Sensitivity and uncertainty analysis of agro-ecological modeling for saffron plant cultivation using GIS spatial decision-making methods

The main objective of this research is to model the uncertainty associated with GIS-based multi-criteria decision analysis (MCDA) for crop suitability assessment. To achieve this goal, an integrated approach using GIS-MCDA in association with Monte Carlo simulation (MCS) and global sensitivity analysis (GSA) were applied for Saffron suitability mapping in East-Azerbaijan Province in Iran. The results of this study indicated that integration of MCDA with MCS and GSA could improve modeling precision by reducing data variance. Results indicated that applying the MCS method using the local training data leads to computing the spatial correlation between criteria weights and characteristics of the study area. Results of the GSA method also allow us to obtain the priority of criteria and identify the most important criteria and the variability of outputs under uncertainty conditions for model inputs. The findings showed that, commonly used primary zoning methods, without considering the interaction effects of variables, had significant errors and uncertainty in the output of MCDA-based suitability models, which should be minimized by the advanced complementarity of sensitivity and uncertainty analysis.     

A WATERSHED-LEVEL ECOLOGICAL RISK ASSESSMENT METHODOLOGY1

ABSTRACT: We present an ecological risk assessment methodology at the watershed level for freshwater ecosystems. The major component is a pollutant transport and fate model (a modified EUTROMOD) with an integrated uncertainty analysis utilizing a two-phase Monte Carlo procedure. The uncertainty analysis methodology distinguishes between knowledge uncertainty and stochastic variability. The model assesses the ecological risk of lentic (lake) ecosystems in response to the stress of excess phosphorus resulting in eutrophication. The methodology and model were tested on the Wister Lake watershed in Oklahoma with the lake and its trophic state as the endpoint for ecological risk assessment. A geographic information system was used to store, manage, and manipulate spatially referenced data for model input.     

Uncertainty analysis of predicted disturbance from off-road vehicular traffic in complex landscapes at fort hood

The US Army Engineering Research Development Center (ERDC) uses a modified form of the Revised Universal Soil Loss Equation (RUSLE) to estimate spatially explicit rates of soil erosion by water across military training facilities. One modification involves the RUSLE support practice factor (P factor), which is used to account for the effect of disturbance by human activities on erosion rates. Since disturbance from off-road military vehicular traffic moving through complex landscapes varies spatially, a spatially explicit nonlinear regression model (disturbance model) is used to predict the distribution of P factor values across a training facility. This research analyzes the uncertainty in this model's disturbance predictions for the Fort Hood training facility in order to determine both the spatial distribution of prediction uncertainty and the contribution of different error sources to that uncertainty. This analysis shows that a three-category vegetation map used by the disturbance model was the greatest source of prediction uncertainty, especially for the map categories shrub and tree. In areas mapped as grass, modeling error (uncertainty associated with the model parameter estimates) was the largest uncertainty source. These results indicate that the use of a high-quality vegetation map that is periodically updated to reflect current vegetation distributions, would produce the greatest reductions in disturbance prediction uncertainty.     

Prediction and uncertainty source analysis of the spatial and temporal disturbance from off-road vehicular traffic in a complex ecosystem

Vehicle use during military training activities results in soil disturbance and vegetation loss. The capacity of lands to sustain training is a function of the sensitivity of lands to vehicle use and the pattern of land use. The sensitivity of land to vehicle use has been extensively studied. Less well understood are the spatial patterns of vehicle disturbance. Since disturbance from off-road vehicular traffic moving through complex landscapes varies spatially, a spatially explicit nonlinear regression model (disturbance model) was used to predict the pattern of vehicle disturbance across a training facility. An uncertainty analysis of the model predictions assessed the spatial distribution of prediction uncertainty and the contribution of different error sources to that uncertainty.For the most part, this analysis showed that mapping and modeling process errors contributed more than 95% of the total uncertainty of predicted disturbance, while satellite imagery error contributed less than 5% of the uncertainty. When the total uncertainty was larger than a threshold, modeling error contributed 60% to 90% of the prediction uncertainty. Otherwise, mapping error contributed about 10% to 50% of the total uncertainty. These uncertainty sources were further partitioned spatially based on other sources of uncertainties associated with vehicle moment, landscape characterization, satellite imagery, etc.     

