Validation of the InVEST nutrient retention model across Europe with attribution of model errors Article Swipe
Danny A. P. Hooftman
,
Guy Ziv
,
Paul M. Evans
,
James M. Bullock
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.envsoft.2025.106657
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.envsoft.2025.106657
Intensive fertilisation of farmland leads to substantial nutrient escape into the environment, polluting land, water, and the atmosphere. We used the InVEST NDR model to investigate nitrogen (N) and phosphorus (P) run-off and retention across the European continent at 25 x 25 m resolution, and validated outputs against empirical measurements at 2,251 river locations. Mean nutrient retention across Europe was estimated as 93% for N and 92% for P, through accumulation by standing vegetation and the soil. Modelled nutrient export to streams matched well to empirical measurements. Model-based uncertainties were related to seasonality, the balance between surface and sub-surface flows, and extremes in slope and rainfall. Uncertainties related to empirical data suggested enhancements to monitoring programmes that would improve nutrient export and erosion modelling, which included higher resolution fertiliser and manure data, differentiation of grassland types, including stocking density categories, and in-river nutrient measurements at low flow.
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- Language
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- Landing Page
- https://doi.org/10.1016/j.envsoft.2025.106657
- OA Status
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https://openalex.org/W4413384486Canonical identifier for this work in OpenAlex
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Validation of the InVEST nutrient retention model across Europe with attribution of model errorsWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-08-21Full publication date if available
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Danny A. P. Hooftman, Guy Ziv, Paul M. Evans, James M. BullockList of authors in order
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https://doi.org/10.1016/j.envsoft.2025.106657Publisher landing page
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hybridOpen access status per OpenAlex
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Attribution, Nutrient, Econometrics, Environmental science, Computer science, Psychology, Economics, Social psychology, Ecology, BiologyTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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| abstract_inverted_index.including | 136 |
| abstract_inverted_index.polluting | 12 |
| abstract_inverted_index.rainfall. | 105 |
| abstract_inverted_index.retention | 33, 56 |
| abstract_inverted_index.suggested | 111 |
| abstract_inverted_index.validated | 45 |
| abstract_inverted_index.fertiliser | 128 |
| abstract_inverted_index.locations. | 53 |
| abstract_inverted_index.modelling, | 123 |
| abstract_inverted_index.monitoring | 114 |
| abstract_inverted_index.phosphorus | 29 |
| abstract_inverted_index.programmes | 115 |
| abstract_inverted_index.resolution | 127 |
| abstract_inverted_index.vegetation | 73 |
| abstract_inverted_index.Model-based | 87 |
| abstract_inverted_index.atmosphere. | 17 |
| abstract_inverted_index.categories, | 139 |
| abstract_inverted_index.investigate | 25 |
| abstract_inverted_index.resolution, | 43 |
| abstract_inverted_index.sub-surface | 98 |
| abstract_inverted_index.substantial | 6 |
| abstract_inverted_index.accumulation | 70 |
| abstract_inverted_index.enhancements | 112 |
| abstract_inverted_index.environment, | 11 |
| abstract_inverted_index.measurements | 49, 143 |
| abstract_inverted_index.seasonality, | 92 |
| abstract_inverted_index.Uncertainties | 106 |
| abstract_inverted_index.fertilisation | 1 |
| abstract_inverted_index.measurements. | 86 |
| abstract_inverted_index.uncertainties | 88 |
| abstract_inverted_index.differentiation | 132 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/2 |
| sustainable_development_goals[0].score | 0.7400000095367432 |
| sustainable_development_goals[0].display_name | Zero hunger |
| citation_normalized_percentile.value | 0.83938097 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |