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In this study, we investigate the compatibility of specific vulnerability indicators and heat exposure data and the suitability of spatial temperature-related data at a range of resolutions, to represent spatial temperature variations within cities using data from Atlanta, Georgia. For this purpose, we include various types of known and theoretically based vulnerability indicators such as specific street-level landscape features and urban form metrics, population-based and zone-based variables as predictors, and different measures of temperature, including air temperature (as vector-based data), land surface temperature (at resolution ranges from 30 m to 305 m), and mean radiant temperature (at resolution ranges from 1 m to 39 m) as dependent variables. Using regression analysis, we examine how different sets of predictors and spatial resolutions can explain spatial heat variation. Our findings suggest that the lower resolution of land surface temperature data, up to 152 m, and mean radiant temperature data, up to 15 m, may still satisfactorily represent spatial urban temperature variation caused by landscape elements. The results of this study have important implications for heat-related policies and planning by providing insights into the appropriate sets of data and relevant resolution of temperature measurements for representing spatial urban heat variations. 

Stakeholder participation in social-ecological systems (SES) modeling is increasingly considered a desirable way to elicit diverse sources of knowledge about SES behavior and to promote inclusive decision-making in SES. Understanding how participatory modeling processes function in the context of long-term adaptive management of SES may allow for better design of participatory processes to achieve the intended outcomes of inclusionary knowledge, representativeness, and social learning, while avoiding unintended outcomes. Long-term adaptive management contexts often include political influences -- attempts to shift or preserve power structures and authority, and efforts to represent the political and economic interests of stakeholders -- in the computer models that are used to shape policy making and implementation. In this research, we examine a period that included a major transition in the watershed model used for management of the Chesapeake Bay in the United States. The Chesapeake Bay watershed model has been in development since the 1980s, and is considered by many to be an exemplary case of participatory modeling. We use documentary analysis and interviews with participants involved in the model application and development transition to reveal a variety of ways in which participatory modeling may be subject to different kinds of political influences, some of which resulted in unintended outcomes, including: perceptions of difficulty updating the model in substantive ways, “gaming” of the model/participatory process by stakeholders, and increasing resistance against considering uncertainty in the system not captured by the model. This research suggests unintended or negative outcomes may be associated with both participatory decision-making and stakeholder learning even though they are so often touted as the benefits of participatory modeling. We end with a hypothesis that further development of a theory of computer model governance to bridge model impact and broader theories of environmental governance at the science-policy interface may result in improved SES modeling outcomes.