Degradation Model: A Quantitative EIA Instrument,Acting as a Decision Support System (DSS) for Environmental Management

Environmental assessment of alternative development plans, programs, and policies may bring conflict among decision-makers, particularly when some quantitative measures for decision-making are needed and where cumulative impacts are neglected. Environmental impact assessment (EIA) and environmental economics theories, despite their usefulness, are not capable of addressing those issues and problems alone. In recent years, the decision support system (DSS) has provided some solutions, but mathematical analysis of the system to show the internal structure of the problem is not always possible. To addres the above shortcomings and ongoing problems of decision-making in Iran, a degradation model (DM) was introduced as an instrument of EIA, to act as a DSS for managers. The model is a compromise between knowledge-based decision support systems, detailed models, digested information models, and the basic theorem of environmental economics. In the present study (1996–2000), the model was applied in three provinces of Iran, representing three of four biogeographical regions of Iran. The study area was divided into a set of grids (100 km²). The degradation coefficient (H) was computed for all grids (1333), representing the degree of degradation in the grid. It is obvious that the higher the coefficient the more area is degraded and less prone to further development, and vice versa. In order to provide decision-makers with a set of quantitative measures to observe impacted areas (critical and noncritical) for resource allocation and further development, the degradation coefficients of all grids were classified into categories and criteria, using a fuzzy set theoretic approach. Accordingly, only 24% of study areas are prone to further development. The degradation model as a knowledge-based decision support system has its strengths and weaknesses, but it has solved managers' ongoing problems in Iran and it could be used elsewhere.     

Opportunity costs of implementing forest plans

Intellectual concern with the National Forest Management Act of 1976 has followed a course emphasizing the planning aspects of the legislation associated with the development of forest plans. Once approved, however, forest plans must be implemented. Due to the complex nature of the ecological systems of interest, and the multiple and often conflicting desires of user clientele groups, the feasibility and costs of implementing forest plans require immediate investigation. For one timber sale on the Coconino National Forest in Arizona, forest plan constraints were applied and resulting resource outputs predicted using the terrestrial ecosystem analysis and modeling system (TEAMS), a computer-based decision support system developed at the School of Forestry, Northern Arizona University, With forest plan constraints for wildlife habitat, visual diversity, riparian area protection, and soil and slope harvesting restrictions, the maximum timber harvest obtainable was reduced 58% from the maximum obtainable without plan constraints.Former Graduate Student at Northern Arizona University.     

QUICKScan as a quick and participatory methodology for problem identification and scoping in policy processes

Policy making is required in cases in which a public good needs to be either maintained or created, and private or civil initiatives cannot deal alone with this. Policy making thus starts with a phase of problem identification and determining whether there is a problem that needs to be dealt with. Rapidly evolving contexts exert influence on policy makers who have to take decisions much faster and more accurately than in the past, also facing greater complexity. There is a need for a method that lowers the lead time of the exploratory phase of the policy cycle. At the same time the method should create a joint understanding of the most important interactions. This paper proposes QUICKScan, a method, process and spatially explicit tool, to jointly scope policy problems in a participatory setting, investigate the most important interactions and feedbacks and assesses the state of knowledge and data of relevance to the problem. QUICKScan uses strongly moderated participatory workshops bringing together a wide range of stakeholders relevant to the policy issue. These moderated workshops jointly build an expert system in a spatially explicit tool using functionality of bayesian belief networks, python programming, simple map algebra and knowledge matrices, with a strong focus on visualization of results. QUICKScan has been applied in 70 different applications in a range of different policy contexts, stakeholders and physical locations. Through these applications participants were able to internalize the knowledge that was usually handed to them in briefs and reports, to develop a joint understanding of the main interactions and their link to impacts and to develop a problem statement and solution space in a reduced lead time. Ultimately, QUICKScan demonstrates another role of science, not solely as a knowledge production, but also facilitating the knowledge consumption.     

