Understanding the impacts of climate change on people and the environment requires an understanding of the dynamics of both climate and land use/land cover changes. A range of future climate scenarios is available for the conterminous United States that have been developed based on widely used international greenhouse gas emissions storylines. Climate scenarios derived from these emissions storylines have not been matched with logically consistent land use/cover maps for the United States. This gap is a critical barrier to conducting effective integrated assessments. This study develops novel national scenarios of housing density and impervious surface cover that are logically consistent with emissions storylines. Analysis of these scenarios suggests that combinations of climate and land use/cover can be important in determining environmental conditions regulated under the Clean Air and Clean Water Acts. We found significant differences in patterns of habitat loss and the distribution of potentially impaired watersheds among scenarios, indicating that compact development patterns can reduce habitat loss and the number of impaired watersheds. These scenarios are also associated with lower global greenhouse gas emissions and, consequently, the potential to reduce both the drivers of anthropogenic climate change and the impacts of changing conditions. The residential housing and impervious surface datasets provide a substantial first step toward comprehensive national land use/land cover scenarios, which have broad applicability for integrated assessments as these data and tools are publicly available.urbanization | land planning | water quality L and-use and land-cover change are recognized to have global consequences (1) and demographic trends drive land development, including residential housing, which define many landscapes (2). However, it remains challenging to develop a deeper understanding of the consequences of these changes because most urban-growth models that can incorporate policy drivers are limited to local and regional scales (e.g., ref. 3, but see ref. 4). This gap limits the effectiveness of integrated assessments of global change impacts, particularly to assess the combined effects of land use and climate change on environmental endpoints. Moreover, in this context it is important for scenarios of growth and development to be consistent with the assumptions used to develop global climate-change scenarios and storylines (5-7).Land-use change plays a central role in determining the consequences of climate change for people and the environment (8, 9), and has consequences for many environmental endpoints, such as water and air quality (10-12). These complex interactions influence the condition of resources regulated under the Clean Water and Clean Air Acts and are important considerations to include in planning and policy analyses. For example, changes in water quality and effects on aquatic ecosystems have a strong linkage with impervious surface cover associated with development (13). Here we prov...
Transportation officials are increasingly faced with challenging decisions about how to design, plan, and manage infrastructure to confront changes in climate and extreme weather events. An understanding of which impacts affect infrastructure and at what point damage begins to occur is a critical step toward assessing overall vulnerability and risk. However, few resources exist to help managers and designers identify key thresholds and indicators of sensitivity to weather and climate impacts. This paper introduces a sensitivity matrix, a tool developed for the U.S. Department of Transportation's Gulf Coast Study, Phase 2, adaptation pilot project in Mobile, Alabama. This matrix is an important step toward a more comprehensive understanding of relationships between climate and transportation. Transportation planners can use this matrix to screen for assets that are particularly sensitive and, therefore, potentially vulnerable to climate change. Where possible, the matrix includes key thresholds at which damage may be observed. This resource can assist the transportation community in conducting climate vulnerability and risk assessments. This sensitivity matrix reveals three main conclusions about the sensitivity of the transportation system to climate stressors. First, transportation assets tend to be more sensitive to extreme events than to incremental changes in the mean of climate variables. Second, services such as maintenance, traffic conveyance, and safety often are more sensitive to climate stressors than are physical assets. Finally, an asset is often sensitive to stressors whose occurrence is relatively unlikely in comparison with typical weather variability.
From installing culverts with larger safety margins to instituting more frequent training for weather emergencies, transportation agencies around the world are adapting to extreme weather and climate change. An understanding of when and how to adapt (i.e., improve infrastructure preparedness) requires evaluating existing and future vulnerabilities to climate change and prioritizing adaptation efforts. A successful vulnerability assessment lays the groundwork for adaptation by building stakeholder relationships, spurring data collection, and prioritizing needs. One barrier faced by transportation agencies in conducting vulnerability assessments is a lack of financial and staff resources. The process of collecting climate and asset data can be particularly onerous for agencies struggling to meet daily operational needs. Two recent projects piloted a cost-effective screening method for highly vulnerable assets that used indicators developed from data already being collected by many state departments of transportation and metropolitan planning organizations. The indicator that libraries developed during the course of these two studies is described. The results of the data-driven vulnerability screen provide transportation managers with a low-cost starting point toward understanding their system's vulnerabilities. Future research should focus on testing the indicators to identify and eliminate areas of overlap and on evaluating the prediction accuracy for exposure, sensitivity, and adaptive capacity.
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