A Framework for Assessing Concentration‐Discharge Catchment Behavior From Low‐Frequency Water Quality Data. Issue 9 (9th September 2021)

• Record Type: Journal Article
• Authors: Pohle, Ina; Baggaley, Nikki; Palarea‐Albaladejo, Javier; Stutter, Marc; Glendell, Miriam
• Is Part Of: Water resources research. Volume 57:Issue 9(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-09-09
• Subjects: solute hydrochemistry -- concentration‐discharge relationships -- catchment typology -- solute export -- rivers -- hysteresis pattern; Hydrology -- Periodicals; 333.91
• DOI: 10.1029/2021WR029692
• Languages: English
• ISSNs: 0043-1397

Abstract

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 ( 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. Key Points: A reproducible classification of concentration‐discharge relationships was developed Export regime and hysteresis pattern of low‐frequency water quality data are considered Catchment concentration‐discharge classification varies spatially and among solutes