Building Consensus in Environmental Impact Assessment Through Multicriteria Modeling and Sensitivity Analysis

Multicriteria decision analysis (MCDA) increasingly is being applied in environmental impact assessment (EIA). In this article, two MCDA techniques, stochastic analytic hierarchy process and compromise programming, are combined to ascertain the environmental impacts of and to rank two alternative sites for Mexico City’s new airport. Extensive sensitivity analyses were performed to determine the probability of changes in rank ordering given uncertainty in the hierarchy structure, decision criteria weights, and decision criteria performances. Results demonstrate that sensitivity analysis is fundamental for attaining consensus among members of interdisciplinary teams and for settling debates in controversial projects. It was concluded that sensitivity analysis is critical for achieving a transparent and technically defensible MCDA implementation in controversial EIA.     

Decision making under uncertainty using PEES–fuzzy AHP–fuzzy TOPSIS methodology for landfill location selection

The location selection for landfill municipal solid waste is an important issue in waste management. Selection of the optimal location requires consideration of multiple alternative solutions and evaluation factors because of system complexity. Further, ranking of the alternative locations and selection of the most optimal and efficient locations for landfill waste are an important multi-criteria decision-making problem. In this study, the candidate locations for landfill are determined based on Political, Economical, Environmental and Social factors are assessed through decision-makers’ opinion and by the methodology that incorporates fuzzy analytic hierarchy process (fuzzy AHP) and fuzzy technique for order preference by similarity to ideal solution (fuzzy TOPSIS). Specifically, the fuzzy AHP technique is applied to determine the weights of selected criteria impacting the location selection process, and the fuzzy AHP is adapted to model the linguistic vagueness and ambiguity, which can also be expressed as triangular fuzzy numbers. Furthermore, fuzzy TOPSIS technique is employed to rank the alternative locations. The applicability of this methodology is demonstrated by a case study of landfill waste location selection in the region of Casablanca, Morocco, and the results are compared with other techniques. Finally, to complete the treatment, a sensitivity analysis is performed to examine the impact of the preferences given by decision-makers to choose the best location.     

Groundwater vulnerability assessment using an improved DRASTIC method in GIS

Groundwater management can be effectively conducted by using groundwater contamination map assessment. In this study, a modified DRASTIC approach using geographic information system (GIS) was applied to evaluate groundwater vulnerability in Kerman plain (Iran). The Wilcoxon rank-sum nonparametric statistical test was applied to modify the rates of DRASTIC. In addition, the analytic hierarchy process (AHP) method was employed to evaluate the validity of the criteria and sub criteria of all the parameters of the DRASTIC model, which proposed as an alternative treatment of the imprecision demands. The GIS offers spatial analysis in which the multi index evaluation can be effectively conducted through the AHP. The non-point source pollution was effectively determined by the modified DRASTIC method compared with the traditional method. The regression coefficient revealed the relationship between the vulnerability index and the nitrate concentration. The best result was obtained by using AHP–AHP, followed by DRASTIC–AHP, modified DRASTIC–AHP, and AHP–DRASTIC models. In this study, the DRASTIC method failed to provide satisfactory result. Additionally, by using both the original DRASTIC and the modified DRASTIC methods in the study area, AHP–AHP performed highly in the Kerman plain, suggesting that the southern and south east parts of the area considerably calls for conservation against contamination.     

A distance-based uncertainty analysis approach to multi-criteria decision analysis for water resource decision making

The choice among alternative water supply sources is generally based on the fundamental objective of maximising the ratio of benefits to costs. There is, however, a need to consider sustainability, the environment and social implications in regional water resources planning, in addition to economics. In order to achieve this, multi-criteria decision analysis (MCDA) techniques can be used. Various sources of uncertainty exist in the application of MCDA methods, including the selection of the MCDA method, elicitation of criteria weights and assignment of criteria performance values. The focus of this paper is on the uncertainty in the criteria weights. Sensitivity analysis can be used to analyse the effects of uncertainties associated with the criteria weights. Two existing sensitivity methods are described in this paper and a new distance-based approach is proposed which overcomes limitations of these methods. The benefits of the proposed approach are the concurrent alteration of the criteria weights, the applicability of the method to a range of MCDA techniques and the identification of the most critical criteria weights. The existing and proposed methods are applied to three case studies and the results indicate that simultaneous consideration of the uncertainty in the criteria weights should be an integral part of the decision making process.     

