REGIONAL SCALE TREND MONITORING OF INDICATORS OF TROPHIC CONDITION OF LAKES1

ABSTRACT: The U.S. Environmental Protection Agency has proposed a sample survey design to answer questions about the ecological condition and trends in condition of U.S. ecological resources. To meet the objectives, the design relies on a probability sample of the resource population of interest (e.g., a random sample of lakes) each year on which measurements are made during an index period. Natural spatial and temporal variability and variability in the sampling process all affect the ability to describe the status of a population and the sensitivity for trend detection. We describe the important components of variance and estimate their magnitude for indicators of trophic condition of lakes to illustrate the process. We also describe models for trend detection and use them to demonstrate the sensitivity of the proposed design to detect trends. If the variance structure that develops during the probability surveys is like that synthesized from available databases and the literature, then the trends in common indicators of trophic condition of the specified magnitude should be detectable within about a decade for Secchi disk transparency (0.5–1 percentiyear) and total phosphorus (2–3 percent/year), but not for chlorophyll-a (> 3–4 percent/year), which will take longer.     

DESIGN CONSIDERATIONS FOR AMBIENT STREAM QUALITY MONITORING1

ABSTRACT: Existing ambient water quality monitoring programs have resulted in data which are often unsuitable for assessment of water quality trends. A primary concern in designing a stream quality monitoring network is the selection of a temporal sampling strategy. It is extremely important that data for trend assessment be collected uniformly in time. Greatly superior trend detection power results for such a strategy as compared to stratified sampling strategies. In general, it is desirable that sampling frequencies be at least monthly but not greater than biweekly; higher sampling frequencies usually result in little additional information. An upper limit on trend detectability exists such that for both five and ten year base periods it is often impossible to detect trends in time series where the ratio of the trend magnitude to time series standard deviation is less than about 0.5. For the same record lengths trends in records with trend to standard deviation ratios greater than about one can usually be detected with very high power when a uniform sampling strategy is followed.     

CONSIDERATIONS OF SCALE IN WATER QUALITY MONITORING AND DATA ANALYSIS1

ABSTRACT: An assumption of scale is inherent in any environmental monitoring exercise. The temporal or spatial scale of interest defines the statistical model which would be most appropriate for a given system and thus affects both sampling design and data analysis. Two monitoring objectives which are strongly tied to scale are the estimation of average conditions and the evaluation of trends. For both of these objectives, the time or spatial scale of interest strongly influences whether a given set of observations should be regarded as independent or serially correlated and affects the importance of serial correlation in choosing statistical methods. In particular serial correlation has a much different effect on the estimation of long-term means than it does on the estimation of specific-period means. For estimating trends, a distinction between serial correlation and trend is scale dependent. An explicit consideration of scale in monitoring system design and data analysis is, therefore, most important for producing meaningful statistical information.     

Evaluation of a Regional Monitoring Program’s Statistical Power to Detect Temporal Trends in Forest Health Indicators

Forests are socioeconomically and ecologically important ecosystems that are exposed to a variety of natural and anthropogenic stressors. As such, monitoring forest condition and detecting temporal changes therein remain critical to sound public and private forestland management. The National Parks Service’s Vital Signs monitoring program collects information on many forest health indicators, including species richness, cover by exotics, browse pressure, and forest regeneration. We applied a mixed-model approach to partition variability in data for 30 forest health indicators collected from several national parks in the eastern United States. We then used the estimated variance components in a simulation model to evaluate trend detection capabilities for each indicator. We investigated the extent to which the following factors affected ability to detect trends: (a) sample design: using simple panel versus connected panel design, (b) effect size: increasing trend magnitude, (c) sample size: varying the number of plots sampled each year, and (d) stratified sampling: post-stratifying plots into vegetation domains. Statistical power varied among indicators; however, indicators that measured the proportion of a total yielded higher power when compared to indicators that measured absolute or average values. In addition, the total variability for an indicator appeared to influence power to detect temporal trends more than how total variance was partitioned among spatial and temporal sources. Based on these analyses and the monitoring objectives of the Vital Signs program, the current sampling design is likely overly intensive for detecting a 5 % trend·year^?1 for all indicators and is appropriate for detecting a 1 % trend·year^?1 in most indicators.     

