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Large uncertainty in global estimates of manure phosphorus runoff
148 viewed

Large uncertainty in global estimates of manure phosphorus runoff

Sheikholeslami, R

Large uncertainty in global estimates of manure phosphorus runoff

Sheikholeslami, R ; Sharif University of Technology | 2024

148 Viewed
  1. Type of Document: Article
  2. DOI: 10.1016/j.envsoft.2024.106067
  3. Publisher: 2024
  4. Abstract:
  5. The estimation of manure phosphorus loss (MPL) in runoff is crucial for addressing environmental problems linked to nonpoint source pollution. Using a data-driven model, we assessed MPL in 520 major global river basins for the year 2000. Global mean MPL was 1.6 kgP/ha with a median of 0.9 kgP/ha. Hotspots were identified in the basins of South and Southeast Asia, Northwestern Europe, and South America. Importantly, 26% of basin areas showed high uncertainty due to input data, while approximately 43% exhibited low uncertainty. Continental analysis highlighted large uncertainties in Asia and South America, particularly in the Amazon, Ganges, Yangtze, and Nile basins, compared to Europe and the eastern United States. Our findings uncovered substantial uncertainties, even with a simple model, suggesting that more complex models can exacerbate uncertainties and lead to misinterpretations. Thus, there is a need for enhanced data quality and recognition of the conditional nature of model-based estimates. © 2024 Elsevier Ltd
  6. Keywords:
  7. Livestock ; Manure ; Runoff ; Water quality ; Amazon Basin ; China ; Europe ; Ganges Basin ; Nile Basin ; Southeast Asia ; United States ; Yangtze Basin ; Agricultural pollution ; Agricultural runoff ; Fertilizers ; Manures ; Quality control ; River pollution ; Environmental problems ; Global estimate ; Nonpoint sources ; Phosphorus loss ; Phosphorus loss in runoffs ; Phosphorus pollution ; Source pollution ; South America ; Uncertainty ; Data quality ; Estimation method ; Global change ; Nonpoint source pollution ; Phosphorus ; Uncertainty analysis
  8. Source: Environmental Modelling and Software ; Volume 177 , 2024 ; 13648152 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/abs/pii/S1364815224001282