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  • Resilience Assessment & CNH Modeling • Historical Sedimentation Rate • Hydrology & Sediment Interactions • Vegetation Responses & Biomass Estimation • Land Pricing & Population Changes • Energy Sustainability • Adaptive Governance & Planning
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Resilience Assessment and Coupled Natural-Human Dynamics Modeling

Researchers:
Dr. Nina Lam (PI)
Dr. Yi Qiang (Postdoc)
Kenan Li (PhD student, graduated 2015)
Heng Cai (PhD student, graduated 2017)
Lei Zou (PhD student, graduated 2017)
Volodymyr Mihunov (PhD student, graduated 2018)

Objectives of this component are to (1) develop criteria and models to assess the resilience of the study system using two approaches, the RIM model and the genetic algorithm approach. Based on the resilience assessment results, we can test the hypothesis that spatial units near the coastal areas (the South) have lower resilience (sustainability) than those in the North. High resiliency indicates that the system is a well-coupled CNH system; (2) develop a CNH model to explain and quantify the changes of the system. The CNH model combines the methods of genetic algorithms, agent-based models, and cellular automata, and will incorporate the linkages and feedbacks with the other six components in the system. The final CNH model can be used to simulate and evaluate under different scenarios (e.g., sea-level rise) whether the region may become unsustainable in terms of population decline and land loss.

We will experiment with different approaches to assess resilience and model its dynamics using genetic algorithms and Bayesian Networks. Then, we will model the dynamical feedbacks of the coastal region using a system dynamic approach. Our objective is to link land loss with population changes, as well as the other six components in a model, and once empirically validated, the model can be used to simulate future scenarios.