Improving the TMDL Process Using Watershed Risk Assessment Principles

Ecological risk assessment (ERA) evaluates potential causal relationships between multiple sources and stressors and impacts on valued ecosystem components. ERAs applied at the watershed scale have many similarities to the place-based analyses that are undertaken to develop Total Maximum Daily Loads (TMDLs), in which linkages are established between stressors, sources, and water quality standards, including support of designated uses. TMDLs focus on achieving water quality standards associated with attainment of designated uses. In attempting to attain the water quality standard, many TMDLs focus on the stressor of concern rather than the ecological endpoint or indicators of the designated use that the standard is meant to protect. A watershed ecological risk assessment (WERA), at least in theory, examines effects of most likely stressors, as well as their probable sources in the watershed, to prioritize management options that will most likely result in meeting environmental goals or uses. Useful WERA principles that can be applied to TMDL development include: development and use of comprehensive conceptual models in the Problem Identification step of TMDLs; use of a transparent process for selecting Numeric Targets for TMDLs based on assessment endpoints derived from the management goal or designated use under consideration; analysis of co-occurring stressors likely to cause beneficial use impairment based on the conceptual model; use of explicit uncertainty analyses in the Linkage Analysis step of TMDL development; and frequent stakeholder interactions throughout the process. WERA principles are currently most applicable to those TMDLs in which there is no numeric standard and, therefore, indicators and targets need to be developed, such as many nutrient or sediment TMDLs. WERA methods can also be useful in determining TMDL targets in situations where simply targeting the water quality standard may re-attain the numeric criterion but not the broader designated use. Better incorporation of problem formulation principles from WERA into the TMDL development process would be helpful in improving the scientific rigor of TMDLs.     

Risk and opportunity in upgrading the US drinking water infrastructure system

This paper presents a practical risk assessment methodology to provide drinking water infrastructure (DWI) decision-makers with an objective risk assessment tool. The purpose of this risk assessment tool is to maintain the desired level-of-service or systems reliability [r(f)], while managing the financial uncertainty of the expected budgetary impact within the capital improvement program (CIP). The goal of this paper is to demonstrate the value of an objective risk assessment tool for estimating the DWI decision-maker's sensitivity to the risk of systems failure (R). The objectives are to: (1) incorporate probability of systems failure [p(f)] into the CIP budgetary analysis process and (2) evaluate the affects of p(f) on the expected CIP budgetary outcome. The magnitude of the expected budgetary impact is managed through the DWI decision-maker's sensitivity to R, which is represented by the level of the rate of reinvestment (RR). The expected result of the proposed risk assessment tool demonstrates that by proactively managing R to maintain a desired r(f) will effectively manage the impact of uncertainty on the expected budgetary outcome within the CIP. The expected contribution of the practical risk assessment methodology is to provide DWI decision-makers with the ability to reduce budgetary uncertainty when allocating limited financial resources among competing operational, repair, maintenance, and expansion activities within the CIP. The conclusions of the paper reveal that if DWI decision-makers assume risk-avoidance positions through proactive asset management (AM) strategies, they will achieve positive affects on expected budgetary outcomes.     

An integrated impact assessment and weighting methodology: evaluation of the environmental consequences of computer display technology substitution

Computer display technology is currently in a state of transition, as the traditional technology of cathode ray tubes is being replaced by liquid crystal display flat-panel technology. Technology substitution and process innovation require the evaluation of the trade-offs among environmental impact, cost, and engineering performance attributes. General impact assessment methodologies, decision analysis and management tools, and optimization methods commonly used in engineering cannot efficiently address the issues needed for such evaluation. The conventional Life Cycle Assessment (LCA) process often generates results that can be subject to multiple interpretations, although the advantages of the LCA concept and framework obtain wide recognition. In the present work, the LCA concept is integrated with Quality Function Deployment (QFD), a popular industrial quality management tool, which is used as the framework for the development of our integrated model. The problem of weighting is addressed by using pairwise comparison of stakeholder preferences. Thus, this paper presents a new integrated analytical approach, Integrated Industrial Ecology Function Deployment (I2-EFD), to assess the environmental behavior of alternative technologies in correlation with their performance and economic characteristics. Computer display technology is used as the case study to further develop our methodology through the modification and integration of various quality management tools (e.g., process mapping, prioritization matrix) and statistical methods (e.g., multi-attribute analysis, cluster analysis). Life cycle thinking provides the foundation for our methodology, as we utilize a published LCA report, which stopped at the characterization step, as our starting point. Further, we evaluate the validity and feasibility of our methodology by considering uncertainty and conducting sensitivity analysis.     

