Index models to evaluate the risk of phosphorus and nitrogen loss at catchment scales

This paper investigates index models as a tool to estimate the risk of N and P source strengths and loss at the catchment scale. The index models assist managers in improving the focus of remediation actions that reduce nutrient delivery to waterbodies. N and P source risk factors (e.g. soil nutrien...

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Veröffentlicht in:Journal of environmental management 2011-03, Vol.92 (3), p.639-649
Hauptverfasser: Drewry, J.J., Newham, L.T.H., Greene, R.S.B.
Format: Artikel
Sprache:eng
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Zusammenfassung:This paper investigates index models as a tool to estimate the risk of N and P source strengths and loss at the catchment scale. The index models assist managers in improving the focus of remediation actions that reduce nutrient delivery to waterbodies. N and P source risk factors (e.g. soil nutrient concentrations) and transport risk factors (e.g. distance-to-streams) are used to determine the overall risk of nutrient loss for a case study in the Tuross River catchment of coastal southeast Australia. In the development of the N index model for Tuross, particulate N was considered important based on the observed event water quality data. In contrast to previous N index models, erosion and contributing distance were therefore included in the Tuross River catchment N index. Event-based water quality monitoring, and soil information, or in data-poor catchments conceptual understanding, are essential to represent catchment-scale processes. The techniques have high applicability in other catchments, and are complementary to other modelling techniques such as process-based semi-distributed modelling. Index models generally provide much more detailed spatial resolution than fully- or semi-distributed conceptual modelling approaches. Semi-distributed models can be used to quantify nutrient loads and provide overall direction to set the broad focus of management. Index models can then be used to refine on-the-ground investigations and investment priorities. In this way semi-distributed models can be combined with index models to provide a set of powerful tools to influence management decisions and outcomes.
ISSN:0301-4797
1095-8630
DOI:10.1016/j.jenvman.2010.10.001