Stream Solutes and Particulates Export Regimes: A New Framework to Optimize Their Monitoring

The quantification of solute and sediment export from drainage basins is challenging. A large proportion of annual or decadal loads of most constituents is exported during relatively short periods of time, a “hot moment,” which vary between constituents and catchments. We developed a new framework based on concentration-discharge (C-Q) relationship to characterize the export regime of stream particulates and solutes during high water periods when the majority of annual and inter-annual load is transported. We evaluated the load flashiness index (percentage of cumulative load that occurs during the highest 2% of daily load, M2), a function of flow flashiness (percentage of cumulative Q during the highest 2% of daily Q, W2), and export pattern (slope of the logC-logQ relationship for Q higher than the daily median Q, b50high). We established this relationship based on long-term water quality and discharge datasets of 580 streams sites of France and USA, corresponding to 2,507 concentration time series of total suspended sediments (TSS), total dissolved solutes (TDS), total phosphorus (TP), nitrate (NO3), and dissolved organic carbon (DOC), generating 1.5 million data points in highly diverse geologic, climatic, and anthropogenic contexts. Load flashiness (M2) increased with b50high and/or W2. Also, M2 varied as a function of the constituent transported. M2 had the highest values for TSS and decreased for the other constituents in the following order: TP, DOC, NO3, TDS. Based on these results, we constructed a load-flashiness diagram to determine optimal monitoring frequency of dissolved or particulate constituents as a function of b50high and W2. Based on M2, optimal temporal monitoring frequency of the studied constituents decreases in the following order: TSS, TP, DOC, NO3, and TDS. Finally, we analyzed relationships between these metrics and catchments characteristics. Depending on the constituent, we explained between 30 and 40% of their M2 variance with simple catchment characteristics, such as stream network density or percentage of intensive agriculture. Therefore, catchment characteristics can be used as a first approach to set up water quality monitoring design where no hydrological and/or water quality monitoring exist