PLANNING METHODOLOGY FOR ANALYSIS AND MANAGEMENT ON LAKE EUTROPHICATION1

ABSTRACT: The effects of changing nutrient inputs through land use management, waste water treatment, or effluent diversion are not clear, and managers are discovering that decisions which were effective in reversing eutrophication for one lake are often unsuccessful when applied to another. Simple empirical relationships are often used to predict the impact of management decisions. Errors in estimation could result in either substantial costs for overdesign or failure to meet desired eutrophication levels. This paper presents and illustrates a methodology to evaluate the impact of land use and water resource management decisions on lake eutrophication. The problems of worth of additional information, and uncertainty of estimates were handled within a cost-effectiveness framework. The probability of exceeding a critical level of eutrophication was considered as a measure of effectiveness. The cost criterion is the expected value of opportunity costs, costs of analysis and costs of additional information. Uncertainty analysis techniques were used to estimate the effectiveness of various management alternatives. Bayesian methods can be utilized to determine the worth of additional information. The methodology was applied to Beseck Lake, Connecticut, and the cost and effectiveness measures estimated for a number of land management alternatives. Worth of additional information was not determined in this initial effort in uncertainty analysis for lake eutrophication management.     

Groundwater Vulnerability Assessment Combining the Drastic and Dyna-Clue Model in the Argentine Pampas

Vulnerability assessment is considered an effective tool in establishing monitoring networks required for controlling potential pollution. The aim of this work is to propose a new integrated methodology to assess actual and forecasted groundwater vulnerability by including land-use change impact on groundwater quality. Land-use changes were simulated by applying a spatial dynamics model in a scenario of agricultural expansion. Groundwater vulnerability methodology DRASTIC-P, was modifyed by adding a land-use parameter in order to assess groundwater vulnerability within a future scenario. This new groundwater vulnerability methodology shows the areas where agricultural activities increase the potential level of groundwater vulnerability to pollution. The Dulce Creek Basin was the study case proposed for the application of this methodology. The study revealed that the area with Very High vulnerability would increase 20% by the year 2020 in the Dulce Creek Basin. This result can be explained by analyzing the land-use map simulated by the Dyna-CLUE model for the year 2020, which shows that the areas with increments in crop and pasture coincide with the area defined by the Very High aquifer vulnerability category in the year 2020. Through scenario analysis, land-use change models can help to identify medium or long term critical locations in the face of environmental change.     

Metapopulations in Agricultural Landscapes: A Spatially Explicit Trade-off Analysis

This paper presents a spatially explicit trade-off analysis of species conservation in agricultural areas. A spatially explicit model is presented that integrates an applied metapopulation model with a farm management model. The model is used to calculate production possibilities frontiers of net monetary benefits from agriculture and conservation of three species with different habitats. Simulations of spatial habitat allocation under European agri-environment schemes are compared to these production possibilities frontiers. The results suggest that the cost-effectiveness of current conservation policies may be greatly enhanced if the spatial dimension is considered explicitly.     

Trade-off analysis in the Northern Andes to study the dynamics in agricultural land use

In this paper we hypothesize that land use change can be induced by non-linearities and thresholds in production systems that impact farmers' decision making. Tradeoffs between environmental and economic indicators is a useful way to represent dynamic properties of agricultural systems. The Tradeoff Analysis (TOA) System is software designed to implement the integrated analysis of tradeoffs in agricultural systems. The TOA methodology is based on spatially explicit econometric simulation models linked to spatially referenced bio-physical simulation models to simulate land use and input decisions. The methodology has been applied for the potato-pasture production system in the Ecuadorian Andes. The land use change literature often describes non-linearity in land use change as a result of sudden changes in the political (e.g. new agricultural policies) or environmental setting (e.g. earthquakes). However, less attention has been paid to the non-linearities in production systems and their consequences for land use change. In this paper, we use the TOA system to study agricultural land use dynamics and to find the underlying processes for non-linearities. Results show that the sources of non-linearities are in the properties of bio-physical processes and in the decision making-process of farmers.     

