The Value of Intensive Sampling—A Comparison of Fluvial Loads
Tóm tắt
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.
Tài liệu tham khảo
Allan IJ et al (2006) Strategic monitoring for the European water framework directive. TrAC Trends Anal Chem 25:704–715. https://doi.org/10.1016/j.trac.2006.05.009
Arabkhedri M, Lai F, Noor-Akma I, Mohamad-Roslan M (2010) An application of adaptive cluster sampling for estimating total suspended sediment load. Hydrol Res 41:63–73
Beck MW, Hagy JD (2015) Adaptation of a weighted regression approach to evaluate water quality trends in an estuary. Environmental Modeling & Assessment 20:637–655. https://doi.org/10.1007/s10666-015-9452-8
Donigian A (2002) Watershed model calibration and validation: The HSPF experience Proc WEF 2002:44–73
Easton Z et al. (2017) Scientific and Technical Advisory Committee Review of the Phase 6 Chesapeake Bay Watershed Model. STAC Publication Number 17–007. Edgewater, MD, USA
FWPCA (2002) Fedral Water Pollution Control Act vol 33 U.S. Code § 1251. United States Senate and House of Representatives, Washington DC
Harmel R, Cooper R, Slade R, Haney R, Arnold J (2006) Cumulative uncertainty in measured streamflow and water quality data for small watersheds. Trans ASABE 49:13
He Y, Gui Z, Su C, Chen X, Chen D, Lin K, Bai X (2018) Response of sediment load to hydrological change in the upstream part of the Lancang-Mekong River over the past 50 years. Water 10:888. https://doi.org/10.3390/w10070888
Helsel D, Hirsch R (2002) Statistical Methods in Water Resources. Techniques of Water-Resources Investigations of the United States Geological Survey, Book 4, Hydrologic Analysis and Interpretation, chapter A3. U.S. Geological Survey, Reston, VA, USA
Hirsch RM, De Cicco LA (2015) User guide to Exploration and Graphics for RivEr Trends (EGRET) and dataRetrieval: R packages for hydrologic data, Version 1.0: Originally posted October 8, 2014; Version 2.0: February 5, 2015 edn., Reston, VA, USA. doi:https://doi.org/10.3133/tm4A10
Hirsch RM, Moyer DL, Archfield SA (2010) Weighted regressions on time, discharge, and season (WRTDS), with an application to Chesapeake Bay River inputs. J Am Water Resour Assoc 46:857–880. https://doi.org/10.1111/j.1752-1688.2010.00482.x
Horowitz AJ, Clarke RT, Merten GH (2015) The effects of sample scheduling and sample numbers on estimates of the annual fluxes of suspended sediment in fluvial systems. Hydrol Process 29:531–543. https://doi.org/10.1002/hyp.10172
Johnes P (2007) Uncertainties in annual riverine phosphorus load estimation: impact of load estimation methodology, sampling frequency, baseflow index and catchment population density. J Hydrol 332:241–258
Kronvang B, Bruhn A (1996) Choice of sampling strategy and estimation method for calculating nitrogen and phosphorus transport in small lowland streams. Hydrol Process 10:1483–1501
Kumar S, Godrej AN, Grizzard TJ (2013) Watershed size effects on applicability of regression-based methods for fluvial loads estimation. Water Resour Res 49:7698–7710. https://doi.org/10.1002/2013WR013704
Lee CJ, Hirsch RM, Schwarz GE, Holtschlag DJ, Preston SD, Crawford CG, Vecchia AV (2016) An evaluation of methods for estimating decadal stream loads. J Hydrol 542:185–203. https://doi.org/10.1016/j.jhydrol.2016.08.059
Maher WA, Cullen PW, Norris RH (1994) Framework for designing sampling programs. Environ Monit Assess 30:139–162. https://doi.org/10.1007/BF00545619
Moyer DL, Hirsch RM, Hyer KE (2012) Comparison of Two Regression-Based Approaches for Determining Nutrient and Sediment Fluxes and Trends in the Chesapeake Bay Watershed, U.S. Geological Survey Scientific Investigations Report 2012–5244. U.S. Geological Survey Richmond, VA, USA
Park YS, Engel BA (2015) Analysis for regression model behavior by sampling strategy for annual pollutant load estimation. J Environ Qual 44:1843–1851
Robertson DM, Roerish ED (1999) Influence of various water quality sampling strategies on load estimates for small streams. Water Resour Res 35:3747–3759
Sadeghi SHR, Saeidi P (2010) Reliability of sediment rating curves for a deciduous forest watershed in Iran. Hydrol Sci J 55:821–831
Sadeghi SHR et al (2008) Development, evaluation and interpretation of sediment rating curves for a Japanese small mountainous reforested watershed. Geoderma 144:198–211. https://doi.org/10.1016/j.geoderma.2007.11.008
Sprague LA, Hirsch RM, Aulenbach BT (2011) Nitrate in the Mississippi River and its tributaries, 1980 to 2008: are we making Progress? Environ Sci Technol 45:7209–7216. https://doi.org/10.1021/es201221s
Stenback GA, Crumpton WG, Schilling KE, Helmers MJ (2011) Rating curve estimation of nutrient loads in Iowa rivers. J Hydrol 396:158–169. https://doi.org/10.1016/j.jhydrol.2010.11.006
von Storch H (1995) Misuses of statistical analysis in climate research. In: von Storch H, Navarra A (eds) Analysis of climate variability. Springer, New York, pp 11–26
Wainger LA (2012) Opportunities for reducing Total maximum daily load (TMDL) compliance costs: lessons from the Chesapeake Bay. Environ Sci Technol 46:9256–9265. https://doi.org/10.1021/es300540k
Zhang Q, Harman CJ, Ball WP (2016) An improved method for interpretation of riverine concentration-discharge relationships indicates long-term shifts in reservoir sediment trapping. Geophys Res Lett 43:10,215–210,224. https://doi.org/10.1002/2016GL069945