Abstract
Most long-term sampling regimes are calendar based, collecting one or two samples per month regardless of the stream conditions. Loads estimated with calendar-based sampling are often used for expensive water quality mitigation measures. In this paper, we have tested the differences between the calendar-based and extensive sampling methods for two watersheds of different sizes, and three parameters—total nitrogen, total phosphorus, and total suspended solids. Based on the results obtained and the costs associated with the remediation, a simple decision-making framework is proposed for watershed managers to decide on the applicability of a calendar-based sampling method. Direct loads (DL) were computed using a method based on an intensive sampling of flow and other water quality parameters. Weighted regression loads (WL) were estimated using the WRTDS model designed for modified calendar-based sampling. The results suggest that for trend analysis and planning on a larger scale, long-term loads obtained from a modified calendar-based sampling regime may be used as a reasonable substitute for loads obtained from intensive sampling. However, for purposes where accurate daily loads are needed (e.g., water quality model calibration) WL may not be an effective substitute for DL. Finally, we recommend that the costs of control measures should be assessed when deciding on a sampling regime.
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Acknowledgments
The authors would like to acknowledge the stakeholders of the Metropolitan Washington Council of Governments for their continued (since 1982) support of the Potomac River (PR01) monitoring program and (since 1973) the Occoquan Watershed Monitoring Program (ST30). We will also like to acknowledge the work by Dr. Thomas Grizzard who was instrumental in developing this study. Unfortunately, he passed away before this manuscript could be written and is not included as an author as per the journal policy.
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Kumar, S., Godrej, A., Post, H. et al. The Value of Intensive Sampling—A Comparison of Fluvial Loads. Water Resour Manage 33, 4303–4318 (2019). https://doi.org/10.1007/s11269-019-02369-7
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DOI: https://doi.org/10.1007/s11269-019-02369-7