Mathematical problem definition for ecological restoration planning

Ecological restoration is an increasingly important tool for managing and improving highly degraded or altered environments. Faced with a large number of sites or ecosystems to restore, and a diverse array of restoration approaches, investments in ecological restoration must be prioritized. Nevertheless, there are relatively few examples of the systematic prioritization of restoration actions. The development of a general theory for ecological restoration that is sufficiently sophisticated and robust to account for the inherent complexity of restoration planning, and yet is flexible and adaptable to ensure applicability to a diverse array of restoration problems is needed. In this paper we draw on principles from systematic conservation planning to explicitly formulate the ‘restoration prioritization problem’. We develop a generalized theory for static and dynamic restoration planning problems, and illustrate how the basic problem formulation can be expanded to allow for many factors characteristic of restoration problems, including spatial dependencies, the possibility of restoration failure, and the choice of multiple restoration techniques. We illustrate the applicability of our generic problem definition by applying it to a case study - restoration prioritization on The Irvine Ranch Natural Landmark in Southern California. Through this case study we illustrate how the definition of the general restoration problem can be extended to account for the specific constraints and considerations of an on-the-ground restoration problem.     

Modeling soil and plant phosphorus within DSSAT

The crop models in the Decision Support System for Agrotechnology Transfer (DSSAT) have served worldwide as a research tool for improving predictions of relationships between soil and plant nitrogen (N) and crop yield. However, without a phosphorus (P) simulation option, the applicability of the DSSAT crop models in P-deficient environments is limited. In this study, a soil-plant P model integrated to DSSAT was described, and results showing the ability of the model to mimic wide differences in maize responses to P in Ghana are presented as preliminary attempts to testing the model on highly weathered soils. The model simulates P transformations between soil inorganic labile, active and stable pools and soil organic microbial and stable pools. Plant growth is limited by P between two concentration thresholds that are species-specific optimum and minimum concentrations of P defined at different stages of plant growth. Phosphorus stress factors are computed to reduce photosynthesis, dry matter accumulation and dry matter partitioning. Testing on two highly weathered soils from Ghana over a wide range of N and P fertilizer application rates indicated that the P model achieved good predictability skill at one site (Kpeve) with a final grain yield root mean squared error (RMSE) of 535 kg ha⁻¹and a final biomass RMSE of 507 kg ha⁻¹. At the other site (Wa), the RMSE was 474 kg ha⁻¹ for final grain yield and 1675 kg ha⁻¹ for final biomass. A local sensitivity analysis indicated that under P-limiting conditions and no P fertilizer application, crop biomass, grain yield, and P uptake could be increased by over 0.10% due to organic P mineralization resulting from a 1% increase in organic carbon. It was also shown that the modeling philosophy that makes P in a root-free zone unavailable to plants resulted in a better agreement of simulated crop biomass and grain yield with field measurements. Because the complex soil P chemistry makes the availability of P to plants extremely variable, testing under a wider range of agro-ecological conditions is needed to complement the initial evaluation presented here, and extend the use of the DSSAT-P model to other P-deficient environments.     

事故定性分析与事故定量预测

分析了影响事故预测准确的原因,综述了国内外许多学者在事故预测方面所做的努力。指出今后事故预测工作的主要途径是由安全系统工程专家建立计算机支持的决策支持系统(DSS),由安全专家灵活运用这个系统中的计算机辅助研究方法(CAR)。     

Decision Tree Algorithm for Detection of Spatial Processes in Landscape Transformation

The conversion of landscapes by human activities results in widespread changes in landscape spatial structure. Regardless of the type of land conversion, there appears to be a limited number of common spatial configurations that result from such land transformation processes. Some of these configurations are considered optimal or more desirable than others. Based on pattern geometry, we define ten processes responsible for pattern change: aggregation, attrition, creation, deformation, dissection, enlargement, fragmentation, perforation, shift, and shrinkage. A novelty in this contribution is the inclusion of transformation processes causing expansion of the land cover of interest. Consequently, we propose a decision tree algorithm that enables detection of these processes, based on three parameters that have to be determined before and after the transformation of the landscape: area, perimeter length, and number of patches of the focal landscape class. As an example, the decision tree algorithm is applied to determine the transformation processes of three divergent land cover change scenarios: deciduous woodland degradation in Cadiz Township (Wisconsin, USA) 1831–1950, canopy gap formation in a terra firme rain forest at the Tiputini Biodiversity Station (Amazonian Ecuador) 1997–1998, and forest regrowth in Petersham Township (Massachusetts, USA) 1830–1985. The examples signal the importance of the temporal resolution of the data, since long-term pattern conversions can be subdivided in stadia in which particular pattern components are altered by specific transformation processes.     

