Confronting Management Challenges in Highly Uncertain Natural Resource Systems: a Robustness–Vulnerability Trade-off Approach

This paper presents a framework for the study of policy implementation in highly uncertain natural resource systems in which uncertainty cannot be characterized by probability distributions. We apply the framework to parametric uncertainty in the traditional Gordon–Schaefer model of a fishery to illustrate how performance can be sacrificed (traded-off) for reduced sensitivity and hence increased robustness, with respect to model parameter uncertainty. With sufficient data, our robustness–vulnerability analysis provides tools to discuss policy options. When less data are available, it can be used to inform the early stages of a learning process. Several key insights emerge from this analysis: (1) the classic optimal control policy can be very sensitive to parametric uncertainty, (2) even mild robustness properties are difficult to achieve for the simple Gordon–Schaefer model, and (3) achieving increased robustness with respect to some parameters (e.g., biological parameters) necessarily results in increased sensitivity (decreased robustness) with respect to other parameters (e.g., economic parameters). We thus illustrate fundamental robustness–vulnerability trade-offs and the limits to robust natural resource management. Finally, we use the framework to explore the effects of infrequent sampling and delays on policy performance.     

Climate change vulnerability in a tropical region based on environmental and socio-economic factors

The understanding of the regional and local dimensions of vulnerability due to climate change is essential to develop appropriate and targeted adaptation efforts. We assessed the local dimensions of vulnerability in the tropical state of Kerala, India, using a purposely developed vulnerability index, which accounts for both environmental and socio-economic factors. The large extents of coastal wetlands and lagoons and high concentration of mangrove forests make the state environmentally vulnerable. Low human development index, large population of socially deprived groups, which are dependent on the primary sector, and high population density make the state vulnerable from a socio-economic point of view. The present study investigates climate change vulnerability at the district level in the State of Kerala relying on a purposely developed composite vulnerability index that encompasses both socio-economic and environmental factors. The Kerala coast contains the socio-economically and ecologically most vulnerable regions, as demonstrated by a composite vulnerability index.     

Compositing climate change vulnerability of a Mediterranean region using spatiotemporally dynamic proxies for ecological and socioeconomic impacts and stabilities

The study presents a new methodology to quantify spatiotemporal dynamics of climate change vulnerability at a regional scale adopting a new conceptual model of vulnerability as a function of climate change impacts, ecological stability, and socioeconomic stability. Spatiotemporal trends of equally weighted proxy variables for the three vulnerability components were generated to develop a composite climate change vulnerability index (CCVI) for a Mediterranean region of Turkey combining Landsat time series data, digital elevation model (DEM)-derived data, ordinary kriging, and geographical information system. Climate change impact was based on spatiotemporal trends of August land surface temperature (LST) between 1987 and 2016. Ecological stability was based on DEM, slope, aspect, and spatiotemporal trends of normalized difference vegetation index (NDVI), while socioeconomic stability was quantified as a function of spatiotemporal trends of land cover, population density, per capita gross domestic product, and illiteracy. The zones ranked on the five classes of no-to-extreme vulnerability were identified where highly and moderately vulnerable lands covered 0.02% (12 km²) and 11.8% (6374 km²) of the study region, respectively, mostly occurring in the interior central part. The adoption of this composite CCVI approach is expected to lead to spatiotemporally dynamic policy recommendations towards sustainability and tailor preventive and mitigative measures to locally specific characteristics of coupled ecological–socioeconomic systems.     

Climatic Implications of Revised IPCC Emissions Scenarios, the Kyoto Protocol and Quantification of Uncertainties

In December 1997, the United Nations Framework Convention on Climate Change (FCCC) adopted the Kyoto Protocol. This paper describes a framework that models the climatic implications of this international agreement, using Monte Carlo simulations and the preliminary Intergovernmental Panel on Climate Change emissions scenarios (SRES). Emissions scenarios (including intervention scenarios), climate sensitivity, and terrestrial carbon sink are the key sampled model parameters. This framework gives prior probability distributions to these parameters and, using a simple climate model, posterior distributions of global temperature change are determined for the future. Our exercise showed that the Kyoto Protocol's effectiveness will be mostly dependent upon which SRES world evolves. In some worlds the Protocol decreases the warming considerably but in others it is almost irrelevant. We exemplified this approach with a current FCCC issue, namely “hot air”. This modelling framework provides a probabilistic assessment of climate policies, which can be useful for decision-makers involved in global climate change management. This revised version was published online in July 2006 with corrections to the Cover Date.     

