Value of information analysis for adequate monitoring of carbon dioxide storage in geological reservoirs under uncertainty

As monitoring is essential for the proper management of geological storage of carbon dioxide (CO₂), the ability to value information from monitoring is indispensable to adequately design a monitoring program. It is necessary to judge whether the expected improvement in management is worth the cost of monitoring. The value of information (VOI) is closely related to the possible increase in expected utility gained by gathering the information, the concept of which can be applied to such judgement. Although VOI analysis has been extensively studied in the context of decision analysis, its application to the management of carbon dioxide capture and storage (CCS) operations is rare. This paper introduces and discusses the methodology of VOI analyses in the context of monitoring CO₂ storage. A motivating problem with discrete probabilities is used to illustrate the concept of VOI. It is demonstrated that information is not always of value; for information to be worthwhile, monitoring under uncertainty must satisfy certain conditions. This concept is then extended to continuous probability distributions. The effects of prior uncertainty and information reliability on the VOI are examined. It is shown that an excessive improvement in information accuracy yields little value and that the optimal level of reliability can be inferred. VOI analyses provide quantitative insights into the value of information-gathering activities and therefore can be an objective means to adequately design and impartially justify a monitoring program.     

Exploring red deer culling strategies using a population-specific calibrated management model

Wildlife management is generally carried out under conditions of uncertainty. The exact population size is unknown, its future dynamics are uncertain and clear management objectives are often not formulated. In order to provide management advice in this situation, a framework is presented for combining different sources of information using a Bayesian approach for calibrating a management model. Harvesting strategies can then be explored based on predictions of future populations size and structure which incorporate parameter uncertainty. This method makes it possible to evaluate the probability of achieving certain objectives with different management strategies. The advantage of the approach presented in this paper lies in that both the model and the harvesting strategies are adaptable to any particular population of interest. The approach is illustrated for two Scottish red deer populations for which culling strategies corresponding to different management objectives are explored and their benefits evaluated. It is found that each population requires different culling rates for keeping population number stable, demonstrating the benefits of the population specific calibration of the management model.     

Calibration and Uncertainty Analysis Using the Sparrow Model for Dissolved‐Solids Transport in the Upper Colorado River Basin

Salinity in the Upper Colorado River Basin (UCRB) is due to both natural sources and processes, and anthropogenic activities. Given economic damage due to salinity of $295 million in 2010, understanding salinity sources and production together with transport are of great importance. SPAtially Referenced Regressions On Watershed (SPARROW) is a nonlinear regression water quality model that simulates sources and transport of contaminants such as dissolved‐solids. However, SPARROW simulations of dissolved‐solids in the UCRB only represent conditions through 1998 due to limited data availability. More importantly, prior simulations focused on a single year calibration and its transferability to other years, and the validity of this approach is questionable, given the changing hydrologic and climatic conditions. This study presents different calibration approaches to assess the best approach for reducing model uncertainty. This study conducted simulations from 1999 to 2011, and the results showed good model accuracy. However, the number of monitoring stations decreased significantly in recent years resulting in higher model uncertainty. The uncertainty analysis was conducted using SPARROW results and bootstrapping. The results suggest that the watershed rankings based on salinity yields changed due to the uncertainty analysis and therefore, uncertainty consideration should be an important part of the management strategy.     

The work of Envirowise in driving forward UK industrial waste reduction

Envirowise — formerly the Environmental Technology Best Practice Programme — is an UK Government Programme designed to help businesses to improve environmental performance and reduce costs. It works closely with industry bodies to prepare a strategic approach to producing and promoting tools that help companies take action to reduce waste at source. By September 2000, the Programme had helped UK companies to save £125 million per year by reducing the use of raw materials and the production of waste. The Programme produces practical advice based on existing practices in industry. As well as producing published information, Envirowise runs a free telephone helpline for UK companies. Envirowise undertakes a considerable amount of marketing to help companies overcome the barriers to action. The Government has set the objectives for the Programme's managing contractors; these are based on the impact of its activities in terms of reduced waste production.     

