Zhang, W., H. Li, and D.W. Hyndman, 2020, Water quality trends under rapid agricultural expansion and enhanced in-stream interception in a hilly watershed in Eastern China, Environmental Research Letters.
Lupi, F., B. Basso, C. Garnache, J. Herriges, D. Hyndman, R. J. Stevenson, 2020. Linking Agricultural Nutrient Pollution to the Value of Freshwater Ecosystem Services, Land Economics 96(4)
Hamlin, Q.F., A.D. Kendall, S.L. Martin, H.D. Whitenack, J.A. Roush, B.A. Hannah, D.W. Hyndman, 2020, Spatially Explicit Nutrient Source Map (SENSMap): Quantifying Landscape Nutrient Applications, Journal of Geophysical Research: Biogeosciences, 125, e2019JG005134. DOI:10.1029/2019JG005134
Abstract
Nutrient management is an essential part of watershed planning worldwide to protect water resources from both widespread landscape inputs of nutrients (N and P) and point source emissions. To provide information to regional watershed planners and better understand nutrient sources, we developed the Spatially Explicit Nutrient Source Estimate Map (SENSEmap) to quantify individual sources of N and P at their entry points in the landscape. We modeled seven sources of N and six sources of P across the U.S. Great Lakes Basin at 30‐m resolution: atmospheric deposition, septic systems, chemical nonagricultural fertilizer, chemical agricultural fertilizer, manure, nitrogen fixation, and point sources. By modeling these sources, we provide a more detailed view of nutrient inputs to the landscape beyond what would be possible from land use alone. We found that 71% and 88% of N and P, respectively, came from agricultural sources. The nature of agricultural nutrient inputs varied significantly across the basin, as relative contributions of chemical agricultural fertilizers, manure, and N fixation changed according to diverse land use practices regionally. We then applied k‐means cluster analysis and identified nine Nutrient Input Landscapes (NILs) with N and P source characteristics, grouped into intensive agricultural, urban, and rural landscapes. These NILs can offer insights into landscape variability that land use data alone cannot; within agricultural NILs, application of chemical fertilizer and manure varied greatly, but land uses were similar. These NILs can provide a framework for broadly categorizing watersheds that may prove useful to both ecological and management practices.