A predictive geospatial approach for modelling phosphorus concentrations in rivers at the landscape scale

Greene, Sheila; McElarney, Yvonne R.; Taylor, David. 2013 A predictive geospatial approach for modelling phosphorus concentrations in rivers at the landscape scale. Journal of Hydrology, 504. 216-225. https://doi.org/10.1016/j.jhydrol.2013.09.040

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Abstract/Summary

Enrichment by phosphorus (P) constitutes a significant pressure on river systems, and is one of the main causes of freshwater pollution globally. Catchment environmental conditions influence the timing and magnitude of P release and transfer to water bodies, and therefore can potentially provide a basis for identifying water bodies vulnerable to impairment by P and/or resistant to restoration efforts. The current research involved construction of a geospatial database, comprising monthly values for flow-weighted concentrations of molybdate reactive phosphorus (fwMRP) sampled in rivers from 2006 to 2008 together with spatially-expressed environmental data relating to 18 different variables for 54 catchments in the Republic of Ireland. A regression–kriging modelling methodology within a landscape-scale, geospatial approach was tested. Environmental conditions relating to hydrological transportation and connectivity (slope, degree of surface saturation, soil water content) were found to exert greater influence over concentrations of P in rivers than direct proxies of sources of P (e.g. human population level or land use). Geospatial models provided greater explanation of P variance than regression models (an improvement in predictive capability of up to 8.5%). Data for fwMRP were segregated sub-annually into two periods, one focused on summer and the other on winter months. A geospatial model for the period including winter months was found to have a better predictive capability than the one that centred upon the summer, with the latter routinely overestimating fwMRP when compared with observed (test) data. Geospatial models potentially provide a means of optimising monitoring regimes for river water quality, and can also be used as a screening tool to focus management and remediation measures where they are likely to prove most effective.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1016/j.jhydrol.2013.09.040
UKCEH and CEH Sections/Science Areas:	Acreman
ISSN:	0022-1694
Additional Keywords:	nutrient, pollution, predictive modelling, regression-kriging, restoration, water quality
NORA Subject Terms:	Earth Sciences Hydrology
Date made live:	24 Mar 2014 15:24 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/506640

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