[Influence of Uncertainty and Uncertainty Appraisal on Self-management in Hemodialysis Patients].

PubMed

Jang, Hyung Suk; Lee, Chang Suk; Yang, Young Hee

2015-04-01

This study was done to examine the relation of uncertainty, uncertainty appraisal, and self-management in patients undergoing hemodialysis, and to identify factors influencing self-management. A convenience sample of 92 patients receiving hemodialysis was selected. Data were collected using a structured questionnaire and medical records. The collected data were analyzed using descriptive statistics, t-test, ANOVA, Pearson correlations and multiple regression analysis with the SPSS/WIN 20.0 program. The participants showed a moderate level of uncertainty with the highest score being for ambiguity among the four uncertainty subdomains. Scores for uncertainty danger or opportunity appraisals were under the mid points. The participants were found to perform a high level of self-management such as diet control, management of arteriovenous fistula, exercise, medication, physical management, measurements of body weight and blood pressure, and social activity. The self-management of participants undergoing hemodialysis showed a significant relationship with uncertainty and uncertainty appraisal. The significant factors influencing self-management were uncertainty, uncertainty opportunity appraisal, hemodialysis duration, and having a spouse. These variables explained 32.8% of the variance in self-management. The results suggest that intervention programs to reduce the level of uncertainty and to increase the level of uncertainty opportunity appraisal among patients would improve the self-management of hemodialysis patients.

A Word to the Wise: Advice for Scientists Engaged in Collaborative Adaptive Management

NASA Astrophysics Data System (ADS)

Hopkinson, Peter; Huber, Ann; Saah, David S.; Battles, John J.

2017-05-01

Collaborative adaptive management is a process for making decisions about the environment in the face of uncertainty and conflict. Scientists have a central role to play in these decisions. However, while scientists are well trained to reduce uncertainty by discovering new knowledge, most lack experience with the means to mitigate conflict in contested situations. To address this gap, we drew from our efforts coordinating a large collaborative adaptive management effort, the Sierra Nevada Adaptive Management Project, to offer advice to our fellow environmental scientists. Key challenges posed by collaborative adaptive management include the confusion caused by multiple institutional cultures, the need to provide information at management-relevant scales, frequent turnover in participants, fluctuations in enthusiasm among key constituencies, and diverse definitions of success among partners. Effective strategies included a dedication to consistency, a commitment to transparency, the willingness to communicate frequently via multiple forums, and the capacity for flexibility. Collaborative adaptive management represents a promising, new model for scientific engagement with the public. Learning the lessons of effective collaboration in environmental management is an essential task to achieve the shared goal of a sustainable future.

Evaluation of potential effects of federal land management alternatives on trends of salmonids and their habitats in the interior Columbia River basin.

Treesearch

Bruce Rieman; James T. Peterson; James Clayton; Philip Howell; Russell Thurow; William Thompson; Danny Lee

2001-01-01

Aquatic species throughout the interior Columbia River basin are at risk. Evaluation of the potential effects of federal land management on aquatic ecosystems across this region is an important but challenging task. Issues include the size and complexity of the systems, uncertainty in important processes and existing states, flexibility and consistency in the...

Selection of climate policies under the uncertainties in the Fifth Assessment Report of the IPCC

NASA Astrophysics Data System (ADS)

Drouet, L.; Bosetti, V.; Tavoni, M.

2015-10-01

Strategies for dealing with climate change must incorporate and quantify all the relevant uncertainties, and be designed to manage the resulting risks. Here we employ the best available knowledge so far, summarized by the three working groups of the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5; refs , , ), to quantify the uncertainty of mitigation costs, climate change dynamics, and economic damage for alternative carbon budgets. We rank climate policies according to different decision-making criteria concerning uncertainty, risk aversion and intertemporal preferences. Our findings show that preferences over uncertainties are as important as the choice of the widely discussed time discount factor. Climate policies consistent with limiting warming to 2 °C above preindustrial levels are compatible with a subset of decision-making criteria and some model parametrizations, but not with the commonly adopted expected utility framework.

Information Seeking in Uncertainty Management Theory: Exposure to Information About Medical Uncertainty and Information-Processing Orientation as Predictors of Uncertainty Management Success.

PubMed

Rains, Stephen A; Tukachinsky, Riva

2015-01-01

Uncertainty management theory outlines the processes through which individuals cope with health-related uncertainty. Information seeking has been frequently documented as an important uncertainty management strategy. The reported study investigates exposure to specific types of medical information during a search, and one's information-processing orientation as predictors of successful uncertainty management (i.e., a reduction in the discrepancy between the level of uncertainty one feels and the level one desires). A lab study was conducted in which participants were primed to feel more or less certain about skin cancer and then were allowed to search the World Wide Web for skin cancer information. Participants' search behavior was recorded and content analyzed. The results indicate that exposure to two health communication constructs that pervade medical forms of uncertainty (i.e., severity and susceptibility) and information-processing orientation predicted uncertainty management success.

Flood risk assessment and robust management under deep uncertainty: Application to Dhaka City

NASA Astrophysics Data System (ADS)

Mojtahed, Vahid; Gain, Animesh Kumar; Giupponi, Carlo

2014-05-01

The socio-economic changes as well as climatic changes have been the main drivers of uncertainty in environmental risk assessment and in particular flood. The level of future uncertainty that researchers face when dealing with problems in a future perspective with focus on climate change is known as Deep Uncertainty (also known as Knightian uncertainty), since nobody has already experienced and undergone those changes before and our knowledge is limited to the extent that we have no notion of probabilities, and therefore consolidated risk management approaches have limited potential.. Deep uncertainty is referred to circumstances that analysts and experts do not know or parties to decision making cannot agree on: i) the appropriate models describing the interaction among system variables, ii) probability distributions to represent uncertainty about key parameters in the model 3) how to value the desirability of alternative outcomes. The need thus emerges to assist policy-makers by providing them with not a single and optimal solution to the problem at hand, such as crisp estimates for the costs of damages of natural hazards considered, but instead ranges of possible future costs, based on the outcomes of ensembles of assessment models and sets of plausible scenarios. Accordingly, we need to substitute optimality as a decision criterion with robustness. Under conditions of deep uncertainty, the decision-makers do not have statistical and mathematical bases to identify optimal solutions, while instead they should prefer to implement "robust" decisions that perform relatively well over all conceivable outcomes out of all future unknown scenarios. Under deep uncertainty, analysts cannot employ probability theory or other statistics that usually can be derived from observed historical data and therefore, we turn to non-statistical measures such as scenario analysis. We construct several plausible scenarios with each scenario being a full description of what may happen in future and based on a meaningful synthesis of parameters' values with control of their correlations for maintaining internal consistencies. This paper aims at incorporating a set of data mining and sampling tools to assess uncertainty of model outputs under future climatic and socio-economic changes for Dhaka city and providing a decision support system for robust flood management and mitigation policies. After constructing an uncertainty matrix to identify the main sources of uncertainty for Dhaka City, we identify several hazard and vulnerability maps based on future climatic and socio-economic scenarios. The vulnerability of each flood management alternative under different set of scenarios is determined and finally the robustness of each plausible solution considered is defined based on the above assessment.

Integrating telecare for chronic disease management in the community: What needs to be done?

PubMed Central

2011-01-01

Background Telecare could greatly facilitate chronic disease management in the community, but despite government promotion and positive demonstrations its implementation has been limited. This study aimed to identify factors inhibiting the implementation and integration of telecare systems for chronic disease management in the community. Methods Large scale comparative study employing qualitative data collection techniques: semi-structured interviews with key informants, task-groups, and workshops; framework analysis of qualitative data informed by Normalization Process Theory. Drawn from telecare services in community and domestic settings in England and Scotland, 221 participants were included, consisting of health professionals and managers; patients and carers; social care professionals and managers; and service suppliers and manufacturers. Results Key barriers to telecare integration were uncertainties about coherent and sustainable service and business models; lack of coordination across social and primary care boundaries, lack of financial or other incentives to include telecare within primary care services; a lack of a sense of continuity with previous service provision and self-care work undertaken by patients; and general uncertainty about the adequacy of telecare systems. These problems led to poor integration of policy and practice. Conclusion Telecare services may offer a cost effective and safe form of care for some people living with chronic illness. Slow and uneven implementation and integration do not stem from problems of adoption. They result from incomplete understanding of the role of telecare systems and subsequent adaption and embeddedness to context, and uncertainties about the best way to develop, coordinate, and sustain services that assist with chronic disease management. Interventions are therefore needed that (i) reduce uncertainty about the ownership of implementation processes and that lock together health and social care agencies; and (ii) ensure user centred rather than biomedical/service-centred models of care. PMID:21619596

Integrating telecare for chronic disease management in the community: what needs to be done?

PubMed

May, Carl R; Finch, Tracy L; Cornford, James; Exley, Catherine; Gately, Claire; Kirk, Sue; Jenkings, K Neil; Osbourne, Janice; Robinson, A Louise; Rogers, Anne; Wilson, Robert; Mair, Frances S

2011-05-27

Telecare could greatly facilitate chronic disease management in the community, but despite government promotion and positive demonstrations its implementation has been limited. This study aimed to identify factors inhibiting the implementation and integration of telecare systems for chronic disease management in the community. Large scale comparative study employing qualitative data collection techniques: semi-structured interviews with key informants, task-groups, and workshops; framework analysis of qualitative data informed by Normalization Process Theory. Drawn from telecare services in community and domestic settings in England and Scotland, 221 participants were included, consisting of health professionals and managers; patients and carers; social care professionals and managers; and service suppliers and manufacturers. Key barriers to telecare integration were uncertainties about coherent and sustainable service and business models; lack of coordination across social and primary care boundaries, lack of financial or other incentives to include telecare within primary care services; a lack of a sense of continuity with previous service provision and self-care work undertaken by patients; and general uncertainty about the adequacy of telecare systems. These problems led to poor integration of policy and practice. Telecare services may offer a cost effective and safe form of care for some people living with chronic illness. Slow and uneven implementation and integration do not stem from problems of adoption. They result from incomplete understanding of the role of telecare systems and subsequent adaption and embeddedness to context, and uncertainties about the best way to develop, coordinate, and sustain services that assist with chronic disease management. Interventions are therefore needed that (i) reduce uncertainty about the ownership of implementation processes and that lock together health and social care agencies; and (ii) ensure user centred rather than biomedical/service-centred models of care.

Confronting uncertainty in wildlife management: performance of grizzly bear management.

PubMed

Artelle, Kyle A; Anderson, Sean C; Cooper, Andrew B; Paquet, Paul C; Reynolds, John D; Darimont, Chris T

2013-01-01

Scientific management of wildlife requires confronting the complexities of natural and social systems. Uncertainty poses a central problem. Whereas the importance of considering uncertainty has been widely discussed, studies of the effects of unaddressed uncertainty on real management systems have been rare. We examined the effects of outcome uncertainty and components of biological uncertainty on hunt management performance, illustrated with grizzly bears (Ursus arctos horribilis) in British Columbia, Canada. We found that both forms of uncertainty can have serious impacts on management performance. Outcome uncertainty alone--discrepancy between expected and realized mortality levels--led to excess mortality in 19% of cases (population-years) examined. Accounting for uncertainty around estimated biological parameters (i.e., biological uncertainty) revealed that excess mortality might have occurred in up to 70% of cases. We offer a general method for identifying targets for exploited species that incorporates uncertainty and maintains the probability of exceeding mortality limits below specified thresholds. Setting targets in our focal system using this method at thresholds of 25% and 5% probability of overmortality would require average target mortality reductions of 47% and 81%, respectively. Application of our transparent and generalizable framework to this or other systems could improve management performance in the presence of uncertainty.

Managing uncertainty in collaborative robotics engineering projects: The influence of task structure and peer interaction

NASA Astrophysics Data System (ADS)

Jordan, Michelle

Uncertainty is ubiquitous in life, and learning is an activity particularly likely to be fraught with uncertainty. Previous research suggests that students and teachers struggle in their attempts to manage the psychological experience of uncertainty and that students often fail to experience uncertainty when uncertainty may be warranted. Yet, few educational researchers have explicitly and systematically observed what students do, their behaviors and strategies, as they attempt to manage the uncertainty they experience during academic tasks. In this study I investigated how students in one fifth grade class managed uncertainty they experienced while engaged in collaborative robotics engineering projects, focusing particularly on how uncertainty management was influenced by task structure and students' interactions with their peer collaborators. The study was initiated at the beginning of instruction related to robotics engineering and preceded through the completion of several long-term collaborative robotics projects, one of which was a design project. I relied primarily on naturalistic observation of group sessions, semi-structured interviews, and collection of artifacts. My data analysis was inductive and interpretive, using qualitative discourse analysis techniques and methods of grounded theory. Three theoretical frameworks influenced the conception and design of this study: community of practice, distributed cognition, and complex adaptive systems theory. Uncertainty was a pervasive experience for the students collaborating in this instructional context. Students experienced uncertainty related to the project activity and uncertainty related to the social system as they collaborated to fulfill the requirements of their robotics engineering projects. They managed their uncertainty through a diverse set of tactics for reducing, ignoring, maintaining, and increasing uncertainty. Students experienced uncertainty from more different sources and used more and different types of uncertainty management strategies in the less structured task setting than in the more structured task setting. Peer interaction was influential because students relied on supportive social response to enact most of their uncertainty management strategies. When students could not garner socially supportive response from their peers, their options for managing uncertainty were greatly reduced.

Reducing uncertainty about objective functions in adaptive management

USGS Publications Warehouse

Williams, B.K.

2012-01-01

This paper extends the uncertainty framework of adaptive management to include uncertainty about the objectives to be used in guiding decisions. Adaptive decision making typically assumes explicit and agreed-upon objectives for management, but allows for uncertainty as to the structure of the decision process that generates change through time. Yet it is not unusual for there to be uncertainty (or disagreement) about objectives, with different stakeholders expressing different views not only about resource responses to management but also about the appropriate management objectives. In this paper I extend the treatment of uncertainty in adaptive management, and describe a stochastic structure for the joint occurrence of uncertainty about objectives as well as models, and show how adaptive decision making and the assessment of post-decision monitoring data can be used to reduce uncertainties of both kinds. Different degrees of association between model and objective uncertainty lead to different patterns of learning about objectives. ?? 2011.

Recognizing and responding to uncertainty: a grounded theory of nurses' uncertainty.

PubMed

Cranley, Lisa A; Doran, Diane M; Tourangeau, Ann E; Kushniruk, Andre; Nagle, Lynn

2012-08-01

There has been little research to date exploring nurses' uncertainty in their practice. Understanding nurses' uncertainty is important because it has potential implications for how care is delivered. The purpose of this study is to develop a substantive theory to explain how staff nurses experience and respond to uncertainty in their practice. Between 2006 and 2008, a grounded theory study was conducted that included in-depth semi-structured interviews. Fourteen staff nurses working in adult medical-surgical intensive care units at two teaching hospitals in Ontario, Canada, participated in the study. The theory recognizing and responding to uncertainty characterizes the processes through which nurses' uncertainty manifested and how it was managed. Recognizing uncertainty involved the processes of assessing, reflecting, questioning, and/or being unable to predict aspects of the patient situation. Nurses' responses to uncertainty highlighted the cognitive-affective strategies used to manage uncertainty. Study findings highlight the importance of acknowledging uncertainty and having collegial support to manage uncertainty. The theory adds to our understanding the processes involved in recognizing uncertainty, strategies and outcomes of managing uncertainty, and influencing factors. Tailored nursing education programs should be developed to assist nurses in developing skills in articulating and managing their uncertainty. Further research is needed to extend, test and refine the theory of recognizing and responding to uncertainty to develop strategies for managing uncertainty. This theory advances the nursing perspective of uncertainty in clinical practice. The theory is relevant to nurses who are faced with uncertainty and complex clinical decisions, to managers who support nurses in their clinical decision-making, and to researchers who investigate ways to improve decision-making and care delivery. ©2012 Sigma Theta Tau International.

Cross-seasonal effects on waterfowl productivity: Implications under climate change

USGS Publications Warehouse

Osnas, Erik; Zhao, Qing; Runge, Michael C.; Boomer, G Scott

2016-01-01

Previous efforts to relate winter-ground precipitation to subsequent reproductive success as measured by the ratio of juveniles to adults in the autumn failed to account for increased vulnerability of juvenile ducks to hunting and uncertainty in the estimated age ratio. Neglecting increased juvenile vulnerability will positively bias the mean productivity estimate, and neglecting increased vulnerability and estimation uncertainty will positively bias the year-to-year variance in productivity because raw age ratios are the product of sampling variation, the year-specific vulnerability, and year-specific reproductive success. Therefore, we estimated the effects of cumulative winter precipitation in the California Central Valley and the Mississippi Alluvial Valley on pintail (Anas acuta) and mallard (Anas platyrhnchos) reproduction, respectively, using hierarchical Bayesian methods to correct for sampling bias in productivity estimates and observation error in covariates. We applied the model to a hunter-collected parts survey implemented by the United States Fish and Wildlife Service and band recoveries reported to the United States Geological Survey Bird Banding Laboratory using data from 1961 to 2013. We compared our results to previous estimates that used simple linear regression on uncorrected age ratios from a smaller subset of years in pintail (1961–1985). Like previous analyses, we found large and consistent effects of population size and wetland conditions in prairie Canada on mallard productivity, and large effects of population size and mean latitude of the observed breeding population on pintail productivity. Unlike previous analyses, we report a large amount of uncertainty in the estimated effects of wintering-ground precipitation on pintail and mallard productivity, with considerable uncertainty in the sign of the estimated main effect, although the posterior medians of precipitation effects were consistent with past studies. We found more consistent estimates in the sign of an interaction effect between population size and precipitation, suggesting that wintering-ground precipitation has a larger effect in years of high population size, especially for pintail. When we used the estimated effects in a population model to derive a sustainable harvest and population size projection (i.e., a yield curve), there was considerable uncertainty in the effect of increased or decreased wintering-ground precipitation on sustainable harvest potential and population size. These results suggest that the mechanism of cross-seasonal effects between winter habitat and reproduction in ducks occurs through a reduction in the strength of density dependence in years of above-average wintering-ground precipitation. We suggest additional investigation of the underlying mechanisms and that habitat managers and decision-makers consider the level of uncertainty in these estimates when attempting to integrate habitat management and harvest management decisions. Collection of annual data on the status of wintering-ground habitat in a rigorous sampling framework would likely be the most direct way to improve understanding of mechanisms and inform management.

A controllable sensor management algorithm capable of learning

NASA Astrophysics Data System (ADS)

Osadciw, Lisa A.; Veeramacheneni, Kalyan K.

2005-03-01

Sensor management technology progress is challenged by the geographic space it spans, the heterogeneity of the sensors, and the real-time timeframes within which plans controlling the assets are executed. This paper presents a new sensor management paradigm and demonstrates its application in a sensor management algorithm designed for a biometric access control system. This approach consists of an artificial intelligence (AI) algorithm focused on uncertainty measures, which makes the high level decisions to reduce uncertainties and interfaces with the user, integrated cohesively with a bottom up evolutionary algorithm, which optimizes the sensor network"s operation as determined by the AI algorithm. The sensor management algorithm presented is composed of a Bayesian network, the AI algorithm component, and a swarm optimization algorithm, the evolutionary algorithm. Thus, the algorithm can change its own performance goals in real-time and will modify its own decisions based on observed measures within the sensor network. The definition of the measures as well as the Bayesian network determine the robustness of the algorithm and its utility in reacting dynamically to changes in the global system.

Against conventional wisdom: when the public, the media, and medical practice collide.

PubMed

Jensen, Jakob D; Krakow, Melinda; John, Kevin K; Liu, Miao

2013-01-01

In 2009, the U.S. Preventive Services Task Force released new mammography screening guidelines that sparked a torrent of criticism. The subsequent conflict was significant and pitted the Task Force against other health organizations, advocacy groups, the media, and the public at large. We argue that this controversy was driven by the systematic removal of uncertainty from science communication. To increase comprehension and adherence, health information communicators remove caveats, limitations, and hedging so science appears simple and more certain. This streamlining process is, in many instances, initiated by researchers as they engage in dissemination of their findings, and it is facilitated by public relations professionals, journalists, public health practitioners, and others whose tasks involve using the results from research for specific purposes. Uncertainty is removed from public communication because many communicators believe that it is difficult for people to process and/or that it is something the audience wants to avoid. Uncertainty management theory posits that people can find meaning and value in uncertainty. We define key terms relevant to uncertainty management, describe research on the processing of uncertainty, identify directions for future research, and offer recommendations for scientists, practitioners, and media professionals confronted with uncertain findings. Science is routinely simplified as it is prepared for public consumption. In line with the model of information overload, this practice may increase short-term adherence to recommendations at the expense of long-term message consistency and trust in science.

Against conventional wisdom: when the public, the media, and medical practice collide

PubMed Central

2013-01-01

Background In 2009, the U.S. Preventive Services Task Force released new mammography screening guidelines that sparked a torrent of criticism. The subsequent conflict was significant and pitted the Task Force against other health organizations, advocacy groups, the media, and the public at large. We argue that this controversy was driven by the systematic removal of uncertainty from science communication. To increase comprehension and adherence, health information communicators remove caveats, limitations, and hedging so science appears simple and more certain. This streamlining process is, in many instances, initiated by researchers as they engage in dissemination of their findings, and it is facilitated by public relations professionals, journalists, public health practitioners, and others whose tasks involve using the results from research for specific purposes. Analysis Uncertainty is removed from public communication because many communicators believe that it is difficult for people to process and/or that it is something the audience wants to avoid. Uncertainty management theory posits that people can find meaning and value in uncertainty. We define key terms relevant to uncertainty management, describe research on the processing of uncertainty, identify directions for future research, and offer recommendations for scientists, practitioners, and media professionals confronted with uncertain findings. Conclusions Science is routinely simplified as it is prepared for public consumption. In line with the model of information overload, this practice may increase short-term adherence to recommendations at the expense of long-term message consistency and trust in science. PMID:24565173

Evaluating a multispecies adaptive management framework: Must uncertainty impede effective decision-making?

USGS Publications Warehouse

Smith, David R.; McGowan, Conor P.; Daily, Jonathan P.; Nichols, James D.; Sweka, John A.; Lyons, James E.

2013-01-01

Application of adaptive management to complex natural resource systems requires careful evaluation to ensure that the process leads to improved decision-making. As part of that evaluation, adaptive policies can be compared with alternative nonadaptive management scenarios. Also, the value of reducing structural (ecological) uncertainty to achieving management objectives can be quantified.A multispecies adaptive management framework was recently adopted by the Atlantic States Marine Fisheries Commission for sustainable harvest of Delaware Bay horseshoe crabs Limulus polyphemus, while maintaining adequate stopover habitat for migrating red knots Calidris canutus rufa, the focal shorebird species. The predictive model set encompassed the structural uncertainty in the relationships between horseshoe crab spawning, red knot weight gain and red knot vital rates. Stochastic dynamic programming was used to generate a state-dependent strategy for harvest decisions given that uncertainty. In this paper, we employed a management strategy evaluation approach to evaluate the performance of this adaptive management framework. Active adaptive management was used by including model weights as state variables in the optimization and reducing structural uncertainty by model weight updating.We found that the value of information for reducing structural uncertainty is expected to be low, because the uncertainty does not appear to impede effective management. Harvest policy responded to abundance levels of both species regardless of uncertainty in the specific relationship that generated those abundances. Thus, the expected horseshoe crab harvest and red knot abundance were similar when the population generating model was uncertain or known, and harvest policy was robust to structural uncertainty as specified.Synthesis and applications. The combination of management strategy evaluation with state-dependent strategies from stochastic dynamic programming was an informative approach to evaluate adaptive management performance and value of learning. Although natural resource decisions are characterized by uncertainty, not all uncertainty will cause decisions to be altered substantially, as we found in this case. It is important to incorporate uncertainty into the decision framing and evaluate the effect of reducing that uncertainty on achieving the desired outcomes

Water supply management using an extended group fuzzy decision-making method: a case study in north-eastern Iran

NASA Astrophysics Data System (ADS)

Minatour, Yasser; Bonakdari, Hossein; Zarghami, Mahdi; Bakhshi, Maryam Ali

2015-09-01

The purpose of this study was to develop a group fuzzy multi-criteria decision-making method to be applied in rating problems associated with water resources management. Thus, here Chen's group fuzzy TOPSIS method extended by a difference technique to handle uncertainties of applying a group decision making. Then, the extended group fuzzy TOPSIS method combined with a consistency check. In the presented method, initially linguistic judgments are being surveyed via a consistency checking process, and afterward these judgments are being used in the extended Chen's fuzzy TOPSIS method. Here, each expert's opinion is turned to accurate mathematical numbers and, then, to apply uncertainties, the opinions of group are turned to fuzzy numbers using three mathematical operators. The proposed method is applied to select the optimal strategy for the rural water supply of Nohoor village in north-eastern Iran, as a case study and illustrated example. Sensitivity analyses test over results and comparing results with project reality showed that proposed method offered good results for water resources projects.

A game theoretic controller for a linear time-invariant system with parameter uncertainty and its application to the Space Station

NASA Technical Reports Server (NTRS)

Rhee, Ihnseok; Speyer, Jason L.

1990-01-01

A game theoretic controller is developed for a linear time-invariant system with parameter uncertainties in system and input matrices. The input-output decomposition modeling for the plant uncertainty is adopted. The uncertain dynamic system is represented as an internal feedback loop in which the system is assumed forced by fictitious disturbance caused by the parameter uncertainty. By considering the input and the fictitious disturbance as two noncooperative players, a differential game problem is constructed. It is shown that the resulting time invariant controller stabilizes the uncertain system for a prescribed uncertainty bound. This game theoretic controller is applied to the momentum management and attitude control of the Space Station in the presence of uncertainties in the moments of inertia. Inclusion of the external disturbance torque to the design procedure results in a dynamical feedback controller which consists of conventional PID control and cyclic disturbance rejection filter. It is shown that the game theoretic design, comparing to the LQR design or pole placement design, improves the stability robustness with respect to inertia variations.

Addressing Uncertainty in the ISCORS Multimedia Radiological Dose Assessment of Municipal Sewage Sludge and Ash

NASA Astrophysics Data System (ADS)

Chiu, W. A.; Bachmaier, J.; Bastian, R.; Hogan, R.; Lenhart, T.; Schmidt, D.; Wolbarst, A.; Wood, R.; Yu, C.

2002-05-01

Managing municipal wastewater at publicly owned treatment works (POTWs) leads to the production of considerable amounts of residual solid material, which is known as sewage sludge or biosolids. If the wastewater entering a POTW contains radioactive material, then the treatment process may concentrate radionuclides in the sludge, leading to possible exposure of the general public or the POTW workers. The Sewage Sludge Subcommittee of the Interagency Steering Committee on Radiation Standards (ISCORS), which consists of representatives from the Environmental Protection Agency, the Nuclear Regulatory Commission, the Department of Energy, and several other federal, state, and local agencies, is developing guidance for POTWs on the management of sewage sludge that may contain radioactive materials. As part of this effort, they are conducting an assessment of potential radiation exposures using the Department of Energy's RESidual RADioactivity (RESRAD) family of computer codes developed by Argonne National Laboratory. This poster describes several approaches used by the Subcommittee to address the uncertainties associated with their assessment. For instance, uncertainties in the source term are addressed through a combination of analytic and deterministic computer code calculations. Uncertainties in the exposure pathways are addressed through the specification of a number of hypothetical scenarios, some of which can be scaled to address changes in exposure parameters. In addition, the uncertainty in some physical and behavioral parameters are addressed through probabilistic methods.

