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A hydrochemically guided landscape classification system for modelling spatial variation in multiple water quality

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Summary
This summary is machine-generated.

This study introduces a new water quality model, Process-Attribute Mapping (PoAM), that incorporates landscape variability. The model accurately predicts water quality by mapping process-attribute gradients (PAGs) derived from hydrochemical data.

Keywords:
Chemical weatheringDominant processesHydrologyLandscape gradientsPhysiographicRedox

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Area of Science:

  • Environmental Science
  • Hydrology
  • Geochemistry

Background:

  • Water quality is influenced by spatial variations in landscape attributes, driven by hydrological, redox, and weathering processes.
  • Existing water quality models often overlook the impact of 'process-attribute' gradients (PAGs), which link landscape features to dominant environmental processes.

Purpose of the Study:

  • To present a novel process-based water quality modeling framework, Process-Attribute Mapping (PoAM), that explicitly integrates landscape variability.
  • To develop and apply PAG maps that represent natural landscape gradients influencing water quality.

Main Methods:

  • Utilized 12 geospatial datasets and 28,626 water/soil analyses to identify and map 11 individual PAGs in Southland, New Zealand.
  • Assessed PAG accuracy in replicating hydrological, redox, and weathering gradients using hydrochemical indicators and 93 monitoring sites (cross-validated R² 0.75–0.95).
  • Combined validated PAGs with land use intensity data to estimate steady-state surface water quality.

Main Results:

  • The PoAM framework demonstrated strong predictive accuracy for nutrient concentrations, with cross-validated R² values ranging from 0.81 to 0.92 for key parameters.
  • Models for particulate matter (E. coli, total suspended sediment) showed reasonable, though less precise, results (R² 0.72–0.73).

Conclusions:

  • Process-Attribute Gradients (PAGs) effectively capture landscape influences on water quality, improving model predictions.
  • Further refinement with finer-scale land use and flow-normalized data is suggested for enhanced modeling of particulate water quality constituents.