Multi-criteria decision analysis for the optimal management of nitrate contamination of aquifers

We present an integrated methodology for the optimal management of nitrate contamination of ground water combining environmental assessment and economic cost evaluation through multi-criteria decision analysis. The proposed methodology incorporates an integrated physical modeling framework accounting for on-ground nitrogen loading and losses, soil nitrogen dynamics, and fate and transport of nitrate in ground water to compute the sustainable on-ground nitrogen loading such that the maximum contaminant level is not violated. A number of protection alternatives to stipulate the predicted sustainable on-ground nitrogen loading are evaluated using the decision analysis that employs the importance order of criteria approach for ranking and selection of the protection alternatives. The methodology was successfully demonstrated for the Sumas-Blaine aquifer in Washington State. The results showed the importance of using this integrated approach which predicts the sustainable on-ground nitrogen loadings and provides an insight into the economic consequences generated in satisfying the environmental constraints. The results also show that the proposed decision analysis framework, within certain limitations, is effective when selecting alternatives with competing demands.     

Decision Support for Road System Analysis and Modification on the Tahoe National Forest

The United States Forest Service is required to analyze road systems on each of the national forests for potential environmental impacts. We have developed a novel and inexpensive way to do this using the Ecosystem Management Decision Support program (EMDS). We used EMDS to integrate a user-developed fuzzy logic knowledge base with a grid-based geographic information system to evaluate the degree of truth for assertions about a roads environmental impact. Using spatial data for natural and human processes in the Tahoc National Forest (TNF, California, USA), we evaluated the assertion the road has a high potential for impacting the environment. We found a high level of agreement between the products of this evaluation and ground observations of a TNF transportation engineer, as well as occurrences of road failures. We used the modeled potential environmental impact to negatively weight roads for a least-cost path network analysis to 1573 points of interest in the forest. The network analysis showed that out of 8233 km of road analyzed in the forest, 3483 km (42%) must be kept in a modified road network to ensure access to these points. We found that the modified network had improved patch characteristics, such as significantly fewer cherry stem roads intruding into patches, an improved area-weighted mean shape index, and larger mean patch sizes, as compared to the original network. This analysis system could be used by any public agency to analyze infrastructure for environmental or other risk and included in other mandated analyses such as risks to watersheds.     

Decision Support System for Managing Oil Spill Events

The Mediterranean environment is exposed to various hazards, including oil spills, forest fires, and floods, making the development of a decision support system (DSS) for emergency management an objective of utmost importance. The present work presents a complete DSS for managing marine pollution events caused by oil spills. The system provides all the necessary tools for early detection of oil-spills from satellite images, monitoring of their evolution, estimation of the accident consequences and provision of support to responsible Public Authorities during clean-up operations. The heart of the system is an image processing–geographic information system and other assistant individual software tools that perform oil spill evolution simulation and all other necessary numerical calculations as well as cartographic and reporting tasks related to a specific management of the oil spill event. The cartographic information is derived from the extant general maps representing detailed information concerning several regional environmental and land-cover characteristics as well as financial activities of the application area. Early notification of the authorities with up-to-date accurate information on the position and evolution of the oil spill, combined with the detailed coastal maps, is of paramount importance for emergency assessment and effective clean-up operations that would prevent environmental hazard. An application was developed for the Region of Crete, an area particularly vulnerable to oil spills due to its location, ecological characteristics, and local economic activities.