Evaluation on uncertainty sources in projecting hydrological changes over the Xijiang River basin in South China

NASA Astrophysics Data System (ADS)

Yuan, Fei; Zhao, Chongxu; Jiang, Yong; Ren, Liliang; Shan, Hongcui; Zhang, Limin; Zhu, Yonghua; Chen, Tao; Jiang, Shanhu; Yang, Xiaoli; Shen, Hongren

2017-11-01

Projections of hydrological changes are associated with large uncertainties from different sources, which should be quantified for an effective implementation of water management policies adaptive to future climate change. In this study, a modeling chain framework to project future hydrological changes and the associated uncertainties in the Xijiang River basin, South China, was established. The framework consists of three emission scenarios (ESs), four climate models (CMs), four statistical downscaling (SD) methods, four hydrological modeling (HM) schemes, and four probability distributions (PDs) for extreme flow frequency analyses. Direct variance method was adopted to analyze the manner by which uncertainty sources such as ES, CM, SD, and HM affect the estimates of future evapotranspiration (ET) and streamflow, and to quantify the uncertainties of PDs in future flood and drought risk assessment. Results show that ES is one of the least important uncertainty sources in most situations. CM, in general, is the dominant uncertainty source for the projections of monthly ET and monthly streamflow during most of the annual cycle, daily streamflow below the 99.6% quantile level, and extreme low flow. SD is the most predominant uncertainty source in the projections of extreme high flow, and has a considerable percentage of uncertainty contribution in monthly streamflow projections in July-September. The effects of SD in other cases are negligible. HM is a non-ignorable uncertainty source that has the potential to produce much larger uncertainties for the projections of low flow and ET in warm and wet seasons than for the projections of high flow. PD contributes a larger percentage of uncertainty in extreme flood projections than it does in extreme low flow estimates. Despite the large uncertainties in hydrological projections, this work found that future extreme low flow would undergo a considerable reduction, and a noticeable increase in drought risk in the Xijiang River basin would be expected. Thus, the necessity of employing effective water-saving techniques and adaptive water resources management strategies for drought disaster mitigation should be addressed.

Uncertainties in historical pollution data from sedimentary records from an Australian urban floodplain lake

NASA Astrophysics Data System (ADS)

Lintern, A.; Leahy, P.; Deletic, A.; Heijnis, H.; Zawadzki, A.; Gadd, P.; McCarthy, D.

2018-05-01

Sediment cores from aquatic environments can provide valuable information about historical pollution levels and sources. However, there is little understanding of the uncertainties associated with these findings. The aim of this study is to fill this knowledge gap by proposing a framework for quantifying the uncertainties in historical heavy metal pollution records reconstructed from sediment cores. This uncertainty framework consists of six sources of uncertainty: uncertainties in (1) metals analysis methods, (2) spatial variability of sediment core heavy metal profiles, (3) sub-sampling intervals, (4) the sediment chronology, (5) the assumption that metal levels in bed sediments reflect the magnitude of metal inputs into the aquatic system, and (6) post-depositional transformation of metals. We apply this uncertainty framework to an urban floodplain lake in South-East Australia (Willsmere Billabong). We find that for this site, uncertainties in historical dated heavy metal profiles can be up to 176%, largely due to uncertainties in the sediment chronology, and in the assumption that the settled heavy metal mass is equivalent to the heavy metal mass entering the aquatic system. As such, we recommend that future studies reconstructing historical pollution records using sediment cores from aquatic systems undertake an investigation of the uncertainties in the reconstructed pollution record, using the uncertainty framework provided in this study. We envisage that quantifying and understanding the uncertainties associated with the reconstructed pollution records will facilitate the practical application of sediment core heavy metal profiles in environmental management projects.

Definition, Capabilities, and Components of a Terrestrial Carbon Monitoring System

NASA Technical Reports Server (NTRS)

West, Tristram O.; Brown, Molly E.; Duren, Riley M.; Ogle, Stephen M.; Moss, Richard H.

2013-01-01

Research efforts for effectively and consistently monitoring terrestrial carbon are increasing in number. As such, there is a need to define carbon monitoring and how it relates to carbon cycle science and carbon management. There is also a need to identify capabilities of a carbon monitoring system and the system components needed to develop the capabilities. Capabilities that enable the effective application of a carbon monitoring system for monitoring and management purposes may include: reconciling carbon stocks and fluxes, developing consistency across spatial and temporal scales, tracking horizontal movement of carbon, attribution of emissions to originating sources, cross-sectoral accounting, uncertainty quantification, redundancy and policy relevance. Focused research is needed to integrate these capabilities for sustained estimates of carbon stocks and fluxes. Additionally, if monitoring is intended to inform management decisions, management priorities should be considered prior to development of a monitoring system.

"I Don't Want to Be an Ostrich": Managing Mothers' Uncertainty during BRCA1/2 Genetic Counseling.

PubMed

Fisher, Carla L; Roccotagliata, Thomas; Rising, Camella J; Kissane, David W; Glogowski, Emily A; Bylund, Carma L

2017-06-01

Families who face genetic disease risk must learn how to grapple with complicated uncertainties about their health and future on a long-term basis. Women who undergo BRCA 1/2 genetic testing describe uncertainty related to personal risk as well as their loved ones', particularly daughters', risk. The genetic counseling setting is a prime opportunity for practitioners to help mothers manage uncertainty in the moment but also once they leave a session. Uncertainty Management Theory (UMT) helps to illuminate the various types of uncertainty women encounter and the important role of communication in uncertainty management. Informed by UMT, we conducted a thematic analysis of 16 genetic counseling sessions between practitioners and mothers at risk for, or carriers of, a BRCA1/2 mutation. Five themes emerged that represent communication strategies used to manage uncertainty: 1) addresses myths, misunderstandings, or misconceptions; 2) introduces uncertainty related to science; 3) encourages information seeking or sharing about family medical history; 4) reaffirms or validates previous behavior or decisions; and 5) minimizes the probability of personal risk or family members' risk. Findings illustrate the critical role of genetic counseling for families in managing emotionally challenging risk-related uncertainty. The analysis may prove beneficial to not only genetic counseling practice but generations of families at high risk for cancer who must learn strategic approaches to managing a complex web of uncertainty that can challenge them for a lifetime.

Reducing, Maintaining, or Escalating Uncertainty? The Development and Validation of Four Uncertainty Preference Scales Related to Cancer Information Seeking and Avoidance.

PubMed

Carcioppolo, Nick; Yang, Fan; Yang, Qinghua

2016-09-01

Uncertainty is a central characteristic of many aspects of cancer prevention, screening, diagnosis, and treatment. Brashers's (2001) uncertainty management theory details the multifaceted nature of uncertainty and describes situations in which uncertainty can both positively and negatively affect health outcomes. The current study extends theory on uncertainty management by developing four scale measures of uncertainty preferences in the context of cancer. Two national surveys were conducted to validate the scales and assess convergent and concurrent validity. Results support the factor structure of each measure and provide general support across multiple validity assessments. These scales can advance research on uncertainty and cancer communication by providing researchers with measures that address multiple aspects of uncertainty management.

Assessment of BTEX-induced health risk under multiple uncertainties at a petroleum-contaminated site: An integrated fuzzy stochastic approach

NASA Astrophysics Data System (ADS)

Zhang, Xiaodong; Huang, Guo H.

2011-12-01

Groundwater pollution has gathered more and more attention in the past decades. Conducting an assessment of groundwater contamination risk is desired to provide sound bases for supporting risk-based management decisions. Therefore, the objective of this study is to develop an integrated fuzzy stochastic approach to evaluate risks of BTEX-contaminated groundwater under multiple uncertainties. It consists of an integrated interval fuzzy subsurface modeling system (IIFMS) and an integrated fuzzy second-order stochastic risk assessment (IFSOSRA) model. The IIFMS is developed based on factorial design, interval analysis, and fuzzy sets approach to predict contaminant concentrations under hybrid uncertainties. Two input parameters (longitudinal dispersivity and porosity) are considered to be uncertain with known fuzzy membership functions, and intrinsic permeability is considered to be an interval number with unknown distribution information. A factorial design is conducted to evaluate interactive effects of the three uncertain factors on the modeling outputs through the developed IIFMS. The IFSOSRA model can systematically quantify variability and uncertainty, as well as their hybrids, presented as fuzzy, stochastic and second-order stochastic parameters in health risk assessment. The developed approach haw been applied to the management of a real-world petroleum-contaminated site within a western Canada context. The results indicate that multiple uncertainties, under a combination of information with various data-quality levels, can be effectively addressed to provide supports in identifying proper remedial efforts. A unique contribution of this research is the development of an integrated fuzzy stochastic approach for handling various forms of uncertainties associated with simulation and risk assessment efforts.

The uncertainty room: strategies for managing uncertainty in a surgical waiting room.

PubMed

Stone, Anne M; Lammers, John C

2012-01-01

To describe experiences of uncertainty and management strategies for staff working with families in a hospital waiting room. A 288-bed, nonprofit community hospital in a Midwestern city. Data were collected during individual, semistructured interviews with 3 volunteers, 3 technical staff members, and 1 circulating nurse (n = 7), and during 40 hours of observation in a surgical waiting room. Interview transcripts were analyzed using constant comparative techniques. The surgical waiting room represents the intersection of several sources of uncertainty that families experience. Findings also illustrate the ways in which staff manage the uncertainty of families in the waiting room by communicating support. Staff in surgical waiting rooms are responsible for managing family members' uncertainty related to insufficient information. Practically, this study provided some evidence that staff are expected to help manage the uncertainty that is typical in a surgical waiting room, further highlighting the important role of communication in improving family members' experiences.

Uncertainty functions of modelled soil organic carbon changes in response to crop management derived from a French long term experiments dataset

NASA Astrophysics Data System (ADS)

Dimassi, Bassem; Guenet, Bertrand; Mary, Bruno; Trochard, Robert; Bouthier, Alain; Duparque, Annie; Sagot, Stéphanie; Houot, Sabine; Morel, Christian; Martin, Manuel

2016-04-01

The land use, land-use change and forestry (LULUCF) activities and crop management (CM) in Europe could be an important carbon sink through soil organic carbon (SOC) sequestration. Recently, the (EU decision 529/2013) requires European Union's member states to assess modalities to include greenhouse gas (GHG) emissions and removals resulting from activities relating to LULUCF and CM into the Union's (GHG) emissions reduction commitment and their national inventories reports (NIR). Tier 1, the commonly used method to estimate emissions for NIR, provides a framework for measuring SOC stocks changes. However, estimations have high uncertainty, especially in response to crop management at regional and specific national contexts. Understanding and quantifying this uncertainty with accurate confidence interval is crucial for reliably reporting and support decision-making and policies that aims to mitigate greenhouse gases through soil C storage. Here, we used the Tier 3 method, consisting of process-based modelling, to address the issue of uncertainty quantification at national scale in France. Specifically, we used 20 Long-term croplands experiments (LTE) in France with more than 100 treatments taking into account different agricultural practices such as tillage, organic amendment, inorganic fertilization, cover crops, etc. These LTE were carefully selected because they are well characterized with periodic SOC stocks monitoring overtime and covered a wide range of pedo-climatic conditions. We applied linear mixed effect model to statistically model, as a function of soil, climate and cropping system characteristics, the uncertainty resulting from applying this Tier 3 approach. The model was fitted on the dataset yielded by comparing the simulated (with the Century model V 4.5) to the observed SOC changes on the LTE at hand. This mixed effect model will then be used to derive uncertainty related to the simulation of SOC stocks changes of the French Soil Monitoring Network (FSMN) where only one measurement is done in 16 Km regular grid. These simulations on the grid will be in turn used for NIR. Preliminary results suggest that the model do not adequately simulate SOC stocks levels but succeeds at capturing SOC changes due to management, despite the fact that the model does not explicitly simulate some management such as tillage. This is probably due to inappropriate model parametrization especially for crops and thus Cinput in the French context and/or model initialization.

Integrating info-gap decision theory with robust population management: a case study using the Mountain Plover.

PubMed

van der Burg, Max Post; Tyre, Andrew J

2011-01-01

Wildlife managers often make decisions under considerable uncertainty. In the most extreme case, a complete lack of data leads to uncertainty that is unquantifiable. Information-gap decision theory deals with assessing management decisions under extreme uncertainty, but it is not widely used in wildlife management. So too, robust population management methods were developed to deal with uncertainties in multiple-model parameters. However, the two methods have not, as yet, been used in tandem to assess population management decisions. We provide a novel combination of the robust population management approach for matrix models with the information-gap decision theory framework for making conservation decisions under extreme uncertainty. We applied our model to the problem of nest survival management in an endangered bird species, the Mountain Plover (Charadrius montanus). Our results showed that matrix sensitivities suggest that nest management is unlikely to have a strong effect on population growth rate, confirming previous analyses. However, given the amount of uncertainty about adult and juvenile survival, our analysis suggested that maximizing nest marking effort was a more robust decision to maintain a stable population. Focusing on the twin concepts of opportunity and robustness in an information-gap model provides a useful method of assessing conservation decisions under extreme uncertainty.

50 CFR 648.231 - Closures.

Code of Federal Regulations, 2011 CFR

2011-10-01

... Dogfish Monitoring Committee shall identify and review the relevant sources of management uncertainty to... management uncertainty that were considered, technical approaches to mitigating these sources of uncertainty..., DEPARTMENT OF COMMERCE FISHERIES OF THE NORTHEASTERN UNITED STATES Management Measures for the Spiny Dogfish...

Probabilistic graphs as a conceptual and computational tool in hydrology and water management

NASA Astrophysics Data System (ADS)

Schoups, Gerrit

2014-05-01

Originally developed in the fields of machine learning and artificial intelligence, probabilistic graphs constitute a general framework for modeling complex systems in the presence of uncertainty. The framework consists of three components: 1. Representation of the model as a graph (or network), with nodes depicting random variables in the model (e.g. parameters, states, etc), which are joined together by factors. Factors are local probabilistic or deterministic relations between subsets of variables, which, when multiplied together, yield the joint distribution over all variables. 2. Consistent use of probability theory for quantifying uncertainty, relying on basic rules of probability for assimilating data into the model and expressing unknown variables as a function of observations (via the posterior distribution). 3. Efficient, distributed approximation of the posterior distribution using general-purpose algorithms that exploit model structure encoded in the graph. These attributes make probabilistic graphs potentially useful as a conceptual and computational tool in hydrology and water management (and beyond). Conceptually, they can provide a common framework for existing and new probabilistic modeling approaches (e.g. by drawing inspiration from other fields of application), while computationally they can make probabilistic inference feasible in larger hydrological models. The presentation explores, via examples, some of these benefits.

An index-based robust decision making framework for watershed management in a changing climate.

PubMed

Kim, Yeonjoo; Chung, Eun-Sung

2014-03-01

This study developed an index-based robust decision making framework for watershed management dealing with water quantity and quality issues in a changing climate. It consists of two parts of management alternative development and analysis. The first part for alternative development consists of six steps: 1) to understand the watershed components and process using HSPF model, 2) to identify the spatial vulnerability ranking using two indices: potential streamflow depletion (PSD) and potential water quality deterioration (PWQD), 3) to quantify the residents' preferences on water management demands and calculate the watershed evaluation index which is the weighted combinations of PSD and PWQD, 4) to set the quantitative targets for water quantity and quality, 5) to develop a list of feasible alternatives and 6) to eliminate the unacceptable alternatives. The second part for alternative analysis has three steps: 7) to analyze all selected alternatives with a hydrologic simulation model considering various climate change scenarios, 8) to quantify the alternative evaluation index including social and hydrologic criteria with utilizing multi-criteria decision analysis methods and 9) to prioritize all options based on a minimax regret strategy for robust decision. This framework considers the uncertainty inherent in climate models and climate change scenarios with utilizing the minimax regret strategy, a decision making strategy under deep uncertainty and thus this procedure derives the robust prioritization based on the multiple utilities of alternatives from various scenarios. In this study, the proposed procedure was applied to the Korean urban watershed, which has suffered from streamflow depletion and water quality deterioration. Our application shows that the framework provides a useful watershed management tool for incorporating quantitative and qualitative information into the evaluation of various policies with regard to water resource planning and management. Copyright © 2013 Elsevier B.V. All rights reserved.

Positive Cervical Artery Testing in a Patient with Chronic Whiplash Syndrome: Clinical Decision-Making in the Presence of Diagnostic Uncertainty

PubMed Central

Graziano, David L.; Nitsch, Wanda; Huijbregts, Peter A.

2007-01-01

This case report describes the diagnosis and management of a 43-year-old female patient who had sustained an injury to her neck in a motor-vehicle accident two years earlier. The major symptoms described by the patient included headache and neck pain, but history and examination also revealed signs and symptoms potentially indicative of cervical artery compromise. Physical therapy management initially consisted of soft tissue and non-thrust joint manipulation of the lower cervical and thoracic spine, specific exercise prescription, and superficial heat. Cervical vascular compromise was re-evaluated by way of the sustained extension-rotation test. When at the fifth visit this test no longer produced symptoms potentially indicative of vascular compromise, upper cervical diagnosis and management consisting of soft tissue and non-thrust joint manipulation was added. A positive outcome was achieved both at the impairment level and with regard to limitations in activities, the latter including increased performance at work, a return to previous reading activities, improved length and quality of sleep, and greater comfort while driving. At discharge, the patient reported only occasional pain and mild limitations in activities. This report describes the positive outcomes in a patient with chronic whiplash syndrome; however, its main emphasis lies in the discussion and critical evaluation of clinical reasoning in the presence of diagnostic uncertainty with regard to cervical artery compromise. PMID:19066653

Dealing with Uncertainty in Water Management: Finding the Right Balance Between Risk and Opportunity to Build Trust and Create Value

NASA Astrophysics Data System (ADS)

Islam, S.; Susskind, L.

2012-12-01

Most difficulties in water management are the product of rigid assumptions about how water ought to be allocated in the face of ever-increasing demand and growing uncertainty. When stakeholders face contending water claims, one of the biggest obstacles to reaching agreement is uncertainty. Specifically, there are three types of uncertainty that need to be addressed: uncertainty of information, uncertainty of action and uncertainty of perception. All three shape water management decisions. Contrary to traditional approaches, we argue that management of uncertainty needs to include both risks and opportunities. When parties treat water as a flexible rather than a fixed resource, opportunities to create value can be invented. When they use the right processes and mechanisms to enhance trust, even parties in conflict can reach agreements that satisfy their competing water needs and interests simultaneously. Using examples from several boundary crossing water cases we will show how this balance between risks and opportunities can be found to manage water resources for an uncertain future.

Seniors' uncertainty management of direct-to-consumer prescription drug advertising usefulness.

PubMed

DeLorme, Denise E; Huh, Jisu

2009-09-01

This study provides insight into seniors' perceptions of and responses to direct-to-consumer prescription drug advertising (DTCA) usefulness, examines support for DTCA regulation as a type of uncertainty management, and extends and gives empirical voice to previous survey results through methodological triangulation. In-depth interview findings revealed that, for most informants, DTCA usefulness was uncertain and this uncertainty stemmed from 4 sources. The majority had negative responses to DTCA uncertainty and relied on 2 uncertainty-management strategies: information seeking from physicians, and inferences of and support for some government regulation of DTCA. Overall, the findings demonstrate the viability of uncertainty management theory (Brashers, 2001, 2007) for mass-mediated health communication, specifically DTCA. The article concludes with practical implications and research recommendations.

Operationalising uncertainty in data and models for integrated water resources management.

PubMed

Blind, M W; Refsgaard, J C

2007-01-01

Key sources of uncertainty of importance for water resources management are (1) uncertainty in data; (2) uncertainty related to hydrological models (parameter values, model technique, model structure); and (3) uncertainty related to the context and the framing of the decision-making process. The European funded project 'Harmonised techniques and representative river basin data for assessment and use of uncertainty information in integrated water management (HarmoniRiB)' has resulted in a range of tools and methods to assess such uncertainties, focusing on items (1) and (2). The project also engaged in a number of discussions surrounding uncertainty and risk assessment in support of decision-making in water management. Based on the project's results and experiences, and on the subsequent discussions a number of conclusions can be drawn on the future needs for successful adoption of uncertainty analysis in decision support. These conclusions range from additional scientific research on specific uncertainties, dedicated guidelines for operational use to capacity building at all levels. The purpose of this paper is to elaborate on these conclusions and anchoring them in the broad objective of making uncertainty and risk assessment an essential and natural part in future decision-making processes.

Parameter and input data uncertainty estimation for the assessment of water resources in two sub-basins of the Limpopo River Basin

NASA Astrophysics Data System (ADS)

Oosthuizen, Nadia; Hughes, Denis A.; Kapangaziwiri, Evison; Mwenge Kahinda, Jean-Marc; Mvandaba, Vuyelwa

2018-05-01

The demand for water resources is rapidly growing, placing more strain on access to water and its management. In order to appropriately manage water resources, there is a need to accurately quantify available water resources. Unfortunately, the data required for such assessment are frequently far from sufficient in terms of availability and quality, especially in southern Africa. In this study, the uncertainty related to the estimation of water resources of two sub-basins of the Limpopo River Basin - the Mogalakwena in South Africa and the Shashe shared between Botswana and Zimbabwe - is assessed. Input data (and model parameters) are significant sources of uncertainty that should be quantified. In southern Africa water use data are among the most unreliable sources of model input data because available databases generally consist of only licensed information and actual use is generally unknown. The study assesses how these uncertainties impact the estimation of surface water resources of the sub-basins. Data on farm reservoirs and irrigated areas from various sources were collected and used to run the model. Many farm dams and large irrigation areas are located in the upper parts of the Mogalakwena sub-basin. Results indicate that water use uncertainty is small. Nevertheless, the medium to low flows are clearly impacted. The simulated mean monthly flows at the outlet of the Mogalakwena sub-basin were between 22.62 and 24.68 Mm3 per month when incorporating only the uncertainty related to the main physical runoff generating parameters. The range of total predictive uncertainty of the model increased to between 22.15 and 24.99 Mm3 when water use data such as small farm and large reservoirs and irrigation were included. For the Shashe sub-basin incorporating only uncertainty related to the main runoff parameters resulted in mean monthly flows between 11.66 and 14.54 Mm3. The range of predictive uncertainty changed to between 11.66 and 17.72 Mm3 after the uncertainty in water use information was added.

A robust momentum management and attitude control system for the space station

NASA Technical Reports Server (NTRS)

Speyer, J. L.; Rhee, Ihnseok

1991-01-01

A game theoretic controller is synthesized for momentum management and attitude control of the Space Station in the presence of uncertainties in the moments of inertia. Full state information is assumed since attitude rates are assumed to be very assurately measured. By an input-output decomposition of the uncertainty in the system matrices, the parameter uncertainties in the dynamic system are represented as an unknown gain associated with an internal feedback loop (IFL). The input and output matrices associated with the IFL form directions through which the uncertain parameters affect system response. If the quadratic form of the IFL output augments the cost criterion, then enhanced parameter robustness is anticipated. By considering the input and the input disturbance from the IFL as two noncooperative players, a linear-quadratic differential game is constructed. The solution in the form of a linear controller is used for synthesis. Inclusion of the external disturbance torques results in a dynamic feedback controller which consists of conventional PID (proportional integral derivative) control and cyclic disturbance rejection filters. It is shown that the game theoretic design allows large variations in the inertias in directions of importance.

Robust momentum management and attitude control system for the Space Station

NASA Technical Reports Server (NTRS)

Rhee, Ihnseok; Speyer, Jason L.

1992-01-01

A game theoretic controller is synthesized for momentum management and attitude control of the Space Station in the presence of uncertainties in the moments of inertia. Full state information is assumed since attitude rates are assumed to be very accurately measured. By an input-output decomposition of the uncertainty in the system matrices, the parameter uncertainties in the dynamic system are represented as an unknown gain associated with an internal feedback loop (IFL). The input and output matrices associated with the IFL form directions through which the uncertain parameters affect system response. If the quadratic form of the IFL output augments the cost criterion, then enhanced parameter robustness is anticipated. By considering the input and the input disturbance from the IFL as two noncooperative players, a linear-quadratic differential game is constructed. The solution in the form of a linear controller is used for synthesis. Inclusion of the external disturbance torques results in a dynamic feedback controller which consists of conventional PID (proportional integral derivative) control and cyclic disturbance rejection filters. It is shown that the game theoretic design allows large variations in the inertias in directions of importance.

Atmospheric climate data: Problems and promises

NASA Technical Reports Server (NTRS)

1986-01-01

The explosive growth in the quantity and diversity of weather and climate data, the growing handicap that the distinction between weather and climate in NOAA imposes on the efficient management and use of data is discussed. Also discussed is the uncertainty induced by the lack of clear commitment and consistent policies regarding federal roles and responsibilities in operating and maintaining the national weather and climate data system.

Development of Risk Uncertainty Factors from Historical NASA Projects

NASA Technical Reports Server (NTRS)

Amer, Tahani R.

2011-01-01

NASA is a good investment of federal funds and strives to provide the best value to the nation. NASA has consistently budgeted to unrealistic cost estimates, which are evident in the cost growth in many of its programs. In this investigation, NASA has been using available uncertainty factors from the Aerospace Corporation, Air Force, and Booz Allen Hamilton to develop projects risk posture. NASA has no insight into the developmental of these factors and, as demonstrated here, this can lead to unrealistic risks in many NASA Programs and projects (P/p). The primary contribution of this project is the development of NASA missions uncertainty factors, from actual historical NASA projects, to aid cost-estimating as well as for independent reviews which provide NASA senior management with information and analysis to determine the appropriate decision regarding P/p. In general terms, this research project advances programmatic analysis for NASA projects.

Wildfire Decision Making Under Uncertainty

NASA Astrophysics Data System (ADS)

Thompson, M.

2013-12-01

Decisions relating to wildfire management are subject to multiple sources of uncertainty, and are made by a broad range of individuals, across a multitude of environmental and socioeconomic contexts. In this presentation I will review progress towards identification and characterization of uncertainties and how this information can support wildfire decision-making. First, I will review a typology of uncertainties common to wildfire management, highlighting some of the more salient sources of uncertainty and how they present challenges to assessing wildfire risk. This discussion will cover the expanding role of burn probability modeling, approaches for characterizing fire effects, and the role of multi-criteria decision analysis, and will provide illustrative examples of integrated wildfire risk assessment across a variety of planning scales. Second, I will describe a related uncertainty typology that focuses on the human dimensions of wildfire management, specifically addressing how social, psychological, and institutional factors may impair cost-effective risk mitigation. This discussion will encompass decision processes before, during, and after fire events, with a specific focus on active management of complex wildfire incidents. An improved ability to characterize uncertainties faced in wildfire management could lead to improved delivery of decision support, targeted communication strategies, and ultimately to improved wildfire management outcomes.

Uncertainty, robustness, and the value of information in managing an expanding Arctic goose population

USGS Publications Warehouse

Johnson, Fred A.; Jensen, Gitte H.; Madsen, Jesper; Williams, Byron K.

