Water quality monitoring—Some practical sampling frequency considerations

Loftis, Jim C.; Ward, Robert C.

doi:10.1007/bf01876889

Cited by 46 publications

(17 citation statements)

References 5 publications

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“…It should be noted that use of Equation 1 to deseason a time-series and failure to correct for serial correlation if present can lead to serious underestimation of the standard error of the trend (Loftis & Ward 1980). Data were analysed using the STATS package (MWD 1982a) and TIDEDA package (MWD 1982b) available on the IBM 3033 of the Vogel Computer Centre, MWD.…”

Section: Data Manipulationmentioning

confidence: 99%

Trends in lake Rotorua water quality

Rutherford¹

1984

New Zealand Journal of Marine and Freshwater Research

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Fourteen determinands relating to the water quality of Lake Rotorua were examined for trend over the period [1966][1967][1968][1969][1970][1971][1972][1973][1974][1975][1976][1977][1978][1979][1980][1981][1982][1983]. A linear regression package was used, but additional manipulations were performed to eliminate markedly nonnormal distributions, remove seasonality, compute monthly averages, fill gaps, and correct for serial correlation. Since lake data came from 3 different organisations, with possibly different analytical techniques, trends were calculated over 3 periods separately. Lake quality deteriorated during 1967-1970 largely because meteorological conditions favoured lake stratification. During 1978-1983, despite favourable meteorological conditions, quality again deteriorated at the same time as, and probably as a result of, an increase of available nutrient input from sewage effluent. Improved sewage nutrient removal could halt this deterioration.

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Section: Data Manipulationmentioning

confidence: 99%

Trends in lake Rotorua water quality

Rutherford¹

1984

New Zealand Journal of Marine and Freshwater Research

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“…However, recent work has contributed to our understanding of water quality as a stochastic process (that is, influenced by the random effects of nature and society). The reader is referred to Hirsch and others (1982), Lettenmaier (1976), Loftis and Ward (1980), Ponce (1980), Loftis and others (1983), and Sanders and others (1983) for discussions of specific statistical methods which are appropriate in monitoring.…”

Section: Framework For Complete Designmentioning

confidence: 98%

The “data-rich but information-poor” syndrome in water quality monitoring

Ward

Loftis

McBride

1986

Environmental Management

Self Cite

147

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/ Water quality monitoring conducted routinely over time at fixed sites has been a part of most water quality management efforts for many years. It has been assumed that such monitoring plays a major role in management. However, the lack of routine data analysis, and reporting of information derived from such analysis, points up the fact that the exact nature of the role of routine, fixed-station monitoring is poorly defined.There is a need to very clearly define this role in the design of such systems if routine monitoring is to efficiently and effectively meet the information expectations placed on it. Design of routine monitoring systems will therefore have to consider not only the where, what, and when of sampling, but also why. A framework for including the "why" of monitoring in the design process is proposed and experience with using the framework in New Zealand is discussed.Most professionals involved in the design or operation of water quality monitoring programs recognize the symptoms: file cabinets bursting with data sheets or boxes and boxes of floppy disks full of numbers; large monthly bills for sampling and laboratory analyses; observations coming in daily, weekly, or monthly and piling on top of other observations; managers, government agencies, legislative bodies, and John Q. Public constantly asking nagging questions like, "Is the water quality in our area actually getting better or worse?"; and, finally, no way to answer them. The disease is, of course, the "data-rich but information-poor" syndrome, which appears to permeate throughout the water quality management field. Many cures have been proposed by practitioners and researchers (including the authors) alike. In spite of these suggestions, water quality data often keep piling up without being utilized to provide needed information. This is indeed a precarious position for monitoring programs to be in during these days of budget slashing.The authors, frustrated by the failure of technological innovation to extract meaningful information from water quality monitoring programs, have spent the past year reviewing their own previous work and that of others in an attempt to identify the weak links in monitoring system design and to suggest ways of strengthening them. It is their hope that this fresh look at the lack-of-information problem will help to improve both well-established monitoring programs, such as those found in the United States, and new efforts, such as those anticipated for New Zealand.

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“…The first step in evaluating a supposed change involves developing estimates of the normal range of variability in the system under study (Liebetrau 1979, Loftis andWard 1980). Severa] levels of variability must be considered in developing benchmarks for an aquatic ecosystem.…”

Section: Rationalementioning

confidence: 99%

An assessment of historical change in two northern Minnesota lakes

Hargis

Shannon

1984

Environmental Management

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/ Two northern Minnesota lakes that had been studied in detail 22 years earlier (1958) were restudied to determine the extent of alteration in ecological conditions. Approximately one year after the original investigation, a coal-fired power plant, which incremented sulfate loading by about 6 kg/ha-yr, began operation nine miles away. These lakes lie within a region judged susceptible to acidic precipitation, though each lake, based on its buffering capacity, would be judged only moderately sensitive. In spite of the influence of this plant and other anthropogenic inputs, the change in lake ecology was apparently minimal. Water clarity decreased in both lakes and some alteration in zooplankton community structure was observed. The long-term utility of lake surveys depends upon how carefully and completely conditions can be reconstructed from records and reports. Past surveys generally omit measures of variability for the data, allowing only r Jalitative comparisons to be drawn. In order to juoge the graded responses of aquatic ecosystems, necessary to sound management, quantitative measures are needed.Recent studies of the apparent effects of acidic deposition on the invertebrate biota of low-alkalinity lakes have raised important questions about the stability of species composition in these systems. In effect, as studies identified areas of potential change in limnological systems, possibly attributable to changes in atmospheric inputs or other local interventions, a number of investigators have questioned whether the dominant plankton species of lake systems, and other characteristics, remain stable even in the absence of anthropogenic stresses.Thus the goal of this study was to examine whether historical lake survey information could be useful in detailing specific conditions of change, relatii~e to recent surveys. A wealth of historical survey data exists on the physical, chemical, and biological state of northern lakes , but unfortunately most of these records concern only single-event summer sampling programs. An opportunity to restudy two northern Minnesota lakes that had previously been studied on a two-week interval led to the comparisons cited here. We emphasize the obstacles encountered in drawing conclusions, based upon even this relatively detailed study from 22 years ago, in order to suggest a more rigorous basis for considering questions of chemical and biological change over time.The lakes we investigated~ Hare and Echo Lakes, Lake County, Minnesota, were selected because an unusually rich information base was available .relative to those on other lakes in the region. Each lake had been intensively sampled by the Minnesota Department of Natural Resources (MDNR) during" the spring, summer, and fall of 1958 (Micklus 1961) in

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Water quality monitoring—Some practical sampling frequency considerations

Cited by 46 publications

References 5 publications

Trends in lake Rotorua water quality

Trends in lake Rotorua water quality

The “data-rich but information-poor” syndrome in water quality monitoring

An assessment of historical change in two northern Minnesota lakes

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