Surface water quality and its control in a river with intensive human impacts–a case study of the Xiangjiang River,China

Surface water quality and its natural and anthropogenic controls in the Xiangjiang River were investigated using multivariate statistical approaches and a comprehensive observation dataset collected from 2004 to 2008. Cluster analysis (CA) grouped the 15 different sampling stations into five clusters with similar hydrochemistry characteristics and pollution levels. Four principal components (PCs), nutrients, heavy metals, natural components, and organic components, were extracted from the entire dataset. Comparison of the different regional characteristics of these four PCs revealed a decreasing trend for heavy metals and an increasing trend for organic factor on an annual scale, and the seasonal trend was only observed for natural factor. We also conducted analysis of variance (ANOVA) in combination with principal component analysis (PCA) to quantify the relative contribution of spatial and temporal variations to each of the four PCs. The results revealed that 62% of the contributions from the spatial sites were responsible for variations in heavy metals, while 83% of the contributions from the sampling time were responsible for natural variations observed. However, no significant spatial or temporal contributions were found to be responsible for the nutrient and organic variations. Finally, some suggestions regarding water management were put forward based on the current status and future trends of surface water quality in the Xiangjiang River.     

Determination of Frequency for Remeasuring Ground and Vegetation Cover Factor Needed for Soil Erosion Modeling

Determining a remeasurement frequency of variables over time is required in monitoring environmental systems. This article demonstrates methods based on regression modeling and spatio-temporal variability to determine the time interval to remeasure the ground and vegetation cover factor on permanent plots for monitoring a soil erosion system. The spatio-temporal variability methods include use of historical data to predict semivariograms, modeling average temporal variability, and temporal interpolation by two-step kriging. The results show that for the cover factor, the relative errors of the prediction increase with an increased length of time interval between remeasurements when using the regression and semivariogram models. Given precision or accuracy requirements, appropriate time intervals can be determined. However, the remeasurement frequency also varies depending on the prediction interval time. As an alternative method, the range parameter of a semivariogram model can be used to quantify average temporal variability that approximates the maximum time interval between remeasurements. This method is simpler than regression and semivariogram modeling, but it requires a long-term dataset based on permanent plots. In addition, the temporal interpolation by two-step kriging is also used to determine the time interval. This method is applicable when remeasurements in time are not sufficient. If spatial and temporal remeasurements are sufficient, it can be expanded and applied to design spatial and temporal sampling simultaneously.     

ESTIMATES OF MEAN CHLOROPHYLL-a CONCENTRATION: PRECISION,ACCURACY, AND SAMPLING DESIGN1

ABSTRACT: Data from 56 north-temperate lakes and reservoirs are used to develop models predicting temporal variance as a function of the mean chlorophyll-a concentration. Trophy, as estimated by mean chlorophyll-a concentration, is shown to have little effect on the sampling effort required to achieve a pre-determined level of precision for lakes sampled year-round. Collecting ten observations results in a coefficient of variation that averaged 20 percent; collecting more than ten observations yields increasingly marginal improvements in precision. The same guidelines apply to mesotrophic or eutrophic lakes sampled in the summer, whereas oligotrophic lakes sampled in the summer require fewer observations to achieve the same level of precision. The bias resulting from collecting too few observations is minimized if five or more observations are collected.     