基于广西河池市胁迫风险的土壤环境脆弱性评价

采用层次分析法划分了土壤环境各因素的敏感性和重要程度,从而建立起能科学表达土壤环境承载能力的土壤环境脆弱性评价体系。基于广西河池市指标数据,借助地理信息系统软件Arc GIS9.3、ENVI5.1、MATLAB7.0等计算平台对空间数据进行模型训练与验证,得出可视化表征土壤环境脆弱性空间分布状况的评价图件。结果表明,河池市北部、南部存在高度脆弱区,占全市国土面积的36.3%,中度脆弱区占38.3%,低度脆弱区占25.4%。总体来看,河池市大部分区域的土壤环境属中高度脆弱。     

Using multi-criteria decision analysis to assess the vulnerability of drinking water utilities

Outbreaks of microbiological waterborne disease have increased governmental concern regarding the importance of drinking water safety. Considering the multi-barrier approach to safe drinking water may improve management decisions to reduce contamination risks. However, the application of this approach must consider numerous and diverse kinds of information simultaneously. This makes it difficult for authorities to apply the approach to decision making. For this reason, multi-criteria decision analysis can be helpful in applying the multi-barrier approach to vulnerability assessment. The goal of this study is to propose an approach based on a multi-criteria analysis method in order to rank drinking water systems (DWUs) based on their vulnerability to microbiological contamination. This approach is illustrated with an application carried out on 28 DWUs supplied by groundwater in the Province of Québec, Canada. The multi-criteria analysis method chosen is measuring attractiveness by a categorical based evaluation technique methodology allowing the assessment of a microbiological vulnerability indicator (MVI) for each DWU. Results are presented on a scale ranking DWUs from less vulnerable to most vulnerable to contamination. MVI results are tested using a sensitivity analysis on barrier weights and they are also compared with historical data on contamination at the utilities. The investigation demonstrates that MVI provides a good representation of the vulnerability of DWUs to microbiological contamination.     

Environmental vulnerability assessment using Grey Analytic Hierarchy Process based model

Environmental management of an area describes a policy for its systematic and sustainable environmental protection. In the present study, regional environmental vulnerability assessment in Hirakud command area of Odisha, India is envisaged based on Grey Analytic Hierarchy Process method (Grey–AHP) using integrated remote sensing (RS) and geographic information system (GIS) techniques. Grey–AHP combines the advantages of classical analytic hierarchy process (AHP) and grey clustering method for accurate estimation of weight coefficients. It is a new method for environmental vulnerability assessment. Environmental vulnerability index (EVI) uses natural, environmental and human impact related factors, e.g., soil, geology, elevation, slope, rainfall, temperature, wind speed, normalized difference vegetation index, drainage density, crop intensity, agricultural DRASTIC value, population density and road density. EVI map has been classified into four environmental vulnerability zones (EVZs) namely: ‘low’, ‘moderate’ ‘high’, and ‘extreme’ encompassing 17.87%, 44.44%, 27.81% and 9.88% of the study area, respectively. EVI map indicates that the northern part of the study area is more vulnerable from an environmental point of view. EVI map shows close correlation with elevation. Effectiveness of the zone classification is evaluated by using grey clustering method. General effectiveness is in between “better” and “common classes”. This analysis demonstrates the potential applicability of the methodology.     

Rabbit Population Landscape-Scale Simulation to Investigate the Relevance of Using Rabbits in Regulatory Environmental Risk Assessment

This paper describes the development and testing of the ALMaSS rabbit model and its baseline, and subsequently its application to the question of lagomorph population vulnerability in environmental risk assessment (ERA). Development and testing following a pattern-oriented modelling protocol resulted in a model able to replicate local and landscape-level rabbit population patterns. We then tested how robust rabbit populations are to an (imaginary) extreme toxic stressor at a landscape level in a variety of landscapes, and to what extremes key uncertain model parameters must be pushed to cause extinctions. This was contrasted with the same (imaginary) toxic stressor applied to the already existing ALMaSS hare model. For EU risk assessment of plant protection products, these results clearly indicate that if the protection goal is population-level impacts, either in abundance and/or distribution, then the hare is a much more vulnerable species than the rabbit under all the conditions tested. Rabbits would only be more vulnerable than hares if the entire population were to be exposed simultaneously, when lower body mass would then be a critical factor. This did not occur even though the toxicant and exposure scenarios tested here were extreme and, in fragmented landscapes at scales used here, will not occur in reality from the use of plant protection products on crop fields. As well as specifically answering the question on rabbit versus hare vulnerability, this study generally illustrates the potential application of models for setting focal species for risk assessments.     

