Wind profile observations are used to estimate turbulent mixing in the atmospheric boundary layer from 1 m up to 300 m height in two locations of pine forests characteristic of the southeast US region, and to 30 m height at one location in the northeast. Basic turbulence characteristics of the boundary layers above and within the canopy were measured near prescribed fires for time periods spanning the burns. Together with theoretical models for the mean horizontal velocity and empirical relations between mean flow and variance, we derive the lateral diffusivity using Taylor’s frozen turbulence hypothesis in the thin surface-fuel layer. This parameter is used in a simple 1D model to predict the spread of surface fires in different wind conditions. Initial assessments of sensitivity of the fire spread rates to the lateral diffusivity are made. The lateral diffusivity with and without fire-induced wind is estimated and associated fire spread rates are explored. Our results support the conceptual framework that eddy dynamics in the fuel layer is set by larger eddies developed in the canopy layer aloft. The presence of fire modifies the wind, hence spread rate, depending on the fire intensity.

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Human adaptation to climate change is the outcome of long-term decisions continuously made and revised by local communities. Adaptation choices can be represented by economic investment models in which the often large upfront cost of adaptation is offset by the future benefits of avoiding losses due to future natural hazards. In this context, we investigate the role that expectations of future natural hazards have on adaptation in the Colorado River basin of the USA. We apply an innovative approach that quantifies the impacts of changes in concurrent climate extremes, with a focus on flooding events. By including the expectation of future natural hazards in adaptation models, we examine how public policies can focus on this component to support local community adaptation efforts. Findings indicate that considering the concurrent distribution of several variables makes quantification and prediction of extremes easier, more realistic, and consequently improves our capability to model human systems adaptation. Hazard expectation is a leading force in adaptation. Even without assuming increases in exposure, the Colorado River basin is expected to face harsh increases in damage from flooding events unless local communities are able to incorporate climate change and expected increases in extremes in their adaptation planning and decision making.