2014-01-01

We explored the application of dynamic-optimization methods to the problem of pink-footed goose (Anser brachyrhynchus) management in western Europe. We were especially concerned with the extent to which uncertainty in population dynamics influenced an optimal management strategy, the gain in management performance that could be expected if uncertainty could be eliminated or reduced, and whether an adaptive or robust management strategy might be most appropriate in the face of uncertainty. We combined three alternative survival models with three alternative reproductive models to form a set of nine annual-cycle models for pink-footed geese. These models represent a wide range of possibilities concerning the extent to which demographic rates are density dependent or independent, and the extent to which they are influenced by spring temperatures. We calculated state-dependent harvest strategies for these models using stochastic dynamic programming and an objective function that maximized sustainable harvest, subject to a constraint on desired population size. As expected, attaining the largest mean objective value (i.e., the relative measure of management performance) depended on the ability to match a model-dependent optimal strategy with its generating model of population dynamics. The nine models suggested widely varying objective values regardless of the harvest strategy, with the density-independent models generally producing higher objective values than models with density-dependent survival. In the face of uncertainty as to which of the nine models is most appropriate, the optimal strategy assuming that both survival and reproduction were a function of goose abundance and spring temperatures maximized the expected minimum objective value (i.e., maxi–min). In contrast, the optimal strategy assuming equal model weights minimized the expected maximum loss in objective value. The expected value of eliminating model uncertainty was an increase in objective value of only 3.0%. This value represents the difference between the best that could be expected if the most appropriate model were known and the best that could be expected in the face of model uncertainty. The value of eliminating uncertainty about the survival process was substantially higher than that associated with the reproductive process, which is consistent with evidence that variation in survival is more important than variation in reproduction in relatively long-lived avian species. Comparing the expected objective value if the most appropriate model were known with that of the maxi–min robust strategy, we found the value of eliminating uncertainty to be an expected increase of 6.2% in objective value. This result underscores the conservatism of the maxi–min rule and suggests that risk-neutral managers would prefer the optimal strategy that maximizes expected value, which is also the strategy that is expected to minimize the maximum loss (i.e., a strategy based on equal model weights). The low value of information calculated for pink-footed geese suggests that a robust strategy (i.e., one in which no learning is anticipated) could be as nearly effective as an adaptive one (i.e., a strategy in which the relative credibility of models is assessed through time). Of course, an alternative explanation for the low value of information is that the set of population models we considered was too narrow to represent key uncertainties in population dynamics. Yet we know that questions about the presence of density dependence must be central to the development of a sustainable harvest strategy. And while there are potentially many environmental covariates that could help explain variation in survival or reproduction, our admission of models in which vital rates are drawn randomly from reasonable distributions represents a worst-case scenario for management. We suspect that much of the value of the various harvest strategies we calculated is derived from the fact that they are state dependent, such that appropriate harvest rates depend on population abundance and weather conditions, as well as our focus on an infinite time horizon for sustainability.

Uncertainty, robustness, and the value of information in managing a population of northern bobwhites

USGS Publications Warehouse

Johnson, Fred A.; Hagan, Greg; Palmer, William E.; Kemmerer, Michael

2014-01-01

The abundance of northern bobwhites (Colinus virginianus) has decreased throughout their range. Managers often respond by considering improvements in harvest and habitat management practices, but this can be challenging if substantial uncertainty exists concerning the cause(s) of the decline. We were interested in how application of decision science could be used to help managers on a large, public management area in southwestern Florida where the bobwhite is a featured species and where abundance has severely declined. We conducted a workshop with managers and scientists to elicit management objectives, alternative hypotheses concerning population limitation in bobwhites, potential management actions, and predicted management outcomes. Using standard and robust approaches to decision making, we determined that improved water management and perhaps some changes in hunting practices would be expected to produce the best management outcomes in the face of uncertainty about what is limiting bobwhite abundance. We used a criterion called the expected value of perfect information to determine that a robust management strategy may perform nearly as well as an optimal management strategy (i.e., a strategy that is expected to perform best, given the relative importance of different management objectives) with all uncertainty resolved. We used the expected value of partial information to determine that management performance could be increased most by eliminating uncertainty over excessive-harvest and human-disturbance hypotheses. Beyond learning about the factors limiting bobwhites, adoption of a dynamic management strategy, which recognizes temporal changes in resource and environmental conditions, might produce the greatest management benefit. Our research demonstrates that robust approaches to decision making, combined with estimates of the value of information, can offer considerable insight into preferred management approaches when great uncertainty exists about system dynamics and the effects of management.

Which uncertainty? Using expert elicitation and expected value of information to design an adaptive program

USGS Publications Warehouse

Runge, Michael C.; Converse, Sarah J.; Lyons, James E.

2011-01-01

Natural resource management is plagued with uncertainty of many kinds, but not all uncertainties are equally important to resolve. The promise of adaptive management is that learning in the short-term will improve management in the long-term; that promise is best kept if the focus of learning is on those uncertainties that most impede achievement of management objectives. In this context, an existing tool of decision analysis, the expected value of perfect information (EVPI), is particularly valuable in identifying the most important uncertainties. Expert elicitation can be used to develop preliminary predictions of management response under a series of hypotheses, as well as prior weights for those hypotheses, and the EVPI can be used to determine how much management could improve if uncertainty was resolved. These methods were applied to management of whooping cranes (Grus americana), an endangered migratory bird that is being reintroduced in several places in North America. The Eastern Migratory Population of whooping cranes had exhibited almost no successful reproduction through 2009. Several dozen hypotheses can be advanced to explain this failure, and many of them lead to very different management responses. An expert panel articulated the hypotheses, provided prior weights for them, developed potential management strategies, and made predictions about the response of the population to each strategy under each hypothesis. Multi-criteria decision analysis identified a preferred strategy in the face of uncertainty, and analysis of the expected value of information identified how informative each strategy could be. These results provide the foundation for design of an adaptive management program.

Insights into water managers' perception and handling of uncertainties - a study of the role of uncertainty in practitioners' planning and decision-making

NASA Astrophysics Data System (ADS)

Höllermann, Britta; Evers, Mariele

2017-04-01

Planning and decision-making under uncertainty is common in water management due to climate variability, simplified models, societal developments, planning restrictions just to name a few. Dealing with uncertainty can be approached from two sites, hereby affecting the process and form of communication: Either improve the knowledge base by reducing uncertainties or apply risk-based approaches to acknowledge uncertainties throughout the management process. Current understanding is that science more strongly focusses on the former approach, while policy and practice are more actively applying a risk-based approach to handle incomplete and/or ambiguous information. The focus of this study is on how water managers perceive and handle uncertainties at the knowledge/decision interface in their daily planning and decision-making routines. How they evaluate the role of uncertainties for their decisions and how they integrate this information into the decision-making process. Expert interviews and questionnaires among practitioners and scientists provided an insight into their perspectives on uncertainty handling allowing a comparison of diverse strategies between science and practice as well as between different types of practitioners. Our results confirmed the practitioners' bottom up approach from potential measures upwards instead of impact assessment downwards common in science-based approaches. This science-practice gap may hinder effective uncertainty integration and acknowledgement in final decisions. Additionally, the implementation of an adaptive and flexible management approach acknowledging uncertainties is often stalled by rigid regulations favouring a predict-and-control attitude. However, the study showed that practitioners' level of uncertainty recognition varies with respect to his or her affiliation to type of employer and business unit, hence, affecting the degree of the science-practice-gap with respect to uncertainty recognition. The level of working experience was examined as a cross-cutting property of science and practice with increasing levels of uncertainty awareness and integration among more experienced researchers and practitioners. In conclusion, our study of water managers' perception and handling of uncertainties provides valuable insights for finding routines for uncertainty communication and integration into planning and decision-making processes by acknowledging the divers perceptions among producers, users and receivers of uncertainty information. These results can contribute to more effective integration of hydrological forecast and improved decisions.

Making framing of uncertainty in water management practice explicit by using a participant-structured approach.

PubMed

Isendahl, Nicola; Dewulf, Art; Pahl-Wostl, Claudia

2010-01-01

By now, the need for addressing uncertainty in the management of water resources is widely recognized, yet there is little expertise and experience how to effectively deal with uncertainty in practice. Uncertainties in water management practice so far are mostly dealt with intuitively or based on experience. That way decisions can be quickly taken but analytic processes of deliberate reasoning are bypassed. To meet the desire of practitioners for better guidance and tools how to deal with uncertainty more practice-oriented systematic approaches are needed. For that purpose we consider it important to understand how practitioners frame uncertainties. In this paper we present an approach where water managers developed criteria of relevance to understand and address uncertainties. The empirical research took place in the Doñana region of the Guadalquivir estuary in southern Spain making use of the method of card sorting. Through the card sorting exercise a broad range of criteria to make sense of and describe uncertainties was produced by different subgroups, which were then merged into a shared list of criteria. That way framing differences were made explicit and communication on uncertainty and on framing differences was enhanced. In that, the present approach constitutes a first step to enabling reframing and overcoming framing differences, which are important features on the way to robust decision-making. Moreover, the elaborated criteria build a basis for the development of more structured approaches to deal with uncertainties in water management practice. Copyright 2009 Elsevier Ltd. All rights reserved.

A structured analysis of uncertainty surrounding modeled impacts of groundwater-extraction rules

NASA Astrophysics Data System (ADS)

Guillaume, Joseph H. A.; Qureshi, M. Ejaz; Jakeman, Anthony J.

2012-08-01

Integrating economic and groundwater models for groundwater-management can help improve understanding of trade-offs involved between conflicting socioeconomic and biophysical objectives. However, there is significant uncertainty in most strategic decision-making situations, including in the models constructed to represent them. If not addressed, this uncertainty may be used to challenge the legitimacy of the models and decisions made using them. In this context, a preliminary uncertainty analysis was conducted of a dynamic coupled economic-groundwater model aimed at assessing groundwater extraction rules. The analysis demonstrates how a variety of uncertainties in such a model can be addressed. A number of methods are used including propagation of scenarios and bounds on parameters, multiple models, block bootstrap time-series sampling and robust linear regression for model calibration. These methods are described within the context of a theoretical uncertainty management framework, using a set of fundamental uncertainty management tasks and an uncertainty typology.

Facing uncertainty in ecosystem services-based resource management.

PubMed

Grêt-Regamey, Adrienne; Brunner, Sibyl H; Altwegg, Jürg; Bebi, Peter

2013-09-01

The concept of ecosystem services is increasingly used as a support for natural resource management decisions. While the science for assessing ecosystem services is improving, appropriate methods to address uncertainties in a quantitative manner are missing. Ignoring parameter uncertainties, modeling uncertainties and uncertainties related to human-environment interactions can modify decisions and lead to overlooking important management possibilities. In this contribution, we present a new approach for mapping the uncertainties in the assessment of multiple ecosystem services. The spatially explicit risk approach links Bayesian networks to a Geographic Information System for forecasting the value of a bundle of ecosystem services and quantifies the uncertainties related to the outcomes in a spatially explicit manner. We demonstrate that mapping uncertainties in ecosystem services assessments provides key information for decision-makers seeking critical areas in the delivery of ecosystem services in a case study in the Swiss Alps. The results suggest that not only the total value of the bundle of ecosystem services is highly dependent on uncertainties, but the spatial pattern of the ecosystem services values changes substantially when considering uncertainties. This is particularly important for the long-term management of mountain forest ecosystems, which have long rotation stands and are highly sensitive to pressing climate and socio-economic changes. Copyright © 2012 Elsevier Ltd. All rights reserved.

Resolving structural uncertainty in natural resources management using POMDP approaches

USGS Publications Warehouse

Williams, B.K.

2011-01-01

In recent years there has been a growing focus on the uncertainties of natural resources management, and the importance of accounting for uncertainty in assessing management effectiveness. This paper focuses on uncertainty in resource management in terms of discrete-state Markov decision processes (MDP) under structural uncertainty and partial observability. It describes the treatment of structural uncertainty with approaches developed for partially observable resource systems. In particular, I show how value iteration for partially observable MDPs (POMDP) can be extended to structurally uncertain MDPs. A key difference between these process classes is that structurally uncertain MDPs require the tracking of system state as well as a probability structure for the structure uncertainty, whereas with POMDPs require only a probability structure for the observation uncertainty. The added complexity of the optimization problem under structural uncertainty is compensated by reduced dimensionality in the search for optimal strategy. A solution algorithm for structurally uncertain processes is outlined for a simple example in conservation biology. By building on the conceptual framework developed for POMDPs, natural resource analysts and decision makers who confront structural uncertainties in natural resources can take advantage of the rapid growth in POMDP methods and approaches, and thereby produce better conservation strategies over a larger class of resource problems. ?? 2011.

Visualizing uncertainties with the North Wyke Farm Platform Data Sets

NASA Astrophysics Data System (ADS)

Harris, Paul; Brunsdon, Chris; Lee, Michael

2016-04-01

The North Wyke Farm Platform (NWFP) is a systems-based, farm-scale experiment with the aim of addressing agricultural productivity and ecosystem responses to different management practices. The 63 ha site captures the spatial and/or temporal data necessary to develop a better understanding of the dynamic processes and underlying mechanisms that can be used to model how agricultural grassland systems respond to different management inputs. Via cattle beef and sheep production, the underlying principle is to manage each of three farmlets (each consisting of five hydrologically-isolated sub-catchments) in three contrasting ways: (i) improvement of permanent pasture through use of mineral fertilizers; (ii) improvement through use of legumes; and (iii) improvement through innovation. The connectivity between the timing and intensity of the different management operations, together with the transport of nutrients and potential pollutants from the NWFP is evaluated using numerous inter-linked data collection exercises. In this paper, we introduce some of the visualization opportunities that are possible with this rich data resource, and methods of analysis that might be applied to it, in particular with respect to data and model uncertainty operating across both temporal and spatial dimensions. An important component of the NWFP experiment is the representation of trade-offs with respect to: (a) economic profits, (b) environmental concerns, and (c) societal benefits, under the umbrella of sustainable intensification. Various visualizations exist to display such trade-offs and here we demonstrate ways to tailor them to relay key uncertainties and assessments of risk; and also consider how these visualizations can be honed to suit different audiences.

Embracing uncertainty in applied ecology.

PubMed

Milner-Gulland, E J; Shea, K

2017-12-01

Applied ecologists often face uncertainty that hinders effective decision-making.Common traps that may catch the unwary are: ignoring uncertainty, acknowledging uncertainty but ploughing on, focussing on trivial uncertainties, believing your models, and unclear objectives.We integrate research insights and examples from a wide range of applied ecological fields to illustrate advances that are generally underused, but could facilitate ecologists' ability to plan and execute research to support management.Recommended approaches to avoid uncertainty traps are: embracing models, using decision theory, using models more effectively, thinking experimentally, and being realistic about uncertainty. Synthesis and applications . Applied ecologists can become more effective at informing management by using approaches that explicitly take account of uncertainty.

Management of California Oak Woodlands: Uncertainties and Modeling

Treesearch

Jay E. Noel; Richard P. Thompson

1995-01-01

A mathematical policy model of oak woodlands is presented. The model illustrates the policy uncertainties that exist in the management of oak woodlands. These uncertainties include: (1) selection of a policy criterion function, (2) woodland dynamics, (3) initial and final state of the woodland stock. The paper provides a review of each of the uncertainty issues. The...

A Corrosion Risk Assessment Model for Underground Piping

NASA Technical Reports Server (NTRS)

Datta, Koushik; Fraser, Douglas R.

2009-01-01

The Pressure Systems Manager at NASA Ames Research Center (ARC) has embarked on a project to collect data and develop risk assessment models to support risk-informed decision making regarding future inspections of underground pipes at ARC. This paper shows progress in one area of this project - a corrosion risk assessment model for the underground high-pressure air distribution piping system at ARC. It consists of a Corrosion Model of pipe-segments, a Pipe Wrap Protection Model; and a Pipe Stress Model for a pipe segment. A Monte Carlo simulation of the combined models provides a distribution of the failure probabilities. Sensitivity study results show that the model uncertainty, or lack of knowledge, is the dominant contributor to the calculated unreliability of the underground piping system. As a result, the Pressure Systems Manager may consider investing resources specifically focused on reducing these uncertainties. Future work includes completing the data collection effort for the existing ground based pressure systems and applying the risk models to risk-based inspection strategies of the underground pipes at ARC.

Methods for Assessing Uncertainties in Climate Change, Impacts and Responses (Invited)

NASA Astrophysics Data System (ADS)

Manning, M. R.; Swart, R.

2009-12-01

Assessing the scientific uncertainties or confidence levels for the many different aspects of climate change is particularly important because of the seriousness of potential impacts and the magnitude of economic and political responses that are needed to mitigate climate change effectively. This has made the treatment of uncertainty and confidence a key feature in the assessments carried out by the Intergovernmental Panel on Climate Change (IPCC). Because climate change is very much a cross-disciplinary area of science, adequately dealing with uncertainties requires recognition of their wide range and different perspectives on assessing and communicating those uncertainties. The structural differences that exist across disciplines are often embedded deeply in the corresponding literature that is used as the basis for an IPCC assessment. The assessment of climate change science by the IPCC has from its outset tried to report the levels of confidence and uncertainty in the degree of understanding in both the underlying multi-disciplinary science and in projections for future climate. The growing recognition of the seriousness of this led to the formation of a detailed approach for consistent treatment of uncertainties in the IPCC’s Third Assessment Report (TAR) [Moss and Schneider, 2000]. However, in completing the TAR there remained some systematic differences between the disciplines raising concerns about the level of consistency. So further consideration of a systematic approach to uncertainties was undertaken for the Fourth Assessment Report (AR4). The basis for the approach used in the AR4 was developed at an expert meeting of scientists representing many different disciplines. This led to the introduction of a broader way of addressing uncertainties in the AR4 [Manning et al., 2004] which was further refined by lengthy discussions among many IPCC Lead Authors, for over a year, resulting in a short summary of a standard approach to be followed for that assessment [IPCC, 2005]. This paper extends a review of the treatment of uncertainty in the IPCC assessments by Swart et al [2009]. It is shown that progress towards consistency has been made but that there also appears to be a need for continued use of several complementary approaches in order to cover the wide range of circumstances across different disciplines involved in climate change. While this reflects the situation in the science community, it also raises the level of complexity for policymakers and other users of the assessments who would prefer one common consensus approach. References IPCC (2005), Guidance Notes for Lead Authors of the IPCC Fourth Assessment Report on Addressing Uncertainties, IPCC, Geneva. Manning, M., et al. (2004), IPCC Workshop on Describing Scientific Uncertainties in Climate Change to Support Analysis of Risk and of Options. IPCC Moss, R., and S. Schneider (2000), Uncertainties, in Guidance Papers on the Cross Cutting Issues of the Third Assessment Report of the IPCC, edited by R. Pachauri, et al., Intergovernmental Panel on Climate Change (IPCC), Geneva. Swart, R., et al. (2009), Agreeing to disagree: uncertainty management in assessing climate change, impacts and responses by the IPCC Climatic Change, 92(1-2), 1 - 29.

Holistic uncertainty analysis in river basin modeling for climate vulnerability assessment

NASA Astrophysics Data System (ADS)

Taner, M. U.; Wi, S.; Brown, C.

2017-12-01

The challenges posed by uncertain future climate are a prominent concern for water resources managers. A number of frameworks exist for assessing the impacts of climate-related uncertainty, including internal climate variability and anthropogenic climate change, such as scenario-based approaches and vulnerability-based approaches. While in many cases climate uncertainty may be dominant, other factors such as future evolution of the river basin, hydrologic response and reservoir operations are potentially significant sources of uncertainty. While uncertainty associated with modeling hydrologic response has received attention, very little attention has focused on the range of uncertainty and possible effects of the water resources infrastructure and management. This work presents a holistic framework that allows analysis of climate, hydrologic and water management uncertainty in water resources systems analysis with the aid of a water system model designed to integrate component models for hydrology processes and water management activities. The uncertainties explored include those associated with climate variability and change, hydrologic model parameters, and water system operation rules. A Bayesian framework is used to quantify and model the uncertainties at each modeling steps in integrated fashion, including prior and the likelihood information about model parameters. The framework is demonstrated in a case study for the St. Croix Basin located at border of United States and Canada.

Comparing the effects of different land management strategies across several land types on California's landscape carbon and associated greenhouse gas budgets

NASA Astrophysics Data System (ADS)

Di Vittorio, A. V.; Simmonds, M.; Nico, P. S.

2017-12-01

Land-based carbon sequestration and GreenHouse Gas (GHG) reduction strategies are often implemented in small patches and evaluated independently from each other, which poses several challenges to determining their potential benefits at the regional scales at which carbon/GHG targets are defined. These challenges include inconsistent methods, uncertain scalability to larger areas, and lack of constraints such as land ownership and competition among multiple strategies. To address such challenges we have developed an integrated carbon and GHG budget model of California's entire landscape, delineated by geographic region, land type, and ownership. This empirical model has annual time steps and includes net ecosystem carbon exchange, wildfire, multiple forest management practices including wood and bioenergy production, cropland and rangeland soil management, various land type restoration activities, and land cover change. While the absolute estimates vary considerably due to uncertainties in initial carbon densities and ecosystem carbon exchange rates, the estimated effects of particular management activities with respect to baseline are robust across these uncertainties. Uncertainty in land use/cover change data is also critical, as different rates of shrubland to grassland conversion can switch the system from a carbon source to a sink. The results indicate that reducing urban area expansion has substantial and consistent benefits, while the effects of direct land management practices vary and depend largely on the available management area. Increasing forest fuel reduction extent over the baseline contributes to annual GHG costs during increased management, and annual benefits after increased management ceases. Cumulatively, it could take decades to recover the cost of 14 years of increased fuel reduction. However, forest carbon losses can be completely offset within 20 years through increases in urban forest fraction and marsh restoration. Additionally, highly uncertain black carbon estimates dominate the overall GHG budget due to wildfire, forest management, and bioenergy production. Overall, this tool is well suited for exploring suites of management options and extents throughout California in order to quantify potential regional carbon sequestration and GHG emission benefits.

Patients' and partners' perspectives of chronic illness and its management.

PubMed

Checton, Maria G; Greene, Kathryn; Magsamen-Conrad, Kate; Venetis, Maria K

2012-06-01

This study is framed in theories of illness uncertainty (Babrow, A. S., 2007, Problematic integration theory. In B. B. Whaley & W. Samter (Eds.), Explaining communication: Contemporary theories and exemplars (pp. 181-200). Mahwah, NJ: Erlbaum; Babrow & Matthias, 2009; Brashers, D. E., 2007, A theory of communication and uncertainty management. In B. B. Whaley & W. Samter (Eds.), Explaining communication: Contemporary theories and exemplars (pp. 201-218). Mahwah, NJ: Erlbaum; Hogan, T. P., & Brashers, D. E. (2009). The theory of communication and uncertainty management: Implications for the wider realm of information behavior. In T. D. Afifi & W. A. Afifi (Eds.), Uncertainty and information regulation in interpersonal contexts: Theories and applications, (pp. 45-66). New York, NY: Routledge; Mishel, M. H. (1999). Uncertainty in chronic illness. Annual Review of Nursing Research, 17, 269-294; Mishel, M. H., & Clayton, M. F., 2003, Theories of uncertainty. In M. J. Smith & P. R. Liehr (Eds.), Middle range theory for nursing (pp. 25-48). New York, NY: Springer) and health information management (Afifi, W. A., & Weiner, J. L., 2004, Toward a theory of motivated information management. Communication Theory, 14, 167-190. doi:10.1111/j.1468-2885.2004.tb00310.x; Greene, K., 2009, An integrated model of health disclosure decision-making. In T. D. Afifi & W. A. Afifi (Eds.), Uncertainty and information regulation in interpersonal contexts: Theories and applications (pp. 226-253). New York, NY: Routledge) and examines how couples experience uncertainty and interference related to one partner's chronic health condition. Specifically, a model is hypothesized in which illness uncertainty (i.e., stigma, prognosis, and symptom) and illness interference predict communication efficacy and health condition management. Participants include 308 dyads in which one partner has a chronic health condition. Data were analyzed using structural equation modeling. Results indicate that there are significant differences in (a) how patients and partners experience illness uncertainty and illness interference and (b) how appraisals of illness uncertainty and illness interference influence communication efficacy and health condition management. We discuss the findings and implications of the study.

A tool for efficient, model-independent management optimization under uncertainty

USGS Publications Warehouse

White, Jeremy; Fienen, Michael N.; Barlow, Paul M.; Welter, Dave E.

2018-01-01

To fill a need for risk-based environmental management optimization, we have developed PESTPP-OPT, a model-independent tool for resource management optimization under uncertainty. PESTPP-OPT solves a sequential linear programming (SLP) problem and also implements (optional) efficient, “on-the-fly” (without user intervention) first-order, second-moment (FOSM) uncertainty techniques to estimate model-derived constraint uncertainty. Combined with a user-specified risk value, the constraint uncertainty estimates are used to form chance-constraints for the SLP solution process, so that any optimal solution includes contributions from model input and observation uncertainty. In this way, a “single answer” that includes uncertainty is yielded from the modeling analysis. PESTPP-OPT uses the familiar PEST/PEST++ model interface protocols, which makes it widely applicable to many modeling analyses. The use of PESTPP-OPT is demonstrated with a synthetic, integrated surface-water/groundwater model. The function and implications of chance constraints for this synthetic model are discussed.

Forest Management Under Uncertainty for Multiple Bird Population Objectives

Treesearch

Clinton T. Moore; W. Todd Plummer; Michael J. Conroy

2005-01-01

We advocate adaptive programs of decision making and monitoring for the management of forest birds when responses by populations to management, and particularly management trade-offs among populations, are uncertain. Models are necessary components of adaptive management. Under this approach, uncertainty about the behavior of a managed system is explicitly captured in...

Incorporating uncertainty into mercury-offset decisions with a probabilistic network for National Pollutant Discharge Elimination System permit holders: an interim report

USGS Publications Warehouse

Wood, Alexander

2004-01-01

This interim report describes an alternative approach for evaluating the efficacy of using mercury (Hg) offsets to improve water quality. Hg-offset programs may allow dischargers facing higher-pollution control costs to meet their regulatory obligations by making more cost effective pollutant-reduction decisions. Efficient Hg management requires methods to translate that science and economics into a regulatory decision framework. This report documents the work in progress by the U.S. Geological Surveys Western Geographic Science Center in collaboration with Stanford University toward developing this decision framework to help managers, regulators, and other stakeholders decide whether offsets can cost effectively meet the Hg total maximum daily load (TMDL) requirements in the Sacramento River watershed. Two key approaches being considered are: (1) a probabilistic approach that explicitly incorporates scientific uncertainty, cost information, and value judgments; and (2) a quantitative approach that captures uncertainty in testing the feasibility of Hg offsets. Current fate and transport-process models commonly attempt to predict chemical transformations and transport pathways deterministically. However, the physical, chemical, and biologic processes controlling the fate and transport of Hg in aquatic environments are complex and poorly understood. Deterministic models of Hg environmental behavior contain large uncertainties, reflecting this lack of understanding. The uncertainty in these underlying physical processes may produce similarly large uncertainties in the decisionmaking process. However, decisions about control strategies are still being made despite the large uncertainties in current Hg loadings, the relations between total Hg (HgT) loading and methylmercury (MeHg) formation, and the relations between control efforts and Hg content in fish. The research presented here focuses on an alternative analytical approach to the current use of safety factors and deterministic methods for Hg TMDL decision support, one that is fully compatible with an adaptive management approach. This alternative approach uses empirical data and informed judgment to provide a scientific and technical basis for helping National Pollutant Discharge Elimination System (NPDES) permit holders make management decisions. An Hg-offset system would be an option if a wastewater-treatment plant could not achieve NPDES permit requirements for HgT reduction. We develop a probabilistic decision-analytical model consisting of three submodels for HgT loading, MeHg, and cost mitigation within a Bayesian network that integrates information of varying rigor and detail into a simple model of a complex system. Hg processes are identified and quantified by using a combination of historical data, statistical models, and expert judgment. Such an integrated approach to uncertainty analysis allows easy updating of prediction and inference when observations of model variables are made. We demonstrate our approach with data from the Cache Creek watershed (a subbasin of the Sacramento River watershed). The empirical models used to generate the needed probability distributions are based on the same empirical models currently being used by the Central Valley Regional Water Quality Control Cache Creek Hg TMDL working group. The significant difference is that input uncertainty and error are explicitly included in the model and propagated throughout its algorithms. This work demonstrates how to integrate uncertainty into the complex and highly uncertain Hg TMDL decisionmaking process. The various sources of uncertainty are propagated as decision risk that allows decisionmakers to simultaneously consider uncertainties in remediation/implementation costs while attempting to meet environmental/ecologic targets. We must note that this research is on going. As more data are collected, the HgT and cost-mitigation submodels are updated and the uncer

Prioritizing Project Risks Using AHP

DOE Office of Scientific and Technical Information (OSTI.GOV)

Thibadeau, Barbara M

2007-01-01

This essay introduces the Analytic Hierarchy Process (AHP) as a method by which to rank project risks, in terms of importance as well as likelihood. AHP is way to handle quantifiable and/or intangible criteria in the decision making process. It is a multi-objective multi-criteria decision-making approach that is based on the idea of pair-wise comparisons of alternatives with respect to a given criterion (e.g., which alternative, A or B, is preferred and by how much more is it preferred) or with respect to an objective (e.g., which is more important, A or B, and by how much more is itmore » important). This approach was pioneered by Thomas Saaty in the late 1970's. It has been suggested that a successful project is one that successfully manages risk and that project management is the management of uncertainty. Risk management relies on the quantification of uncertainty which, in turn, is predicated upon the accuracy of probabilistic approaches (in terms of likelihood as well as magnitude). In many cases, the appropriate probability distribution (or probability value) is unknown. And, researchers have shown that probability values are not made very accurately, that the use of verbal expressions is not a suitable alternative, that there is great variability in the use and interpretation of these values and that there is a great reluctance to assign them in the first place. Data from an ongoing project is used to show that AHP can be used to obtain these values, thus overcoming some of the problems associated with the direct assignment of discrete probability values. A novel method by which to calculate the consistency of the data is introduced. The AHP approach is easily implemented and, typically, offers results that are consistent with the decision maker's intuition.« less

Non-parametric data-based approach for the quantification and communication of uncertainties in river flood forecasts

NASA Astrophysics Data System (ADS)

Van Steenbergen, N.; Willems, P.

