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A Framework for Assessing Concentration‐Discharge Catchment Behavior From Low‐Frequency Water Quality Data
Author(s) -
Pohle Ina,
Baggaley Nikki,
PalareaAlbaladejo Javier,
Stutter Marc,
Glendell Miriam
Publication year - 2021
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2021wr029692
Subject(s) - environmental science , streamflow , hydrograph , water quality , hydrology (agriculture) , drainage basin , geography , ecology , geology , cartography , geotechnical engineering , biology
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 ( c − Q ) 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 c − Q 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 c − Q models with separate parametrization for low and high discharge and rising and falling hydrograph limb, enabling a better representation of c − Q 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 c − Q 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, c − Q classification, and catchment characteristics. The data‐driven c − Q 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.