Correlations between land use and stream nitrate-nitrite concentrations in the Yahara River Watershed in south-central Wisconsin.

To better inform land management decisions, we explored relationships between land use data and stream nitrate-nitrite (NO3NO2) concentration data in the Yahara River Watershed (YRW) in south-central Wisconsin, USA. Three metrics were used to evaluate the extent of different land uses in the watershed: (1) the area percentage of each land use in both the watershed and in a range of riparian zone widths, (2) the area factor, which refers to the ratio of the area of woodland, recreational, open and vacant subdivided land, or wetlands in the riparian zone (6.1-213.4 m widths) to agricultural areas in the rest of watershed, which indicates the buffering capacity of the riparian zone, and (3) the inverse-distance-weighted (IDW) area percentage with proximity to sub-watershed outlet and to stream, which characterizes spatial arrangement in the watershed by assigning a higher weight to patches closer to the outlet or stream and a lower weight to those farther away. We found significant, positive correlations between the extent of agricultural areas and stream NO3NO2 concentrations. NO3NO2 concentrations were highly correlated to area factor metrics for all riparian zone widths such that as area factor decreased, NO3NO2 concentrations increased. There was also a marked increase in NO3NO2 concentrations at a threshold of approximately 60% agricultural area with IDW proximity to stream. Wetland area percentage in the entire watershed and IDW wetland area percentage with proximity to stream were negatively correlated to stream NO3NO2 concentrations. Compared to the simple area percentage metric, area factor and IDW wetland area percentage with proximity to stream were better indicators of stream NO3NO2 concentrations. Results from this study indicate that, in addition to land use area percentage, spatial distributions of land uses should be considered when managing watersheds. This study also demonstrates the value of citizen-based sampling data and reveals opportunities to improve the utility of such data.

Novel determination of effective freeze-thaw cycles as drivers of ecosystem change.

Soil freeze-thaw cycles (FTCs) profoundly influence biophysical conditions and modify biogeochemical processes across many northern-hemisphere and alpine ecosystems. How FTCs will contribute to global processes in seasonally snow-covered ecosystems in the future is of particular importance as climate change progresses and winter snowpacks decline. Our understanding of these contributions is limited because there has been little consideration of inter- and intrayear variability in the characteristics of FTCs, in part due to a limited appreciation for which of these characteristics matters most with respect to a given biogeochemical process. Here, we introduce the concept of effective FTCs: those that are most likely linked to changes in key soil processes. We also propose a set of parameters to quantify and characterize effective FTCs using standard field soil temperature data. To put these proposed parameters into effective practice, we present FTCQuant, an R package of functions that quantifies FTCs based on a set of user-defined parameter criteria and, importantly, summarizes the individual characteristics of each FTC counted. To demonstrate the utility of these new concepts and tools, we applied the FTCQuant package to re-analyze data from two published studies to help explain over-winter changes to N2 O emissions and wet-aggregate stability. We found that effective FTCs would be defined differently for each of these response variables and that effective FTCs provided a 76 and 33% increase in model fit for wet-aggregate stability and cumulative N2 O emission, respectively, relative to conventional FTC quantification methods focusing on fluctuations around 0 °C. These results demonstrate the importance of identifying effective FTCs when scaling soil processes to regional or global levels. We hope our contributions will inform future deductions, hypothesis generation, and experimentation with respect to expected changes in freeze-thaw cycling globally.