2012-04-01

Reliable flood forecasts are the most important non-structural measures to reduce the impact of floods. However flood forecasting systems are subject to uncertainty originating from the input data, model structure and model parameters of the different hydraulic and hydrological submodels. To quantify this uncertainty a non-parametric data-based approach has been developed. This approach analyses the historical forecast residuals (differences between the predictions and the observations at river gauging stations) without using a predefined statistical error distribution. Because the residuals are correlated with the value of the forecasted water level and the lead time, the residuals are split up into discrete classes of simulated water levels and lead times. For each class, percentile values are calculated of the model residuals and stored in a 'three dimensional error' matrix. By 3D interpolation in this error matrix, the uncertainty in new forecasted water levels can be quantified. In addition to the quantification of the uncertainty, the communication of this uncertainty is equally important. The communication has to be done in a consistent way, reducing the chance of misinterpretation. Also, the communication needs to be adapted to the audience; the majority of the larger public is not interested in in-depth information on the uncertainty on the predicted water levels, but only is interested in information on the likelihood of exceedance of certain alarm levels. Water managers need more information, e.g. time dependent uncertainty information, because they rely on this information to undertake the appropriate flood mitigation action. There are various ways in presenting uncertainty information (numerical, linguistic, graphical, time (in)dependent, etc.) each with their advantages and disadvantages for a specific audience. A useful method to communicate uncertainty of flood forecasts is by probabilistic flood mapping. These maps give a representation of the probability of flooding of a certain area, based on the uncertainty assessment of the flood forecasts. By using this type of maps, water managers can focus their attention on the areas with the highest flood probability. Also the larger public can consult these maps for information on the probability of flooding for their specific location, such that they can take pro-active measures to reduce the personal damage. The method of quantifying the uncertainty was implemented in the operational flood forecasting system for the navigable rivers in the Flanders region of Belgium. The method has shown clear benefits during the floods of the last two years.

Systematic Evaluation of Stochastic Methods in Power System Scheduling and Dispatch with Renewable Energy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Yishen; Zhou, Zhi; Liu, Cong

2016-08-01

As more wind power and other renewable resources are being integrated into the electric power grid, the forecast uncertainty brings operational challenges for the power system operators. In this report, different operational strategies for uncertainty management are presented and evaluated. A comprehensive and consistent simulation framework is developed to analyze the performance of different reserve policies and scheduling techniques under uncertainty in wind power. Numerical simulations are conducted on a modified version of the IEEE 118-bus system with a 20% wind penetration level, comparing deterministic, interval, and stochastic unit commitment strategies. The results show that stochastic unit commitment provides amore » reliable schedule without large increases in operational costs. Moreover, decomposition techniques, such as load shift factor and Benders decomposition, can help in overcoming the computational obstacles to stochastic unit commitment and enable the use of a larger scenario set to represent forecast uncertainty. In contrast, deterministic and interval unit commitment tend to give higher system costs as more reserves are being scheduled to address forecast uncertainty. However, these approaches require a much lower computational effort Choosing a proper lower bound for the forecast uncertainty is important for balancing reliability and system operational cost in deterministic and interval unit commitment. Finally, we find that the introduction of zonal reserve requirements improves reliability, but at the expense of higher operational costs.« less

Accounting for uncertainty in marine reserve design.

PubMed

Halpern, Benjamin S; Regan, Helen M; Possingham, Hugh P; McCarthy, Michael A

2006-01-01

Ecosystems and the species and communities within them are highly complex systems that defy predictions with any degree of certainty. Managing and conserving these systems in the face of uncertainty remains a daunting challenge, particularly with respect to developing networks of marine reserves. Here we review several modelling frameworks that explicitly acknowledge and incorporate uncertainty, and then use these methods to evaluate reserve spacing rules given increasing levels of uncertainty about larval dispersal distances. Our approach finds similar spacing rules as have been proposed elsewhere - roughly 20-200 km - but highlights several advantages provided by uncertainty modelling over more traditional approaches to developing these estimates. In particular, we argue that uncertainty modelling can allow for (1) an evaluation of the risk associated with any decision based on the assumed uncertainty; (2) a method for quantifying the costs and benefits of reducing uncertainty; and (3) a useful tool for communicating to stakeholders the challenges in managing highly uncertain systems. We also argue that incorporating rather than avoiding uncertainty will increase the chances of successfully achieving conservation and management goals.

An adaptive approach to invasive plant management on U.S. Fish and Wildlife Service-owned native prairies in the Prairie Pothole Region: decision support under uncertainity

USGS Publications Warehouse

Gannon, Jill J.; Moore, Clinton T.; Shaffer, Terry L.; Flanders-Wanner, Bridgette

2011-01-01

Much of the native prairie managed by the U.S. Fish and Wildlife Service (Service) in the Prairie Pothole Region (PPR) is extensively invaded by the introduced cool-season grasses smooth brome (Bromus inermis) and Kentucky bluegrass (Poa pratensis). The central challenge to managers is selecting appropriate management actions in the face of biological and environmental uncertainties. We describe the technical components of a USGS management project, and explain how the components integrate and inform each other, how data feedback from individual cooperators serves to reduce uncertainty across the whole region, and how a successful adaptive management project is coordinated and maintained on a large scale. In partnership with the Service, the U.S. Geological Survey is developing an adaptive decision support framework to assist managers in selecting management actions under uncertainty and maximizing learning from management outcomes. The framework is built around practical constraints faced by refuge managers and includes identification of the management objective and strategies, analysis of uncertainty and construction of competing decision models, monitoring, and mechanisms for model feedback and decision selection. Nineteen Service field stations, spanning four states of the PPR, are participating in the project. They share a common management objective, available management strategies, and biological uncertainties. While the scope is broad, the project interfaces with individual land managers who provide refuge-specific information and receive updated decision guidance that incorporates understanding gained from the collective experience of all cooperators.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cardenas, Ibsen C., E-mail: c.cardenas@utwente.nl; Halman, Johannes I.M., E-mail: J.I.M.Halman@utwente.nl

Uncertainty is virtually unavoidable in environmental impact assessments (EIAs). From the literature related to treating and managing uncertainty, we have identified specific techniques for coping with uncertainty in EIAs. Here, we have focused on basic steps in the decision-making process that take place within an EIA setting. More specifically, we have identified uncertainties involved in each decision-making step and discussed the extent to which these can be treated and managed in the context of an activity or project that may have environmental impacts. To further demonstrate the relevance of the techniques identified, we have examined the extent to which themore » EIA guidelines currently used in Colombia consider and provide guidance on managing the uncertainty involved in these assessments. Some points that should be considered in order to provide greater robustness in impact assessments in Colombia have been identified. These include the management of stakeholder values, the systematic generation of project options, and their associated impacts as well as the associated management actions, and the evaluation of uncertainties and assumptions. We believe that the relevant and specific techniques reported here can be a reference for future evaluations of other EIA guidelines in different countries. - Highlights: • uncertainty is unavoidable in environmental impact assessments, EIAs; • we have identified some open techniques to EIAs for treating and managing uncertainty in these assessments; • points for improvement that should be considered in order to provide greater robustness in EIAs in Colombia have been identified; • the paper provides substantiated a reference for possible examinations of EIAs guidelines in other countries.« less

The value of information for woodland management: Updating a state–transition model

USGS Publications Warehouse

Morris, William K.; Runge, Michael C.; Vesk, Peter A.

2017-01-01

Value of information (VOI) analyses reveal the expected benefit of reducing uncertainty to a decision maker. Most ecological VOI analyses have focused on population models rarely addressing more complex community models. We performed a VOI analysis for a complex state–transition model of Box-Ironbark Forest and Woodland management. With three management alternatives (limited harvest/firewood removal (HF), ecological thinning (ET), and no management), managing the system optimally (for 150 yr) with the original information would, on average, increase the amount of forest in a desirable state from 19% to 35% (a 16-percentage point increase). Resolving all uncertainty would, on average, increase the final percentage to 42% (a 19-percentage point increase). However, only resolving the uncertainty for a single parameter was worth almost two-thirds the value of resolving all uncertainty. We found the VOI to depend on the number of management options, increasing as the management flexibility increased. Our analyses show it is more cost-effective to monitor low-density regrowth forest than other states and more cost-effective to experiment with the no-management alternative than the other management alternatives. Importantly, the most cost-effective strategies did not include either the most desired forest states or the least understood management strategy, ET. This implies that managers cannot just rely on intuition to tell them where the most VOI will lie, as critical uncertainties in a complex system are sometimes cryptic.

Out of the black box: expansion of a theory-based intervention to self-manage the uncertainty associated with active surveillance (AS) for prostate cancer.

PubMed

Kazer, Meredith Wallace; Bailey, Donald E; Whittemore, Robin

2010-01-01

Active surveillance (AS) (sometimes referred to as watchful waiting) is an alternative approach to managing low-risk forms of prostate cancer. This management approach allows men to avoid expensive prostate cancer treatments and their well-documented adverse events of erectile dysfunction and incontinence. However, AS is associated with illness uncertainty and reduced quality of life (QOL; Wallace, 2003). An uncertainty management intervention (UMI) was developed by Mishel et al. (2002) to manage uncertainty in women treated for breast cancer and men treated for prostate cancer. However, the UMI was not developed for men undergoing AS for prostate cancer and has not been adequately tested in this population. This article reports on the expansion of a theory-based intervention to manage the uncertainty associated with AS for prostate cancer. Intervention Theory (Sidani & Braden, 1998) is discussed as a framework for revising the UMI intervention for men undergoing AS for prostate cancer (UMI-AS). The article concludes with plans for testing of the expanded intervention and implications for the extended theory.

How much is new information worth? Evaluating the financial benefit of resolving management uncertainty

USGS Publications Warehouse

Maxwell, Sean L.; Rhodes, Jonathan R.; Runge, Michael C.; Possingham, Hugh P.; Ng, Chooi Fei; McDonald Madden, Eve

2015-01-01

Conservation decision-makers face a trade-off between spending limited funds on direct management action, or gaining new information in an attempt to improve management performance in the future. Value-of-information analysis can help to resolve this trade-off by evaluating how much management performance could improve if new information was gained. Value-of-information analysis has been used extensively in other disciplines, but there are only a few examples where it has informed conservation planning, none of which have used it to evaluate the financial value of gaining new information. We address this gap by applying value-of-information analysis to the management of a declining koala Phascolarctos cinereuspopulation. Decision-makers responsible for managing this population face uncertainty about survival and fecundity rates, and how habitat cover affects mortality threats. The value of gaining new information about these uncertainties was calculated using a deterministic matrix model of the koala population to find the expected population growth rate if koala mortality threats were optimally managed under alternative model hypotheses, which represented the uncertainties faced by koala managers. Gaining new information about survival and fecundity rates and the effect of habitat cover on mortality threats will do little to improve koala management. Across a range of management budgets, no more than 1·7% of the budget should be spent on resolving these uncertainties. The value of information was low because optimal management decisions were not sensitive to the uncertainties we considered. Decisions were instead driven by a substantial difference in the cost efficiency of management actions. The value of information was up to forty times higher when the cost efficiencies of different koala management actions were similar. Synthesis and applications. This study evaluates the ecological and financial benefits of gaining new information to inform a conservation problem. We also theoretically demonstrate that the value of reducing uncertainty is highest when it is not clear which management action is the most cost efficient. This study will help expand the use of value-of-information analyses in conservation by providing a cost efficiency metric by which to evaluate research or monitoring.

Uncertainty and extreme events in future climate and hydrologic projections for the Pacific Northwest: providing a basis for vulnerability and core/corridor assessments

USGS Publications Warehouse

Littell, Jeremy S.; Mauger, Guillaume S.; Salathe, Eric P.; Hamlet, Alan F.; Lee, Se-Yeun; Stumbaugh, Matt R.; Elsner, Marketa; Norheim, Robert; Lutz, Eric R.; Mantua, Nathan J.

2014-01-01

The purpose of this project was to (1) provide an internally-consistent set of downscaled projections across the Western U.S., (2) include information about projection uncertainty, and (3) assess projected changes of hydrologic extremes. These objectives were designed to address decision support needs for climate adaptation and resource management actions. Specifically, understanding of uncertainty in climate projections – in particular for extreme events – is currently a key scientific and management barrier to adaptation planning and vulnerability assessment. The new dataset fills in the Northwest domain to cover a key gap in the previous dataset, adds additional projections (both from other global climate models and a comparison with dynamical downscaling) and includes an assessment of changes to flow and soil moisture extremes. This new information can be used to assess variations in impacts across the landscape, uncertainty in projections, and how these differ as a function of region, variable, and time period. In this project, existing University of Washington Climate Impacts Group (UW CIG) products were extended to develop a comprehensive data archive that accounts (in a reigorous and physically based way) for climate model uncertainty in future climate and hydrologic scenarios. These products can be used to determine likely impacts on vegetation and aquatic habitat in the Pacific Northwest (PNW) region, including WA, OR, ID, northwest MT to the continental divide, northern CA, NV, UT, and the Columbia Basin portion of western WY New data series and summaries produced for this project include: 1) extreme statistics for surface hydrology (e.g. frequency of soil moisture and summer water deficit) and streamflow (e.g. the 100-year flood, extreme 7-day low flows with a 10-year recurrence interval); 2) snowpack vulnerability as indicated by the ratio of April 1 snow water to cool-season precipitation; and, 3) uncertainty analyses for multiple climate scenarios.

Uncertainty

USGS Publications Warehouse

Hunt, Randall J.

2012-01-01

Management decisions will often be directly informed by model predictions. However, we now know there can be no expectation of a single ‘true’ model; thus, model results are uncertain. Understandable reporting of underlying uncertainty provides necessary context to decision-makers, as model results are used for management decisions. This, in turn, forms a mechanism by which groundwater models inform a risk-management framework because uncertainty around a prediction provides the basis for estimating the probability or likelihood of some event occurring. Given that the consequences of management decisions vary, it follows that the extent of and resources devoted to an uncertainty analysis may depend on the consequences. For events with low impact, a qualitative, limited uncertainty analysis may be sufficient for informing a decision. For events with a high impact, on the other hand, the risks might be better assessed and associated decisions made using a more robust and comprehensive uncertainty analysis. The purpose of this chapter is to provide guidance on uncertainty analysis through discussion of concepts and approaches, which can vary from heuristic (i.e. the modeller’s assessment of prediction uncertainty based on trial and error and experience) to a comprehensive, sophisticated, statistics-based uncertainty analysis. Most of the material presented here is taken from Doherty et al. (2010) if not otherwise cited. Although the treatment here is necessarily brief, the reader can find citations for the source material and additional references within this chapter.

Uncertainty and risk in wildland fire management: A review

Treesearch

Matthew P. Thompson; Dave E. Calkin

2011-01-01

Wildland fire management is subject to manifold sources of uncertainty. Beyond the unpredictability of wildfire behavior, uncertainty stems from inaccurate/missing data, limited resource value measures to guide prioritization across fires and resources at risk, and an incomplete scientific understanding of ecological response to fire, of fire behavior response to...

Social, institutional, and psychological factors affecting wildfire incident decision making

Treesearch

Matthew P. Thompson

2014-01-01

Managing wildland fire incidents can be fraught with complexity and uncertainty. Myriad human factors can exert significant influence on incident decision making, and can contribute additional uncertainty regarding programmatic evaluations of wildfire management and attainment of policy goals. This article develops a framework within which human sources of uncertainty...

Adaptive management of rangeland systems

USGS Publications Warehouse

Allen, Craig R.; Angeler, David G.; Fontaine, Joseph J.; Garmestani, Ahjond S.; Hart, Noelle M.; Pope, Kevin L.; Twidwell, Dirac

2017-01-01

Adaptive management is an approach to natural resource management that uses structured learning to reduce uncertainties for the improvement of management over time. The origins of adaptive management are linked to ideas of resilience theory and complex systems. Rangeland management is particularly well suited for the application of adaptive management, having sufficient controllability and reducible uncertainties. Adaptive management applies the tools of structured decision making and requires monitoring, evaluation, and adjustment of management. Adaptive governance, involving sharing of power and knowledge among relevant stakeholders, is often required to address conflict situations. Natural resource laws and regulations can present a barrier to adaptive management when requirements for legal certainty are met with environmental uncertainty. However, adaptive management is possible, as illustrated by two cases presented in this chapter. Despite challenges and limitations, when applied appropriately adaptive management leads to improved management through structured learning, and rangeland management is an area in which adaptive management shows promise and should be further explored.

Adaptive management for ecosystem services (j/a)

EPA Science Inventory

Management of natural resources for the production of ecosystem services, which are vital for human well-being, is necessary even when there is uncertainty regarding system response to management action. This uncertainty is the result of incomplete controllability, complex intern...

4. International reservoir characterization technical conference

DOE Office of Scientific and Technical Information (OSTI.GOV)

NONE

1997-04-01

This volume contains the Proceedings of the Fourth International Reservoir Characterization Technical Conference held March 2-4, 1997 in Houston, Texas. The theme for the conference was Advances in Reservoir Characterization for Effective Reservoir Management. On March 2, 1997, the DOE Class Workshop kicked off with tutorials by Dr. Steve Begg (BP Exploration) and Dr. Ganesh Thakur (Chevron). Tutorial presentations are not included in these Proceedings but may be available from the authors. The conference consisted of the following topics: data acquisition; reservoir modeling; scaling reservoir properties; and managing uncertainty. Selected papers have been processed separately for inclusion in the Energymore » Science and Technology database.« less

Uncertainty Analysis of Consequence Management (CM) Data Products.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hunt, Brian D.; Eckert-Gallup, Aubrey Celia; Cochran, Lainy Dromgoole

The goal of this project is to develop and execute methods for characterizing uncertainty in data products that are deve loped and distributed by the DOE Consequence Management (CM) Program. A global approach to this problem is necessary because multiple sources of error and uncertainty from across the CM skill sets contribute to the ultimate p roduction of CM data products. This report presents the methods used to develop a probabilistic framework to characterize this uncertainty and provides results for an uncertainty analysis for a study scenario analyzed using this framework.

Uncertainty in BMP evaluation and optimization for watershed management

NASA Astrophysics Data System (ADS)

Chaubey, I.; Cibin, R.; Sudheer, K.; Her, Y.

2012-12-01

Use of computer simulation models have increased substantially to make watershed management decisions and to develop strategies for water quality improvements. These models are often used to evaluate potential benefits of various best management practices (BMPs) for reducing losses of pollutants from sources areas into receiving waterbodies. Similarly, use of simulation models in optimizing selection and placement of best management practices under single (maximization of crop production or minimization of pollutant transport) and multiple objective functions has increased recently. One of the limitations of the currently available assessment and optimization approaches is that the BMP strategies are considered deterministic. Uncertainties in input data (e.g. precipitation, streamflow, sediment, nutrient and pesticide losses measured, land use) and model parameters may result in considerable uncertainty in watershed response under various BMP options. We have developed and evaluated options to include uncertainty in BMP evaluation and optimization for watershed management. We have also applied these methods to evaluate uncertainty in ecosystem services from mixed land use watersheds. In this presentation, we will discuss methods to to quantify uncertainties in BMP assessment and optimization solutions due to uncertainties in model inputs and parameters. We have used a watershed model (Soil and Water Assessment Tool or SWAT) to simulate the hydrology and water quality in mixed land use watershed located in Midwest USA. The SWAT model was also used to represent various BMPs in the watershed needed to improve water quality. SWAT model parameters, land use change parameters, and climate change parameters were considered uncertain. It was observed that model parameters, land use and climate changes resulted in considerable uncertainties in BMP performance in reducing P, N, and sediment loads. In addition, climate change scenarios also affected uncertainties in SWAT simulated crop yields. Considerable uncertainties in the net cost and the water quality improvements resulted due to uncertainties in land use, climate change, and model parameter values.

50 CFR 648.141 - Closure.

Code of Federal Regulations, 2011 CFR

2011-10-01

... Bass Monitoring Committee shall identify and review the relevant sources of management uncertainty to... sources of management uncertainty that were considered, technical approaches to mitigating these sources..., DEPARTMENT OF COMMERCE FISHERIES OF THE NORTHEASTERN UNITED STATES Management Measures for the Black Sea Bass...

50 CFR 648.121 - Closures.

Code of Federal Regulations, 2011 CFR

2011-10-01

... management uncertainty to recommend ACTs for the commercial and recreational fishing sectors as part of the... sources of management uncertainty that were considered, technical approaches to mitigating these sources..., DEPARTMENT OF COMMERCE FISHERIES OF THE NORTHEASTERN UNITED STATES Management Measures for the Scup Fishery...

Life-cycle assessment of municipal solid waste management alternatives with consideration of uncertainty: SIWMS development and application.

PubMed

Hanandeh, Ali El; El-Zein, Abbas

2010-05-01

This paper describes the development and application of the Stochastic Integrated Waste Management Simulator (SIWMS) model. SIWMS provides a detailed view of the environmental impacts and associated costs of municipal solid waste (MSW) management alternatives under conditions of uncertainty. The model follows a life-cycle inventory approach extended with compensatory systems to provide more equitable bases for comparing different alternatives. Economic performance is measured by the net present value. The model is verified against four publicly available models under deterministic conditions and then used to study the impact of uncertainty on Sydney's MSW management 'best practices'. Uncertainty has a significant effect on all impact categories. The greatest effect is observed in the global warming category where a reversal of impact direction is predicted. The reliability of the system is most sensitive to uncertainties in the waste processing and disposal. The results highlight the importance of incorporating uncertainty at all stages to better understand the behaviour of the MSW system. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

A climate robust integrated modelling framework for regional impact assessment of climate change

NASA Astrophysics Data System (ADS)

Janssen, Gijs; Bakker, Alexander; van Ek, Remco; Groot, Annemarie; Kroes, Joop; Kuiper, Marijn; Schipper, Peter; van Walsum, Paul; Wamelink, Wieger; Mol, Janet

2013-04-01

Decision making towards climate proofing the water management of regional catchments can benefit greatly from the availability of a climate robust integrated modelling framework, capable of a consistent assessment of climate change impacts on the various interests present in the catchments. In the Netherlands, much effort has been devoted to developing state-of-the-art regional dynamic groundwater models with a very high spatial resolution (25x25 m2). Still, these models are not completely satisfactory to decision makers because the modelling concepts do not take into account feedbacks between meteorology, vegetation/crop growth, and hydrology. This introduces uncertainties in forecasting the effects of climate change on groundwater, surface water, agricultural yields, and development of groundwater dependent terrestrial ecosystems. These uncertainties add to the uncertainties about the predictions on climate change itself. In order to create an integrated, climate robust modelling framework, we coupled existing model codes on hydrology, agriculture and nature that are currently in use at the different research institutes in the Netherlands. The modelling framework consists of the model codes MODFLOW (groundwater flow), MetaSWAP (vadose zone), WOFOST (crop growth), SMART2-SUMO2 (soil-vegetation) and NTM3 (nature valuation). MODFLOW, MetaSWAP and WOFOST are coupled online (i.e. exchange information on time step basis). Thus, changes in meteorology and CO2-concentrations affect crop growth and feedbacks between crop growth, vadose zone water movement and groundwater recharge are accounted for. The model chain WOFOST-MetaSWAP-MODFLOW generates hydrological input for the ecological prediction model combination SMART2-SUMO2-NTM3. The modelling framework was used to support the regional water management decision making process in the 267 km2 Baakse Beek-Veengoot catchment in the east of the Netherlands. Computations were performed for regionalized 30-year climate change scenarios developed by KNMI for precipitation and reference evapotranspiration according to Penman-Monteith. Special focus in the project was on the role of uncertainty. How valid is the information that is generated by this modelling framework? What are the most important uncertainties of the input data, how do they affect the results of the model chain and how can the uncertainties of the data, results, and model concepts be quantified and communicated? Besides these technical issues, an important part of the study was devoted to the perception of stakeholders. Stakeholder analysis and additional working sessions yielded insight into how the models, their results and the uncertainties are perceived, how the modelling framework and results connect to the stakeholders' information demands and what kind of additional information is needed for adequate support on decision making.

Assessing and reporting uncertainties in dietary exposure analysis - Part II: Application of the uncertainty template to a practical example of exposure assessment.

PubMed

Tennant, David; Bánáti, Diána; Kennedy, Marc; König, Jürgen; O'Mahony, Cian; Kettler, Susanne

2017-11-01

A previous publication described methods for assessing and reporting uncertainty in dietary exposure assessments. This follow-up publication uses a case study to develop proposals for representing and communicating uncertainty to risk managers. The food ingredient aspartame is used as the case study in a simple deterministic model (the EFSA FAIM template) and with more sophisticated probabilistic exposure assessment software (FACET). Parameter and model uncertainties are identified for each modelling approach and tabulated. The relative importance of each source of uncertainty is then evaluated using a semi-quantitative scale and the results expressed using two different forms of graphical summary. The value of this approach in expressing uncertainties in a manner that is relevant to the exposure assessment and useful to risk managers is then discussed. It was observed that the majority of uncertainties are often associated with data sources rather than the model itself. However, differences in modelling methods can have the greatest impact on uncertainties overall, particularly when the underlying data are the same. It was concluded that improved methods for communicating uncertainties for risk management is the research area where the greatest amount of effort is suggested to be placed in future. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

The NASA Langley Multidisciplinary Uncertainty Quantification Challenge

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Kenny, Sean P.; Giesy, Daniel P.

2014-01-01

This paper presents the formulation of an uncertainty quantification challenge problem consisting of five subproblems. These problems focus on key aspects of uncertainty characterization, sensitivity analysis, uncertainty propagation, extreme-case analysis, and robust design.

Understanding the ways in which health visitors manage anxiety in cross-cultural work: a qualitative study.

PubMed

Cuthill, Fiona

2014-10-01

This paper is a report of part of a study that explored the ways in which health visitors manage uncertainty and anxiety when working with clients across cultures. Internationally health care professionals are required to deliver a high standard of culturally appropriate care to increasingly diverse communities and yet problems persist. Research evidence informing cultural 'competence' is focused largely around student experience and consequently little is known about the day-to-day experiences of health professionals in diverse community settings. Anxiety and uncertainty are increasingly recognised as important emotions experienced by a variety of health care professionals when working across cultures and yet the ways in which anxiety and uncertainty is managed in practice is less well understood. Grounded theory methodology was used and 21 semi-structured interviews were conducted with participating health visitors in the North East of England between May 2008 and September 2009. All participants described themselves as white. This study identified three different positions adopted by the health visitors to manage uncertainty and anxiety in their work across cultures. Identified as, 'Fixing a culture', 'Reworking the equality agenda' and 'Asserting the professional self', these strategies identify the ways in which health visitors try to manage the uncertainty and anxiety they feel when working in diverse communities. All of these strategies attempt in different ways to negate cultural difference and to render culture as static and known. Given that health professionals report anxiety and uncertainty when working across diverse community settings, identification of the strategies used by health visitors to manage that anxiety is important for both policy and practice. New strategies need to be developed to help health professionals to manage uncertainty and anxiety in ways that promote both cultural safe care and health equity.