Groundwater risk assessment at a heavily industrialised catchment and the associated impacts on a peri-urban wetland

Industrial and agricultural activities often impose significant pressures to the groundwater quality and consequently degrade wetland ecosystems that depend mostly on subsurface water flow. Groundwater vulnerability and risk mapping is a widely used approach to assess the natural protection of aquifers and the associated pollution potential from human activities. In the particular study, the relatively new Pan-European methodology (COP method) has been applied in a highly industrialized peri-urban wetland catchment, located close to Athens city, to map the intrinsic vulnerability of the aquifer and evaluate the risk potential originating from local land uses. Groundwater analysis results for various parameters, including Phenols, PCBs and nutrients, have been used to validate the vulnerability and risk estimations while a biological assessment occurred to associate the mapping results with the wetland's ecological status. The results indicated that even though the natural protection of the aquifer is relatively high due to the dominant hydrogeologic and geomorphologic conditions, the groundwater pollution risk is considerable, mainly because of the existing hazardous land uses. The water quality of the groundwater accredited these findings and the ecological status of this peri-urban wetland also indicated significant impacts from industrial effluents.     

Groundwater Vulnerability Assessment for Organic Compounds: Fuzzy Multicriteria Approach for Mexico City

This study was based on a groundwater vulnerability assessment approach implemented for the Mexico City Metropolitan Area (MCMA). The approach is based on a fuzzy multicriteria procedure integrated in a geographic information system. The approach combined the potential contaminant sources with the permeability of geological materials. Initially, contaminant sources were ranked by experts through the Analytic Hierarchy Process. An aggregated contaminant sources map layer was obtained through the simple additive weighting method, using a scalar multiplication of criteria weights and binary maps showing the location of each source. A permeability map layer was obtained through the reclassification of a geology map using the respective hydraulic conductivity values, followed by a linear normalization of these values against a compatible scale. A fuzzy logic procedure was then applied to transform and combine the two map layers, resulting in a groundwater vulnerability map layer of five classes: very low, low, moderate, high, and very high. Results provided a more coherent assessment of the policy-making priorities considered when discussing the vulnerability of groundwater to organic compounds. The very high and high vulnerability areas covered a relatively small area (71 km² or 1.5% of the total study area), allowing the identification of the more critical locations. The advantage of a fuzzy logic procedure is that it enables the best possible use to be made of the information available regarding groundwater vulnerability in the MCMA.     

Combining Analytical Hierarchy Process and Agglomerative Hierarchical Clustering in Search of Expert Consensus in Green Corridors Development Management

Environmental management and planning are instrumental in resolving conflicts arising between societal needs for economic development on the one hand and for open green landscapes on the other hand. Allocating green corridors between fragmented core green areas may provide a partial solution to these conflicts. Decisions regarding green corridor development require the assessment of alternative allocations based on multiple criteria evaluations. Analytical Hierarchy Process provides a methodology for both a structured and consistent extraction of such evaluations and for the search for consensus among experts regarding weights assigned to the different criteria. Implementing this methodology using 15 Israeli experts—landscape architects, regional planners, and geographers—revealed inherent differences in expert opinions in this field beyond professional divisions. The use of Agglomerative Hierarchical Clustering allowed to identify clusters representing common decisions regarding criterion weights. Aggregating the evaluations of these clusters revealed an important dichotomy between a pragmatist approach that emphasizes the weight of statutory criteria and an ecological approach that emphasizes the role of the natural conditions in allocating green landscape corridors.     

Terrestrial metapopulation dynamics: a nonlinear bioeconomic model analysis

This paper presents a spatially explicit bioeconomic analysis of species conservation in agricultural areas. Wild species in fragmented agricultural landscapes are best approached as metapopulations consisting of a finite number of local populations. Economic analysis of species conservation in fragmented habitat needs to deal with metapopulation theory and its theoretical implications. This paper presents a spatially explicit bioeconomic model consisting of a straightforward economic land use model and an applied metapopulation model. This paper demonstrates that multiple equilibria and multiple local optima in metapopulations might lead to nonconvexities in the production possibilities set of agricultural profits and species conservation.     

Assessing representativeness of places for conservation reservation and heritage listing

Problems arising from application of the representative criterion for conservation and natural heritage evaluation are discussed. An ecological basis to this criterion is suggested that focuses on those key environmental factors dominating biotic response. A methodology is proposed that utilizes computer-based methods of establishing and interrogating spatial data bases (geographic information systems), environmental modeling, and numeric analysis. An example is presented illustrating some of the advantages and limitations of classification and dimension reduction techniques in both defining bioenvironments and displaying their spatial distribution. The advantages of this method for representativeness evaluation are that it maximizes the utility of available data, is explicit and repeatable, and enables large areas to be analyzed at relatively fine scales.