CONFLICTING PERSPECTWES ABOUT DETECTION LIMITS AND ABOUT THE CENSORING OF ENVIRONMENTAL DATA1

ABSTRACT: Current conventions for reporting analytical results from environmental samples brings the objectives of laboratory scientists into conflict with those of environmental scientists. The objective of chemical analyses is to provide estimates of the true composition of samples. Reported results must reflect the analytical uncertainty. Current conventions require left-censoring of those results below the Limit of Detection. The objective of statistical interpretation of environmental data is to provide estimates of the characteristics of ecosystems. Such statistical analyses are often confounded by left-censoring of analytical results. We review the different points of view and propose a compromise which recognizes these conflicting perspectives.     

SOURCES OF VARIABILITY IN PHOSPHORUS AND CHLOROPHYLL AND THEIR EFFECTS ON USE OF LAKE SURVEY DATA1

Summer lake survey measurements of total phosphorus (TP) and chlorophyll a (CHLa) from 188 reserviors and natural lakes in the midwest were analyzed to determine the magnitude of major sources of variability. Median variance among replicate samples collected at the same location and time was about 7-8 percent of the mean for both TP and CHLa. Median observed temporal variability within summers was 27 percent of the mean for TP and 45 percent of the mean for CHLa. Median values of year-to-year variance in average TP and CHLa were 22 percent and 31 percent of the mean, respectively. A range of approximately two orders of magnitude was observed among individual estimates of variance in each of these categories. The magnitude of observed temporal variability was affected only slightly by variance among replicate samples on individual days and was weakly correlated with the length of time during which samples were collected from individual lakes. Observed temporal variation was similar between reservoirs and natural lakes when variances were calculated with logtransformed data. The magnitude of temporal and year-to-year variance can severely limit the power of statistical comparisons of TP and CHLa means, but has less effect on establishing relative rankings of lake means, Sources and relative magnitude of variability are important in the use of TP and CHLa data in regression models and in the planning of lake surveys and subsequent data analysis.     

Near-term forecasts of stream temperature using deep learning and data assimilation in support of management decisions

Deep learning (DL) models are increasingly used to make accurate hindcasts of management-relevant variables, but they are less commonly used in forecasting applications. Data assimilation (DA) can be used for forecasts to leverage real-time observations, where the difference between model predictions and observations today is used to adjust the model to make better predictions tomorrow. In this use case, we developed a process-guided DL and DA approach to make 7-day probabilistic forecasts of daily maximum water temperature in the Delaware River Basin in support of water management decisions. Our modeling system produced forecasts of daily maximum water temperature with an average root mean squared error (RMSE) from 1.1 to 1.4°C for 1-day-ahead and 1.4 to 1.9°C for 7-day-ahead forecasts across all sites. The DA algorithm marginally improved forecast performance when compared with forecasts produced using the process-guided DL model alone (0%–14% lower RMSE with the DA algorithm). Across all sites and lead times, 65%–82% of observations were within 90% forecast confidence intervals, which allowed managers to anticipate probability of exceedances of ecologically relevant thresholds and aid in decisions about releasing reservoir water downstream. The flexibility of DL models shows promise for forecasting other important environmental variables and aid in decision-making.     

DETERMINING SIGNIFICANCE AND PRECISION OF ESTIMATED PARAMETERS FOR RUNOFF FROM SEMIARID WATERSHEDS1

ABSTRACT Significant parameters for predicting thunderstorm runoff from small semiarid watersheds are determined using data from the Walnut Gulch watershed in southern Arizona. Based on these data, thunderstorm rainfall is dominant over watershed parameters for predicting runoff from multiple linear regression equations. In some cases antecedent moisture added significantly to the models. A technique is developed for estimating precision of predicted values from multiple linear regression equations. The technique involves matrix methods in estimating the variance of mean predicted values from a regression equation. The estimated variance of the mean predicted value is then used to estimate the variance of an individual predicted value. A computer program is developed to implement these matrix methods and to form confidence limits on predicted values based on both a normality assumption and the Chebyshev inequality.     

