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Natural wetlands intrinsically heterogeneous and are typically composed of a mosaic of ecosystem patches with different plant types. The adaptation of these plants' communities to water-dominated environment is the basis for their use in improving the water quality in constructed wetlands. The understanding of wetland vegetation effects on the environment is the key to determine which plant to grow in a constructed wetland in term of nutrients removal. Wetland vegetation can influence water movement. The plant density and life form affect the drag and thus controls the residence time of water in different parts of the wetlands, as well as the rate of deposition of suspended solids. Furthermore, emergent plants with high transpiration rates can lower the water level. Accurately identifying the vegetation patches is important to understanding their hydrological effects and further effects on nutrients removal. This dissertation demonstrated an approach that using NDVI time series constructed from remote sensing imagery to classify the vegetation at coastal wetland. The classification of wetland vegetation were then utilized to characterize the distinct ecohydrological patches in an hydrodynamic model, which provides the information such as water depth, flow rate and residence time for each ecohydrological patch. While the simulations we conducted focused on the barrier-open events, future research will be performed on the barrier-closed event.

Urban areas are increasingly exposed to weather-related environmental hazards under climate change, and equity concerns exist in adaptation and mitigation programs for these exposures and their consequences. To address these issues above, we used data-driven approaches to investigate three unique cases. We first developed a novel, cost-effective, and spatially-explicit classification of urbanization trends over time, using time-series nighttime light remote sensing images and unsupervised classification, in mainland China between 1992 and 2013. We identified five temporal typologies of urbanization, namely stable urban activity, high-level steady growth, acceleration, low-level steady growth, and fluctuation. Our classification characterizes distinct urbanization patterns over time and can be applied to environmentally sensitive and hazard-prone areas where monitoring of urbanization is critical. Next, we perform a multitemporal and multi-scenario projection of exposure to flooding, caused by sea level rise and storm surge, in the highly urbanized San Francisco Bay Area. We found increased uncertainty in exposure over time and in scenarios with higher greenhouse gas concentrations. Such elevated uncertainty suggests that stakeholders should employ adaptation strategies that are no-regret, reversible, and flexible, and that regulators may explicitly require a long-term planning horizon for adaptation programs and new developments. Finally, we investigated the allocation of a widely-adopted adaptation and mitigation program, clean vehicle rebates, from two major policy programs in California. We evaluated rebate allocation rates with respect to community characteristics including socioeconomic and environmental disadvantages, household income, racial and ethnical composition, and ambient air pollution. We found that when rebate assignment and amount were only based on vehicle technology and did not consider the varied socioeconomic backgrounds of potential applicants, rebate allocation rates were higher in advantaged, wealthier communities, and communities with intermediate levels of nitrogen dioxide concentration, but the rates were lower in communities with higher percentages of Hispanics and Non-Hispanic Blacks. After introducing an income cap, expanded vehicle eligibility, and income- and geography-tiered rebate amounts, rebate allocation rates increased in communities with lower-household income, higher percentages of Hispanics, and slightly higher nitrogen dioxide concentration. The findings of this study implied the need for and effectiveness of equity-related policy designs to spread the benefits of adaptation and mitigation programs to more diverse populations. In all, these studies seek to engage a broader conversation about environmental and societal challenges in urban areas under climate change, as well as how data-driven approaches can reveal the underlying processes of these challenges and inform better policy and decision-making.