Pohle, I. and Baggaley, N. and Palarea-Albaladejo, J. and Stutter, M. and Glendell, M. (2021) A Framework for Assessing Concentration-Discharge Catchment Behavior From Low-Frequency Water Quality Data. Water Resources Research, 57 (9). ISSN 0043-1397
Full text not available from this repository.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. © 2021. The Authors.