The United Kingdom's assessment of greenhouse gas mitigation options

The UK published its Climate Change Programme in January 1994 and was the first country to do so. The published program contains market measures, voluntary agreements, and regulation. It was established following national consultation. The program was quantified using a combination of econometric modeling (for price based measures) and bottom-up analysis. It is monitored through the National Emissions Inventory with provisional CO₂ trends updated every quarter. The program is being updated to take account of developments since its publication, including the publication of new national energy projections in March 1995.

     

Assessing and communicating data quality in policy-relevant research

The quality of scientific information in policy-relevant fields of research is difficult to assess, and quality control in these important areas is correspondingly difficult to maintain. Frequently there are insufficient high-quality measurements for the presentation of the statistical uncertainty in the numerical estimates that are crucial to policy decisions. We propose and develop a grading system for numerical estimates that can deal with the full range of data quality—from statistically valid estimates to informed guesses. By analyzing the underlying quality of numerical estimates, summarized as spread and grade, we are able to provide simple rules whereby input data can be coded for quality, and these codings carried through arithmetical calculations for assessing the quality of model results. For this we use the NUSAP (numeral, unit, spread, assessment, pedigree) notational system. It allows the more quantitative and the more qualitative aspects of data uncertainty to be managed separately. By way of example, we apply the system to an ecosystem valuation study that used several different models and data of widely varying quality to arrive at a single estimate of the economic value of wetlands. The NUSAP approach illustrates the major sources of uncertainty in this study and can guide new research aimed at the improvement of the quality of outputs and the efficiency of the procedures.     

The influence of metal source uncertainty on cost-effective allocation of mine water pollution abatement in catchments

In mine water pollution abatement, it is commonly assumed that known mine waste sites are the major pollution sources, thus neglecting the possibility of significant contribution from other old and diffuse sources within a catchment. We investigate the influence of different types of pollution source uncertainty on cost-effective allocation of abatement measures for mine water pollution. A catchment-scale cost-minimization model is developed and applied to the catchment of the river Dalälven, Sweden, in order to exemplify important effects of such source uncertainty. Results indicate that, if the pollution distribution between point and diffuse sources is partly unknown, downstream abatement measures, such as constructed wetlands, at given compliance boundaries are often cost-effective. If downstream abatement measures are not practically feasible, the pollution source distribution between point and diffuse mine water sources is critical for cost-effective solutions to abatement measure allocation in catchments. In contrast, cost-effective solutions are relatively insensitive to uncertainty in total pollutant discharge from mine water sources.     

MODELING INSTREAM RECREATIONAL BENEFITS1

ABSTRACT: A basic problem in the management of rivers has been how to balance the tradeoffs between instream and out-of-stream uses. Traditionally, the problem has been addressed by optimizing the economic benefits of flow diversions and regulated releases with instream uses as a flow constraint. An alternative method is to model the effect different river flows have on various recreational uses (e.g., boating, fishing) and then use the results as an additional function or piece of information to determine river project operations and benefits. A methodology that is based on multiobjective decision theory and that relates instream recreational preferences to river flow is proposed. The methodology consists of determining, standardizing, and combining recreational benefit functions, and incorporating potential sources of uncertainty into an estimate of total instream benefits. Thus different types of flow patterns, resulting from reservoir regulation (out-of-stream water uses), can be analyzed to determine their potential instream impact. The methodology is applied to the New River Gorge, West Virginia, which has been designated as a National River.     

空气质量自动监测中测量不确定度的应用

根据测量原理建立数学模型,分析各种不确定分量的来源,评定标准不确定度,确定合成不确定度和扩展不确定度。通过不确定影响分量的分析,找出最大不确定分量,重点控制其分量,可保证测量的准确性和精度,也可通过重新评估显著性不确定分量,找出方法存在的不足和问题,提出控制不确定分量的步骤和方法,改善测量方法和手段提高测量准确性和精度。     

Quantifying sources of climate uncertainty to inform risk analysis for climate change decision-making