Water quality trends at inflows to Everglades National Park, 1977-2005

Restoration of the Florida Everglades is important for the health of the natural system, including both the "River of Grass" and its downstream estuaries. Water quality improvement is one indicator of successful restoration in this complex ecosystem. Using the period of record of 1977 through 2005, we evaluated data from seven inflow sites to the Everglades National Park (ENP) for temporal trends of various forms of phosphorus (P) and nitrogen (N) and analyzed them using principal component analysis and factor analysis without flow adjustments. Locally estimated scatter plot smoothing (LOESS) trend lines identified two inflection points (three time periods) of changing trend in total P (TP) concentration at the seven sites. Results indicated that overall water quality in ENP inflow improved from 1977 to 2005, with significant downward trends in TP concentration. The overall trend ofTP is probably mediated by hydrology, which is evident by a negative relationship between flow and annual average TP concentration at the majority of stations within the available data, although additional changes in vegetation due to hydroperiod may have some effects. Total N (TN), total Kjeldahl N, and total organic N concentrations also generally decreased at inflow sites. Water quality standards for TP, TN, and NH4+ -N were exceeded at selected sites during the study period. Principle component analysis and factor analysis detected a grouping of sampling sites related to the water delivery system that could be used as indicators to better manage monitoring resources. Study results suggest that water quality data analyses could provide additional insight into the success of a restoration management plan and on how monitoring may be modified for more efficient use ofresources.     

Grouping Lakes for Water Quality Assessment and Monitoring: The Roles of Regionalization and Spatial Scale

Regionalization frameworks cluster geographic data to create contiguous regions of similar climate, geology and hydrology by delineating land into discrete regions, such as ecoregions or watersheds, often at several spatial scales. Although most regionalization schemes were not originally designed for aquatic ecosystem classification or management, they are often used for such purposes, with surprisingly few explicit tests of the relative ability of different regionalization frameworks to group lakes for water quality monitoring and assessment. We examined which of 11 different lake grouping schemes at two spatial scales best captures the maximum amount of variation in water quality among regions for total nutrients, water clarity, chlorophyll, overall trophic state, and alkalinity in 479 lakes in Michigan (USA). We conducted analyses on two data sets: one that included all lakes and one that included only minimally disturbed lakes. Using hierarchical linear models that partitioned total variance into within-region and among-region components, we found that ecological drainage units and 8-digit hydrologic units most consistently captured among-region heterogeneity at their respective spatial scales using all lakes (variation among lake groups = 3% to 50% and 12% to 52%, respectively). However, regionalization schemes capture less among-region variance for minimally disturbed lakes. Diagnostics of spatial autocorrelation provided insight into the relative performance of regionalization frameworks but also demonstrated that region size is only partly responsible for capturing variation among lakes. These results suggest that regionalization schemes can provide useful frameworks for lake water quality assessment and monitoring but that we must identify the appropriate spatial scale for the questions being asked, the type of management applied, and the metrics being assessed.     

Optimal water quality monitoring network design for river systems

Typical tasks of a river monitoring network design include the selection of the water quality parameters, selection of sampling and measurement methods for these parameters, identification of the locations of sampling stations and determination of the sampling frequencies. These primary design considerations may require a variety of objectives, constraints and solutions. In this study we focus on the optimal river water quality monitoring network design aspect of the overall monitoring program and propose a novel methodology for the analysis of this problem. In the proposed analysis, the locations of sampling sites are determined such that the contaminant detection time is minimized for the river network while achieving maximum reliability for the monitoring system performance. Altamaha river system in the State of Georgia, USA is chosen as an example to demonstrate the proposed methodology. The results show that the proposed model can be effectively used for the optimal design of monitoring networks in river systems.     

TESTING FOR TREND IN LAKE AND GROUND WATER QUALITY TIME SERIES1

ABSTRACT: The detection of gradual trends in water quality time series is increasing in importance as concern grows for diffuse sources of pollution such as acid precipitation and agricultural non-point sources. A significant body of literature has arisen dealing with trend detection in water quality variables that exhibit seasonal patterns. Much of the literature has dealt with seasonality of the first moment. However, little has been mentioned about seasonality in the variance, and its effect upon the performance of trend detection techniques. In this paper, eight methods of trend detection that arise from both the statistical literature as well as the water quality literature have been compared by means of a simulation study. Varying degrees of seasonality in both the variances and the means have been introduced into the artificial data, and the performances of these procedures are analyzed. Since the focus is on lake and ground water quality monitoring, quarterly sampling and short to moderate record lengths are examined.     

