Stochastic modelling of phosphorus transfers from agricultural land to aquatic ecosystems

This paper describes a simple model of phosphorus (P) transfer from agricultural land to surface waters which incorporates the effects of spatial variability in catchment properties and uncertainty in model parameter values. TOPMODEL concepts are used to estimate water, solute and sediment fluxes to...

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Publié dans:Water science and technology : a journal of the International Association on Water Pollution Research. - 1986. - 45(2002), 9 vom: 10., Seite 167-75
Auteur principal: Whelan, M J (Auteur)
Autres auteurs: Hope, E G, Fox, K
Format: Article
Langue:English
Publié: 2002
Accès à la collection:Water science and technology : a journal of the International Association on Water Pollution Research
Sujets:Journal Article Research Support, Non-U.S. Gov't Water Pollutants Phosphorus 27YLU75U4W
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520 |a This paper describes a simple model of phosphorus (P) transfer from agricultural land to surface waters which incorporates the effects of spatial variability in catchment properties and uncertainty in model parameter values. TOPMODEL concepts are used to estimate water, solute and sediment fluxes to water bodies. The model predicts the spatial distribution of water table depth and saturation-excess overland flow based on topography. Dissolved P (DP) transfer is assumed to occur vertically in the unsaturated zone and laterally in the saturated zone. Readily soluble P is assumed to decrease exponentially with soil depth. Particulate P (PP) transfers are modelled by estimating overland flow discharge and associated sediment transport capacity. Uncertainty in the distribution of soil surface P concentrations and model parameters controlling the mobility of soil P are incorporated using Monte Carlo simulation. Predicted losses of DP are well correlated with discharge and those of PP are episodic. Highest losses of P tended to be predicted near to the stream where the water table is close to the surface. The combination of a deterministic model core with a stochastic generation of model parameters or state variables provides an attractive way of embracing variability and uncertainty in models of this kind 
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650 4 |a Research Support, Non-U.S. Gov't 
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700 1 |a Fox, K  |e verfasserin  |4 aut 
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