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dc.contributor.authorRissmann, C. W. F.en
dc.contributor.authorPearson, L. K.en
dc.contributor.authorMartin, A. P.en
dc.contributor.authorLeybourne, M. I.en
dc.contributor.authorBaisden, W. T.en
dc.contributor.authorClough, Timothy J.en
dc.contributor.authorMcDowell, Richarden
dc.contributor.authorWebster Brown, J. G.en
dc.date.accessioned2022-01-13T21:30:31Z
dc.date.issued2021-07-20en
dc.identifier.urihttps://hdl.handle.net/10182/14516
dc.description.abstractSpatial variation in landscape attributes can account for much of the variability in water quality compared to land use factors. Spatial variability arises from gradients in topographic, edaphic, and geologic landscape attributes that govern the four dominant processes (atmospheric, hydrological, microbially mediated redox, physical and chemical weathering) that generate, store, attenuate, and transport contaminants. This manuscript extends the application of Process Attribute Mapping (PoAM), a hydrochemically guided landscape classification system for modelling spatial variation in multiple water quality indices, using New Zealand (268,021 km²) as an example. Twelve geospatial datasets and >10,000 ground and surface water samples from 2,921 monitoring sites guided the development of 16 process-attribute gradients (PAG) within a geographic information system. Hydrochemical tracers were used to test the ability of PAG to replicate each dominant process (cross validated R² of 0.96 to 0.54). For water quality, land use intensity was incorporated and the performance of PAGwasevaluated using an independent dataset of 811 long-term surface water quality monitoring sites (R² values for total nitrogen of 0.90 -0.71 (median = 0.78), nitrate-nitrite nitrogen 0.83 -0.71 (0.79), total phosphorus 0.85 -0.63 (0.73), dissolved reactive phosphorus 0.76 -0.57 (0.73), turbidity 0.92 -0.48 (0.69), clarity 0.89 -0.50 (0.62) and E. coli 0.75 -0.59 (0.74)). The PAGs retain significant regional variation, with relative sensitivities related to variable geological and climatic histories. Numerical models or policies that do not consider landscape variation likely produce outputs or rule frameworks that may not support improved water quality.en
dc.format.extent39en
dc.language.isoenen
dc.publisherAmerican Geophysical Union (AGU) and Wileyen
dc.relationThe original publication is available from - ESSOAr - https://doi.org/10.1002/essoar.10507536.1 - https://www.essoar.org/doi/10.1002/essoar.10507536.1en
dc.relation.urihttps://doi.org/10.1002/essoar.10507536.1en
dc.rights© The Authors.en
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectwater qualityen
dc.subjectland useen
dc.subjecthydrochemistryen
dc.subjectlandscapeen
dc.titleA hydrochemically guided landscape-based classification for water quality: A case study application of process-attribute mapping (PoAM) at a national scaleen
dc.typePreprint Server Paper
lu.contributor.unitLincoln Universityen
lu.contributor.unitFaculty of Agriculture and Life Sciencesen
lu.contributor.unitDepartment of Soil and Physical Sciencesen
dc.identifier.doi10.1002/essoar.10507536.1en
dc.relation.isPartOfESSOAren
pubs.notesEarth and Space Science Open Archiveen
pubs.organisational-group/LU
pubs.organisational-group/LU/Agriculture and Life Sciences
pubs.organisational-group/LU/Agriculture and Life Sciences/SOILS
pubs.organisational-group/LU/Research Management Office
pubs.organisational-group/LU/Research Management Office/QE18
pubs.publication-statusPublisheden
pubs.publisher-urlhttps://www.essoar.org/doi/10.1002/essoar.10507536.1en
dc.rights.licenceAttributionen
lu.identifier.orcid0000-0002-5978-5274
lu.identifier.orcid0000-0003-3911-4825
dc.subject.anzsrc2020300201 Agricultural hydrologyen
dc.subject.anzsrc2020370704 Surface water hydrologyen
dc.subject.anzsrc2020410503 Groundwater quality processes and contaminated land assessmenten
dc.subject.anzsrc2020410504 Surface water quality processes and contaminated sediment assessmenten
dc.subject.anzsrc2020370703 Groundwater hydrologyen
dc.subject.anzsrc2020300206 Agricultural spatial analysis and modellingen


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