Post-development Auditing of EIA Predictive Techniques: A Spatial Analytical Approach

Despite widespread recognition that post-development auditing has the potential to provide feedback which could improve future Environmental Impact Assessment (EIA), there remains a paucity of research which relates specifically to the evaluation of EIA predictive techniques, with even less progress in the development of audit methodologies. This paper describes a spatial analytical approach to post-development auditing that focuses upon the identification and analysis of the residual errors between the impacts predicted at a site using a particular predictive method and the actual impacts found to occur through monitoring. For three case studies, relevant impact predictions are tested (to determine the residual errors) and statistical models of the errors are developed in order to explore factors which may explain the performance of the predictive technique. The paper then considers the broader lessons and limitations that can be drawn out from the research both for auditing and EIA practice, including feedback on predictive techniques, the potential role of scoping decisions in generating errors in impact prediction, and the implications of uncertainty over future baseline conditions for auditing and impact prediction/interpretation.     

Risk models for wildfowl diseases

The purpose of this paper is to explore the use of computer models, including artificial intelligence systems, in the context of risk assessment and management for wildfowl diseases. In particular, the paper focuses on avian cholera and botulism in U.S. wildfowl, which are a source of continuing concern to the U.S. Fish and Wildlife Service (USFWS). Severe outbreaks occur frequently, and some have been estimated to claim the lives of as many as 300 000 waterfowl. A potentially valuable support to the USFWS would be a set of models that assist in recognizing situations with high risk of an outbreak, assessing the anticipated severity of an outbreak, and advising on possible preventive measures. The USFWS has already initiated development of several potentially useful models. This paper reviews these as well as other related efforts, within the overall context of risk management for avian botulism and cholera.     

A Probabilistic,Parcel‐Level Inundation Prediction Tool for Medium‐Range Flood Forecasting in Large Lake Systems

This study contributes a bathtub‐style inundation prediction model with abstractions of coastal processes (i.e., storm surge and wave runup) for flood forecasting at medium‐range (weekly to monthly) timescales along the coastline of large lakes. Uncertainty from multiple data sources are propagated through the model to establish probabilistic bounds of inundation, providing a conservative measure of risk. The model is developed in a case study of the New York Lake Ontario shoreline, which has experienced two record‐setting floods over the course of three years (2017–2019). Predictions are developed at a parcel‐level and are validated using inundation accounts from an online survey and flyover imagery taken during the recent flood events. Model predictions are compared against a baseline, deterministic model that accounts for the same processes but does not propagate forward data uncertainties. Results suggest that a probabilistic approach helps capture observed instances of inundation that would otherwise be missed by a deterministic inundation model. However, downward biases are still present in probabilistic predictions, especially for parcels impacted by wave runup. The goal of the tool is to provide community planners and property owners with a conservative, parcel‐level assessment of flood risk to help inform short‐term emergency response and better prepare for future flood events.     

Explaining human settlement patterns in a recreational lake district: Vilas County,Wisconsin, USA

Lakeshore development in Vilas County, northern Wisconsin (USA) is heterogeneous, ranging from lakes that are surrounded by homes and commercial establishments to lakes that have no buildings on their shorelines. Development in this recreational area has increased, and since the 1960s over half of new homes have been built on the lakeshore. We examined building density around lakes in relationship to 11 variables, including in-lake, shoreline, and social characteristics. Buildings in many parts of northern Wisconsin tend to be concentrated around shorelines; in Vilas County 61% of all medium-sized buildings (our proxy for residential development) on private land were ≤100 m of a lake. The probability of development on a lake was largely related to lake surface area, with larger, more accessible lakes showing a higher probability of development. Building density along shorelines varied with travel cost, lake surface area, presence of wetlands, and extent of public land ownership. Building density was greater on larger, more accessible lakes that were surrounded by forest (as opposed to wetlands) and public lands. Gaining a more precise understanding of human settlement patterns can help direct planning and resource protection efforts to lakes most likely to experience future development.     

MAN-MADE LAKES: ATTITUDE SURVEYS OF THEIR VALUE IN RESIDENTIAL AREAS1

ABSTRACT: Attitudinal surveys of residents living near four small residential lakes were conducted to determine how they perceived the advantages and disadvantages associated with the lakes. The survey results indicate that lakes can be used as a tool for land development creating a variety of benefits and problems. Most people felt that lakes have a positive economic impact on the value of their home, are a positive factor in their decision to purchase a home, and provide them with some benefits. They were also aware that lakes create continual maintenance and water quality problems but were willing to pay for the maintenance if they had access to and could utilize the facilities associated with the lakes. Based on the results of the surveys, recommendations are formulated pertaining to the planning development and utilization of residential lakes.     