Uncertainty characterization approaches for risk assessment of DBPs in drinking water: a review.

PubMed

Chowdhury, Shakhawat; Champagne, Pascale; McLellan, P James

2009-04-01

The management of risk from disinfection by-products (DBPs) in drinking water has become a critical issue over the last three decades. The areas of concern for risk management studies include (i) human health risk from DBPs, (ii) disinfection performance, (iii) technical feasibility (maintenance, management and operation) of treatment and disinfection approaches, and (iv) cost. Human health risk assessment is typically considered to be the most important phase of the risk-based decision-making or risk management studies. The factors associated with health risk assessment and other attributes are generally prone to considerable uncertainty. Probabilistic and non-probabilistic approaches have both been employed to characterize uncertainties associated with risk assessment. The probabilistic approaches include sampling-based methods (typically Monte Carlo simulation and stratified sampling) and asymptotic (approximate) reliability analysis (first- and second-order reliability methods). Non-probabilistic approaches include interval analysis, fuzzy set theory and possibility theory. However, it is generally accepted that no single method is suitable for the entire spectrum of problems encountered in uncertainty analyses for risk assessment. Each method has its own set of advantages and limitations. In this paper, the feasibility and limitations of different uncertainty analysis approaches are outlined for risk management studies of drinking water supply systems. The findings assist in the selection of suitable approaches for uncertainty analysis in risk management studies associated with DBPs and human health risk.

Development of perspective-based water management strategies for the Rhine and Meuse basins.

PubMed

van Deursen, W P A; Middelkoop, H

2005-01-01

Water management is surrounded by uncertainties. Water management thus has to answer the question: given the uncertainties, what is the best management strategy? This paper describes the application of the perspectives method on water management in the Rhine and Meuse basins. In the perspectives method, a structured framework to analyse water management strategies under uncertainty is provided. Various strategies are clustered in perspectives according to their underlying assumptions. This framework allows for an analysis of current water management strategies, but also allows for evaluation of the robustness of proposed future water strategies. It becomes clear that no water management strategy is superior to the others, but that inherent choices on risk acceptance and costs make a real political dilemma which will not be solved by further optimisation.

Forest management under uncertainty for multiple bird population objectives

USGS Publications Warehouse

Moore, C.T.; Plummer, W.T.; Conroy, M.J.; Ralph, C. John; Rich, Terrell D.

2005-01-01

We advocate adaptive programs of decision making and monitoring for the management of forest birds when responses by populations to management, and particularly management trade-offs among populations, are uncertain. Models are necessary components of adaptive management. Under this approach, uncertainty about the behavior of a managed system is explicitly captured in a set of alternative models. The models generate testable predictions about the response of populations to management, and monitoring data provide the basis for assessing these predictions and informing future management decisions. To illustrate these principles, we examine forest management at the Piedmont National Wildlife Refuge, where management attention is focused on the recovery of the Red-cockaded Woodpecker (Picoides borealis) population. However, managers are also sensitive to the habitat needs of many non-target organisms, including Wood Thrushes (Hylocichla mustelina) and other forest interior Neotropical migratory birds. By simulating several management policies on a set of-alternative forest and bird models, we found a decision policy that maximized a composite response by woodpeckers and Wood Thrushes despite our complete uncertainty regarding system behavior. Furthermore, we used monitoring data to update our measure of belief in each alternative model following one cycle of forest management. This reduction of uncertainty translates into a reallocation of model influence on the choice of optimal decision action at the next decision opportunity.

Uncertainty in Twenty-First-Century CMIP5 Sea Level Projections

NASA Technical Reports Server (NTRS)

Little, Christopher M.; Horton, Radley M.; Kopp, Robert E.; Oppenheimer, Michael; Yip, Stan

2015-01-01

The representative concentration pathway (RCP) simulations included in phase 5 of the Coupled Model Intercomparison Project (CMIP5) quantify the response of the climate system to different natural and anthropogenic forcing scenarios. These simulations differ because of 1) forcing, 2) the representation of the climate system in atmosphere-ocean general circulation models (AOGCMs), and 3) the presence of unforced (internal) variability. Global and local sea level rise projections derived from these simulations, and the emergence of distinct responses to the four RCPs depend on the relative magnitude of these sources of uncertainty at different lead times. Here, the uncertainty in CMIP5 projections of sea level is partitioned at global and local scales, using a 164-member ensemble of twenty-first-century simulations. Local projections at New York City (NYSL) are highlighted. The partition between model uncertainty, scenario uncertainty, and internal variability in global mean sea level (GMSL) is qualitatively consistent with that of surface air temperature, with model uncertainty dominant for most of the twenty-first century. Locally, model uncertainty is dominant through 2100, with maxima in the North Atlantic and the Arctic Ocean. The model spread is driven largely by 4 of the 16 AOGCMs in the ensemble; these models exhibit outlying behavior in all RCPs and in both GMSL and NYSL. The magnitude of internal variability varies widely by location and across models, leading to differences of several decades in the local emergence of RCPs. The AOGCM spread, and its sensitivity to model exclusion and/or weighting, has important implications for sea level assessments, especially if a local risk management approach is utilized.

Robustness for slope stability modelling under deep uncertainty

NASA Astrophysics Data System (ADS)

Almeida, Susana; Holcombe, Liz; Pianosi, Francesca; Wagener, Thorsten

2015-04-01

Landslides can have large negative societal and economic impacts, such as loss of life and damage to infrastructure. However, the ability of slope stability assessment to guide management is limited by high levels of uncertainty in model predictions. Many of these uncertainties cannot be easily quantified, such as those linked to climate change and other future socio-economic conditions, restricting the usefulness of traditional decision analysis tools. Deep uncertainty can be managed more effectively by developing robust, but not necessarily optimal, policies that are expected to perform adequately under a wide range of future conditions. Robust strategies are particularly valuable when the consequences of taking a wrong decision are high as is often the case of when managing natural hazard risks such as landslides. In our work a physically based numerical model of hydrologically induced slope instability (the Combined Hydrology and Stability Model - CHASM) is applied together with robust decision making to evaluate the most important uncertainties (storm events, groundwater conditions, surface cover, slope geometry, material strata and geotechnical properties) affecting slope stability. Specifically, impacts of climate change on long-term slope stability are incorporated, accounting for the deep uncertainty in future climate projections. Our findings highlight the potential of robust decision making to aid decision support for landslide hazard reduction and risk management under conditions of deep uncertainty.

Development of a European Ensemble System for Seasonal Prediction: Application to crop yield

NASA Astrophysics Data System (ADS)

Terres, J. M.; Cantelaube, P.

2003-04-01

Western European agriculture is highly intensive and the weather is the main source of uncertainty for crop yield assessment and for crop management. In the current system, at the time when a crop yield forecast is issued, the weather conditions leading up to harvest time are unknown and are therefore a major source of uncertainty. The use of seasonal weather forecast would bring additional information for the remaining crop season and has valuable benefit for improving the management of agricultural markets and environmentally sustainable farm practices. An innovative method for supplying seasonal forecast information to crop simulation models has been developed in the frame of the EU funded research project DEMETER. It consists in running a crop model on each individual member of the seasonal hindcasts to derive a probability distribution of crop yield. Preliminary results of cumulative probability function of wheat yield provides information on both the yield anomaly and the reliability of the forecast. Based on the spread of the probability distribution, the end-user can directly quantify the benefits and risks of taking weather-sensitive decisions.

Impact of policy on greenhouse gas emissions and economics of biodiesel production.

PubMed

Olivetti, Elsa; Gülşen, Ece; Malça, João; Castanheira, Erica; Freire, Fausto; Dias, Luis; Kirchain, Randolph

2014-07-01

As an alternative transportation fuel to petrodiesel, biodiesel has been promoted within national energy portfolio targets across the world. Early estimations of low lifecycle greenhouse gas (GHG) emissions of biodiesel were a driver behind extensive government support in the form of financial incentives for the industry. However, studies consistently report a high degree of uncertainty in these emissions estimates, raising questions concerning the carbon benefits of biodiesel. Furthermore, the implications of feedstock blending on GHG emissions uncertainty have not been explicitly addressed despite broad practice by the industry to meet fuel quality standards and to control costs. This work investigated the impact of feedstock blending on the characteristics of biodiesel by using a chance-constrained (CC) blend optimization method. The objective of the optimization is minimization of feedstock costs subject to fuel standards and emissions constraints. Results indicate that blending can be used to manage GHG emissions uncertainty characteristics of biodiesel, and to achieve cost reductions through feedstock diversification. Simulations suggest that emissions control policies that restrict the use of certain feedstocks based on their GHG estimates overlook blending practices and benefits, increasing the cost of biodiesel. In contrast, emissions control policies which recognize the multifeedstock nature of biodiesel provide producers with feedstock selection flexibility, enabling them to manage their blend portfolios cost effectively, potentially without compromising fuel quality or emissions reductions.

Uncertainty and risk in wildland fire management: a review.

PubMed

Thompson, Matthew P; Calkin, Dave E

2011-08-01

Wildland fire management is subject to manifold sources of uncertainty. Beyond the unpredictability of wildfire behavior, uncertainty stems from inaccurate/missing data, limited resource value measures to guide prioritization across fires and resources at risk, and an incomplete scientific understanding of ecological response to fire, of fire behavior response to treatments, and of spatiotemporal dynamics involving disturbance regimes and climate change. This work attempts to systematically align sources of uncertainty with the most appropriate decision support methodologies, in order to facilitate cost-effective, risk-based wildfire planning efforts. We review the state of wildfire risk assessment and management, with a specific focus on uncertainties challenging implementation of integrated risk assessments that consider a suite of human and ecological values. Recent advances in wildfire simulation and geospatial mapping of highly valued resources have enabled robust risk-based analyses to inform planning across a variety of scales, although improvements are needed in fire behavior and ignition occurrence models. A key remaining challenge is a better characterization of non-market resources at risk, both in terms of their response to fire and how society values those resources. Our findings echo earlier literature identifying wildfire effects analysis and value uncertainty as the primary challenges to integrated wildfire risk assessment and wildfire management. We stress the importance of identifying and characterizing uncertainties in order to better quantify and manage them. Leveraging the most appropriate decision support tools can facilitate wildfire risk assessment and ideally improve decision-making. Published by Elsevier Ltd.

Handling Uncertain Gross Margin and Water Demand in Agricultural Water Resources Management using Robust Optimization

NASA Astrophysics Data System (ADS)

Chaerani, D.; Lesmana, E.; Tressiana, N.

2018-03-01

In this paper, an application of Robust Optimization in agricultural water resource management problem under gross margin and water demand uncertainty is presented. Water resource management is a series of activities that includes planning, developing, distributing and managing the use of water resource optimally. Water resource management for agriculture can be one of the efforts to optimize the benefits of agricultural output. The objective function of agricultural water resource management problem is to maximizing total benefits by water allocation to agricultural areas covered by the irrigation network in planning horizon. Due to gross margin and water demand uncertainty, we assume that the uncertain data lies within ellipsoidal uncertainty set. We employ robust counterpart methodology to get the robust optimal solution.

Testing the robustness of management decisions to uncertainty: Everglades restoration scenarios.

PubMed

Fuller, Michael M; Gross, Louis J; Duke-Sylvester, Scott M; Palmer, Mark

2008-04-01

To effectively manage large natural reserves, resource managers must prepare for future contingencies while balancing the often conflicting priorities of different stakeholders. To deal with these issues, managers routinely employ models to project the response of ecosystems to different scenarios that represent alternative management plans or environmental forecasts. Scenario analysis is often used to rank such alternatives to aid the decision making process. However, model projections are subject to uncertainty in assumptions about model structure, parameter values, environmental inputs, and subcomponent interactions. We introduce an approach for testing the robustness of model-based management decisions to the uncertainty inherent in complex ecological models and their inputs. We use relative assessment to quantify the relative impacts of uncertainty on scenario ranking. To illustrate our approach we consider uncertainty in parameter values and uncertainty in input data, with specific examples drawn from the Florida Everglades restoration project. Our examples focus on two alternative 30-year hydrologic management plans that were ranked according to their overall impacts on wildlife habitat potential. We tested the assumption that varying the parameter settings and inputs of habitat index models does not change the rank order of the hydrologic plans. We compared the average projected index of habitat potential for four endemic species and two wading-bird guilds to rank the plans, accounting for variations in parameter settings and water level inputs associated with hypothetical future climates. Indices of habitat potential were based on projections from spatially explicit models that are closely tied to hydrology. For the American alligator, the rank order of the hydrologic plans was unaffected by substantial variation in model parameters. By contrast, simulated major shifts in water levels led to reversals in the ranks of the hydrologic plans in 24.1-30.6% of the projections for the wading bird guilds and several individual species. By exposing the differential effects of uncertainty, relative assessment can help resource managers assess the robustness of scenario choice in model-based policy decisions.

MANAGING UNCERTAINTIES ASSOCIATED WITH RADIOACTIVE WASTE DISPOSAL: TASK GROUP 4 OF THE IAEA PRISM PROJECT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Seitz, R.

2011-03-02

It is widely recognized that the results of safety assessment calculations provide an important contribution to the safety arguments for a disposal facility, but cannot in themselves adequately demonstrate the safety of the disposal system. The safety assessment and a broader range of arguments and activities need to be considered holistically to justify radioactive waste disposal at any particular site. Many programs are therefore moving towards the production of what has become known as a Safety Case, which includes all of the different activities that are conducted to demonstrate the safety of a disposal concept. Recognizing the growing interest inmore » the concept of a Safety Case, the International Atomic Energy Agency (IAEA) is undertaking an intercomparison and harmonization project called PRISM (Practical Illustration and use of the Safety Case Concept in the Management of Near-surface Disposal). The PRISM project is organized into four Task Groups that address key aspects of the Safety Case concept: Task Group 1 - Understanding the Safety Case; Task Group 2 - Disposal facility design; Task Group 3 - Managing waste acceptance; and Task Group 4 - Managing uncertainty. This paper addresses the work of Task Group 4, which is investigating approaches for managing the uncertainties associated with near-surface disposal of radioactive waste and their consideration in the context of the Safety Case. Emphasis is placed on identifying a wide variety of approaches that can and have been used to manage different types of uncertainties, especially non-quantitative approaches that have not received as much attention in previous IAEA projects. This paper includes discussions of the current results of work on the task on managing uncertainty, including: the different circumstances being considered, the sources/types of uncertainties being addressed and some initial proposals for approaches that can be used to manage different types of uncertainties.« less

An experiment with interactive planning models

NASA Technical Reports Server (NTRS)

Beville, J.; Wagner, J. H.; Zannetos, Z. S.

1970-01-01

Experiments on decision making in planning problems are described. Executives were tested in dealing with capital investments and competitive pricing decisions under conditions of uncertainty. A software package, the interactive risk analysis model system, was developed, and two controlled experiments were conducted. It is concluded that planning models can aid management, and predicted uses of the models are as a central tool, as an educational tool, to improve consistency in decision making, to improve communications, and as a tool for consensus decision making.

Validation Study of Wave Breaking Influence in a Coupled Wave Model for Hurricane Wind Conditions

DTIC Science & Technology

2008-08-27

ACRONYM(S) Grant Management Organisation, The University of New South Wales, Sydney 2052, GMO Australia 11. SPONSOR/MONITOR’S REPORT NUMBER(S) None 12...of Snyder et al.(1981) and laboratory measurements ( Plant , 1982). The differences between forms (i) and (ii) are indicative of the level of uncertainty...parameterizations (Snyder, 1981; Plant , 1982; Hsaio-Shemdin, 1983) for growing seas (U10o/c-2). The Janssen9l parameterization is consistent with Snyder8l for

Methods for exploring uncertainty in groundwater management predictions

USGS Publications Warehouse

Guillaume, Joseph H. A.; Hunt, Randall J.; Comunian, Alessandro; Fu, Baihua; Blakers, Rachel S; Jakeman, Anthony J.; Barreteau, Olivier; Hunt, Randall J.; Rinaudo, Jean-Daniel; Ross, Andrew

2016-01-01

Models of groundwater systems help to integrate knowledge about the natural and human system covering different spatial and temporal scales, often from multiple disciplines, in order to address a range of issues of concern to various stakeholders. A model is simply a tool to express what we think we know. Uncertainty, due to lack of knowledge or natural variability, means that there are always alternative models that may need to be considered. This chapter provides an overview of uncertainty in models and in the definition of a problem to model, highlights approaches to communicating and using predictions of uncertain outcomes and summarises commonly used methods to explore uncertainty in groundwater management predictions. It is intended to raise awareness of how alternative models and hence uncertainty can be explored in order to facilitate the integration of these techniques with groundwater management.

Ensemble-based flash-flood modelling: Taking into account hydrodynamic parameters and initial soil moisture uncertainties

NASA Astrophysics Data System (ADS)

Edouard, Simon; Vincendon, Béatrice; Ducrocq, Véronique

2018-05-01

Intense precipitation events in the Mediterranean often lead to devastating flash floods (FF). FF modelling is affected by several kinds of uncertainties and Hydrological Ensemble Prediction Systems (HEPS) are designed to take those uncertainties into account. The major source of uncertainty comes from rainfall forcing and convective-scale meteorological ensemble prediction systems can manage it for forecasting purpose. But other sources are related to the hydrological modelling part of the HEPS. This study focuses on the uncertainties arising from the hydrological model parameters and initial soil moisture with aim to design an ensemble-based version of an hydrological model dedicated to Mediterranean fast responding rivers simulations, the ISBA-TOP coupled system. The first step consists in identifying the parameters that have the strongest influence on FF simulations by assuming perfect precipitation. A sensitivity study is carried out first using a synthetic framework and then for several real events and several catchments. Perturbation methods varying the most sensitive parameters as well as initial soil moisture allow designing an ensemble-based version of ISBA-TOP. The first results of this system on some real events are presented. The direct perspective of this work will be to drive this ensemble-based version with the members of a convective-scale meteorological ensemble prediction system to design a complete HEPS for FF forecasting.

Adaptive management: Chapter 1

USGS Publications Warehouse

Allen, Craig R.; Garmestani, Ahjond S.; Allen, Craig R.; Garmestani, Ahjond S.

2015-01-01

Adaptive management is an approach to natural resource management that emphasizes learning through management where knowledge is incomplete, and when, despite inherent uncertainty, managers and policymakers must act. Unlike a traditional trial and error approach, adaptive management has explicit structure, including a careful elucidation of goals, identification of alternative management objectives and hypotheses of causation, and procedures for the collection of data followed by evaluation and reiteration. The process is iterative, and serves to reduce uncertainty, build knowledge and improve management over time in a goal-oriented and structured process.

Adaptive management

USGS Publications Warehouse

Allen, Craig R.; Garmestani, Ahjond S.

2015-01-01

Adaptive management is an approach to natural resource management that emphasizes learning through management where knowledge is incomplete, and when, despite inherent uncertainty, managers and policymakers must act. Unlike a traditional trial and error approach, adaptive management has explicit structure, including a careful elucidation of goals, identification of alternative management objectives and hypotheses of causation, and procedures for the collection of data followed by evaluation and reiteration. The process is iterative, and serves to reduce uncertainty, build knowledge and improve management over time in a goal-oriented and structured process.

An adaptive decision framework for the conservation of a threatened plant

USGS Publications Warehouse

Moore, Clinton T.; Fonnesbeck, Christopher J.; Shea, Katriona; Lah, Kristopher J.; McKenzie, Paul M.; Ball, Lianne C.; Runge, Michael C.; Alexander, Helen M.

2011-01-01

Mead's milkweed Asclepias meadii, a long-lived perennial herb of tallgrass prairie and glade communities of the central United States, is a species designated as threatened under the U.S. Endangered Species Act. Challenges to its successful management include the facts that much about its life history is unknown, its age at reproductive maturity is very advanced, certain life stages are practically unobservable, its productivity is responsive to unpredictable environmental events, and most of the known populations occur on private lands unprotected by any legal conservation instrument. One critical source of biological uncertainty is the degree to which fire promotes growth and reproductive response in the plant. To aid in its management, we developed a prototype population-level state-dependent decision-making framework that explicitly accounts for this uncertainty and for uncertainties related to stochastic environmental effects and vital rates. To parameterize the decision model, we used estimates found in the literature, and we analyzed data from a long-term monitoring program where fates of individual plants were observed through time. We demonstrate that different optimal courses of action are followed according to how one believes that fire influences reproductive response, and we show that the action taken for certain population states is informative for resolving uncertainty about competing beliefs regarding the effect of fire. We advocate the use of a model-predictive approach for the management of rare populations, particularly when management uncertainty is profound. Over time, an adaptive management approach should reduce uncertainty and improve management performance as predictions of management outcome generated under competing models are continually informed and updated by monitoring data.

Setting the most robust effluent level under severe uncertainty: application of information-gap decision theory to chemical management.

PubMed

Yokomizo, Hiroyuki; Naito, Wataru; Tanaka, Yoshinari; Kamo, Masashi

2013-11-01

Decisions in ecological risk management for chemical substances must be made based on incomplete information due to uncertainties. To protect the ecosystems from the adverse effect of chemicals, a precautionary approach is often taken. The precautionary approach, which is based on conservative assumptions about the risks of chemical substances, can be applied selecting management models and data. This approach can lead to an adequate margin of safety for ecosystems by reducing exposure to harmful substances, either by reducing the use of target chemicals or putting in place strict water quality criteria. However, the reduction of chemical use or effluent concentrations typically entails a financial burden. The cost effectiveness of the precautionary approach may be small. Hence, we need to develop a formulaic methodology in chemical risk management that can sufficiently protect ecosystems in a cost-effective way, even when we do not have sufficient information for chemical management. Information-gap decision theory can provide the formulaic methodology. Information-gap decision theory determines which action is the most robust to uncertainty by guaranteeing an acceptable outcome under the largest degree of uncertainty without requiring information about the extent of parameter uncertainty at the outset. In this paper, we illustrate the application of information-gap decision theory to derive a framework for setting effluent limits of pollutants for point sources under uncertainty. Our application incorporates a cost for reduction in pollutant emission and a cost to wildlife species affected by the pollutant. Our framework enables us to settle upon actions to deal with severe uncertainty in ecological risk management of chemicals. Copyright © 2013 Elsevier Ltd. All rights reserved.

Unexpectedly large impact of forest management and grazing on global vegetation biomass

PubMed Central

Erb, K.-H.; Bais, A.L.S.; Carvalhais, N.; Fetzel, T.; Gingrich, S.; Haberl, H.; Lauk, C.; Niedertscheider, M.; Pongratz, J.; Thurner, M.; Luyssaert, S.

2017-01-01

Carbon stocks in vegetation play a key role in the climate system1–4, but their magnitude and patterns, their uncertainties, and the impact of land use on them remain poorly quantified. Based on a consistent integration of state-of-the art datasets, we show that vegetation currently stores ~450 PgC. In the hypothetical absence of land use, potential vegetation would store ~916 PgC, under current climate. This difference singles out the massive effect land use has on biomass stocks. Deforestation and other land-cover changes are responsible for 53-58% of the difference between current and potential biomass stocks. Land management effects, i.e. land-use induced biomass stock changes within the same land cover, contribute 42-47% but are underappreciated in the current literature. Avoiding deforestation hence is necessary but not sufficient for climate-change mitigation. Our results imply that trade-offs exist between conserving carbon stocks on managed land and raising the contribution of biomass to raw material and energy supply for climate change mitigation. Efforts to raise biomass stocks are currently only verifiable in temperate forests, where potentials are limited. In contrast, large uncertainties hamper verification in the tropical forest where the largest potentials are located, pointing to challenges for the upcoming stocktaking exercises under the Paris agreement. PMID:29258288

Analysis of air quality management with emphasis on transportation sources

NASA Technical Reports Server (NTRS)

English, T. D.; Divita, E.; Lees, L.

1980-01-01

The current environment and practices of air quality management were examined for three regions: Denver, Phoenix, and the South Coast Air Basin of California. These regions were chosen because the majority of their air pollution emissions are related to mobile sources. The impact of auto exhaust on the air quality management process is characterized and assessed. An examination of the uncertainties in air pollutant measurements, emission inventories, meteorological parameters, atmospheric chemistry, and air quality simulation models is performed. The implications of these uncertainties to current air quality management practices is discussed. A set of corrective actions are recommended to reduce these uncertainties.

Supporting Fisheries Management by Means of Complex Models: Can We Point out Isles of Robustness in a Sea of Uncertainty?

PubMed Central

Gasche, Loïc; Mahévas, Stéphanie; Marchal, Paul

2013-01-01

Ecosystems are usually complex, nonlinear and strongly influenced by poorly known environmental variables. Among these systems, marine ecosystems have high uncertainties: marine populations in general are known to exhibit large levels of natural variability and the intensity of fishing efforts can change rapidly. These uncertainties are a source of risks that threaten the sustainability of both fish populations and fishing fleets targeting them. Appropriate management measures have to be found in order to reduce these risks and decrease sensitivity to uncertainties. Methods have been developed within decision theory that aim at allowing decision making under severe uncertainty. One of these methods is the information-gap decision theory. The info-gap method has started to permeate ecological modelling, with recent applications to conservation. However, these practical applications have so far been restricted to simple models with analytical solutions. Here we implement a deterministic approach based on decision theory in a complex model of the Eastern English Channel. Using the ISIS-Fish modelling platform, we model populations of sole and plaice in this area. We test a wide range of values for ecosystem, fleet and management parameters. From these simulations, we identify management rules controlling fish harvesting that allow reaching management goals recommended by ICES (International Council for the Exploration of the Sea) working groups while providing the highest robustness to uncertainties on ecosystem parameters. PMID:24204873

Supporting fisheries management by means of complex models: can we point out isles of robustness in a sea of uncertainty?

PubMed

Gasche, Loïc; Mahévas, Stéphanie; Marchal, Paul

2013-01-01

Ecosystems are usually complex, nonlinear and strongly influenced by poorly known environmental variables. Among these systems, marine ecosystems have high uncertainties: marine populations in general are known to exhibit large levels of natural variability and the intensity of fishing efforts can change rapidly. These uncertainties are a source of risks that threaten the sustainability of both fish populations and fishing fleets targeting them. Appropriate management measures have to be found in order to reduce these risks and decrease sensitivity to uncertainties. Methods have been developed within decision theory that aim at allowing decision making under severe uncertainty. One of these methods is the information-gap decision theory. The info-gap method has started to permeate ecological modelling, with recent applications to conservation. However, these practical applications have so far been restricted to simple models with analytical solutions. Here we implement a deterministic approach based on decision theory in a complex model of the Eastern English Channel. Using the ISIS-Fish modelling platform, we model populations of sole and plaice in this area. We test a wide range of values for ecosystem, fleet and management parameters. From these simulations, we identify management rules controlling fish harvesting that allow reaching management goals recommended by ICES (International Council for the Exploration of the Sea) working groups while providing the highest robustness to uncertainties on ecosystem parameters.

Understanding Grief and Loss

MedlinePlus

... Cancer Coping With Uncertainty Managing Stress Coping with Anger Anxiety Depression Fear of Treatment-Related Side Effects Coping with ... Cancer Coping With Uncertainty Managing Stress Coping with Anger Anxiety Depression Fear of Treatment-Related Side Effects Coping with ...

Management applications of discontinuity theory

EPA Science Inventory

1.Human impacts on the environment are multifaceted and can occur across distinct spatiotemporal scales. Ecological responses to environmental change are therefore difficult to predict, and entail large degrees of uncertainty. Such uncertainty requires robust tools for management...

