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Research about northern Wisconsin

Source-linked reports with geographic coverage including northern Wisconsin.

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Wildfire risk in the wildland-urban interface: A simulation study in northwestern Wisconsin

The rapid growth of housing in and near the wildland–urban interface (WUI) increases wildfire risk to lives and structures. To reduce fire risk, it is necessary to identify WUI housing areas that are more susceptible to wildfire. This is challenging, because wildfire patterns depend on fire behavior and spread, which in turn depend on ignition locations, weather conditions, the spatial arrangement of fuels, and topography. The goal of our study was to assess wildfire risk to a 60,000 ha WUI area in northwestern Wisconsin while accounting for all of these factors. We conducted 6000 simulations with two dynamic fire models: Fire Area Simulator (FARSITE) and Minimum Travel Time (MTT) in order to map the spatial pattern of burn probabilities. Simulations were run under normal and extreme weather conditions to assess the effect of weather on fire spread, burn probability, and risk to structures. The resulting burn probability maps were intersected with maps of structure locations and land cover types. The simulations revealed clear hotspots of wildfire activity and a large range of wildfire risk to structures in the study area. As expected, the extreme weather conditions yielded higher burn probabilities over the entire landscape, as well as to different land cover classes and individual structures. Moreover, the spatial pattern of risk was significantly different between extreme and normal weather conditions. The results highlight the fact that extreme weather conditions not only produce higher fire risk than normal weather conditions, but also change the fine-scale locations of high risk areas in the landscape, which is of great importance for fire management in WUI areas. In addition, the choice of weather data may limit the potential for comparisons of risk maps for different areas and for extrapolating risk maps to future scenarios where weather conditions are unknown. Our approach to modeling wildfire risk to structures can aid fire risk reduction management activities by identifying areas with elevated wildfire risk and those most vulnerable under extreme weather conditions.

Wisconsin

Factors influencing mercury concentrations in walleyes in northern Wisconsin lakes

We examined relations between mercury concentrations in walleyes Stizostedion vitreum and the characteristics ofclear‐water Wisconsin lakes, which spanned a broad range of pH values (5.0–8.1) and acid‐neutralizing capacities (–9 to 1,017 μeq/L). Total concentrations of mercury in axial muscle tissue of walleyes (total length, 25–56 cm) varied from 0.12 to 1.74 μg/g wet weight. Concentrations were greatest in fish from the eight lakes with pH less than 7.0; concentrations in these fish equaled or exceeded 0.5 μg/g in 88% of the samples analyzed and 1.0 μg/g in 44%. In the five lakes with pH of 7.0 and above, concentrations exceeded 0.5 μg/g in only 1 of 21 walleyes. Multiple regression revealed that lake pH and total length offish accounted for 69% of the variation in mercury concentration in walleyes. Regression models with total length and either waterborne calcium or acid‐neutralizing capacity as independent variables accounted for 67% of the variation in concentration. The observed differences in fish mercury concentration between the low‐pH and high‐pH lakes could not be logically attributed to differences in growth rate or diet among the walleye populations. Moreover, it is improbable that mercury influxes to the low‐pH lakes were greater than those to the high‐pH lakes, because of the close proximity and spatial interspersion of low‐ and high‐pH lakes. We attributed the observed pH‐related trend in mercury concentration in walleyes to variation among lakes in within‐lake processes that affected the production and bioavailability of methylmercury.

Wisconsin