ESTIMATION OF PRE- AND POST-DEVELOPMENT NONPOINT WATER QUALITY LOADINGS1

ABSTRACT: A simple procedure for estimating pre- and post-development water quality loadings from residential communities is discussed. The procedure deals with: (a) gathering basic water quality loading numbers observed by others at several watersheds with various land uses; (b) obtaining the breakdown of proposed land uses at various phases of the community development; and (c) estimating pre- and post-development water quality loading numbers by taking the weighted average of the basic loading numbers in terms of areal coverages of different land uses at various phases of development. Results of this simplified procedure have been verified indirectly by comparing them with the estimates derived independently through a more fundamental but time-consuming approach. The procedure was used to evaluate the anticipated water quality impact of two future residential communities in South Florida by analyzing four water quality parameters: Suspended Soils (SS), Total Nitrogen (TN), Total Phosphorus (TP), and Biochemical Oxygen Demand (BOD₅). Although computation of loading numbers with mixed land uses is not an exact science at the present time, the recommended approach appears to be the best available technique to analyze quantitatively the water quality-quantity-land use interactions.     

A STATISTICAL MODEL FOR SMALL LAKE WATER QUALITY MANAGEMENT1

ABSTRACT: Phosphorus loading tolerances of small lakes are analyzed by means of a statistical model of lake eutrophication based upon the work of Vollenweider and Dillon. Using a sample of 195 midwestern and eastern U. S. lakes, it was found that Vollenweider and Dillon's method of predicting the trophic status of relatively deep, slow-flushing lakes can be applied to shallower lakes with much shorter retention times. The statistical model used to replicate the results of Vollenweider and Dillon is stated in detail, for convenience of application to small lake water quality management problems. The model extends the Vollenweider and Dillon results by associating each alternative phosphorous loading with a probability that a given lake can achieve or maintain noneutrophis status. It is applicable to lakes for which only minimal data are available. The major policy conclusion is that the highly variable tolerance for phosphorus loading must be considered in legislating efficient effluent limitations. The paper concludes with a comparison to a recent contribution employing a similar approach.     

Landscape-Based Assessment of Human Disturbance for Michigan Lakes

Assessment of lake impairment status and identification of threats’ type and source is essential for protection of intact, enhancement of modified, and restoration of impaired lakes. For regions in which large numbers of lakes occur, such assessment has usually been done for only small fractions of lakes due to resource and time limitation. This study describes a process for assessing lake impairment status and identifying which human disturbances have the greatest impact on each lake for all lakes that are 2 ha or larger in the state of Michigan using readily available, georeferenced natural and human disturbance databases. In-lake indicators of impairment are available for only a small subset of lakes in Michigan. Using statistical relationships between the in-lake indicators and landscape natural and human-induced measures from the subset lakes, we assessed the likely human impairment condition of lakes for which in-lake indicator data were unavailable using landscape natural and human disturbance measures. Approximately 92% of lakes in Michigan were identified as being least to marginally impacted and about 8% were moderately to heavily impacted by landscape human disturbances. Among lakes that were heavily impacted, more inline lakes (92%) were impacted by human disturbances than disconnected (6%) or headwater lakes (2%). More small lakes were impacted than medium to large lakes. For inline lakes, 90% of the heavily impacted lakes were less than 40 ha, 10% were between 40 and 405 ha, and 1% was greater than 405 ha. For disconnected and headwater lakes, all of the heavily impacted lakes were less than 40 ha. Among the anthropogenic disturbances that contributed the most to lake disturbance index scores, nutrient yields and farm animal density affected the highest number of lakes, agricultural land use affected a moderate number of lakes, and point-source pollution and road measures affected least number of lakes. Our process for assessing lake condition represents a significant advantage over other routinely used methods. It permits the evaluation of lake condition across large regions and yields an overall disturbance index that is a physicochemical and biological indicator weighted sum of multiple disturbance factors. The robustness of our approach can be improved with increased availability of high-resolution disturbance datasets.     

An evaluation of the predictive performance of distributional models for flora and fauna in north-east New South Wales.

