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 (urn:x-wiley:00431397:media:wrcr25509:wrcr25509-math-0001) 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 urn:x-wiley:00431397:media:wrcr25509:wrcr25509-math-0002 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 urn:x-wiley:00431397:media:wrcr25509:wrcr25509-math-0003 models with separate parametrization for low and high discharge and rising and falling hydrograph limb, enabling a better representation of urn:x-wiley:00431397:media:wrcr25509:wrcr25509-math-0004 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 urn:x-wiley:00431397:media:wrcr25509:wrcr25509-math-0005 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, urn:x-wiley:00431397:media:wrcr25509:wrcr25509-math-0006 classification, and catchment characteristics. The data-driven urn:x-wiley:00431397:media:wrcr25509:wrcr25509-math-0007 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