Quantitative estimates of future climate change and its various impacts are often based on complex climate models which incorporate a number of physical processes. As these models continue to become more sophisticated, it is commonly assumed that the latest generation of climate models will provide us with better estimates of climate change. Here, we quantify the uncertainty in future climate change projections using two multi-model ensembles of climate model simulations and divide it into different components: internal, scenario and model. The contributions of these sources of uncertainty changes as a function of variable, temporal and spatial scale and especially lead time in the future. In the new models, uncertainty intervals for each of the components have increased. For temperature, importance of scenario uncertainty is the largest over low latitudes and increases nonlinearly after the mid-century. It has a small importance for precipitation simulations on all time scales, which hampers estimating the effect which any mitigation efforts might have. In line with current state-of-the-art adaptation approaches, we argue that despite these uncertainties climate models can provide useful information to support adaptation decision-making. Moreover, adaptation decisions should not be postponed in the hope that future improved scientific understanding will result in more accurate predictions of future climate change. Such simulations might not become available. On the contrary, while planning adaptation initiatives, a rational framework for decision-making under uncertainty should be employed. We suggest that there is an urgent need for continued development and use of improved risk analysis methods for climate change adaptation.     

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.     

测定水样中化学需氧量的不确定度

对化学需氧量测定的不确定度进行合理评定,应充分考虑测定过程中的不确定度来源。建立了重铬酸钾法测定水样中化学需氧量不确定度的数学模型,对组成化学需氧量的各个因子的不确定度分量进行了详尽的分析和计算,得出了本次样品CODCr的测定结果为72.2mg/L,扩展不确定度为3.8mg/L。     

Point‐Nonpoint Trading – Can It Work?1

Ribaudo, Marc O. and Jessica Gottlieb, 2011. Point‐Nonpoint Trading – Can It Work? Journal of the American Water Resources Association (JAWRA) 47(1):5‐14. DOI: 10.1111/j.1752‐1688.2010.00454.x Abstract: Water quality trading between point and nonpoint sources is of great interest as an alternative to strict command and control regulations on point sources for achieving water quality goals. The expectation is that trading will reduce the costs of water quality protection, and may speed compliance. The United States Environmental Protection Agency has issued guidance to the States on developing point‐nonpoint trading programs, and United States Department of Agriculture is encouraging farmer participation. However, existing point‐nonpoint trading programs have resulted in very few trades. Supply side and demand side impediments seem to be preventing trades from occurring in most trading programs. These include uncertainty over the number of discharge allowances different management practices can produce, high transactions costs of identifying trading partners, baseline requirements that eliminate low‐cost credits, the reluctance of point sources to trade with unfamiliar agents, and the perception of some farmers that entering contracts with regulated point sources leads to greater scrutiny and potential future regulation. Many of these problems can be addressed through research and program design.     

Comparing two sets of forest cover change knowledge used in forest landscape management planning

Forecasts of future resource states are central to resource management planning. Many simulation models and planning tools are used to produce such forecasts and apply knowledge of resource change dynamics as key input. Consistency among knowledge sources is therefore important to avoid knowledge ambiguity and uncertainty in resource forecasts and management plan outcomes. Using Ontario's boreal forest landscape as a case study, this paper examined two knowledge sources of forest resource change, practitioner expertise and research studies, commonly applied in plans and policies for large forest landscapes. The two knowledge sources were quantitatively compared by constructing networks of forest cover change for both sources and determining their agreement in structure and transition times. Some networks agreed well, indicating little knowledge ambiguity and comparatively low uncertainty if they were used to forecast forest landscapes. Other networks showed low agreement, thus indicating higher knowledge ambiguity and a dilemma of choice for forest landscape planners who may have to select from these knowledge sets. It is suggested that knowledge disagreements may be widespread in knowledge-driven management planning of many natural resource types and their causes similar. These disagreements signal areas of knowledge uncertainty, where resource planners must address resulting uncertainty of management outcomes and research should focus on improving resource change knowledge.     