To use models of species distributions effectively in conservation planning, it is important to determine the predictive accuracy of such models. Extensive modelling of the distribution of vascular plant and vertebrate fauna species within north-east New South Wales has been undertaken by linking field survey data to environmental and geographical predictors using logistic regression. These models have been used in the development of a comprehensive and adequate reserve system within the region. We evaluate the predictive accuracy of models for 153 small reptile, arboreal marsupial, diurnal bird and vascular plant species for which independent evaluation data were available. The predictive performance of each model was evaluated using the relative operating characteristic curve to measure discrimination capacity. Good discrimination ability implies that a model's predictions provide an acceptable index of species occurrence. The discrimination capacity of 89% of the models was significantly better than random, with 70% of the models providing high levels of discrimination. Predictions generated by this type of modelling therefore provide a reasonably sound basis for regional conservation planning. The discrimination ability of models was highest for the less mobile biological groups, particularly the vascular plants and small reptiles. In the case of diurnal birds, poor performing models tended to be for species which occur mainly within specific habitats not well sampled by either the model development or evaluation data, highly mobile species, species that are locally nomadic or those that display very broad habitat requirements. Particular care needs to be exercised when employing models for these types of species in conservation planning.     

A Hydrological Model for Predicting the Effects of Dams on the Shoreline Vegetation of Lakes and Reservoirs

/ The species richness of shoreline vegetation of unregulated lakes in Nova Scotia, Canada, is known to increase as a function of catchment area, a topographic variable governing water level fluctuations. Predictions based on catchment area however, fail to account for richness patterns at the margins of lakes enlarged by dams. Here, we compare the vegetation and hydrological regimes of regulated and unregulated systems. Hydrological regimes of regulated systems deviated from natural systems of similar catchment area by being either hypovariable or hypervariable for both within-year and among-year fluctuations in water level. Plant communities of dammed systems were less diverse, contained more exotic species, and were, with one exception, devoid of rare shoreline herbs. Data from "recovering," or previously dammed systems indicated that shoreline communities can be restored upon return of the appropriate hydrological regime. Using observed within-year and among-year water level fluctuation data, we propose a general model for the maintenance or restoration of diverse herbaceous wetlands on shorelines of temperate lakes or reservoirs. Managers can manipulate the within-year water level variation within prescribed limits (1-2 m), while ensuring that among-year variation (SD of summer levels) is less than 25% of within-year variation. This preliminary model is based on data from low-fertility, temperate lakes in river systems. To calibrate the model, plant community data from other regions are needed, as are long-term water-level data for unregulated lakes, data which are essential but largely lacking in many areas.KEY WORDS: Catchment area; Regulated lakes; Shoreline restoration; Rare plants; Exotic plants; Diversity     

Violation analysis for solid waste management systems: an interval fuzzy programming approach

This paper introduces a violation analysis approach for the planning of regional solid waste management systems under uncertainty, based on an interval-parameter fuzzy integer programming (IPFIP) model. In this approach, several given levels of tolerable violation for system constraints are permitted. This is realized through a relaxation of the critical constraints using violation variables, such that the model's decision space can be expanded. Thus, solutions from the violation analysis will not necessarily satisfy all of the model's original constraints. Application of the developed methodology to the planning of a waste management system indicates that reasonable solutions can be generated through this approach. Considerable information regarding decisions of facility expansion and waste flow allocation within the waste management system were generated. The modeling results help to generate a number of decision alternatives under various system conditions, allowing for more in-depth analyses of tradeoffs between environmental and economic objectives as well as those between system optimality and reliability.     

A Multilevel Model of the Impact of Farm‐Level Best Management Practices on Phosphorus Runoff1

Abstract: Multilevel or hierarchical models have been applied for a number of years in the social sciences but only relatively recently in the environmental sciences. These models can be developed in either a frequentist or Bayesian context and have similarities to other methods such as empirical Bayes analysis and random coefficients regression. In essence, multilevel models take advantage of the hierarchical structure that exists in many multivariate datasets; for example, water quality measurements may be taken from individual lakes, lakes are located in various climatic zones, lakes may be natural or man‐made, and so on. The groups, or levels, may effectively yield different responses or behaviors (e.g., nutrient load response in lakes) that often make retaining group membership more effective when developing a predictive model than when working with either all of the data together or working separately with the individuals. Here, we develop a multilevel model of the impact of farm level best management practices (BMPs) on phosphorus runoff. The result of this research is a model with parameters which vary with key practice categories and thus may be used to evaluate the effectiveness of these practices on phosphorus runoff. For example, it was found that the effect of fertilizer application rate on farm‐scale phosphorus loss is a function of the application method, the hydrologic soil group, and the land use (crop type). Further, results indicate that the most effective method for controlling fertilizer loss is through soil injection. In summary, the resultant multilevel model can be used to estimate phosphorus loss from farms and hence serve as a useful tool for BMP selection.     

