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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - A Framework for Assessing Concentration-Discharge Catchment Behavior From Low-Frequency Water Quality Data
AU - Pohle, I.
AU - Baggaley, N.
AU - Palarea-Albaladejo, J.
AU - Stutter, M.
AU - Glendell, M.
PY - 2021/9/30
Y1 - 2021/9/30
N2 - Effective nutrient pollution mitigation measures require in-depth understanding of spatio-temporal controls on water quality which can be obtained by analyzing export regime and hysteresis patterns in concentration-discharge ((Formula presented.)) relationships. Such analyses require high-frequency data (hourly or higher resolution), hampering the assessment of hysteresis patterns in widely available low-frequency (monthly, biweekly) regulatory water quality data. We propose a reproducible classification of (Formula presented.) relationships considering export regime (dilution, constancy, enrichment) and long-term average hysteresis pattern (clockwise, no hysteresis, anticlockwise) applicable to low-frequency water quality data. The classification is based on power-law (Formula presented.) models with separate parametrization for low and high discharge and rising and falling hydrograph limb, enabling a better representation of (Formula presented.) dynamics. The classification has been applied to a 30-years record of daily streamflow and monthly spot samples of solute concentrations in 45 Scottish catchments with contrasting characteristics in terms of topography, climate, soil and land cover. We found that (Formula presented.) classification is solute- and catchment-specific and linked to upland versus lowland catchments and streamflow variability. However as the relationship between solute behavior and catchment characteristics is variable, we propose that future typologies should integrate both water quality response, that is, (Formula presented.) classification, and catchment characteristics. The data-driven (Formula presented.) classification allows us to increase the information content of low-frequency water quality data and thus inform mitigation measures, monitoring strategies, and modeling approaches. Such approaches open up an ability to characterize processes and best management for a wider number of catchments, subject to regulatory surveillance and outside of research catchments. © 2021. The Authors.
AB - Effective nutrient pollution mitigation measures require in-depth understanding of spatio-temporal controls on water quality which can be obtained by analyzing export regime and hysteresis patterns in concentration-discharge ((Formula presented.)) relationships. Such analyses require high-frequency data (hourly or higher resolution), hampering the assessment of hysteresis patterns in widely available low-frequency (monthly, biweekly) regulatory water quality data. We propose a reproducible classification of (Formula presented.) relationships considering export regime (dilution, constancy, enrichment) and long-term average hysteresis pattern (clockwise, no hysteresis, anticlockwise) applicable to low-frequency water quality data. The classification is based on power-law (Formula presented.) models with separate parametrization for low and high discharge and rising and falling hydrograph limb, enabling a better representation of (Formula presented.) dynamics. The classification has been applied to a 30-years record of daily streamflow and monthly spot samples of solute concentrations in 45 Scottish catchments with contrasting characteristics in terms of topography, climate, soil and land cover. We found that (Formula presented.) classification is solute- and catchment-specific and linked to upland versus lowland catchments and streamflow variability. However as the relationship between solute behavior and catchment characteristics is variable, we propose that future typologies should integrate both water quality response, that is, (Formula presented.) classification, and catchment characteristics. The data-driven (Formula presented.) classification allows us to increase the information content of low-frequency water quality data and thus inform mitigation measures, monitoring strategies, and modeling approaches. Such approaches open up an ability to characterize processes and best management for a wider number of catchments, subject to regulatory surveillance and outside of research catchments. © 2021. The Authors.
KW - catchment typology
KW - concentration-discharge relationships
KW - hysteresis pattern
KW - rivers
KW - solute export
KW - solute hydrochemistry
KW - Catchments
KW - Classification (of information)
KW - Hydrochemistry
KW - River pollution
KW - Rivers
KW - Runoff
KW - Stream flow
KW - Topography
KW - Water quality
KW - Catchment characteristics
KW - Catchment typology
KW - Concentration-discharge relationship
KW - Hysteresis pattern
KW - Lower frequencies
KW - Mitigation measures
KW - Nutrient pollution
KW - Solute export
KW - Solute hydrochemistry
KW - Water quality data
KW - Hysteresis
U2 - 10.1029/2021WR029692
DO - 10.1029/2021WR029692
M3 - Journal article
VL - 57
JO - Water Resources Research
JF - Water Resources Research
SN - 0043-1397
IS - 9
ER -