A multi criteria analog model for assessing the vulnerability of rural catchments to road spills of hazardous substances

Road spills of hazardous substances are common in developing countries due to increasing industrialization and traffic accidents, and represent a serious threat to soils and water in catchments. There is abundant literature on equations describing the wash-off of pollutants from roads during a storm event and there are a number of watershed models incorporating those equations in storm water quality algorithms that route runoff and pollution yields through a drainage system towards the catchment outlet. However, methods describing catchment vulnerability to contamination by road spills based solely on biophysical parameters are scarce. These methods could be particularly attractive to managers because they can operate with a limited amount of easily collectable data, while still being able to provide important insights on the areas more prone to contamination within the studied watershed. The purpose of this paper was then to contribute with a new vulnerability model. To accomplish the goal, a selection of medium properties appearing in wash-off equations and routing algorithms were assembled and processed in a parametric framework based on multi criteria analysis to define the watershed vulnerability. However, parameters had to be adapted because wash-off equations and water quality models have been developed to operate primarily in the urban environment while the vulnerability model is meant to run in rural watersheds. The selected parameters were hillside slope, ground roughness (depending on land use), soil permeability (depending on soil type), distance to water courses and stream density. The vulnerability model is a spatially distributed algorithm that was prepared to run under the IDRISI Selva software, a GIS platform capable of handling spatial and alphanumeric data and execute the necessary terrain model, hydrographic and thematic analyses. For illustrative purposes, the vulnerability model was applied to the legally protected Environmental Protection Area (APA), located in the Uberaba region, state of Minas Gerais, Brazil. In this region, the risk of accidents causing chemical spills is preoccupying because large quantities of dangerous materials are transported in two important distribution highways while the APA is fundamental for the protection of water resources, the riverine ecosystems and remnants of native vegetation. In some tested scenarios, model results show 60% of vulnerable areas within the studied area. The most sensitive parameter to vulnerability is soil type. To prevent soils from contamination, specific measures were proposed involving minimization of land use conflicts that would presumably raise the soil's organic matter and in the sequel restore the soil's structural functions. Additionally, the present study proposed the preservation and reinforcement of riparian forests as one measure to protect the quality of surface water.     

Using multiple-criteria decision-making techniques for eco-environmental vulnerability assessment: a case study on the Chi-Jia-Wan Stream watershed, Taiwan

The Chi-Jia-Wan Stream watershed, located in the area of the upstream Da-Chia River in central Taiwan, is famous for slopeland agriculture and the land-locked salmon. Improper agricultural activities have caused apparent ecosystem vulnerability and sensitivity. In this study, a system that combined three watershed-based environmental indicators with multiple-criteria decision-making techniques, the Analytical Hierarchy Process, and the Preference Ranking Organization METHod for Enrichment Evaluations was developed to assess eco-environmental vulnerability. The composite evaluation index system was set up including sediment, runoff, and nutrient factors. Supported by geographic information system and K-means clustering and taking the subwatershed as the evaluation unit, the vulnerability is classified into four levels: potential, low, moderate, and high. The evaluated results show that 8.82% of subwatersheds (six subwatersheds) are in the moderately and highly vulnerable zones. These subwatersheds represent vertical-belt distribution, mainly concentrated in the right side of the studied area and near the riparian zone along the Chi-Jia-Wan Stream. The exploited farmland in the moderately and highly vulnerable zones is about 142.21 ha, occupying 75.38% of the total farmland in the studied watershed. These seriously vulnerable zones that have caused degradation in the quality of the eco-environment should be treated with more best management practices for eco-environmental rehabilitation. Additionally, the proposed model can effectively evaluate the eco-environmental vulnerability grade for reference in policy planning and ecological restoration in this area.