Development and Comparison of Multiple Regression Models to Predict Bankfull Channel Dimensions for Use in Hydrologic Models

Channel dimensions are important input variables for many hydrologic models. As measurements of channel geometry are not available in most watersheds, they are often predicted using bankfull hydraulic geometry relationships. This study aims at improving existing equations that relate bankfull width, depth, and cross‐sectional area to drainage area (DA) without limiting their use to well‐gauged watersheds. We included seven additional variables in the equations that can be derived from data that are generally required by hydrologic models anyway and conducted several multiple regression analyses to identify the ideal combination of additional variables for nationwide and regional models for each Physiographic Division of the United States (U.S.). Results indicate that including the additional variables in the regression equations generally improves predictions considerably. The selection of relevant variables varies by Physiographic Division, but average annual precipitation (PCP) and temperature (TMP) were generally found to improve the models the most. Therefore, we recommend using regression equations with three independent variables (DA, PCP, and TMP) to predict bankfull channel dimensions for hydrologic models. Furthermore, we recommend using the regional equations for watersheds within regions from which data were used for model development, whereas in all other parts of the U.S. and the rest of the world, the nationwide equations should be given preference.     

A review on the integration of artificial intelligence into coastal modeling

With the development of computing technology, mechanistic models are often employed to simulate processes in coastal environments. However, these predictive tools are inevitably highly specialized, involving certain assumptions and/or limitations, and can be manipulated only by experienced engineers who have a thorough understanding of the underlying theories. This results in significant constraints on their manipulation as well as large gaps in understanding and expectations between the developers and practitioners of a model. The recent advancements in artificial intelligence (AI) technologies are making it possible to integrate machine learning capabilities into numerical modeling systems in order to bridge the gaps and lessen the demands on human experts. The objective of this paper is to review the state-of-the-art in the integration of different AI technologies into coastal modeling. The algorithms and methods studied include knowledge-based systems, genetic algorithms, artificial neural networks, and fuzzy inference systems. More focus is given to knowledge-based systems, which have apparent advantages over the others in allowing more transparent transfers of knowledge in the use of models and in furnishing the intelligent manipulation of calibration parameters. Of course, the other AI methods also have their individual contributions towards accurate and reliable predictions of coastal processes. The integrated model might be very powerful, since the advantages of each technique can be combined.     

Thomas Malthus,Ester Boserup,and Agricultural Development Models in the Age of Limits

Two competing models have served as the basis for agricultural development policies. One is based on observations and assumptions of The Reverend Thomas Malthus in late eighteenth century Britain, and the other from the Danish economist Ester Boserup in the mid-twentieth century. However, rational agricultural development decisions can only be made using a model that incorporates assumptions based on a technically appropriate model that takes into account the currently status of global systems. A new development model may incorporate elements of both Neo-Malthusian and Boserupian economic-demographic models, but because the world has changed substantially, it can be neither of them alone, nor a hybrid of the two models without significant expansion and refinement. The principles espoused by Malthus and Boserup can thus be used as the starting points in a dialectic argument to arrive at a new agricultural development paradigm.     

Regional Scale Stressor‐Response Models in Aquatic Ecosystems1

Abstract: A systematic method for identification and estimation of regional scale stressor‐response models in aquatic ecosystems will be useful in monitoring and assessment of aquatic resources, determination of regional nutrient criteria and for increased understanding of the differences between regions. The model response variable is chlorophyll a, a measure of algal density, while the stressors include nutrient concentrations from the USEPA Nutrient Criteria Database (NCD) for lakes/ponds and reservoirs of the continental United States. The NCD has observations for both stressors and biological responses determined using methods that are not consistently available at the continental scale. To link multiple environmental stressors to biological responses and quantify uncertainty in model predictions, we take a multilevel modeling approach to the estimation of a linear model for prediction of log Chlorophyll a using predictors log TP and log TN. The multilevel modeling approach allows us to adjust the impact of covariates at all levels (observation, higher level groups) for the simultaneous operation of contextual and individual variability in the outcome. Here, we wish to allow separate regression coefficients for inference regarding similarities and differences between each of 14 ecoregions, and between the two water‐body types, lakes/ponds and reservoirs. We are also interested in the nuisance effects of the categorical variables indicating the type of nitrogen measurements (three levels) and the type of chlorophyll a measurements (four levels) used. Model‐based determination of nutrient criteria points to an apparent incompatibility of criteria developed for nutrient stressors and eutrophication responses using current Environmental Protection Agency’s guidance.     

