Temporal variability in water quality of agricultural tailwaters: Implications for water quality monitoring

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Abstract

Accurate assessments of non-point source pollution and the associated evaluation of mitigation strategies depend on effective water quality monitoring programs. Intensive irrigation season water quality monitoring was conducted on three agricultural drains (6 h to daily sampling) along with analysis of decade long records from two larger agricultural drains (biweekly to monthly sampling) in the San Joaquin Valley, California. Analyses revealed significant temporal variability in concentrations of nutrients, salts, and turbidity over short time-scales (<1 day), as well as significant differences in monthly and annual mean concentrations. Statistical techniques were used to evaluate the sampling intensity required to meet rigorous confidence and accuracy criteria, as well as to evaluate the efficacy of different sampling strategies (e.g. grab samples versus composite samples). The number of samples required to determine mean constituent concentrations within 20% of the mean at a 95% confidence level ranged from 2 to 39 samples per month (SPM) for total phosphorus, 1–16 SPM for total nitrogen, 5–25 SPM for turbidity, and 1–3 SPM for electrical conductivity. Using a daily composite sample (4 subsamples per composite) instead of discrete samples was shown to maintain the same accuracy and confidence standards, while reducing the required sample number by up to 50%. This study emphasizes the value of a statistical approach for evaluating water quality monitoring strategies, and provides a framework through which cost–benefit analysis can be implemented in the development of monitoring plans.

Introduction

The 2002 National Water Quality Inventory of the Environmental Protection Agency (EPA) identified agricultural non-point source (NPS) pollution as the leading cause of water quality impairment to rivers and lakes in the U.S. (U.S. - EPA, 2002). On irrigated lands, much of the NPS pollution is delivered to surface waters from tailwaters originating from gravity flow (flood or furrow) irrigation methods. Currently about 94,000 km2, 44% of the irrigated land area in the U.S., utilizes gravity flow irrigation (USDA-ASS, 2002). Due to its diffuse nature, agricultural return flows have remained largely unregulated. In 2003, California began the process of regulating agricultural water dischargers through adoption of the Irrigated Lands Conditional Waiver's Program (ILCWP). The ILCWP mandates that individual landowners or coalition groups develop monitoring programs to document that their contribution of NPS pollutants will not negatively impact surface waters. The current version of the ILCWP requires collection and analysis of one grab sample per month throughout the year (California Regional Water Quality Control Board, Central Valley Region, 2008). As regulation of agricultural NPS pollution becomes a more widespread reality, it is essential that regulators and growers have appropriate information for developing monitoring programs that: (i) document existing background conditions, (ii) identify exceedences of water quality constituents, (iii) verify the effectiveness of mitigation strategies, and (iv) are economically feasible to implement.

The aim of regulatory monitoring is to assess compliance with water quality objectives, usually concentrations or mass loading rates, for a given water body. However, most water quality monitoring programs have been designed on an arbitrary, rather than a statistically defensible basis (Strobl and Robillard, 2008). Proper evaluation of compliance is crucial, as growers may be unfairly punished for “false exceedances”, and the efficacy of the program is compromised if exceedances go undetected. The frequency of sampling necessary to accurately characterize water quality is dependent on the statistical distribution of the monitoring data (e.g., seasonal peaks, distribution, variance, and degree of autocorrelation) (Valiela and Whitfield, 1989). In many cases, cost constraints severely limit sampling frequency, despite the fact that accuracy and precision are a direct function of sampling frequency (Moustafa and Havens, 2001). A rational criterion for selecting sampling frequencies for regulatory monitoring is to choose a large enough sample number, based on statistical parameters (e.g., variance), to achieve a reasonably small and uniform confidence interval about the mean value (Loftis and Ward, 1980a).

Water quality monitoring involves sampling a “population” that is changing over time. In irrigation tailwater systems, sample statistics (e.g., sample mean) computed from water quality data are affected by: (i) random changes induced by irrigation timing and amount, fertilizer application, contributions from specific fields, etc., (ii) seasonal changes resulting from crop rotation, fertilizer application, plant nutrient demands, etc., and (iii) serial correlation of data (Loftis and Ward, 1980b). Given the diversity of cropping systems, nutrient management, irrigation practices, soils, and watershed size, it is difficult to recommend a universally acceptable monitoring program that transcends all water quality constituents of concern (e.g., nutrients, sediments, salts, and pesticides). There is currently a paucity of data quantifying the variability of water quality contaminants in agricultural tailwaters. Thus, it is generally not possible to evaluate the effectiveness of monitoring programs.

A large portion of the cost of monitoring is related directly to the collection and processing of water samples, so it is important to devise a monitoring scheme that minimizes sample number while preserving accuracy. Methods such as time-composite samples can be used to capture variability without increasing sample number (Moustafa and Havens, 2001). Continuous monitoring for certain constituents, such as salt (specific conductance) and sediment (turbidity) is feasible using microprocessor controlled sensors. However, the technology to quantify many pollutants (e.g., pesticides, nutrients) at a high frequency is either not available or prohibitively expensive. However, in some cases it may be possible to determine an easily measured proxy that displays a strong correlation to more difficult to measure constituents (e.g., turbidity versus total phosphorus, specific conductivity versus nitrate).

The goal of this study was to provide a statistical basis to quantify the variability of selected water quality constituents in five agricultural watersheds in the San Joaquin Valley, California, for the purpose of optimizing monitoring protocols both in terms of cost and accuracy. The number of samples necessary to calculate seasonal mean concentrations within given confidence bounds was evaluated for various sampling strategies (composite versus grab sampling). This is the first study of its type for California irrigation tailwaters, and therefore, it provides information critical for evaluation of the current ILCWP monitoring requirements, as well as a template for regulators and growers to design economically feasible and effective monitoring programs.

Section snippets

Study sites

Two sets of agricultural watersheds in California's San Joaquin Valley (SJV) were studied at two different temporal monitoring resolutions. Three small watersheds (<5000 ha) were monitored by the University of California, Davis (UC Davis) at a high frequency (6 h to daily), and two large watersheds (86 and 1245 km2) were monitored by the U.S. Geological Survey and UC Davis (USGS-UC Davis) at a low frequency (biweekly to monthly) over a decade. The high-resolution data were used to evaluate

Temporal variability

Temporal variation in water quality was considerably different among sites. Time-series plots of TN (Fig. 2) are shown to illustrate the magnitude of variability within a given site, and the heterogeneity of variability among sites. TN was highly variable at W-1 throughout both the 2006 and 2007 irrigation seasons, and showed the greatest range of TN values, <0.01–60 mg L−1 in 2006 and <0.01–40 mg L−1 in 2007. W-1 TN values fluctuated on short-time scales, often more than 20 mg L−1 in a 24-h period (

Conclusions

The dynamic nature of water quality variability in agricultural tailwaters in the San Joaquin Valley poses unique challenges for regulators to design and implement effective monitoring programs that are economically feasible. The current ILCWP takes a first step towards monitoring agricultural tailwaters. Results from this study show that the mandated monthly sampling frequency was grossly inadequate in addressing the short-term water quality variability inherent in tailwater systems with

Acknowledgements

Funding for this project has been provided in part through an agreement with the California State Water Resources Control Board. The contents of this document do not necessarily reflect the views and policies of the State Water Resources Control Board, nor does mention of trade names or commercial products constitute endorsement or recommendation for use. We gratefully acknowledge the assistance of Jiayou Deng, Tony Orozco, Jon Maynard, and Jeannie Evatt.

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