Applying Statistical Causal Analyses to Agricultural Conservation: A Case Study Examining P Loss Impacts

Estimating the effect of agricultural conservation practices on reducing nutrient loss using observational data can be confounded by factors such as differing crop types and management practices. As we may not have the full knowledge of these confounding factors, conventional statistical meta‐analysis methods can be misleading. We discuss the use of two statistical causal analysis methods for quantifying the effects of water and soil conservation practices in reducing P loss from agricultural fields. With the propensity score method, a subset of data was used to form a treatment group and a control group with similar distributions of confounding factors. With the multilevel modeling method, data were stratified based on important confounding factors, and the conservation practice effect was evaluated for each stratum. Both methods resulted in similar estimates of the conservation practice effect (total P load reduction avg. ~70%). In addition, both methods show evidence of conservation practices reducing the incremental increase in total P export per unit increase in fertilizer application. These results are presented as examples of the types of outcomes provided by statistical causal analyses, not to provide definitive estimates of P loss reduction. The enhanced meta‐analysis methods presented within are applicable for improved assessment of agricultural practices and their effects and can be used for providing realistic parameter values for watershed‐scale modeling.     

An analysis of variables influencing the material composition of automobiles

The use of materials is studied broadly, because of the environmental problems related to extraction, production, consumption and waste treatment. The use and substitution of materials in products is therefore a relevant issue for environmental policy making. Studies have been done to describe the material flow or to measure the impact of materials or products on the environment. However, these studies do not often consider economic, substitution and dynamic aspects of material flows. Other studies on material flows analyse the relationship between the use of materials and economic growth, but they do not consider substitution between materials. For environmental policy making economic, technological and environmental aspects of the use of materials need to be considered. Especially, substitution of materials is important. In various countries material and product policies are imposed on a variety of materials and products. For evaluation of these policies their environmental and economic effects need to be examined in detail. This study aims to analyse the economic and technological factors influencing the use of materials and the substitution between different materials dynamically. The goal is to obtain an insight in the effect that material levies may have on the use and substitution of materials. The statistical analysis is performed on a specific product-group because decisions on the use of materials are taken on a product-level. The case study is performed on automobiles. The results show that the material use is largely an autonomous development. The price of aluminium has a positive, significant effect on the use of that material. The price of plastics has a positive, but not significant effect on the use of plastics. Reasons may be that the costs of a raw material are small relative to the processing costs, and that the production process can only be changed slowly. Other factors, like competitiveness and consumers' tastes, may be more important for substitution. This implies that levies or subsidies on certain materials is not a promising policy to change the use of materials. Besides time, there are two other factors that have a positive and significant relationship with the use of aluminium and plastics: the fuel efficiency, which is the distance driven divided by the energy used; and, the road tax, which depends on the weight of a car. However, these effects are caused by their positive relationship with time. The main conclusion of the case study is that imposing a levy on materials may not have the desired or expected effect of reduction in material use.     

SMOOTHED DATA AND GRADIENTS USING SLIDING POLYNOMIALS WITH OPTIONAL CONTROLS1

ABSTRACT: Sliding polynomials differ from other piecewise interpolation and smoothing methods in their functional continuity at the nodes. This functional continuity was used to establish optional spacing of nodes and optional boundary controls in data smoothing while still maintaining mathematically continuous rates or gradients. Cyclic as well as noncyclic data can be smoothed. Variance of the individual nodal values. derived through least-squares optimization, can be calculated using the rigorously determined weighting coefficients between data points and nodes. Such nodal variances are estimates of localized uncertainty in the data which complement the localization of smoothing through use of piecewise functions. Choice of controls in smoothing and calculation of variance have been incorporated in a computer program for user convenience.     

