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A hydrochemically guided landscape-based classification for water quality: a case study application of process-attribute mapping (PoAM) at a national scale
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  • Clinton WF Rissmann,
  • Lisa K Pearson,
  • Adam P Martin,
  • Matthew I Leybourne,
  • W Troy Baisden,
  • Timothy J Clough,
  • Richard W McDowell,
  • Jenny G Webster Brown
Clinton WF Rissmann
Univeristy of Canterbury
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Lisa K Pearson
Land and Water Science

Corresponding Author:[email protected]

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Adam P Martin
GNS Science
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Matthew I Leybourne
Queens University
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W Troy Baisden
University of Waikato
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Timothy J Clough
Lincoln University
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Richard W McDowell
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Jenny G Webster Brown
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Spatial 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 PAG was evaluated 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.