Modeling global water use for the 21st century: the Water Futures and Solutions (WFaS) initiative and its approaches

NASA Astrophysics Data System (ADS)

Wada, Y.; Flörke, M.; Hanasaki, N.; Eisner, S.; Fischer, G.; Tramberend, S.; Satoh, Y.; van Vliet, M. T. H.; Yillia, P.; Ringler, C.; Burek, P.; Wiberg, D.

2016-01-01

To sustain growing food demand and increasing standard of living, global water use increased by nearly 6 times during the last 100 years, and continues to grow. As water demands get closer and closer to the water availability in many regions, each drop of water becomes increasingly valuable and water must be managed more efficiently and intensively. However, soaring water use worsens water scarcity conditions already prevalent in semi-arid and arid regions, increasing uncertainty for sustainable food production and economic development. Planning for future development and investments requires that we prepare water projections for the future. However, estimations are complicated because the future of the world's waters will be influenced by a combination of environmental, social, economic, and political factors, and there is only limited knowledge and data available about freshwater resources and how they are being used. The Water Futures and Solutions (WFaS) initiative coordinates its work with other ongoing scenario efforts for the sake of establishing a consistent set of new global water scenarios based on the shared socio-economic pathways (SSPs) and the representative concentration pathways (RCPs). The WFaS "fast-track" assessment uses three global water models, namely H08, PCR-GLOBWB, and WaterGAP. This study assesses the state of the art for estimating and projecting water use regionally and globally in a consistent manner. It provides an overview of different approaches, the uncertainty, strengths and weaknesses of the various estimation methods, types of management and policy decisions for which the current estimation methods are useful. We also discuss additional information most needed to be able to improve water use estimates and be able to assess a greater range of management options across the water-energy-climate nexus.

Modeling Global Water Use for the 21st Century: Water Futures and Solutions (WFaS) Initiative and Its Approaches

NASA Technical Reports Server (NTRS)

Wada, Y.; Florke, M.; Hanasaki, N.; Eisner, S.; Fischer, G.; Tramberend, S.; Satoh, Y.; van Vliet, M. T. H.; Yillia, P.; Ringler, C.;

Modeling global water use for the 21st century: Water Futures and Solutions (WFaS) initiative and its approaches

NASA Astrophysics Data System (ADS)

Wada, Y.; Flörke, M.; Hanasaki, N.; Eisner, S.; Fischer, G.; Tramberend, S.; Satoh, Y.; van Vliet, M. T. H.; Yillia, P.; Ringler, C.; Wiberg, D.

2015-08-01

To sustain growing food demand and increasing standard of living, global water use increased by nearly 6 times during the last 100 years and continues to grow. As water demands get closer and closer to the water availability in many regions, each drop of water becomes increasingly valuable and water must be managed more efficiently and intensively. However, soaring water use worsens water scarcity condition already prevalent in semi-arid and arid regions, increasing uncertainty for sustainable food production and economic development. Planning for future development and investments requires that we prepare water projections for the future. However, estimations are complicated because the future of world's waters will be influenced by a combination of environmental, social, economic, and political factors, and there is only limited knowledge and data available about freshwater resources and how they are being used. The Water Futures and Solutions initiative (WFaS) coordinates its work with other on-going scenario efforts for the sake of establishing a consistent set of new global water scenarios based on the Shared Socioeconomic Pathways (SSPs) and the Representative Concentration Pathways (RCPs). The WFaS "fast-track" assessment uses three global water models, namely H08, PCR-GLOBWB, and WaterGAP. This study assesses the state of the art for estimating and projecting water use regionally and globally in a consistent manner. It provides an overview of different approaches, the uncertainty, strengths and weaknesses of the various estimation methods, types of management and policy decisions for which the current estimation methods are useful. We also discuss additional information most needed to be able to improve water use estimates and be able to assess a greater range of management options across the water-energy-climate nexus.

The effects of harvest on waterfowl populations

USGS Publications Warehouse

Cooch, Evan G.; Guillemain, Matthieu; Boomer, G Scott; Lebreton, Jean-Dominique; Nichols, James D.

2014-01-01

Overall, there is substantial uncertainty about system dynamics, about the impacts of potential management and conservation decisions on those dynamics, and how to optimise management decisions in the presence of such uncertainties. Such relationships are unlikely to be stationary over space or time, and selective harvest of some individuals can potentially alter life history allocation of resources over time – both of which will potentially influence optimal harvest strategies. These sources of variation and uncertainty argue for the use of adaptive approaches to waterfowl harvest management.

Use of social network analysis and global sensitivity and uncertainty analyses to better understand an influenza outbreak.

PubMed

Liu, Jianhua; Jiang, Hongbo; Zhang, Hao; Guo, Chun; Wang, Lei; Yang, Jing; Nie, Shaofa

2017-06-27

In the summer of 2014, an influenza A(H3N2) outbreak occurred in Yichang city, Hubei province, China. A retrospective study was conducted to collect and interpret hospital and epidemiological data on it using social network analysis and global sensitivity and uncertainty analyses. Results for degree (χ2=17.6619, P<0.0001) and betweenness(χ2=21.4186, P<0.0001) centrality suggested that the selection of sampling objects were different between traditional epidemiological methods and newer statistical approaches. Clique and network diagrams demonstrated that the outbreak actually consisted of two independent transmission networks. Sensitivity analysis showed that the contact coefficient (k) was the most important factor in the dynamic model. Using uncertainty analysis, we were able to better understand the properties and variations over space and time on the outbreak. We concluded that use of newer approaches were significantly more efficient for managing and controlling infectious diseases outbreaks, as well as saving time and public health resources, and could be widely applied on similar local outbreaks.

Serum albumin: accuracy and clinical use.

PubMed

Infusino, Ilenia; Panteghini, Mauro

2013-04-18

Albumin is the major plasma protein and its determination is used for the prognostic assessment of several diseases. Clinical guidelines call for monitoring of serum albumin with specific target cut-offs that are independent of the assay used. This requires accurate and equivalent results among different commercially available methods (i.e., result standardization) through a consistent definition and application of a reference measurement system. This should be associated with the definition of measurement uncertainty goals based on medical relevance of serum albumin to make results reliable for patient management. In this paper, we show that, in the current situation, if one applies analytical goals for serum albumin measurement derived from its biologic variation, the uncertainty budget derived from each step of the albumin traceability chain is probably too high to fulfil established quality levels for albumin measurement and to guarantee the accuracy needed for clinical usefulness of the test. The situation is further worsened if non-specific colorimetric methods are used for albumin measurement as they represent an additional random source of uncertainty. Copyright © 2013 Elsevier B.V. All rights reserved.

Understanding earth system models: how Global Sensitivity Analysis can help

NASA Astrophysics Data System (ADS)

Pianosi, Francesca; Wagener, Thorsten

2017-04-01

Computer models are an essential element of earth system sciences, underpinning our understanding of systems functioning and influencing the planning and management of socio-economic-environmental systems. Even when these models represent a relatively low number of physical processes and variables, earth system models can exhibit a complicated behaviour because of the high level of interactions between their simulated variables. As the level of these interactions increases, we quickly lose the ability to anticipate and interpret the model's behaviour and hence the opportunity to check whether the model gives the right response for the right reasons. Moreover, even if internally consistent, an earth system model will always produce uncertain predictions because it is often forced by uncertain inputs (due to measurement errors, pre-processing uncertainties, scarcity of measurements, etc.). Lack of transparency about the scope of validity, limitations and the main sources of uncertainty of earth system models can be a strong limitation to their effective use for both scientific and decision-making purposes. Global Sensitivity Analysis (GSA) is a set of statistical analysis techniques to investigate the complex behaviour of earth system models in a structured, transparent and comprehensive way. In this presentation, we will use a range of examples across earth system sciences (with a focus on hydrology) to demonstrate how GSA is a fundamental element in advancing the construction and use of earth system models, including: verifying the consistency of the model's behaviour with our conceptual understanding of the system functioning; identifying the main sources of output uncertainty so to focus efforts for uncertainty reduction; finding tipping points in forcing inputs that, if crossed, would bring the system to specific conditions we want to avoid.

A Bayesian-based two-stage inexact optimization method for supporting stream water quality management in the Three Gorges Reservoir region.

PubMed

Hu, X H; Li, Y P; Huang, G H; Zhuang, X W; Ding, X W

2016-05-01

In this study, a Bayesian-based two-stage inexact optimization (BTIO) method is developed for supporting water quality management through coupling Bayesian analysis with interval two-stage stochastic programming (ITSP). The BTIO method is capable of addressing uncertainties caused by insufficient inputs in water quality model as well as uncertainties expressed as probabilistic distributions and interval numbers. The BTIO method is applied to a real case of water quality management for the Xiangxi River basin in the Three Gorges Reservoir region to seek optimal water quality management schemes under various uncertainties. Interval solutions for production patterns under a range of probabilistic water quality constraints have been generated. Results obtained demonstrate compromises between the system benefit and the system failure risk due to inherent uncertainties that exist in various system components. Moreover, information about pollutant emission is accomplished, which would help managers to adjust production patterns of regional industry and local policies considering interactions of water quality requirement, economic benefit, and industry structure.

PDF Weaving - Linking Inventory Data and Monte Carlo Uncertainty Analysis in the Study of how Disturbance Affects Forest Carbon Storage

NASA Astrophysics Data System (ADS)

Healey, S. P.; Patterson, P.; Garrard, C.

2014-12-01

Altered disturbance regimes are likely a primary mechanism by which a changing climate will affect storage of carbon in forested ecosystems. Accordingly, the National Forest System (NFS) has been mandated to assess the role of disturbance (harvests, fires, insects, etc.) on carbon storage in each of its planning units. We have developed a process which combines 1990-era maps of forest structure and composition with high-quality maps of subsequent disturbance type and magnitude to track the impact of disturbance on carbon storage. This process, called the Forest Carbon Management Framework (ForCaMF), uses the maps to apply empirically calibrated carbon dynamics built into a widely used management tool, the Forest Vegetation Simulator (FVS). While ForCaMF offers locally specific insights into the effect of historical or hypothetical disturbance trends on carbon storage, its dependence upon the interaction of several maps and a carbon model poses a complex challenge in terms of tracking uncertainty. Monte Carlo analysis is an attractive option for tracking the combined effects of error in several constituent inputs as they impact overall uncertainty. Monte Carlo methods iteratively simulate alternative values for each input and quantify how much outputs vary as a result. Variation of each input is controlled by a Probability Density Function (PDF). We introduce a technique called "PDF Weaving," which constructs PDFs that ensure that simulated uncertainty precisely aligns with uncertainty estimates that can be derived from inventory data. This hard link with inventory data (derived in this case from FIA - the US Forest Service Forest Inventory and Analysis program) both provides empirical calibration and establishes consistency with other types of assessments (e.g., habitat and water) for which NFS depends upon FIA data. Results from the NFS Northern Region will be used to illustrate PDF weaving and insights gained from ForCaMF about the role of disturbance in carbon storage.

Problems with the application of hydrogeological science to regulation of Australian mining projects: Carmichael Mine and Doongmabulla Springs

NASA Astrophysics Data System (ADS)

Currell, Matthew J.; Werner, Adrian D.; McGrath, Chris; Webb, John A.; Berkman, Michael

2017-05-01

Understanding and managing impacts from mining on groundwater-dependent ecosystems (GDEs) and other groundwater users requires development of defensible science supported by adequate field data. This usually leads to the creation of predictive models and analysis of the likely impacts of mining and their accompanying uncertainties. The identification, monitoring and management of impacts on GDEs are often a key component of mine approvals, which need to consider and attempt to minimise the risks that negative impacts may arise. Here we examine a case study where approval for a large mining project in Australia (Carmichael Coal Mine) was challenged in court on the basis that it may result in more extensive impacts on a GDE (Doongmabulla Springs) of high ecological and cultural significance than predicted by the proponent. We show that throughout the environmental assessment and approval process, significant data gaps and scientific uncertainties remained unresolved. Evidence shows that the assumed conceptual hydrogeological model for the springs could be incorrect, and that at least one alternative conceptualisation (that the springs are dependent on a deep fault) is consistent with the available field data. Assumptions made about changes to spring flow as a consequence of mine-induced drawdown also appear problematic, with significant implications for the spring-fed wetlands. Despite the large scale of the project, it appears that critical scientific data required to resolve uncertainties and construct robust models of the springs' relationship to the groundwater system were lacking at the time of approval, contributing to uncertainty and conflict. For this reason, we recommend changes to the approval process that would require a higher standard of scientific information to be collected and reviewed, particularly in relation to key environmental assets during the environmental impact assessment process in future projects.

Assessing climate change and socio-economic uncertainties in long term management of water resources

NASA Astrophysics Data System (ADS)

Jahanshahi, Golnaz; Dawson, Richard; Walsh, Claire; Birkinshaw, Stephen; Glenis, Vassilis

2015-04-01

Long term management of water resources is challenging for decision makers given the range of uncertainties that exist. Such uncertainties are a function of long term drivers of change, such as climate, environmental loadings, demography, land use and other socio economic drivers. Impacts of climate change on frequency of extreme events such as drought make it a serious threat to water resources and water security. The release of probabilistic climate information, such as the UKCP09 scenarios, provides improved understanding of some uncertainties in climate models. This has motivated a more rigorous approach to dealing with other uncertainties in order to understand the sensitivity of investment decisions to future uncertainty and identify adaptation options that are as far as possible robust. We have developed and coupled a system of models that includes a weather generator, simulations of catchment hydrology, demand for water and the water resource system. This integrated model has been applied in the Thames catchment which supplies the city of London, UK. This region is one of the driest in the UK and hence sensitive to water availability. In addition, it is one of the fastest growing parts of the UK and plays an important economic role. Key uncertainties in long term water resources in the Thames catchment, many of which result from earth system processes, are identified and quantified. The implications of these uncertainties are explored using a combination of uncertainty analysis and sensitivity testing. The analysis shows considerable uncertainty in future rainfall, river flow and consequently water resource. For example, results indicate that by the 2050s, low flow (Q95) in the Thames catchment will range from -44 to +9% compared with the control scenario (1970s). Consequently, by the 2050s the average number of drought days are expected to increase 4-6 times relative to the 1970s. Uncertainties associated with urban growth increase these risks further. Adaptation measures, such as new reservoirs can manage these risks to a certain extent, but our sensitivity testing demonstrates that they are less robust to future uncertainties than measures taken to reduce water demand. Keywords: Climate change, Uncertainty, Decision making, Drought, Risk, Water resources management.

Grieving the Loss of a Sibling

MedlinePlus

... Cancer Coping With Uncertainty Managing Stress Coping with Anger Anxiety Depression Fear of Treatment-Related Side Effects Coping with ... Cancer Coping With Uncertainty Managing Stress Coping with Anger Anxiety Depression Fear of Treatment-Related Side Effects Coping with ...

Multi-criteria dynamic decision under uncertainty: a stochastic viability analysis and an application to sustainable fishery management.

PubMed

De Lara, M; Martinet, V

2009-02-01

Managing natural resources in a sustainable way is a hard task, due to uncertainties, dynamics and conflicting objectives (ecological, social, and economical). We propose a stochastic viability approach to address such problems. We consider a discrete-time control dynamical model with uncertainties, representing a bioeconomic system. The sustainability of this system is described by a set of constraints, defined in practice by indicators - namely, state, control and uncertainty functions - together with thresholds. This approach aims at identifying decision rules such that a set of constraints, representing various objectives, is respected with maximal probability. Under appropriate monotonicity properties of dynamics and constraints, having economic and biological content, we characterize an optimal feedback. The connection is made between this approach and the so-called Management Strategy Evaluation for fisheries. A numerical application to sustainable management of Bay of Biscay nephrops-hakes mixed fishery is given.

Optimization and resilience in natural resources management

USGS Publications Warehouse

Williams, Byron K.; Johnson, Fred A.

2015-01-01

We consider the putative tradeoff between optimization and resilience in the management of natural resources, using a framework that incorporates different sources of uncertainty that are common in natural resources management. We address one-time decisions, and then expand the decision context to the more complex problem of iterative decision making. For both cases we focus on two key sources of uncertainty: partial observability of system state and uncertainty as to system dynamics. Optimal management strategies will vary considerably depending on the timeframe being considered and the amount and quality of information that is available to characterize system features and project the consequences of potential decisions. But in all cases an optimal decision making framework, if properly identified and focused, can be useful in recognizing sound decisions. We argue that under the conditions of deep uncertainty that characterize many resource systems, an optimal decision process that focuses on robustness does not automatically induce a loss of resilience.

Knowledge management system for risk mitigation in supply chain uncertainty: case from automotive battery supply chain

NASA Astrophysics Data System (ADS)

Marie, I. A.; Sugiarto, D.; Surjasa, D.; Witonohadi, A.

2018-01-01

Automotive battery supply chain include battery manufacturer, sulphuric acid suppliers, polypropylene suppliers, lead suppliers, transportation service providers, warehouses, retailers and even customers. Due to the increasingly dynamic condition of the environment, supply chain actors were required to improve their ability to overcome various uncertainty issues in the environment. This paper aims to describe the process of designing a knowledge management system for risk mitigation in supply chain uncertainty. The design methodology began with the identification of the knowledge needed to solve the problems associated with uncertainty and analysis of system requirements. The design of the knowledge management system was described in the form of a data flow diagram. The results of the study indicated that key knowledge area that needs to be managed were the knowledge to maintain the stability of process in sulphuric acid process and knowledge to overcome the wastes in battery manufacturing process. The system was expected to be a media acquisition, dissemination and storage of knowledge associated with the uncertainty in the battery supply chain and increase the supply chain performance.

Adaptive management of social-ecological systems: the path forward

USGS Publications Warehouse

Allen, Craig R.

2015-01-01

Adaptive management remains at the forefront of environmental management nearly 40 years after its original conception, largely because we have yet to develop other methodologies that offer the same promise. Despite the criticisms of adaptive management and the numerous failed attempts to implement it, adaptive management has yet to be replaced with a better alternative. The concept persists because it is simple, allows action despite uncertainty, and fosters learning. Moving forward, adaptive management of social-ecological systems provides policymakers, managers and scientists a powerful tool for managing for resilience in the face of uncertainty.

A transient stochastic weather generator incorporating climate model uncertainty

NASA Astrophysics Data System (ADS)

Glenis, Vassilis; Pinamonti, Valentina; Hall, Jim W.; Kilsby, Chris G.

2015-11-01

Stochastic weather generators (WGs), which provide long synthetic time series of weather variables such as rainfall and potential evapotranspiration (PET), have found widespread use in water resources modelling. When conditioned upon the changes in climatic statistics (change factors, CFs) predicted by climate models, WGs provide a useful tool for climate impacts assessment and adaption planning. The latest climate modelling exercises have involved large numbers of global and regional climate models integrations, designed to explore the implications of uncertainties in the climate model formulation and parameter settings: so called 'perturbed physics ensembles' (PPEs). In this paper we show how these climate model uncertainties can be propagated through to impact studies by testing multiple vectors of CFs, each vector derived from a different sample from a PPE. We combine this with a new methodology to parameterise the projected time-evolution of CFs. We demonstrate how, when conditioned upon these time-dependent CFs, an existing, well validated and widely used WG can be used to generate non-stationary simulations of future climate that are consistent with probabilistic outputs from the Met Office Hadley Centre's Perturbed Physics Ensemble. The WG enables extensive sampling of natural variability and climate model uncertainty, providing the basis for development of robust water resources management strategies in the context of a non-stationary climate.

Use of Atmospheric Budget to Reduce Uncertainty in Estimated Water Availability over South Asia from Different Reanalyses

NASA Astrophysics Data System (ADS)

Sebastian, Dawn Emil; Pathak, Amey; Ghosh, Subimal

2016-07-01

Disagreements across different reanalyses over South Asia result into uncertainty in assessment of water availability, which is computed as the difference between Precipitation and Evapotranspiration (P-E). Here, we compute P-E directly from atmospheric budget with divergence of moisture flux for different reanalyses and find improved correlation with observed values of P-E, acquired from station and satellite data. We also find reduced closure terms for water cycle computed with atmospheric budget, analysed over South Asian landmass, when compared to that obtained with individual values of P and E. The P-E value derived with atmospheric budget is more consistent with energy budget, when we use top-of-atmosphere radiation for the same. For analysing water cycle, we use runoff from Global Land Data Assimilation System, and water storage from Gravity Recovery and Climate Experiment. We find improvements in agreements across different reanalyses, in terms of inter-annual cross correlation when atmospheric budget is used to estimate P-E and hence, emphasize to use the same for estimations of water availability in South Asia to reduce uncertainty. Our results on water availability with reduced uncertainty over highly populated monsoon driven South Asia will be useful for water management and agricultural decision making.

Use of Atmospheric Budget to Reduce Uncertainty in Estimated Water Availability over South Asia from Different Reanalyses.

PubMed

Sebastian, Dawn Emil; Pathak, Amey; Ghosh, Subimal

2016-07-08

Disagreements across different reanalyses over South Asia result into uncertainty in assessment of water availability, which is computed as the difference between Precipitation and Evapotranspiration (P-E). Here, we compute P-E directly from atmospheric budget with divergence of moisture flux for different reanalyses and find improved correlation with observed values of P-E, acquired from station and satellite data. We also find reduced closure terms for water cycle computed with atmospheric budget, analysed over South Asian landmass, when compared to that obtained with individual values of P and E. The P-E value derived with atmospheric budget is more consistent with energy budget, when we use top-of-atmosphere radiation for the same. For analysing water cycle, we use runoff from Global Land Data Assimilation System, and water storage from Gravity Recovery and Climate Experiment. We find improvements in agreements across different reanalyses, in terms of inter-annual cross correlation when atmospheric budget is used to estimate P-E and hence, emphasize to use the same for estimations of water availability in South Asia to reduce uncertainty. Our results on water availability with reduced uncertainty over highly populated monsoon driven South Asia will be useful for water management and agricultural decision making.

Use of Atmospheric Budget to Reduce Uncertainty in Estimated Water Availability over South Asia from Different Reanalyses

PubMed Central

Sebastian, Dawn Emil; Pathak, Amey; Ghosh, Subimal

2016-01-01

Disagreements across different reanalyses over South Asia result into uncertainty in assessment of water availability, which is computed as the difference between Precipitation and Evapotranspiration (P–E). Here, we compute P–E directly from atmospheric budget with divergence of moisture flux for different reanalyses and find improved correlation with observed values of P–E, acquired from station and satellite data. We also find reduced closure terms for water cycle computed with atmospheric budget, analysed over South Asian landmass, when compared to that obtained with individual values of P and E. The P–E value derived with atmospheric budget is more consistent with energy budget, when we use top-of-atmosphere radiation for the same. For analysing water cycle, we use runoff from Global Land Data Assimilation System, and water storage from Gravity Recovery and Climate Experiment. We find improvements in agreements across different reanalyses, in terms of inter-annual cross correlation when atmospheric budget is used to estimate P–E and hence, emphasize to use the same for estimations of water availability in South Asia to reduce uncertainty. Our results on water availability with reduced uncertainty over highly populated monsoon driven South Asia will be useful for water management and agricultural decision making. PMID:27388837

Implications of Simulation Conceptual Model Development for Simulation Management and Uncertainty Assessment

NASA Technical Reports Server (NTRS)

Pace, Dale K.

2000-01-01

A simulation conceptual model is a simulation developers way of translating modeling requirements (i. e., what is to be represented by the simulation or its modification) into a detailed design framework (i. e., how it is to be done), from which the software, hardware, networks (in the case of distributed simulation), and systems/equipment that will make up the simulation can be built or modified. A conceptual model is the collection of information which describes a simulation developers concept about the simulation and its pieces. That information consists of assumptions, algorithms, characteristics, relationships, and data. Taken together, these describe how the simulation developer understands what is to be represented by the simulation (entities, actions, tasks, processes, interactions, etc.) and how that representation will satisfy the requirements to which the simulation responds. Thus the conceptual model is the basis for judgment about simulation fidelity and validity for any condition that is not specifically tested. The more perspicuous and precise the conceptual model, the more likely it is that the simulation development will both fully satisfy requirements and allow demonstration that the requirements are satisfied (i. e., validation). Methods used in simulation conceptual model development have significant implications for simulation management and for assessment of simulation uncertainty. This paper suggests how to develop and document a simulation conceptual model so that the simulation fidelity and validity can be most effectively determined. These ideas for conceptual model development apply to all simulation varieties. The paper relates these ideas to uncertainty assessments as they relate to simulation fidelity and validity. The paper also explores implications for simulation management from conceptual model development methods, especially relative to reuse of simulation components.

Uncertainty-accounting environmental policy and management of water systems.

PubMed

Baresel, Christian; Destouni, Georgia

2007-05-15

Environmental policies for water quality and ecosystem management do not commonly require explicit stochastic accounts of uncertainty and risk associated with the quantification and prediction of waterborne pollutant loads and abatement effects. In this study, we formulate and investigate a possible environmental policy that does require an explicit stochastic uncertainty account. We compare both the environmental and economic resource allocation performance of such an uncertainty-accounting environmental policy with that of deterministic, risk-prone and risk-averse environmental policies under a range of different hypothetical, yet still possible, scenarios. The comparison indicates that a stochastic uncertainty-accounting policy may perform better than deterministic policies over a range of different scenarios. Even in the absence of reliable site-specific data, reported literature values appear to be useful for such a stochastic account of uncertainty.

The Social Construction of Uncertainty in Healthcare Delivery

NASA Astrophysics Data System (ADS)

Begun, James W.; Kaissi, Amer A.

We explore the following question: How would healthcare delivery be different if uncertainty were widely recognized, accurately diagnosed, and appropriately managed? Unlike most studies of uncertainty, we examine uncertainty at more than one level of analysis, considering uncertainty that arises at the patient-clinician interaction level and at the organizational level of healthcare delivery. We consider the effects of history, as the forces and systems that currently shape and manage uncertainty have emerged over a long time period. The purpose of this broad and speculative "thought exercise" is to generate greater sensemaking of the current state of healthcare delivery, particularly in the realm of organizational and public policy, and to generate new research questions about healthcare delivery. The discussion is largely based on experience in the United States, which may limit its generalizability.

Uncertainty Categorization, Modeling, and Management for Regional Water Supply Planning

NASA Astrophysics Data System (ADS)

Fletcher, S.; Strzepek, K. M.; AlSaati, A.; Alhassan, A.

2016-12-01

Many water planners face increased pressure on water supply systems from growing demands, variability in supply and a changing climate. Short-term variation in water availability and demand; long-term uncertainty in climate, groundwater storage, and sectoral competition for water; and varying stakeholder perspectives on the impacts of water shortages make it difficult to assess the necessity of expensive infrastructure investments. We categorize these uncertainties on two dimensions: whether they are the result of stochastic variation or epistemic uncertainty, and whether the uncertainties can be described probabilistically or are deep uncertainties whose likelihood is unknown. We develop a decision framework that combines simulation for probabilistic uncertainty, sensitivity analysis for deep uncertainty and Bayesian decision analysis for uncertainties that are reduced over time with additional information. We apply this framework to two contrasting case studies - drought preparedness in Melbourne, Australia and fossil groundwater depletion in Riyadh, Saudi Arabia - to assess the impacts of different types of uncertainty on infrastructure decisions. Melbourne's water supply system relies on surface water, which is impacted by natural variation in rainfall, and a market-based system for managing water rights. Our results show that small, flexible investment increases can mitigate shortage risk considerably at reduced cost. Riyadh, by contrast, relies primarily on desalination for municipal use and fossil groundwater for agriculture, and a centralized planner makes allocation decisions. Poor regional groundwater measurement makes it difficult to know when groundwater pumping will become uneconomical, resulting in epistemic uncertainty. However, collecting more data can reduce the uncertainty, suggesting the need for different uncertainty modeling and management strategies in Riyadh than in Melbourne. We will categorize the two systems and propose appropriate decision making under uncertainty methods from the state of the art. We will compare the efficiency of alternative approaches to the two case studies. Finally, we will present a hybrid decision analytic tool to address the synthesis of uncertainties.