QUANTITATIVE METHODS FOR PRELIMINARY DESIGN OF WATER QUALITY SURVEILLANCE SYSTEMS1

ABSTRACT: To assure attainment and maintenance of desired water quality levels in our rivers and streams, systematic monitoring must be performed. A preliminary phase of the design of water quality surveillance systems is the specification of sampling frequencies and station locations throughout the basin; that is, the development of an adequate space/time sampling plan. The purpose of this paper is to present some quantitative methods which have been developed to identify candidate sets of sampling frequencies and station locations, and to establish priorities for implementing the different frequencies and locations. These methods are useful in the cost/effectiveness trade-off analyses in surveillance system design, and are based on the surveillance system objective of pollution abatement in which it is desired to detect violations in state-federal water quality standards. A spatial priority measure is developed which is dependent both on the water quality profile in the stream and on the information obtained from monitoring stations located in other reaches. Also, a temporal sampling priority rating is presented which is a measure of the effectiveness of the surveillance system with respect to its ability to detect the violations in the standards. To illustrate the quantitative methods, the procedures are applied to the Wabash River Basin.     

基于MODIS数据的太湖蓝藻水华时空分布规律研究

利用EOS/MODIS遥感影像数据,采用蓝藻水华分级评价方法和蓝藻水华发生频率分析方法,对2010年～2013年不同级别太湖蓝藻水华时空分布规律进行分析和研究,以期为太湖蓝藻水华预警、监控、治理工作提供技术支持.结果表明：（1） 2010年～2013年,全太湖共发生蓝藻水华354次,其中小型水华发生次数最多,蓝藻水华级别越高,发生次数越少.（2）年际变化上,蓝藻水华发生次数总体趋于平稳,蓝藻水华发生呈现“小型多发、中大型少发、重大型偶发”趋势,蓝藻水华发生规模呈显著缩小趋势.（3）月际变化上,蓝藻水华主要发生在8 ～10月,8月和9月尤其是太湖蓝藻水华的高发月份.发生规模上,4～6月蓝藻水华发生规模较小,7月开始规模逐步扩大,到9月达到顶峰,10月规模有所缩小.（4）空间变化上,太湖西部沿岸是太湖蓝藻水华首次爆发最频繁的水域.从空间分布频率来看,太湖西部沿岸区尤其宜兴沿岸是蓝藻水华爆发频率最高的水域.     

Optimizing Liquid Effluent Monitoring at a Large Nuclear Complex

Effluent monitoring typically requires a large number of analytes and samples during the initial or startup phase of a facility. Once a baseline is established, the analyte list and sampling frequency may be reduced. Although there is a large body of literature relevant to the initial design, few, if any, published papers exist on updating established effluent monitoring programs. This paper statistically evaluates four years of baseline data to optimize the liquid effluent monitoring efficiency of a centralized waste treatment and disposal facility at a large defense nuclear complex. Specific objectives were to: (1) assess temporal variability in analyte concentrations, (2) determine operational factors contributing to waste stream variability, (3) assess the probability of exceeding permit limits, and (4) streamline the sampling and analysis regime. Results indicated that the probability of exceeding permit limits was one in a million under normal facility operating conditions, sampling frequency could be reduced, and several analytes could be eliminated. Furthermore, indicators such as gross alpha and gross beta measurements could be used in lieu of more expensive specific isotopic analyses (radium, cesium-137, and strontium-90) for routine monitoring. Study results were used by the state regulatory agency to modify monitoring requirements for a new discharge permit, resulting in an annual cost savings of US $223,000. This case study demonstrates that statistical evaluation of effluent contaminant variability coupled with process knowledge can help plant managers and regulators streamline analyte lists and sampling frequencies based on detection history and environmental risk.     

