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A Framework for Assessing Concentration-Discharge Catchment Behavior From Low-Frequency Water Quality Data

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A Framework for Assessing Concentration-Discharge Catchment Behavior From Low-Frequency Water Quality Data. / Pohle, I.; Baggaley, N.; Palarea-Albaladejo, J. et al.

In: Water Resources Research, Vol. 57, No. 9, 30.09.2021.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Pohle, I, Baggaley, N, Palarea-Albaladejo, J, Stutter, M & Glendell, M 2021, 'A Framework for Assessing Concentration-Discharge Catchment Behavior From Low-Frequency Water Quality Data', Water Resources Research, vol. 57, no. 9. https://doi.org/10.1029/2021WR029692

APA

Pohle, I., Baggaley, N., Palarea-Albaladejo, J., Stutter, M., & Glendell, M. (2021). A Framework for Assessing Concentration-Discharge Catchment Behavior From Low-Frequency Water Quality Data. Water Resources Research, 57(9). https://doi.org/10.1029/2021WR029692

Vancouver

Pohle I, Baggaley N, Palarea-Albaladejo J, Stutter M, Glendell M. A Framework for Assessing Concentration-Discharge Catchment Behavior From Low-Frequency Water Quality Data. Water Resources Research. 2021 Sep 30;57(9). Epub 2021 Sep 9. doi: 10.1029/2021WR029692

Author

Pohle, I. ; Baggaley, N. ; Palarea-Albaladejo, J. et al. / A Framework for Assessing Concentration-Discharge Catchment Behavior From Low-Frequency Water Quality Data. In: Water Resources Research. 2021 ; Vol. 57, No. 9.

Bibtex

@article{82041f8ce776463e8809de1fe68daec0,
title = "A Framework for Assessing Concentration-Discharge Catchment Behavior From Low-Frequency Water Quality Data",
abstract = "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. {\textcopyright} 2021. The Authors.",
keywords = "catchment typology, concentration-discharge relationships, hysteresis pattern, rivers, solute export, solute hydrochemistry, Catchments, Classification (of information), Hydrochemistry, River pollution, Rivers, Runoff, Stream flow, Topography, Water quality, Catchment characteristics, Catchment typology, Concentration-discharge relationship, Hysteresis pattern, Lower frequencies, Mitigation measures, Nutrient pollution, Solute export, Solute hydrochemistry, Water quality data, Hysteresis",
author = "I. Pohle and N. Baggaley and J. Palarea-Albaladejo and M. Stutter and M. Glendell",
year = "2021",
month = sep,
day = "30",
doi = "10.1029/2021WR029692",
language = "English",
volume = "57",
journal = "Water Resources Research",
issn = "0043-1397",
publisher = "AMER GEOPHYSICAL UNION",
number = "9",

}

RIS

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 -