The use of valuation and weighting sets in environmental impact assessment

In environmental impact assessment of policies and product design results need to be presented in a comprehensible way to make alternatives easily comparable. One way of doing this is to aggregate results to a manageable set by using weighting methods. Valuing the environmental impacts can be a challenging task that can also be quite time-consuming. To the aid of practitioners, several weighting sets with readily available weights have been developed over the last decade. The scope and coverage of these sets vary, and it is important to be aware of the implications of using different valuation methods and weighting sets.The aim of this paper is to map valuation and weighting techniques and indicate the methods that are suitable to use, depending on the purpose of the analysis. Furthermore, we give an overview over sets of generic values or weights and their properties, and give an illustration of how different sets may influence the results. It is very useful to use several weighting sets, and discuss the results thoroughly. It is often a very interesting and fruitful exercise to see if and how the results differ, why they differ, and which one seems to be the best alternative to base any recommendation on.The example provided in this article demonstrates that looking at aggregate results is not enough. Since many weighting sets are not sufficiently transparent as to how they are constructed and what their impact categories actually include, a general recommendation is to provide weighting sets with a declaration of content, providing a clear picture of what is included and what is not, and a recommendation of suitable uses of the weighting set.     

Prediction of diffuse solar radiation using satellite data

In the project of the solar systems, the values of the solar radiation of a region must be known. The global solar radiation measurements are performed by the Turkish Meteorological Service in Turkey, while the diffuse solar radiation measurements are unavailable. In this study, some new models for predicting the monthly average daily diffuse solar radiation on a horizontal surface for Erzincan, Turkey are developed using satellite data. The evaluation of the models is carried out with the statistical analysis methods of mean bias error (MBE), root mean square error (RMSE) and correlation coefficient (r). The results are proved that the correlation equations obtained in here can be used to forecast diffuse solar radiation reasonably well.     

QUANTIFICATION FOR VISUALIZATION OF TIME TREND1

ABSTRACT: A method for quantifying fluctuations in time-series data was developed and tested to aid the process of visualization. The methodology is based on free-form sliding polynomials and identifies (a) short-period variability about the mean value, (b) a long-term trend or cycle, and (c) random errors residual to these two structured components. Consistent results were obtained for designed synthetic data and natural data from seven sites in Georgia. Statistics of fit of the analytical model for the natural data were not significant on a site-by-site basis. An unexpected finding for the study was obtained when the statistical results for the seven data sets for temperature were pooled. The smoothing model yielded consistent long-term trends even though the individual station results were not significant. Also, the correlation coefficients, while low, showed a statistically significant trend toward higher values toward the northwest and away from the Georgia coast line. This study thus supports the concept that multiple-site, and regionally based, analyses are necessary for the detection of trends. Secondarily, such consistency of results strengthens the conclusion that the proposed smoothing method is an effective procedure in the presence of varying amounts of random content in the natural data sets.     

ANTIDEGRADATION WATER QUALITY CRITERIA FOR THE DELAWARE RIVER: A DISTRIBUTION-FREE STATISTICAL APPROACH1

ABSTRACT: Existing water quality for the Middle Delaware Scenic and Recreational River is significantly better than is required by current standards, leaving a potential for degradation. A method is presented for deriving candidate antidegradation water quality criteria for this segment of the Delaware River using statistical analysis of historic (ambient) water quality data. Data for 34 water quality parameters are first evaluated for data density, serial correlation, trend, seasonality, and other factors. These preliminary analyses are based on observation of data plots and application of distribution-free statistical techniques that are insensitive to outliers and are robust to relatively mild violations of basic assumptions. Data for 12 of the parameters have sufficient density for further analysis and can reasonably be modeled as independent and identically distributed over time (either seasonally or for the entire data sets). For these 12 parameters, distribution-free statistical methods are developed and used to derive intervals within which there is high confidence (usually greater than 95 percent) that the quantiles with potential use as anti-degradation criteria (the 0.85^th, 0.90^th, and 0.95^th quantiles in this study) for a particular parameter lie.     

