ALBERT

Notes: Abstract Intervention analysis is a relatively new branch of time series analysis. The power of this technique, which gives the probability that changes in mean level can be distinguished from natural data variability, is quite sensitive to the way the data are collected. The principal independent variables influenced by the data collection design are overall sample size, sampling frequency, and the relative length of record before the occurrence of the event (intervention) that is postulated to have caused a change in mean process level. For three of the four models investigated, data should be collected so that the post-intervention record is substantially longer than the pre-intervention record. This is in conflict with the intuitive approach, which would be to collect equal amounts of data before and after the intervention. The threshold (minimum) level of change that can be detected is quite high unless sample sizes of at least 50 and preferably 100 are available; this minimum level is dependent on the complexity of the model required to describe the response of the process mean to the intervention. More complex models tend to require larger sample sizes for the same threshold detectable change level. Uniformity of sampling frequency is a key consideration. Environmental data collection programs have not historically been oriented toward data analysis using time series techniques, thus eliminating a potentially powerful tool from use in many environmental assessment applications.

Notes: Monitoring of water supply wells has long been a responsibility of public health agencies. With the exception of large municipal water supply systems, most monitoring has been bacteriological, and sampling priority and frequency have been governed by the size of the water supply system. Current concerns about the health effects of agricultural chemicals has created a dilemma for public health agencies, because sampling costs for trace organic chemicals are much higher than for bacteriological parameters. A three-level hierarchical strategy for jointly assessing contamination risk of water supply wells by agricultural chemicals, and prioritizing wells for sampling, is described. At the first level, a determination is made as to data needs and availability, monitoring objectives are defined, and the scope of the monitoring program is established. At the second level, risk or contamination susceptibility factors are identified, and algorithms for quantifying contamination risk as a function of the susceptibility factors are developed. At the third level, the algorithms are calibrated using pilot data, and an optimization scheme for prioritizing additional wells for sampling is developed based on either minimization of the aggregate health risk and risk uncertainty, or on information gained through sampling. The monitoring of Whatcom County, Washington, domestic wells for ethylene dibromide (EDB), a highly toxic soil fumigant whose registration was suspended in 1983 by the U.S. Environmental Protection Agency, is used as a case study for testing of Levels Two and Three of the hierarchy. In the case study, the initial data consisted of EDB concentrations and ancillary data, such as well depth and pumping rate, for 24 wells already sampled. Susceptibility factors were identified, and a Level Three algorithm was used to prioritize the next 10 wells from among 54 additional candidate wells for a second stage of sampling. The sensitivity of the monitoring well selection, and risk-benefit tradeoff, are evaluated for different monitoring budgets, risk susceptibility parameters, and risk-benefit tradeoff criteria.〈section xml:id="abs1-2"〉〈title type="main"〉SummaryThe design of sampling programs for detection of agricultural chemicals in ground water is a difficult task. A review of ground water pesticide sampling programs that have been undertaken to date indicate that a lack of direction has existed in many past studies. This lack of focus generally has resulted from failure to clearly define sampling program objectives and to relate data collection priorities to those objectives. Even in California, where a very large sampling program for DBCP was undertaken (more than 8000 wells sampled), Holden (1986) reported that due to the lack of specific study objectives, the data obtained were limited in the scope of their applicability. In this paper, a methodology has been described that provides a framework for clear definition of objectives and data needs, enabling efficient design of sampling programs. The framework consists of a three-level hierarchy. Once study objectives and data needs have been defined at the top level of the hierarchy, specific analytical methods are used (in the second level) to link well contamination probability to certain susceptibility factors and (in the third level) to select wells for sampling in a multistage sampling process.The methodology was applied to a specific problem, the contamination of drinking water in Whatcom County, Washington, by the pesticide EDB. The application study demonstrated the use of available data to estimate the probability of contamination for each well in the study area, and the use of such estimates to guide future sampling as part of a multistage, interactive sampling program. The proposed approach is generally applicable to the problem of ground water contamination by agricultural chemicals. Many pesticides and herbicides are currently of concern because of their potential presence in drinking water. Sampling programs currently being undertaken to study these problems could clearly benefit from structuring of the monitoring design decision process as proposed and demonstrated.

Notes: The performance of a simplified water resource system consisting of a single surface reservoir and adjacent aquifer storage operated as a coupled flow buffering device is investigated on an annual scale to provide insight into the most important physical and climatic (streamflow) parameters governing cyclic storage performance. The hypo thetical system is fully characterized by aquifer capacity, pumping and recharge capacity, surface storage size, annual demand, and reservoir inflow statistics, including annual mean, coefficient of variation, skew coefficient, lag one correlation coefficient, and Hurst coefficient. System performance under a range of these parameters is reviewed via Monte Carlo simulation; for the cases considered system performance is almost always limited by total system storage (sum of surface and aquifer storage). A preliminary economic analysis indicates that the cost of providing flow buffering via development of subsurface storage is about an order of magnitude less than for surface storage in the cases considered.

Notes: : National and state fixed station stream quality monitoring networks have now been in existence for over ten years. The resulting data bases provide opportunities and challenges for statistical trend assessment. Although nonparametric tests have been developed that are well suited to such problems, the interpretation of variations in trend significance between seasons and variables remains a problem. One recently developed test is based on the sum of Mann-Kendall statistics over seasons or variables, with the test statistic variance computed as the sum of the covariances of the individual Mann-Kendall statistics. In this method, up- and downtrends can cancel, giving an overall indication of no trend. A related test which is sensitive to trend regardless of direction has been shown to behave poorly for typical stream quality record lengths. An alternative formulation which is sensitive to up- and downtrends and has power approaching that of the covariance sum method, is described. In addition, a variation of a contrast test for discriminating trend directions and magnitudes among variables or seasons where correlation between seasons or variables is present is described, and tests of its performance reported.

Notes: . A relatively straightforward illustration of the potential uses of State Estimation techniques in water resources modeling is given. Background theory for Linear and Extended Kalman Filters is given; application of the filter techniques to modeling BOD and oxygen deficit in a stream illustrates the importance of model conceptualization, model completeness, uncertainty in model dynamics and incorporation of measurements and measurement errors. Potential applications of state estimation techniques to measurement system design; model building, assessment and calibration; and data extension are explored.

Notes: [Auszug] Hydrologists yearn above all to close Earth's water balance. This is essentially a housekeeping task: to measure precisely where and in what quantities Earth stores water, and how the water moves between those stores. Constant changes, both natural and anthropogenic, to the global water cycle make ...