Seasonal leaching and biodegradation of dicamba in turfgrass

The leaching of surface-applied herbicides, such as dicamba (2methoxy-3,6-dichlorobenzoic acid), to ground water is an environmental concern. Seasonal changes in soil temperature and water content, affecting infiltration and biodegradation, may control leaching. The objectives of this study were to (i) investigate the leaching of dicamba applied to turfgrass, (ii) measure the degradation rate of dicamba in soil and thatch in the laboratory under simulated field conditions, and (iii) test the ability of the model EXPRES (containing LEACHM) to simulate the field transport and degradation processes. Four field lysimeters, packed with sandy loam soil and topped with Kentucky bluegrass (Poa pratensis L.) sod, were monitored after receiving three applications (May, September, November) of dicamba. Concentrations of dicamba greater than 1 mg L(-1) were detected in soil water. Although drying of the soil during the summer prevented deep transport, greater leaching occurred in late autumn due to increased infiltration. From the batch experiment, the degradation rate for dicamba in thatch was 5.9 to 8.4 times greater than for soil, with a calculated half-life as low as 5.5 d. Computer modeling indicated that the soil and climatic conditions would influence the effectiveness of greater degradation in thatch for reducing dicamba leaching. In general, EXPRES predictions were similar to observed concentration profiles, though peak dicamba concentrations at the 10-cm depth tended to be higher than predicted in May and November. Differences between predictions and observations are probably a result of minor inaccuracies in the water-flow simulation and the model's inability to modify degradation rates with changing climatic conditions.     

Modeling surface water critical loads with PROFILE: possibilities and challenges

The critical load concept has become a valuable tool for policymakers in the European negotiations on emission reductions. Despite the international acceptance, ongoing validation of critical load methodology is of the utmost importance to avoid a situation where the calculation results are difficult to defend. In this paper we explore the potential of using the steady state soil chemistry model PROFILE as an alternative to the Steady State Water Chemistry (SSWC) method for calculating critical loads of acidity. The hypothesis is that the uncertainty in prediction of preindustrial leaching of base cations is reduced when soil properties instead of lake chemistry are used as input data. Paleolimnological reconstructions of preindustrial lake chemistry are used to test PROFILE. As PROFILE requires soil data that are not generally available on a catchment level, we used distributions of crucial parameters from soil survey data within the vicinity of five lakes for which paleoecological pH reconstructions were available. An important concern is the characterization of catchment hydrology. A calibration of the "effective" soil depth, needed to give PROFILE predictions that coincided with paleolimnology, suggested that approximately 0.6 m of the total soil depth was hydrologically active in supplying acid neutralizing capacity (ANC) to runoff through weathering. At present, there is insufficient evidence to either recommend or reject the PROFILE model for surface water critical loads. Before such a judgement can be made, the approach presented here has to be tested for other regions, and the definition of catchment hydrology needs to be investigated further.     

A Tool for Modeling the Winter Oxygen Depletion Rate in Arctic Lakes1

Abstract: Many arctic lakes freeze completely in winter. The few that retain unfrozen water for the entire winter period serve as overwintering fish habitat. In addition to serving as fish habitat, water in arctic lakes is needed for industrial and domestic use. Permits for water extraction seek to maximize water use without impacting dissolved oxygen (DO) levels and endangering fish habitat. The relationship between lake volume, winter DO budget, and extraction of water through pumping has historically not been well understood. A management model that could estimate end‐of‐winter DO would improve our understanding of the potential impacts of different management strategies. Using under‐ice DO measurements (November to April) taken from two natural lakes and one flooded gravel mine on the North Slope of Alaska, a physically based model was developed to predict end‐of‐winter DO concentration, water‐column DO profiles, and winter oxygen depletion rate in arctic lakes during periods of ice cover. Comparisons between the measured and model‐predicted oxygen profiles in the three study lakes suggest that the depth‐based DO modeling tool presented herein can be used to adequately predict the amount of DO available in arctic lakes throughout winter.     

INPUT/OUTPUT MODELS AS DECISION AIDS FOR LAKE RESTORATION1

ABSTRACT: Over the past several years, input/output models have been used increasingly as decisionmaking aids in the design of lake restoration activities because they provide an approximation of the link between nutrient influx and lake trophic status. To evaluate the applicability of these models as design tools, a study was conducted in which “before” and “after” data were obtained for 25 lakes which experienced reductions in nutrient inflow, and comparisons were made of observed and predicted changes in lake conditions. Three input/output models were used as predictive tools to describe lake response: those reported by Dillon and Rigler (1974) and by Vollenweider (1975, 1976). Based on described trophic states of oligotrophic, mesotrophic, and eutrophic, it was found that all three models yielded accurate predictions for at least 70 percent of the study lakes. The model of Vollenweider (1976) performed slightly better than the other two (80 percent correct) on the data set studied.