Quantifying Landscape Nutrient Inputs With Spatially Explicit Nutrient Source Estimate Maps Article Swipe
Q. F. Hamlin
,
A. D. Kendall
,
Sherry L. Martin
,
H. D. Whitenack
,
Jacob Roush
,
B. A. Hannah
,
D. W. Hyndman
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1029/2019jg005134
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1029/2019jg005134
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1029/2019jg005134
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1029/2019JG005134
- OA Status
- bronze
- Cited By
- 35
- References
- 104
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3003722160
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3003722160Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1029/2019jg005134Digital Object Identifier
- Title
-
Quantifying Landscape Nutrient Inputs With Spatially Explicit Nutrient Source Estimate MapsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-01-30Full publication date if available
- Authors
-
Q. F. Hamlin, A. D. Kendall, Sherry L. Martin, H. D. Whitenack, Jacob Roush, B. A. Hannah, D. W. HyndmanList of authors in order
- Landing page
-
https://doi.org/10.1029/2019jg005134Publisher landing page
- PDF URL
-
https://onlinelibrary.wiley.com/doi/pdfdirect/10.1029/2019JG005134Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1029/2019JG005134Direct OA link when available
- Concepts
-
Watershed, Nutrient, Environmental science, Agriculture, Fertilizer, Land use, Manure, Agricultural land, Nonpoint source pollution, Nutrient management, Intensive farming, Hydrology (agriculture), Water resource management, Ecology, Computer science, Biology, Geology, Geotechnical engineering, Machine learningTop concepts (fields/topics) attached by OpenAlex
- Cited by
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35Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 1, 2023: 12, 2022: 9, 2021: 4Per-year citation counts (last 5 years)
- References (count)
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104Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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