IMPLICATIONS OF ON‐GROUND NITROGEN LOADING AND SOIL TRANSFORMATIONS ON GROUND WATER QUALITY MANAGEMENT1

ABSTRACT: This paper presents a modeling approach based on a geographic information system (GIS) to estimate the variability of on‐ground nitrogen loading and the corresponding nitrate leaching to ground water. The methodology integrates all point and nonpoint sources of nitrogen, the national land cover database, soil nitrogen transformations, and the uncertainty of key soil and land use‐related parameters to predict the nitrate mass leaching to ground water. The analysis considered 21 different land use classes with information derived from nitrogen sources such as fertilizer and dairy manure applications, dairy lagoons, septic systems, and dry and wet depositions. Simulations were performed at a temporal resolution of one month to capture seasonal trends. The model was applied to a large aquifer of 376 square miles in Washington State that serves more than 100,000 residents with drinking water. The results showed that dairy manure is the main source of nitrogen in the area followed by fertilizers. It was also seen that nitrate leaching is controlled by the recharge rate, and there can be a substantial buildup of soil nitrogen over long periods of time. Uncertainty analysis showed that denitrification rate is the most influential parameter on nitrate leaching. The results showed that combining management alternatives is a successful strategy, especially with the use of nitrification inhibitors. Also, change in the land use pattern has a noticeable impact on nitrate leaching.     

Policy planning under uncertainty: efficient starting populations for simulation-optimization methods applied to municipal solid waste management

Evolutionary simulation-optimization (ESO) techniques can be adapted to model a wide variety of problem types in which system components are stochastic. Grey programming (GP) methods have been previously applied to numerous environmental planning problems containing uncertain information. In this paper, ESO is combined with GP for policy planning to create a hybrid solution approach named GESO. It can be shown that multiple policy alternatives meeting required system criteria, or modelling-to-generate-alternatives (MGA), can be quickly and efficiently created by applying GESO to this case data. The efficacy of GESO is illustrated using a municipal solid waste management case taken from the regional municipality of Hamilton-Wentworth in the Province of Ontario, Canada. The MGA capability of GESO is especially meaningful for large-scale real-world planning problems and the practicality of this procedure can easily be extended from MSW systems to many other planning applications containing significant sources of uncertainty.     

A response surface methodology to address uncertainties in cap rock failure assessment for CO2 geological storage in deep aquifers

Cap rock failure assessment, either tensile fracturing or shear slip reactivation of pre-existing fault, is a key issue for preventing CO₂ leakage from deep aquifer reservoirs up to the surface. For an appropriate use in risk management, the uncertainties associated with such studies should be investigated. Nevertheless, uncertainty analysis requires multiple simulations and a direct use of conventional numerical approaches might be too computer time consuming. An alternative is to use conventional analytical models, but their assumptions appear to be too conservative. An intermediate approach is then proposed based on the response surface methodology, consisting in estimating the effective stress state after CO₂ injection as a linear combination of the most influential site properties based on a limited number of numerical simulations. The decision maker is provided with three levels of information: (1) the identification of the most important site properties; (2) an analytical model for a quick assessment of the maximal sustainable overpressure and (3) a simplified model to be used in a computationally intensive uncertainty analysis framework. This generic methodology is illustrated with the Paris Basin case using a large-scale hydromechanical model to assess cap rock failure in the injector zone.     

低温试验能力验证中的不确定度分析评估

本文简述电气领域如何评定测量不确定度过程,并结合CNAS T0346低温试验能力验证计划,识别和具体分析低温试验中各测量不确定度的来源,对各种影响因素进行评价,论述人、机、料、法、环、测各环节对测量结果的影响,给出了低温试验测量结果不确定度的评估过程。     

Enhancing environmental integration in strategic environmental assessment (SEA): insight from sensitivity analysis

This paper explores the potential of sensitivity analysis to improve the Strategic Environmental Assessment (SEA) process and enhance environmental integration. It applies the Sensitivity Model Prof. Vester® to the England SEA process, following two objectives: (1) establishing how different strategic environmental assessment (SEA) elements affect environmental integration; and (2) establishing the sensitivity of the SEA process in terms of response to change. The findings showed that no single element was critical to enhancing environmental integration. Several elements, acting as sources of uncertainty, leverage or stability in the process, were also identified, highlighting their potential as priority targets for process improvement. It is concluded that sensitivity analysis can supplement current expert-led approaches to SEA improvement by (1) identifying elements likely to influence environmental integration and (2) testing the likely performance of recommendations for process improvement.