Stochastic uncertainties and sensitivities of a regional-scale transport model of nitrate in groundwater

Groundwater quality management relies more and more on models in recent years. These models are used to predict the risk of groundwater contamination for various land uses. This paper presents an assessment of uncertainties and sensitivities to input parameters for a regional model. The model had be...

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Veröffentlicht in:Journal of hydrology (Amsterdam) 2008-11, Vol.361 (3), p.309-318
Hauptverfasser: Brink, Cors van den, Zaadnoordijk, Willem Jan, Burgers, Saskia, Griffioen, Jasper
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Sprache:eng
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Zusammenfassung:Groundwater quality management relies more and more on models in recent years. These models are used to predict the risk of groundwater contamination for various land uses. This paper presents an assessment of uncertainties and sensitivities to input parameters for a regional model. The model had been set up to improve and facilitate the decision-making process between stakeholders and in a groundwater quality conflict. The stochastic uncertainty and sensitivity analysis comprised a Monte Carlo simulation technique in combination with a Latin hypercube sampling procedure. The uncertainty of the calculated concentrations of nitrate leached into groundwater was assessed for the various combinations of land use, soil type, and depth of the groundwater table in a vulnerable, sandy region in The Netherlands. The uncertainties in the shallow groundwater were used to assess the uncertainty of the nitrate concentration in the abstracted groundwater. The confidence intervals of the calculated nitrate concentrations in shallow groundwater for agricultural land use functions did not overlap with those of non-agricultural land use such as nature, indicating significantly different nitrate leaching in these areas. The model results were sensitive for almost all input parameters analyzed. However, the NSS is considered pretty robust because no shifts in uncertainty between factors occurred between factors towards systematic changes in fertilizer and manure inputs of the scenarios. In view of these results, there is no need to collect more data to allow science based decision-making in this planning process.
ISSN:0022-1694
1879-2707
DOI:10.1016/j.jhydrol.2008.08.001