Efficient Data-Worth Analysis Using a Multilevel Monte Carlo Method Applied in Oil Reservoir Simulations

NASA Astrophysics Data System (ADS)

Lu, D.; Ricciuto, D. M.; Evans, K. J.

2017-12-01

Data-worth analysis plays an essential role in improving the understanding of the subsurface system, in developing and refining subsurface models, and in supporting rational water resources management. However, data-worth analysis is computationally expensive as it requires quantifying parameter uncertainty, prediction uncertainty, and both current and potential data uncertainties. Assessment of these uncertainties in large-scale stochastic subsurface simulations using standard Monte Carlo (MC) sampling or advanced surrogate modeling is extremely computationally intensive, sometimes even infeasible. In this work, we propose efficient Bayesian analysis of data-worth using a multilevel Monte Carlo (MLMC) method. Compared to the standard MC that requires a significantly large number of high-fidelity model executions to achieve a prescribed accuracy in estimating expectations, the MLMC can substantially reduce the computational cost with the use of multifidelity approximations. As the data-worth analysis involves a great deal of expectation estimations, the cost savings from MLMC in the assessment can be very outstanding. While the proposed MLMC-based data-worth analysis is broadly applicable, we use it to a highly heterogeneous oil reservoir simulation to select an optimal candidate data set that gives the largest uncertainty reduction in predicting mass flow rates at four production wells. The choices made by the MLMC estimation are validated by the actual measurements of the potential data, and consistent with the estimation obtained from the standard MC. But compared to the standard MC, the MLMC greatly reduces the computational costs in the uncertainty reduction estimation, with up to 600 days cost savings when one processor is used.

Can agent based models effectively reduce fisheries management implementation uncertainty?

NASA Astrophysics Data System (ADS)

Drexler, M.

2016-02-01

Uncertainty is an inherent feature of fisheries management. Implementation uncertainty remains a challenge to quantify often due to unintended responses of users to management interventions. This problem will continue to plague both single species and ecosystem based fisheries management advice unless the mechanisms driving these behaviors are properly understood. Equilibrium models, where each actor in the system is treated as uniform and predictable, are not well suited to forecast the unintended behaviors of individual fishers. Alternatively, agent based models (AMBs) can simulate the behaviors of each individual actor driven by differing incentives and constraints. This study evaluated the feasibility of using AMBs to capture macro scale behaviors of the US West Coast Groundfish fleet. Agent behavior was specified at the vessel level. Agents made daily fishing decisions using knowledge of their own cost structure, catch history, and the histories of catch and quota markets. By adding only a relatively small number of incentives, the model was able to reproduce highly realistic macro patterns of expected outcomes in response to management policies (catch restrictions, MPAs, ITQs) while preserving vessel heterogeneity. These simulations indicate that agent based modeling approaches hold much promise for simulating fisher behaviors and reducing implementation uncertainty. Additional processes affecting behavior, informed by surveys, are continually being added to the fisher behavior model. Further coupling of the fisher behavior model to a spatial ecosystem model will provide a fully integrated social, ecological, and economic model capable of performing management strategy evaluations to properly consider implementation uncertainty in fisheries management.

Decision-making under surprise and uncertainty: Arsenic contamination of water supplies

NASA Astrophysics Data System (ADS)

Randhir, Timothy O.; Mozumder, Pallab; Halim, Nafisa

2018-05-01

With ignorance and potential surprise dominating decision making in water resources, a framework for dealing with such uncertainty is a critical need in hydrology. We operationalize the 'potential surprise' criterion proposed by Shackle, Vickers, and Katzner (SVK) to derive decision rules to manage water resources under uncertainty and ignorance. We apply this framework to managing water supply systems in Bangladesh that face severe, naturally occurring arsenic contamination. The uncertainty involved with arsenic in water supplies makes the application of conventional analysis of decision-making ineffective. Given the uncertainty and surprise involved in such cases, we find that optimal decisions tend to favor actions that avoid irreversible outcomes instead of conventional cost-effective actions. We observe that a diversification of the water supply system also emerges as a robust strategy to avert unintended outcomes of water contamination. Shallow wells had a slight higher optimal level (36%) compare to deep wells and surface treatment which had allocation levels of roughly 32% under each. The approach can be applied in a variety of other cases that involve decision making under uncertainty and surprise, a frequent situation in natural resources management.

The importance of parameterization when simulating the hydrologic response of vegetative land-cover change

NASA Astrophysics Data System (ADS)

White, Jeremy; Stengel, Victoria; Rendon, Samuel; Banta, John

2017-08-01

Computer models of hydrologic systems are frequently used to investigate the hydrologic response of land-cover change. If the modeling results are used to inform resource-management decisions, then providing robust estimates of uncertainty in the simulated response is an important consideration. Here we examine the importance of parameterization, a necessarily subjective process, on uncertainty estimates of the simulated hydrologic response of land-cover change. Specifically, we applied the soil water assessment tool (SWAT) model to a 1.4 km2 watershed in southern Texas to investigate the simulated hydrologic response of brush management (the mechanical removal of woody plants), a discrete land-cover change. The watershed was instrumented before and after brush-management activities were undertaken, and estimates of precipitation, streamflow, and evapotranspiration (ET) are available; these data were used to condition and verify the model. The role of parameterization in brush-management simulation was evaluated by constructing two models, one with 12 adjustable parameters (reduced parameterization) and one with 1305 adjustable parameters (full parameterization). Both models were subjected to global sensitivity analysis as well as Monte Carlo and generalized likelihood uncertainty estimation (GLUE) conditioning to identify important model inputs and to estimate uncertainty in several quantities of interest related to brush management. Many realizations from both parameterizations were identified as behavioral in that they reproduce daily mean streamflow acceptably well according to Nash-Sutcliffe model efficiency coefficient, percent bias, and coefficient of determination. However, the total volumetric ET difference resulting from simulated brush management remains highly uncertain after conditioning to daily mean streamflow, indicating that streamflow data alone are not sufficient to inform the model inputs that influence the simulated outcomes of brush management the most. Additionally, the reduced-parameterization model grossly underestimates uncertainty in the total volumetric ET difference compared to the full-parameterization model; total volumetric ET difference is a primary metric for evaluating the outcomes of brush management. The failure of the reduced-parameterization model to provide robust uncertainty estimates demonstrates the importance of parameterization when attempting to quantify uncertainty in land-cover change simulations.

The importance of parameterization when simulating the hydrologic response of vegetative land-cover change

USGS Publications Warehouse

White, Jeremy; Stengel, Victoria G.; Rendon, Samuel H.; Banta, John

2017-01-01

Computer models of hydrologic systems are frequently used to investigate the hydrologic response of land-cover change. If the modeling results are used to inform resource-management decisions, then providing robust estimates of uncertainty in the simulated response is an important consideration. Here we examine the importance of parameterization, a necessarily subjective process, on uncertainty estimates of the simulated hydrologic response of land-cover change. Specifically, we applied the soil water assessment tool (SWAT) model to a 1.4 km2 watershed in southern Texas to investigate the simulated hydrologic response of brush management (the mechanical removal of woody plants), a discrete land-cover change. The watershed was instrumented before and after brush-management activities were undertaken, and estimates of precipitation, streamflow, and evapotranspiration (ET) are available; these data were used to condition and verify the model. The role of parameterization in brush-management simulation was evaluated by constructing two models, one with 12 adjustable parameters (reduced parameterization) and one with 1305 adjustable parameters (full parameterization). Both models were subjected to global sensitivity analysis as well as Monte Carlo and generalized likelihood uncertainty estimation (GLUE) conditioning to identify important model inputs and to estimate uncertainty in several quantities of interest related to brush management. Many realizations from both parameterizations were identified as behavioral in that they reproduce daily mean streamflow acceptably well according to Nash–Sutcliffe model efficiency coefficient, percent bias, and coefficient of determination. However, the total volumetric ET difference resulting from simulated brush management remains highly uncertain after conditioning to daily mean streamflow, indicating that streamflow data alone are not sufficient to inform the model inputs that influence the simulated outcomes of brush management the most. Additionally, the reduced-parameterization model grossly underestimates uncertainty in the total volumetric ET difference compared to the full-parameterization model; total volumetric ET difference is a primary metric for evaluating the outcomes of brush management. The failure of the reduced-parameterization model to provide robust uncertainty estimates demonstrates the importance of parameterization when attempting to quantify uncertainty in land-cover change simulations.

Forest processes from stands to landscapes: exploring model forecast uncertainties using cross-scale model comparison

Treesearch

Michael J. Papaik; Andrew Fall; Brian Sturtevant; Daniel Kneeshaw; Christian Messier; Marie-Josee Fortin; Neal Simon

2010-01-01

Forest management practices conducted primarily at the stand scale result in simplified forests with regeneration problems and low structural and biological diversity. Landscape models have been used to help design management strategies to address these problems. However, there remains a great deal of uncertainty that the actual management practices result in the...

Students' Uncertainty Management in the College Classroom

ERIC Educational Resources Information Center

Sollitto, Michael; Brott, Jan; Cole, Catherine; Gil, Elia; Selim, Heather

2018-01-01

The uncertainty experienced by college students can have serious repercussions for their success and subsequent retention. Drawing parallels between instructional context and organizational context will enrich theory and research about students' experiences of uncertainty in their college courses. Therefore, this study used Uncertainty Management…

Climate Risk Informed Decision Analysis (CRIDA): A novel practical guidance for Climate Resilient Investments and Planning

NASA Astrophysics Data System (ADS)

Jeuken, Ad; Mendoza, Guillermo; Matthews, John; Ray, Patrick; Haasnoot, Marjolijn; Gilroy, Kristin; Olsen, Rolf; Kucharski, John; Stakhiv, Gene; Cushing, Janet; Brown, Casey

2016-04-01

Engineers and water managers have always incorporated uncertainty in water resources operations, design and planning. In recent years, concern has been growing concern that many of the fundamental principles to address uncertainty in planning and design are insufficient for coping with unprecedented shifts in climate, especially given the long lifetimes of water investments - spanning decades, even centuries. Can we design and operate new flood risk management, energy, water supply and sanitation, and agricultural projects that are robust to shifts over 20, 50, or more years? Since about 2009, better approaches to planning and designing under climate uncertainty have been gaining ground worldwide. The main challenge is to operationalize these approaches and bring them from science to practice, embed them within the existing decision-making processes of particular institutions, and shift from highly specialized "boutique" applications to methods that result in consistent, replicable outcomes accessible to water managers worldwide. With CRIDA a serious step is taken to achieve these goals. CRIDA is built on two innovative but complementary approaches that have developed in isolation across the Atlantic over the past seven years: diagnosing and assessing risk (decision scaling), and developing sequential decision steps to compensate for uncertainty within regulatory / performance standards (adaptation pathways). First, the decision scaling or "bottom up" framework to climate change adaptation was first conceptualized during the US/Canada Great Lakes regulation study and has recently been placed in a decision-making context for water-related investments published by the World Bank Second, the adaptation pathways approach was developed in the Netherlands to cope with the level of climate uncertainty we now face. Adaptation pathways is a tool for maintaining options and flexibility while meeting operational goals by envisioning how sequences of decisions can be navigated over time. They are part of the Dutch adaptive planning approach Adaptive Delta Management, executed and develop by the Dutch Delta program. Both decision scaling and adaptation pathways have been piloted in studies worldwide. The objective of CRIDA is to mainstream effective climate adaptation for professional water managers. The CRIDA publication, due in april 2016, follows the generic water design planning design cycle. At each step, CRIDA describes stepwise guidance for incorporating climate robustness: problem definition, stress test, alternatives formulation and recommendation, evaluation and selection. In the presentation the origin, goal, steps and practical tools available at each step of CRIDA will be explained. In two other abstracts ("Climate Risk Informed Decision Analysis: A Hypothetical Application to the Waas Region" by Gilroy et al., "The Application of Climate Risk Informed Decision Analysis to the Ioland Water Treatment Plant in Lusaka, Zambia, by Kucharski et al.), the application of CRIDA to cases is explained

Group elicitations yield more consistent, yet more uncertain experts in understanding risks to ecosystem services in New Zealand bays

PubMed Central

Sinner, Jim; Ellis, Joanne; Kandlikar, Milind; Halpern, Benjamin S.; Satterfield, Terre; Chan, Kai

2017-01-01

The elicitation of expert judgment is an important tool for assessment of risks and impacts in environmental management contexts, and especially important as decision-makers face novel challenges where prior empirical research is lacking or insufficient. Evidence-driven elicitation approaches typically involve techniques to derive more accurate probability distributions under fairly specific contexts. Experts are, however, prone to overconfidence in their judgements. Group elicitations with diverse experts can reduce expert overconfidence by allowing cross-examination and reassessment of prior judgements, but groups are also prone to uncritical “groupthink” errors. When the problem context is underspecified the probability that experts commit groupthink errors may increase. This study addresses how structured workshops affect expert variability among and certainty within responses in a New Zealand case study. We find that experts’ risk estimates before and after a workshop differ, and that group elicitations provided greater consistency of estimates, yet also greater uncertainty among experts, when addressing prominent impacts to four different ecosystem services in coastal New Zealand. After group workshops, experts provided more consistent ranking of risks and more consistent best estimates of impact through increased clarity in terminology and dampening of extreme positions, yet probability distributions for impacts widened. The results from this case study suggest that group elicitations have favorable consequences for the quality and uncertainty of risk judgments within and across experts, making group elicitation techniques invaluable tools in contexts of limited data. PMID:28767694

Optimal Groundwater Extraction under Uncertainty and a Spatial Stock Externality

EPA Science Inventory

We introduce a model that incorporates two important elements to estimating welfare gains from groundwater management: stochasticity and a spatial stock externality. We estimate welfare gains resulting from optimal management under uncertainty as well as a gradual stock externali...

Cross-sectional associations between psychological traits, and HPV vaccine uptake and intentions in young adults from the United States.

PubMed

Scherer, Aaron M; Schacht Reisinger, Heather; Schweizer, Marin L; Askelson, Natoshia M; Fagerlin, Angela; Lynch, Charles F

2018-01-01

Human papillomavirus (HPV) is the most prevalent sexually transmitted infection worldwide and can lead to the development of genital warts, and cancers throughout the body. Despite the availability of HPV vaccines for over a decade, uptake in the United States among adolescents and young adults remains well below national targets. While most efforts to improve HPV vaccine uptake have rightly focused on adolescents, there is still a tremendous opportunity to improve vaccination among young adults who have not been vaccinated against HPV. To that end, we report an exploratory examination of associations between HPV vaccination status and intentions with psychological traits that may impact HPV vaccine uptake with a national, demographically diverse sample of young adults (N = 1358) who completed an online survey. These psychological traits conceptually mapped onto motivations to: 1) understand health-related information, 2) deliberate, 3) manage uncertainty, and 4) manage threats. We found notable gender differences for the association of these motivations and vaccination status. For women, higher interest in and ability to understand health-related information seemed to distinguish those who reported receiving the HPV vaccine from those who did not. For men, less need to deliberate and greater needs to manage threat and uncertainty seemed to be the distinguishing motives for those who reported receiving the HPV vaccine compared to those who did not. Results for vaccination intentions were less consistent, but there was some evidence to indicate that, regardless of gender, greater health-related information interest and understanding and need to manage uncertainty and threats were associated with increased intention to receive the HPV vaccine, while greater need to deliberate was associated with decreased vaccination intentions. These results suggest that there are psychological differences that are associated with HPV vaccination decisions and that these motivations should be considered in efforts to improve HPV vaccine uptake.

Incorporating uncertainty of management costs in sensitivity analyses of matrix population models.

PubMed

Salomon, Yacov; McCarthy, Michael A; Taylor, Peter; Wintle, Brendan A

2013-02-01

The importance of accounting for economic costs when making environmental-management decisions subject to resource constraints has been increasingly recognized in recent years. In contrast, uncertainty associated with such costs has often been ignored. We developed a method, on the basis of economic theory, that accounts for the uncertainty in population-management decisions. We considered the case where, rather than taking fixed values, model parameters are random variables that represent the situation when parameters are not precisely known. Hence, the outcome is not precisely known either. Instead of maximizing the expected outcome, we maximized the probability of obtaining an outcome above a threshold of acceptability. We derived explicit analytical expressions for the optimal allocation and its associated probability, as a function of the threshold of acceptability, where the model parameters were distributed according to normal and uniform distributions. To illustrate our approach we revisited a previous study that incorporated cost-efficiency analyses in management decisions that were based on perturbation analyses of matrix population models. Incorporating derivations from this study into our framework, we extended the model to address potential uncertainties. We then applied these results to 2 case studies: management of a Koala (Phascolarctos cinereus) population and conservation of an olive ridley sea turtle (Lepidochelys olivacea) population. For low aspirations, that is, when the threshold of acceptability is relatively low, the optimal strategy was obtained by diversifying the allocation of funds. Conversely, for high aspirations, the budget was directed toward management actions with the highest potential effect on the population. The exact optimal allocation was sensitive to the choice of uncertainty model. Our results highlight the importance of accounting for uncertainty when making decisions and suggest that more effort should be placed on understanding the distributional characteristics of such uncertainty. Our approach provides a tool to improve decision making. © 2013 Society for Conservation Biology.

Ecosystem Services and Climate Change Considerations for ...

EPA Pesticide Factsheets

Freshwater habitats provide fishable, swimmable and drinkable resources and are a nexus of geophysical and biological processes. These processes in turn influence the persistence and sustainability of populations, communities and ecosystems. Climate change and landuse change encompass numerous stressors of potential exposure, including the introduction of toxic contaminants, invasive species, and disease in addition to physical drivers such as temperature and hydrologic regime. A systems approach that includes the scientific and technologic basis of assessing the health of ecosystems is needed to effectively protect human health and the environment. The Integrated Environmental Modeling Framework “iemWatersheds” has been developed as a consistent and coherent means of forecasting the cumulative impact of co-occurring stressors. The Framework consists of three facilitating technologies: Data for Environmental Modeling (D4EM) that automates the collection and standardization of input data; the Framework for Risk Assessment of Multimedia Environmental Systems (FRAMES) that manages the flow of information between linked models; and the Supercomputer for Model Uncertainty and Sensitivity Evaluation (SuperMUSE) that provides post-processing and analysis of model outputs, including uncertainty and sensitivity analysis. Five models are linked within the Framework to provide multimedia simulation capabilities for hydrology and water quality processes: the Soil Water

Adaptive management for improving species conservation across the captive-wild spectrum

USGS Publications Warehouse

Canessa, Stefano; Guillera-Arroita, Gurutzeta; Lahoz-Monfort, José J.; Southwell, Darren M; Armstrong, Doug P.; Chadès, Iadine; Lacy, Robert C; Converse, Sarah J.

2016-01-01

Conservation of endangered species increasingly envisages complex strategies that integrate captive and wild management actions. Management decisions in this context must be made in the face of uncertainty, often with limited capacity to collect information. Adaptive management (AM) combines management and monitoring, with the aim of updating knowledge and improving decision-making over time. We provide a guide for managers who may realize the potential of AM, but are unsure where to start. The urgent need for iterative management decisions, the existence of uncertainty, and the opportunity for learning offered by often highly-controlled captive environments create favorable conditions for AM. However, experiments and monitoring may be complicated by small sample sizes, and the ability to control the system, including stochasticity and observability, may be limited toward the wild end of the spectrum. We illustrate the key steps to implementing AM in threatened species management using four case studies, including the management of captive programs for cheetah (Acinonyx jubatus) and whooping cranes (Grus americana), of a translocation protocol for Arizona cliffroses Purshia subintegra and of ongoing supplementary feeding of reintroduced hihi (Notiomystis cincta) populations. For each case study, we explain (1) how to clarify whether the decision can be improved by learning (i.e. it is iterative and complicated by uncertainty) and what the management objectives are; (2) how to articulate uncertainty via alternative, testable hypotheses such as competing models or parameter distributions; (3) how to formally define how additional information can be collected and incorporated in future management decisions.

Using info-Gap Decision Theory for Water Resources Planning Under Severe Uncertainty

NASA Astrophysics Data System (ADS)

Korteling, B.; Brazier, R.; Kapelan, Z.; Dessai, S.

2012-12-01

Water resource managers are required to develop comprehensive water resource plans based on severely uncertain information of the effects of climate change on local hydrology and future socio-economic changes on localised demand. In England and Wales, current water resource planning methodologies include a headroom estimation process that quantifies uncertainty based on only one point of an assumed range of deviations from the expected climate and projected demand 25 years into the future. There are many situations where there is not enough knowledge to be able to estimate a representative probability of occurrence, or to be confident that the tails of an assumed probability distribution will not exhibit unexpected skewness, or that the kurtosis of a distribution differs from the norm. These situations can be considered severely uncertain. Information-Gap decision theory offers a method to sample a wider range of uncertainty than with traditional methods, and as a result, compare the robustness of various water resource management options under conditions of severe uncertainty. A more robust management option is one that delivers the same level of performance as other options at higher levels of uncertainty. A case study is based on a Water Supply Area that encompasses the county of Cornwall in southwest England containing 17 reservoirs and 19 demand nodes. The performance success of management options are evaluated primarily by measures of water availability including a reservoir risk measure that tests the probability and magnitude that strategic reservoir storage levels fall below the drought management curve under adverse conditions and also a safety margin deficit that tests how quickly reservoir levels can return to optimum operating levels in favourable conditions. Multi-Criteria Decision Analysis (MCDA) is used to test the effectiveness of different management options with different weightings for metrics other than water availability including; capital and operating costs, costs to customers, carbon emissions, environmental impact and social acceptability. Findings show that beyond the uncertainty range explored with the traditional headroom method, preference reversals can occur, i.e. some management options that underperform at lower uncertainties, outperform at higher levels of uncertainty. This study also shows that when 50% or more of the population adopts demand side management, efficiency related measures and innovative options such as rainwater collection can perform equally well or better than some supply side options. The additional use of MCDA shifts the focus away from reservoir expansion options that perform best with respect to water availability, to combined strategies that include innovative demand side management actions of rainwater collection and greywater reuse as well as efficiency measures and additional regional transfers. This research illustrates how an Info-Gap based approach can offer a comprehensive picture of potential supply/demand futures and a rich variety of information to support adaptive management of water systems under severe uncertainty.

A duality theorem-based algorithm for inexact quadratic programming problems: Application to waste management under uncertainty

NASA Astrophysics Data System (ADS)

Kong, X. M.; Huang, G. H.; Fan, Y. R.; Li, Y. P.

2016-04-01

In this study, a duality theorem-based algorithm (DTA) for inexact quadratic programming (IQP) is developed for municipal solid waste (MSW) management under uncertainty. It improves upon the existing numerical solution method for IQP problems. The comparison between DTA and derivative algorithm (DAM) shows that the DTA method provides better solutions than DAM with lower computational complexity. It is not necessary to identify the uncertain relationship between the objective function and decision variables, which is required for the solution process of DAM. The developed method is applied to a case study of MSW management and planning. The results indicate that reasonable solutions have been generated for supporting long-term MSW management and planning. They could provide more information as well as enable managers to make better decisions to identify desired MSW management policies in association with minimized cost under uncertainty.

Regional Sea Level Scenarios for Coastal Risk Management: Managing the Uncertainty of Future Sea Level Change and Extreme Water Levels for Department of Defense Coastal Sites Worldwide

DTIC Science & Technology

2016-04-01

SERDP NOAA USACE Ocean MANAGING THE UNCERTAINTY OF FUTURE SEA LEVEL CHANGE AND EXTREME WATER LEVELS FOR DEPARTMENT OF DEFENSE COASTAL SITES...WORLDWIDE APRIL 2016 REGIONAL SEA LEVEL SCENARIOS FOR COASTAL RISK MANAGEMENT: COVER PHOTOS, FROM LEFT TO RIGHT: - Overwash of the island of Roi-Namur on...J.A., S. Gill, J. Obeysekera, W. Sweet, K. Knuuti, and J. Marburger. 2016. Regional Sea Level Scenarios for Coastal Risk Management: Managing the

Can integrative catchment management mitigate future water quality issues caused by climate change and socio-economic development?

NASA Astrophysics Data System (ADS)

Honti, Mark; Schuwirth, Nele; Rieckermann, Jörg; Stamm, Christian

2017-03-01

The design and evaluation of solutions for integrated surface water quality management requires an integrated modelling approach. Integrated models have to be comprehensive enough to cover the aspects relevant for management decisions, allowing for mapping of larger-scale processes such as climate change to the regional and local contexts. Besides this, models have to be sufficiently simple and fast to apply proper methods of uncertainty analysis, covering model structure deficits and error propagation through the chain of sub-models. Here, we present a new integrated catchment model satisfying both conditions. The conceptual iWaQa model was developed to support the integrated management of small streams. It can be used to predict traditional water quality parameters, such as nutrients and a wide set of organic micropollutants (plant and material protection products), by considering all major pollutant pathways in urban and agricultural environments. Due to its simplicity, the model allows for a full, propagative analysis of predictive uncertainty, including certain structural and input errors. The usefulness of the model is demonstrated by predicting future surface water quality in a small catchment with mixed land use in the Swiss Plateau. We consider climate change, population growth or decline, socio-economic development, and the implementation of management strategies to tackle urban and agricultural point and non-point sources of pollution. Our results indicate that input and model structure uncertainties are the most influential factors for certain water quality parameters. In these cases model uncertainty is already high for present conditions. Nevertheless, accounting for today's uncertainty makes management fairly robust to the foreseen range of potential changes in the next decades. The assessment of total predictive uncertainty allows for selecting management strategies that show small sensitivity to poorly known boundary conditions. The identification of important sources of uncertainty helps to guide future monitoring efforts and pinpoints key indicators, whose evolution should be closely followed to adapt management. The possible impact of climate change is clearly demonstrated by water quality substantially changing depending on single climate model chains. However, when all climate trajectories are combined, the human land use and management decisions have a larger influence on water quality against a time horizon of 2050 in the study.

Ensembles vs. information theory: supporting science under uncertainty

NASA Astrophysics Data System (ADS)

Nearing, Grey S.; Gupta, Hoshin V.

2018-05-01

Multi-model ensembles are one of the most common ways to deal with epistemic uncertainty in hydrology. This is a problem because there is no known way to sample models such that the resulting ensemble admits a measure that has any systematic (i.e., asymptotic, bounded, or consistent) relationship with uncertainty. Multi-model ensembles are effectively sensitivity analyses and cannot - even partially - quantify uncertainty. One consequence of this is that multi-model approaches cannot support a consistent scientific method - in particular, multi-model approaches yield unbounded errors in inference. In contrast, information theory supports a coherent hypothesis test that is robust to (i.e., bounded under) arbitrary epistemic uncertainty. This paper may be understood as advocating a procedure for hypothesis testing that does not require quantifying uncertainty, but is coherent and reliable (i.e., bounded) in the presence of arbitrary (unknown and unknowable) uncertainty. We conclude by offering some suggestions about how this proposed philosophy of science suggests new ways to conceptualize and construct simulation models of complex, dynamical systems.

A coupled stochastic inverse-management framework for dealing with nonpoint agriculture pollution under groundwater parameter uncertainty

NASA Astrophysics Data System (ADS)

Llopis-Albert, Carlos; Palacios-Marqués, Daniel; Merigó, José M.

