Carolina, the UK, and Central Europe, and 2) qualitative systems analysis (also known as collaborative or mediated causal mapping) that relies on soft systems thinking to identify the most critical interactions among system components in collaboration with stakeholders.

PERC seeks to answer questions related to aspects of resilience, risk management and catastrophe intervention. Through a PERC, we will examine what has worked well (identifying best practice) during and immediately after Harvey and what failed to meet expectations offering opportunities for further improvements. Causal mapping will allow us to explicitly recognize these connections and identify potential feedback interactions that may have played critical roles in system performance. Based on the nature of interactions identified during the causal mapping phase/component, the stakeholders may agree on a prioritization (in terms of funding and urgency) scheme to address the deficiencies observed and experienced during the disaster response and recovery.