NONPARAMETRIC TEST PERFORMANCE FOR TRENDS IN WATER QUALITY WITH SAMPLING DESIGN APPLICATIONS1

ABSTRACT: In this paper four nonparametric tests for monotonic trend detection are compared with respect to their power and accuracy. The importance of comparing powers at equal empirical significance levels rather than nominal levels is stressed. Therefore, an appropriate graphical method is presented. The effect of the sampling frequency is also assessed using Monte Carlo simulations and a trajectory representation that visualizes the dynamics of the trade-off between the type I and type II errors. These methods are applied to compare four nonparametrical tests (seasonal Mann. Kendall, modified seasonal Mann-Kendall, covariance eigenvalue and covariance inversion) under several conditions. It is concluded with respect to the power that it is not worthwhile for the modified seasonal Mann-Kendall test applied to the AR(1) process considered in this paper to increase the sampling frequency from monthly to biweekly for detecting a monotonic trend of 5 percent, 10 percent, or 15 percent of the process variance. Under these conditions the seasonal Mann-Kendall test is highly liberal, while the covariance inversion and the covariance eigenvalue test are conservative. This research is situated in the development of an efficient sampling design for the Flemish water quality monitoring network.     

Introduction to the Biological Monitoring and Abatement Program

This paper provides an introduction to a long-term biological monitoring program and the Environmental Management special issue titled Long-term Biological Monitoring of an Impaired Stream: Implications for Environmental Management. The Biological Monitoring and Abatement Program, or BMAP, was implemented to assess biological impairment downstream of U.S. Department of Energy (DOE) facilities in Oak Ridge, Tennessee, beginning in 1985. Several of the unique aspects of the program include its long-term consistent sampling, a focus on evaluating the effectiveness of specific facility abatement and remedial actions, and the use of quantitative sampling protocols using a multidisciplinary approach. This paper describes the need and importance of long-term watershed-based biological monitoring strategies, in particular for addressing long-term stewardship goals at DOE sites, and provides a summary of the BMAP’s objectives, spatial and temporal extent, and overall focus. The primary components of the biological monitoring program for East Fork Poplar Creek in Oak Ridge, Tennessee are introduced, as are the additional 9 papers in this Environmental Management special issue.     

Application of nutrient loading models to the analysis of trophic conditions in Lake Okeechobee,Florida

Lake Okeechobee (surface area = 1830 km², mean depth = 3.5 m), the largest lake in Florida, is eutrophic and has nitrogen and phosphorus loading rates in excess of nearly all established criteria. The lake is not homogeneous regarding trophic conditions, and spatial and temporal variations occur regarding nutrient limitation. Nonetheless, phosphorus loading rate and trophic state data fit reasonably well to various input-output models developed for temperate lakes. Modification of the models by regression analysis to fit data for Florida lakes resulted in improved predictions for most parameters. Analysis of nutrient management alternatives for the lake indicates that a 75% reduction of phosphorus loading from the largest source (the Taylor Creek-Nubbins Slough watershed) would reduce the average chlorophyll a concentration by less than 20%. Complete elimination of inputs from the largest nitrogen source (the Everglades Agricultural Area) would decrease the average nitrogen concentration in the lake by about 20%. Limitations of nutrient inputoutput models regarding analysis of trophic conditions and management alternatives for the lake are discussed.     