ANTIDEGRADATION WATER QUALITY CRITERIA FOR THE DELAWARE RIVER: A DISTRIBUTION-FREE STATISTICAL APPROACH1

ABSTRACT: Existing water quality for the Middle Delaware Scenic and Recreational River is significantly better than is required by current standards, leaving a potential for degradation. A method is presented for deriving candidate antidegradation water quality criteria for this segment of the Delaware River using statistical analysis of historic (ambient) water quality data. Data for 34 water quality parameters are first evaluated for data density, serial correlation, trend, seasonality, and other factors. These preliminary analyses are based on observation of data plots and application of distribution-free statistical techniques that are insensitive to outliers and are robust to relatively mild violations of basic assumptions. Data for 12 of the parameters have sufficient density for further analysis and can reasonably be modeled as independent and identically distributed over time (either seasonally or for the entire data sets). For these 12 parameters, distribution-free statistical methods are developed and used to derive intervals within which there is high confidence (usually greater than 95 percent) that the quantiles with potential use as anti-degradation criteria (the 0.85^th, 0.90^th, and 0.95^th quantiles in this study) for a particular parameter lie.     

ESTIMATING THE MEAN AND VARIANCE OF CENSORED PHOSPHORUS CONCENTRATIONS IN FLORIDA RAINFALL1

ABSTRACT: The total phosphorous (TP) concentrations in the South Florida rainfall have been recorded in weekly intervals with a detection limit (D_L) of 3.5 μg/L. As a large amount of the data is reported as below the D_L, appropriate statistical methods are needed for data analysis. Thus, an attempt was made to identify an appropriate method to estimate the mean and variance of the data. In particular, a method to separate the statistics for the below D_L portion from the estimated population statistics is proposed. The estimated statistics of the censored data are compared with the statistics of the uncensored data available from the recent years’ laboratory records. It was found that the one-step restricted maximum likelihood method is the most accurate for the wet TP data, and that the proposed method to combine the estimated statistics for TP < D_L portion and the sample statistics for TP ≥ D_L portion improves estimates compared to the conventional maximum likelihood estimates.     

Spatial analysis of resources and environmental carrying capacity in Iran

Urbanization and mass movement of the population from rural areas and small cities to megacities have led to environmental, economic, and social problems in Iran. In dealing with these challenges, assessing resource and environmental carrying capacity (RECC) is considered an effective method to leverage space and capital to achieve sustainable development. This study aimed to rank the provincial RECC in Iran. Toward this purpose, environmental indices were generated from remotely sensed and statistical census data. Then, the provinces were scored in terms of environmental, economic, and infrastructural carrying capacities, and RECC using the mean variance analysis method. Results demonstrated that in most areas, there is no relationship between economic and infrastructural capacities and development. Statistically, a correlation coefficient of −0.53 between economic and environmental carrying capacities indicated excessive use of environmental capacities. Moreover, the spatial distribution pattern of environmental, economic, and infrastructural carrying capacity was entirely heterogeneous between the provinces; there was a northeast–southwest pattern in terms of infrastructural capacity and an economic pattern from north to south. The distribution pattern of RECC is most consistent with the environmental capacity, pointing at the high weight of the indicators of the RECC model. In conclusion, this research offers a new vision for policymakers and provides a theoretical and applicable framework for implementing sustainable strategies in land-use planning. It is recommended that the RECC concept and tools can be used not only for planning but also for measuring the efficiency of spatial development programs and establishing land balances in the region.     

