Submitted to: Book Chapter
Publication Type: Book / Chapter
Publication Acceptance Date: 6/10/1995
Publication Date: N/A
Citation: N/A Interpretive Summary: Non-point source pollutants (e.g., fertilizers, pesticides, trace elements and salinity) potentially pose a greater threat to soil and water resources than point sources of pollution due to their long-term stresses imposed across millions of hectares. Assessments of non-point source pollution, with mathematical models designed to produce multicolored maps, are now being used in the decision management arena. This has been possible primarily because of the marriage of solute transport models to geographic information systems which add a geo-referenced dimension to transport models. Albert Einstein said that "everything must be made as simple as possible, but not simpler." The utility of relatively simple vulnerability maps, which have been produced at regional scales with geographic information system technology, is undermined by significant uncertainties related to model and data errors. In this paper, the three most commonly used methods for characterizing simulation uncertainties are discussed: sensitivity analysis, first-order analysis, and Monte Carlo analysis. Examples of each method are presented.
Technical Abstract: Contamination of soil and water resources by non-point source (NPS) pollutants is a global environmental concern. The increasing availability of NPS pollutant model software and geographic information system (GIS) software to those involved in the technical support of land use decisions has resulted in the generation of multicolored management maps for regional targeting and risk assessment. In general, these assessments rest upon data that are extremely sparse and contain considerable uncertainty. An understanding of the level of uncertainty associated with the generated predictions of vulnerability assessment maps is central to the utility of the maps as decision-making tools. Uncertainties are pervasive in risk-based environmental assessment problems and thereby impact the decisions made to address those problems. Even so, risk-assessment and risk-management decisions generally rely on nominal predictions from models with little or no knowledge of the reliability of those predictions. Uncertainty analysis (i.e., the computation of the total uncertainty associated with a model's output by quantifying uncertainty in the inputs, parameters, or model indispensable in evaluating the reliability of predicted values structure) is indispensable in evaluatiing the reliability of predicted values which contribute to the decision-making process. This paper presents a review of the methods associated with uncertainty analysis as related to the modeling of non-point source pollutants.