Mesoscale survey of western and northwestern Irish lakes – Spatial and aestival patterns in trophic status and phytoplankton community structure

Inland water bodies are considered as integrated parts of the landscape and the monitoring of water quality and aquatic resources need to be addressed on a regional basis for optimal assessment and management. In this study, a simple stratified sampling scheme was applied to a mesoscale survey of western and northwestern Irish lakes, which was carried out to identify, based on the distribution patterns of phytoplankton biomass, potential associations between lake trophic state and land cover attributes. Phytoplankton community analysis was also performed to determine whether taxa associations reflected meteorology-linked aestival succession or specific spatial distributions. The assessment was based on the typology of hydrogeomorphological and land cover attributes of river catchments through ArcGIS analysis. Sampling was carried out in 50 lakes and during a 15-week period in summer 2009. Results showed a general longitudinal gradient in the trophic status of the lakes sampled, with a greater frequency of mesotrophic lakes in the eastern part of the study area where land cover is dominated by agricultural surfaces. Significant relationships (p < 0.010) were found between chlorophyll-a concentration and the proportion of river catchment surface covered by agriculture land and wetlands, findings which might be considered further as proxies for developing an eutrophication risk index. Multivariate analysis of phytoplankton community data clustered the sampled lakes into three assemblages, with ordination along axis 1 being significantly correlated to time and temperature (p < 0.006). There was greater frequency of occurrence of diatoms in lakes from cluster III (Kruskal–Wallis, p < 0.05, H = 6.34, df = 2, n = 49), concomitant to lower chlorophyll-a concentrations, lake surface temperatures and Secchi depths, reflecting meteorological conditions dominated by precipitations. Those results support the potential of mesoscale surveys to assess water quality variables and detect environmental patterns at regional scales.     

REVIEW OF A SEVENTEEN-YEAR CHLOROPHYLL RECORD AS IT PERTAINS TO THE TROPIIIC STATUS OF LAKE OKEECHOBEE,FLORIDA, USA1

ABSTRACT: A 17-year record of chlorophyll a at eight limnetic sampling stations was used to evaluate putative changes in the trophic status of Lake Okeechobee, a shallow polymictic lake located in the subtropical environment of South Florida. Significant spatial differences were observed in the temporal patterns and variability of chlorophyll a concentrations. The highest chlorophyll a values were found in the northern and northwestern regions of the lake. The center of the lake, subject to high levels of non-algal suspended solids, exhibited relatively low chlorophyll a values and coefficient of variation. The lowest chlorophyll a values were observed at the southernmost sampling station in the lake. This was also the station that showed a significant upward trend in annual mean chlorophyll a values over the 17-year period of record. Examination of the relationship between chlorophyll a and three key environmental variables (i.e., total phosphorus concentration, phosphorus loading, and lake stage) revealed significant correlations at two out of the eight stations. The overall results of this study indicate that spatial and temporal disparities in the distribution and dynamics of chlorophyll a in Lake Okeechobee mandate more temporally and spatially intense approaches to the evaluation of trophic state than used in previous studies.     

WATER QUALITY SAMPLING SCHEMES FOR VARIABLE FLOW CANALS AT REMOTE SITES1

ABSTRACT: Growing interest in water quality has resulted in the development of monitoring networks and intensive sampling for various constituents. Common purposes are regulatory, source and sink understanding, and trend observations. Water quality monitoring involves monitoring system design; sampling site instrumentation; and sampling, analysis, quality control, and assurance. Sampling is a process to gather information with the least cost and least error. Various water quality sampling schemes have been applied for different sampling objectives and time frames. In this study, a flow proportional composite sampling scheme is applied to variable flow remote canals where the flow rate is not known a priori. In this scheme, historical weekly flow data are analyzed to develop high flow and low flow sampling trigger volumes for auto‐samplers. The median flow is used to estimate low flow sampling trigger volume and the five percent exceedence probability flow is used for high flow sampling trigger volume. A computer simulation of high resolution sampling is used to demonstrate the comparative bias in load estimation and operational cost among four sampling schemes. Weekly flow proportional composite auto‐sampling resulted in the least bias in load estimation with competitive operational cost compared to daily grab, weekly grab sampling and time proportional auto‐sampling.