AN APPLICATION OF COMPUTER GRAPHICS TO ANALYSIS OF EXTREMES1

ABSTRACT: A convenient method for the statistical analysis of hydrologic extremes is to use probability papers to fit selected theoretical distributions to extremal observations. Three commonly accepted statistical distributions of extreme hydrologic events are: the double exponential distribution, the bounded exponential distribution, and the Log Pearson Type III distribution. In most cases, probability papers are distribution specific. But, for the Log Pearson Type III distribution, the probability paper is characterized by a population-specific parameter, namely, the coefficient of skewness. It is not practicable to procure probability papers for all possible values of this parameter. Therefore, a computer program is developed to generate population-specific probability papers and to perform statistical analysis of the data using computer graphics. Probability papers covering return periods up to 1000 years or more are generated for the three distributions mentioned above. Using a plot routine, available extremal observations are plotted on selected probability papers and a linear regression analysis is used to fit a straight line to the data. Predictions of hydrologic extremes for higher recurrence intervals can be made by extrapolating the fitted straight lines.     

Environmental mapping based on spatial variability

Environmental maps show the probable environmental states of different types of land use or development of landscape in a geographic context. Remotely sensed data are particularly efficient for environmental mapping in order to outline major environmental types. Multiple schemes of image classification used in environmental mapping are either traditionally statistical or heuristic. While the former methods do not take account of spatial variability in space and aerial data, the latter ones does not lend themselves to optimal solutions we present. Novel probabilistic models of piecewise-homogeneous images are used in environmental mapping to segment real images. The models consider both an image and a land cover map. Such a pair constitutes an example of a Markov random field specified by a joint Gibbs probability distribution of images and maps. Parameters of the model are estimated by using a stochastic approximation technique. Its convergence to the desired values is studied experimentally. Addition of spatial attributes appears to be necessary in most areas where the differences in spatial data between regions in the image occur. Experiments in generating the pairs of images and environmental maps and in segmenting the simulated as well as real images are discussed.     

Multi-criteria risk management with the use of <Emphasis Type="Italic">DecernsMCDA</Emphasis>: methods and case studies

Emerging challenges of risk management, environmental protection, and land-use planning requires integration of stakeholder values and expert judgment. The process of decision making in situation of high uncertainty can be assisted through the use of decision support systems (DSSs). Such DSSs are often based on tools for spatial data representation (GIS) and environmental models that are integrated using multi-criteria decision analysis (MCDA). This paper presents DecernsMCDA implementing all major types of multi-criteria methods and tools (AHP, MAUT, Outranking) under the same user interface. In addition to providing ability for testing model uncertainty associated with selection of specific MCDA algorithms, DecernsMCDA implements new algorithms for parameter uncertainty analysis based on probabilistic approaches and fuzzy sets. The paper illustrates application of DecernsMCDA for selecting remedial alternative at radiologically contaminated sites.     

Analyzing Human–Landscape Interactions: Tools That Integrate

Humans have transformed much of Earth’s land surface, giving rise to loss of biodiversity, climate change, and a host of other environmental issues that are affecting human and biophysical systems in unexpected ways. To confront these problems, environmental managers must consider human and landscape systems in integrated ways. This means making use of data obtained from a broad range of methods (e.g., sensors, surveys), while taking into account new findings from the social and biophysical science literatures. New integrative methods (including data fusion, simulation modeling, and participatory approaches) have emerged in recent years to address these challenges, and to allow analysts to provide information that links qualitative and quantitative elements for policymakers. This paper brings attention to these emergent tools while providing an overview of the tools currently in use for analysis of human–landscape interactions. Analysts are now faced with a staggering array of approaches in the human–landscape literature—in an attempt to bring increased clarity to the field, we identify the relative strengths of each tool, and provide guidance to analysts on the areas to which each tool is best applied. We discuss four broad categories of tools: statistical methods (including survival analysis, multi-level modeling, and Bayesian approaches), GIS and spatial analysis methods, simulation approaches (including cellular automata, agent-based modeling, and participatory modeling), and mixed-method techniques (such as alternative futures modeling and integrated assessment). For each tool, we offer an example from the literature of its application in human–landscape research. Among these tools, participatory approaches are gaining prominence for analysts to make the broadest possible array of information available to researchers, environmental managers, and policymakers. Further development of new approaches of data fusion and integration across sites or disciplines pose an important challenge for future work in integrating human and landscape components.