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. 10.1016/j.jhydrol.2013.09.040
Full text not available from this repository.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 |
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Digital Object Identifier (DOI): | 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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