2014-04-01

In this paper a methodology for the stochastic management of groundwater quality problems is presented, which can be used to provide agricultural advisory services. A stochastic algorithm to solve the coupled flow and mass transport inverse problem is combined with a stochastic management approach to develop methods for integrating uncertainty; thus obtaining more reliable policies on groundwater nitrate pollution control from agriculture. The stochastic inverse model allows identifying non-Gaussian parameters and reducing uncertainty in heterogeneous aquifers by constraining stochastic simulations to data. The management model determines the spatial and temporal distribution of fertilizer application rates that maximizes net benefits in agriculture constrained by quality requirements in groundwater at various control sites. The quality constraints can be taken, for instance, by those given by water laws such as the EU Water Framework Directive (WFD). Furthermore, the methodology allows providing the trade-off between higher economic returns and reliability in meeting the environmental standards. Therefore, this new technology can help stakeholders in the decision-making process under an uncertainty environment. The methodology has been successfully applied to a 2D synthetic aquifer, where an uncertainty assessment has been carried out by means of Monte Carlo simulation techniques.

Randomized trial of an uncertainty self-management telephone intervention for patients awaiting liver transplant.

PubMed

Bailey, Donald E; Hendrix, Cristina C; Steinhauser, Karen E; Stechuchak, Karen M; Porter, Laura S; Hudson, Julie; Olsen, Maren K; Muir, Andrew; Lowman, Sarah; DiMartini, Andrea; Salonen, Laurel Williams; Tulsky, James A

2017-03-01

We tested an uncertainty self-management telephone intervention (SMI) with patients awaiting liver transplant and their caregivers. Participants were recruited from four transplant centers and completed questionnaires at baseline, 10, and 12 weeks from baseline (generally two and four weeks after intervention delivery, respectively). Dyads were randomized to either SMI (n=56) or liver disease education (LDE; n=59), both of which involved six weekly telephone sessions. SMI participants were taught coping skills and uncertainty management strategies while LDE participants learned about liver function and how to stay healthy. Outcomes included illness uncertainty, uncertainty management, depression, anxiety, self-efficacy, and quality of life. General linear models were used to test for group differences. No differences were found between the SMI and LDE groups for study outcomes. This trial offers insight regarding design for future interventions that may allow greater flexibility in length of delivery beyond our study's 12-week timeframe. Our study was designed for the time constraints of today's clinical practice setting. This trial is a beginning point to address the unmet needs of these patients and their caregivers as they wait for transplants that could save their lives. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Uncertainty-based simulation-optimization using Gaussian process emulation: Application to coastal groundwater management

NASA Astrophysics Data System (ADS)

Rajabi, Mohammad Mahdi; Ketabchi, Hamed

2017-12-01

Combined simulation-optimization (S/O) schemes have long been recognized as a valuable tool in coastal groundwater management (CGM). However, previous applications have mostly relied on deterministic seawater intrusion (SWI) simulations. This is a questionable simplification, knowing that SWI models are inevitably prone to epistemic and aleatory uncertainty, and hence a management strategy obtained through S/O without consideration of uncertainty may result in significantly different real-world outcomes than expected. However, two key issues have hindered the use of uncertainty-based S/O schemes in CGM, which are addressed in this paper. The first issue is how to solve the computational challenges resulting from the need to perform massive numbers of simulations. The second issue is how the management problem is formulated in presence of uncertainty. We propose the use of Gaussian process (GP) emulation as a valuable tool in solving the computational challenges of uncertainty-based S/O in CGM. We apply GP emulation to the case study of Kish Island (located in the Persian Gulf) using an uncertainty-based S/O algorithm which relies on continuous ant colony optimization and Monte Carlo simulation. In doing so, we show that GP emulation can provide an acceptable level of accuracy, with no bias and low statistical dispersion, while tremendously reducing the computational time. Moreover, five new formulations for uncertainty-based S/O are presented based on concepts such as energy distances, prediction intervals and probabilities of SWI occurrence. We analyze the proposed formulations with respect to their resulting optimized solutions, the sensitivity of the solutions to the intended reliability levels, and the variations resulting from repeated optimization runs.

A strategy for monitoring and managing declines in an amphibian community.

PubMed

Grant, Evan H Campbell; Zipkin, Elise F; Nichols, James D; Campbell, J Patrick

2013-12-01

Although many taxa have declined globally, conservation actions are inherently local. Ecosystems degrade even in protected areas, and maintaining natural systems in a desired condition may require active management. Implementing management decisions under uncertainty requires a logical and transparent process to identify objectives, develop management actions, formulate system models to link actions with objectives, monitor to reduce uncertainty and identify system state (i.e., resource condition), and determine an optimal management strategy. We applied one such structured decision-making approach that incorporates these critical elements to inform management of amphibian populations in a protected area managed by the U.S. National Park Service. Climate change is expected to affect amphibian occupancy of wetlands and to increase uncertainty in management decision making. We used the tools of structured decision making to identify short-term management solutions that incorporate our current understanding of the effect of climate change on amphibians, emphasizing how management can be undertaken even with incomplete information. Estrategia para Monitorear y Manejar Disminuciones en una Comunidad de Anfibios. © 2013 Society for Conservation Biology.

An interval-based possibilistic programming method for waste management with cost minimization and environmental-impact abatement under uncertainty.

PubMed

Li, Y P; Huang, G H

2010-09-15

Considerable public concerns have been raised in the past decades since a large amount of pollutant emissions from municipal solid waste (MSW) disposal of processes pose risks on surrounding environment and human health. Moreover, in MSW management, various uncertainties exist in the related costs, impact factors and objectives, which can affect the optimization processes and the decision schemes generated. In this study, an interval-based possibilistic programming (IBPP) method is developed for planning the MSW management with minimized system cost and environmental impact under uncertainty. The developed method can deal with uncertainties expressed as interval values and fuzzy sets in the left- and right-hand sides of constraints and objective function. An interactive algorithm is provided for solving the IBPP problem, which does not lead to more complicated intermediate submodels and has a relatively low computational requirement. The developed model is applied to a case study of planning a MSW management system, where mixed integer linear programming (MILP) technique is introduced into the IBPP framework to facilitate dynamic analysis for decisions of timing, sizing and siting in terms of capacity expansion for waste-management facilities. Three cases based on different waste-management policies are examined. The results obtained indicate that inclusion of environmental impacts in the optimization model can change the traditional waste-allocation pattern merely based on the economic-oriented planning approach. The results obtained can help identify desired alternatives for managing MSW, which has advantages in providing compromised schemes under an integrated consideration of economic efficiency and environmental impact under uncertainty. Copyright 2010 Elsevier B.V. All rights reserved.

Valuing flexibilities in the design of urban water management systems.

PubMed

Deng, Yinghan; Cardin, Michel-Alexandre; Babovic, Vladan; Santhanakrishnan, Deepak; Schmitter, Petra; Meshgi, Ali

2013-12-15

Climate change and rapid urbanization requires decision-makers to develop a long-term forward assessment on sustainable urban water management projects. This is further complicated by the difficulties of assessing sustainable designs and various design scenarios from an economic standpoint. A conventional valuation approach for urban water management projects, like Discounted Cash Flow (DCF) analysis, fails to incorporate uncertainties, such as amount of rainfall, unit cost of water, and other uncertainties associated with future changes in technological domains. Such approach also fails to include the value of flexibility, which enables managers to adapt and reconfigure systems over time as uncertainty unfolds. This work describes an integrated framework to value investments in urban water management systems under uncertainty. It also extends the conventional DCF analysis through explicit considerations of flexibility in systems design and management. The approach incorporates flexibility as intelligent decision-making mechanisms that enable systems to avoid future downside risks and increase opportunities for upside gains over a range of possible futures. A water catchment area in Singapore was chosen to assess the value of a flexible extension of standard drainage canals and a flexible deployment of a novel water catchment technology based on green roofs and porous pavements. Results show that integrating uncertainty and flexibility explicitly into the decision-making process can reduce initial capital expenditure, improve value for investment, and enable decision-makers to learn more about system requirements during the lifetime of the project. Copyright © 2013 Elsevier Ltd. All rights reserved.

Modeling for waste management associated with environmental-impact abatement under uncertainty.

PubMed

Li, P; Li, Y P; Huang, G H; Zhang, J L

2015-04-01

Municipal solid waste (MSW) treatment can generate significant amounts of pollutants, and thus pose a risk on human health. Besides, in MSW management, various uncertainties exist in the related costs, impact factors, and objectives, which can affect the optimization processes and the decision schemes generated. In this study, a life cycle assessment-based interval-parameter programming (LCA-IPP) method is developed for MSW management associated with environmental-impact abatement under uncertainty. The LCA-IPP can effectively examine the environmental consequences based on a number of environmental impact categories (i.e., greenhouse gas equivalent, acid gas emissions, and respiratory inorganics), through analyzing each life cycle stage and/or major contributing process related to various MSW management activities. It can also tackle uncertainties existed in the related costs, impact factors, and objectives and expressed as interval numbers. Then, the LCA-IPP method is applied to MSW management for the City of Beijing, the capital of China, where energy consumptions and six environmental parameters [i.e., CO2, CO, CH4, NOX, SO2, inhalable particle (PM10)] are used as systematic tool to quantify environmental releases in entire life cycle stage of waste collection, transportation, treatment, and disposal of. Results associated with system cost, environmental impact, and the related policy implication are generated and analyzed. Results can help identify desired alternatives for managing MSW flows, which has advantages in providing compromised schemes under an integrated consideration of economic efficiency and environmental impact under uncertainty.

Deciding to institutionalize: caregiving crisis, intergenerational communication, and uncertainty management for elders and their children in Shanghai.

PubMed

Chen, Lin

2015-01-01

This phenomenological study integrated crisis theory, social identity theory, and uncertainty management theory to conceptualize the decision-making process around institutionalization among nursing home residents and their children in Shanghai. I conducted face-to-face, semistructured interviews with 12 dyads of matched elders and their children (N = 24). The findings suggest that caregiving crises triggered intergenerational communication about caregiving alternatives and new arrangements, although each generation had different stances and motivations. Children finalized the decision by helping their parents to manage the uncertainties pertaining to institutionalization. This study sheds light on caregiving decision-making dynamics for the increasing aging population across cultures.

Conservation of northern bobwhite on private lands in Georgia, USA under uncertainty about landscape-level habitat effects

USGS Publications Warehouse

Howell, J.E.; Moore, C.T.; Conroy, M.J.; Hamrick, R.G.; Cooper, R.J.; Thackston, R.E.; Carroll, J.P.

2009-01-01

Large-scale habitat enhancement programs for birds are becoming more widespread, however, most lack monitoring to resolve uncertainties and enhance program impact over time. Georgia?s Bobwhite Quail Initiative (BQI) is a competitive, proposal-based system that provides incentives to landowners to establish habitat for northern bobwhites (Colinus virginianus). Using data from monitoring conducted in the program?s first years (1999?2001), we developed alternative hierarchical models to predict bobwhite abundance in response to program habitat modifications on local and regional scales. Effects of habitat and habitat management on bobwhite population response varied among geographical scales, but high measurement variability rendered the specific nature of these scaled effects equivocal. Under some models, BQI had positive impact at both local farm scales (1, 9 km2), particularly when practice acres were clustered, whereas other credible models indicated that bird response did not depend on spatial arrangement of practices. Thus, uncertainty about landscape-level effects of management presents a challenge to program managers who must decide which proposals to accept. We demonstrate that optimal selection decisions can be made despite this uncertainty and that uncertainty can be reduced over time, with consequent improvement in management efficacy. However, such an adaptive approach to BQI program implementation would require the reestablishment of monitoring of bobwhite abundance, an effort for which funding was discontinued in 2002. For landscape-level conservation programs generally, our approach demonstrates the value in assessing multiple scales of impact of habitat modification programs, and it reveals the utility of addressing management uncertainty through multiple decision models and system monitoring.

Creating dialogue: a workshop on "Uncertainty in Decision Making in a Changing Climate"

NASA Astrophysics Data System (ADS)

Ewen, Tracy; Addor, Nans; Johnson, Leigh; Coltekin, Arzu; Derungs, Curdin; Muccione, Veruska

2014-05-01

Uncertainty is present in all fields of climate research, spanning from projections of future climate change, to assessing regional impacts and vulnerabilities, to adaptation policy and decision-making. In addition to uncertainties, managers and planners in many sectors are often confronted with large amounts of information from climate change research whose complex and interdisciplinary nature make it challenging to incorporate into the decision-making process. An overarching issue in tackling this problem is the lack of institutionalized dialogue between climate researchers, decision-makers and user groups. Forums that facilitate such dialogue would allow climate researchers to actively engage with end-users and researchers in different disciplines to better characterize uncertainties and ultimately understand which ones are critically considered and incorporated into decisions made. We propose that the introduction of students to these challenges at an early stage of their education and career is a first step towards improving future dialogue between climate researchers, decision-makers and user groups. To this end, we organized a workshop at the University of Zurich, Switzerland, entitled "Uncertainty in Decision Making in a Changing Climate". It brought together 50 participants, including Bachelor, Master and PhD students and academic staff, and nine selected speakers from academia, industry, government, and philanthropy. Speakers introduced participants to topics ranging from uncertainties in climate model scenarios to managing uncertainties in development and aid agencies. The workshop consisted of experts' presentations, a panel discussion and student group work on case studies. Pedagogical goals included i) providing participants with an overview of the current research on uncertainty and on how uncertainty is dealt with by decision-makers, ii) fostering exchange between practitioners, students, and scientists from different backgrounds, iii) exposing students, at an early stage of their professional life, to multidisciplinary collaborations and real-world problems involving decisions under uncertainty. An opinion survey conducted before and after the workshop enabled us to observe changes in participants' perspectives on what information and tools should be exchanged between researchers and decision-makers to better address uncertainty. Responses demonstrated a marked shift from a pre-workshop vertical conceptualizations of researcher—user group interaction to a post-workshop horizontal mode: in the former, researchers were portrayed as bestowing data-based products to decision-makers, while in the latter, both sets of actors engaged in institutionalized dialogues and frequent communication, exchanging their needs, expertise, and personnel. In addition to the survey, we will draw on examples from the course evaluation to illustrate the strengths and weaknesses of our approach. By doing so, we seek to encourage the organization of similar events by other universities, with the mid-term goal to improve future dialogue. From a pedagogical perspective, introducing students to these ideas at a very early stage in their research careers is an ideal opportunity to establish new modes of communication with an interdisciplinary perspective and strengthen dialogue between climate researchers, decision-makers and user groups.

Assessing Uncertainty in Expert Judgments About Natural Resources

Treesearch

David A. Cleaves

1994-01-01

Judgments are necessary in natural resources management, but uncertainty about these judgments should be assessed. When all judgments are rejected in the absence of hard data, valuable professional experience and knowledge are not utilized fully. The objective of assessing uncertainty is to get the best representation of knowledge and its bounds. Uncertainty...

Managing North American waterfowl in the face of uncertainty

USGS Publications Warehouse

Robbins, C.S.

1995-01-01

Informed management of waterfowl (or any animal population) requires management goals and objectives, the ability to implement management actions, periodic information about population and goal-related varlables, and knowledge of effects of management actions on population and goal-related variables. In North America, international treaties mandate a primary objective of protecting migratory bird populations, with a secondary objective of providing hunting opportunity in a manner compatible with such protection. Through the years, annual establishment of hunting regulations and acquisition and man-agement of habitat have been the primary management actions taken by federal agencies. Various information-gathering programs were established and, by the 1960s, had developed into arguably the best monitoring system in the world for continentally distributed animal populations. Retrospective analyses using estimates from this monitoring system have been used to investigate effects of management actions on waterfowl population and harvest dynamics, but key relationships arc still characterized by uncertainty. We recommend actively adaptive management as an approach that can meet short-term harvest objectives, while reducing uncertainty and ensuring sustainable populations over the long-term.

The HTA Risk Analysis Chart: Visualising the Need for and Potential Value of Managed Entry Agreements in Health Technology Assessment.

PubMed

Grimm, Sabine Elisabeth; Strong, Mark; Brennan, Alan; Wailoo, Allan J

2017-12-01

Recent changes to the regulatory landscape of pharmaceuticals may sometimes require reimbursement authorities to issue guidance on technologies that have a less mature evidence base. Decision makers need to be aware of risks associated with such health technology assessment (HTA) decisions and the potential to manage this risk through managed entry agreements (MEAs). This work develops methods for quantifying risk associated with specific MEAs and for clearly communicating this to decision makers. We develop the 'HTA risk analysis chart', in which we present the payer strategy and uncertainty burden (P-SUB) as a measure of overall risk. The P-SUB consists of the payer uncertainty burden (PUB), the risk stemming from decision uncertainty as to which is the truly optimal technology from the relevant set of technologies, and the payer strategy burden (PSB), the additional risk of approving a technology that is not expected to be optimal. We demonstrate the approach using three recent technology appraisals from the UK National Institute for Health and Clinical Excellence (NICE), each of which considered a price-based MEA. The HTA risk analysis chart was calculated using results from standard probabilistic sensitivity analyses. In all three HTAs, the new interventions were associated with substantial risk as measured by the P-SUB. For one of these technologies, the P-SUB was reduced to zero with the proposed price reduction, making this intervention cost effective with near complete certainty. For the other two, the risk reduced substantially with a much reduced PSB and a slightly increased PUB. The HTA risk analysis chart shows the risk that the healthcare payer incurs under unresolved decision uncertainty and when considering recommending a technology that is not expected to be optimal given current evidence. This allows the simultaneous consideration of financial and data-collection MEA schemes in an easily understood format. The use of HTA risk analysis charts will help to ensure that MEAs are considered within a standard utility-maximising health economic decision-making framework.

Incorporating parametric uncertainty into population viability analysis models

USGS Publications Warehouse

McGowan, Conor P.; Runge, Michael C.; Larson, Michael A.

2011-01-01

Uncertainty in parameter estimates from sampling variation or expert judgment can introduce substantial uncertainty into ecological predictions based on those estimates. However, in standard population viability analyses, one of the most widely used tools for managing plant, fish and wildlife populations, parametric uncertainty is often ignored in or discarded from model projections. We present a method for explicitly incorporating this source of uncertainty into population models to fully account for risk in management and decision contexts. Our method involves a two-step simulation process where parametric uncertainty is incorporated into the replication loop of the model and temporal variance is incorporated into the loop for time steps in the model. Using the piping plover, a federally threatened shorebird in the USA and Canada, as an example, we compare abundance projections and extinction probabilities from simulations that exclude and include parametric uncertainty. Although final abundance was very low for all sets of simulations, estimated extinction risk was much greater for the simulation that incorporated parametric uncertainty in the replication loop. Decisions about species conservation (e.g., listing, delisting, and jeopardy) might differ greatly depending on the treatment of parametric uncertainty in population models.

A PROBABILISTIC APPROACH FOR ANALYSIS OF UNCERTAINTY IN THE EVALUATION OF WATERSHED MANAGEMENT PRACTICES

EPA Science Inventory

A computational framework is presented for analyzing the uncertainty in model estimates of water quality benefits of best management practices (BMPs) in two small (<10 km²) watersheds in Indiana. The analysis specifically recognizes the significance of the difference b...

Bayesian Monte Carlo and Maximum Likelihood Approach for Uncertainty Estimation and Risk Management: Application to Lake Oxygen Recovery Model

EPA Science Inventory

Model uncertainty estimation and risk assessment is essential to environmental management and informed decision making on pollution mitigation strategies. In this study, we apply a probabilistic methodology, which combines Bayesian Monte Carlo simulation and Maximum Likelihood e...

Leadership Values, Trust and Negative Capability: Managing the Uncertainties of Future English Higher Education

ERIC Educational Resources Information Center

Jameson, Jill

2012-01-01

The complex leadership attribute of "negative capability" in managing uncertainty and engendering trust may be amongst the qualities enabling institutions to cope with multiple recent government policy challenges affecting English higher education, including significant increases in student fees. Research findings are reported on changes…

Patterns of zone management uncertainty in cotton using tarnished plant bug distributions, NDVI, soil EC, yield and thermal imagery

USDA-ARS?s Scientific Manuscript database

Management zones for various crops have been delineated using NDVI (Normalized Difference Vegetation Index), apparent bulk soil electrical conductivity (ECa - Veris), and yield data; however, estimations of uncertainty for these data layers are equally important considerations. The objective of this...

Investment, regulation, and uncertainty: managing new plant breeding techniques.

PubMed

Smyth, Stuart J; McDonald, Jillian; Falck-Zepeda, Jose

2014-01-01

As with any technological innovation, time refines the technology, improving upon the original version of the innovative product. The initial GM crops had single traits for either herbicide tolerance or insect resistance. Current varieties have both of these traits stacked together and in many cases other abiotic and biotic traits have also been stacked. This innovation requires investment. While this is relatively straight forward, certain conditions need to exist such that investments can be facilitated. The principle requirement for investment is that regulatory frameworks render consistent and timely decisions. If the certainty of regulatory outcomes weakens, the potential for changes in investment patterns increases.   This article provides a summary background to the leading plant breeding technologies that are either currently being used to develop new crop varieties or are in the pipeline to be applied to plant breeding within the next few years. Challenges for existing regulatory systems are highlighted. Utilizing an option value approach from investment literature, an assessment of uncertainty regarding the regulatory approval for these varying techniques is undertaken. This research highlights which technology development options have the greatest degree of uncertainty and hence, which ones might be expected to see an investment decline.

Report from the Workshop on Coregonine Restoration Science

USGS Publications Warehouse

Bronte, Charles R.; Bunnell, David B.; David, Solomon R.; Gordon, Roger; Gorsky, Dimitry; Millard, Michael J.; Read, Jennifer; Stein, Roy A.; Vaccaro, Lynn

2017-08-03

SummaryGreat Lakes fishery managers have the opportunity and have expressed interest in reestablishing a native forage base in the Great Lakes consisting of various forms and species within the genus Coregonus. This report summarizes the proceedings of a workshop focused on a subset of the genus, and the term “coregonines” is used to refer to several species of deepwater ciscoes (also known as “chubs”) and the one more pelagic-oriented cisco species (Coregonus artedi, also known as “lake herring”). As the principal conservation agency for the United States Government, the Department of Interior has unique and significant authorities and capacities to support a coregonine reestablishment program in the Great Lakes. To identify and discuss key uncertainties associated with such a program and develop a coordinated approach, the U.S. Geological Survey (USGS) and the U.S. Fish and Wildlife Service (FWS), the principal Department of the Interior bureaus to address Great Lakes fishery issues, held the first of a series of workshops on coregonine science in Ann Arbor, Michigan, on October 11–13, 2016. Workshop objectives were to identify (1) perceived key uncertainties associated with coregonine restoration in the Great Lakes and (2) DOI capacities for addressing these key uncertainties.

A hierarchical spatial model for well yield in complex aquifers

NASA Astrophysics Data System (ADS)

Montgomery, J.; O'sullivan, F.

2017-12-01

Efficiently siting and managing groundwater wells requires reliable estimates of the amount of water that can be produced, or the well yield. This can be challenging to predict in highly complex, heterogeneous fractured aquifers due to the uncertainty around local hydraulic properties. Promising statistical approaches have been advanced in recent years. For instance, kriging and multivariate regression analysis have been applied to well test data with limited but encouraging levels of prediction accuracy. Additionally, some analytical solutions to diffusion in homogeneous porous media have been used to infer "effective" properties consistent with observed flow rates or drawdown. However, this is an under-specified inverse problem with substantial and irreducible uncertainty. We describe a flexible machine learning approach capable of combining diverse datasets with constraining physical and geostatistical models for improved well yield prediction accuracy and uncertainty quantification. Our approach can be implemented within a hierarchical Bayesian framework using Markov Chain Monte Carlo, which allows for additional sources of information to be incorporated in priors to further constrain and improve predictions and reduce the model order. We demonstrate the usefulness of this approach using data from over 7,000 wells in a fractured bedrock aquifer.

A global probabilistic tsunami hazard assessment from earthquake sources

USGS Publications Warehouse

Davies, Gareth; Griffin, Jonathan; Lovholt, Finn; Glimsdal, Sylfest; Harbitz, Carl; Thio, Hong Kie; Lorito, Stefano; Basili, Roberto; Selva, Jacopo; Geist, Eric L.; Baptista, Maria Ana

2017-01-01

Large tsunamis occur infrequently but have the capacity to cause enormous numbers of casualties, damage to the built environment and critical infrastructure, and economic losses. A sound understanding of tsunami hazard is required to underpin management of these risks, and while tsunami hazard assessments are typically conducted at regional or local scales, globally consistent assessments are required to support international disaster risk reduction efforts, and can serve as a reference for local and regional studies. This study presents a global-scale probabilistic tsunami hazard assessment (PTHA), extending previous global-scale assessments based largely on scenario analysis. Only earthquake sources are considered, as they represent about 80% of the recorded damaging tsunami events. Globally extensive estimates of tsunami run-up height are derived at various exceedance rates, and the associated uncertainties are quantified. Epistemic uncertainties in the exceedance rates of large earthquakes often lead to large uncertainties in tsunami run-up. Deviations between modelled tsunami run-up and event observations are quantified, and found to be larger than suggested in previous studies. Accounting for these deviations in PTHA is important, as it leads to a pronounced increase in predicted tsunami run-up for a given exceedance rate.

Assessment of Uncertainty-Infused Scientific Argumentation

ERIC Educational Resources Information Center

Lee, Hee-Sun; Liu, Ou Lydia; Pallant, Amy; Roohr, Katrina Crotts; Pryputniewicz, Sarah; Buck, Zoë E.

2014-01-01

Though addressing sources of uncertainty is an important part of doing science, it has largely been neglected in assessing students' scientific argumentation. In this study, we initially defined a scientific argumentation construct in four structural elements consisting of claim, justification, uncertainty qualifier, and uncertainty…

Estimating the spatial distribution of wintering little brown bat populations in the eastern United States

USGS Publications Warehouse

Russell, Robin E.; Tinsley, Karl; Erickson, Richard A.; Thogmartin, Wayne E.; Jennifer A. Szymanski,

2014-01-01

Depicting the spatial distribution of wildlife species is an important first step in developing management and conservation programs for particular species. Accurate representation of a species distribution is important for predicting the effects of climate change, land-use change, management activities, disease, and other landscape-level processes on wildlife populations. We developed models to estimate the spatial distribution of little brown bat (Myotis lucifugus) wintering populations in the United States east of the 100th meridian, based on known hibernacula locations. From this data, we developed several scenarios of wintering population counts per county that incorporated uncertainty in the spatial distribution of the hibernacula as well as uncertainty in the size of the current little brown bat population. We assessed the variability in our results resulting from effects of uncertainty. Despite considerable uncertainty in the known locations of overwintering little brown bats in the eastern United States, we believe that models accurately depicting the effects of the uncertainty are useful for making management decisions as these models are a coherent organization of the best available information.

Unconscious Vigilance: Worldview Defense Without Adaptations for Terror, Coalition, or Uncertainty Management

PubMed Central

Holbrook, Colin; Sousa, Paulo; Hahn-Holbrook, Jennifer

2012-01-01

Individuals subtly reminded of death, coalitional challenges, or feelings of uncertainty display exaggerated preferences for affirmations and against criticisms of their cultural in-groups. Terror management, coalitional psychology, and uncertainty management theories postulate this “worldview defense” effect as the output of mechanisms evolved either to allay the fear of death, foster social support, or reduce anxiety by increasing adherence to cultural values. In 4 studies, we report evidence for an alternative perspective. We argue that worldview defense owes to unconscious vigilance, a state of accentuated reactivity to affective targets (which need not relate to cultural worldviews) that follows detection of subtle alarm cues (which need not pertain to death, coalitional challenges, or uncertainty). In Studies 1 and 2, death-primed participants produced exaggerated ratings of worldview-neutral affective targets. In Studies 3 and 4, subliminal threat manipulations unrelated to death, coalitional challenges, or uncertainty evoked worldview defense. These results are discussed as they inform evolutionary interpretations of worldview defense and future investigations of the influence of unconscious alarm on judgment. PMID:21644809

Decision Making Under Uncertainty and Complexity: A Model-Based Scenario Approach to Supporting Integrated Water Resources Management

NASA Astrophysics Data System (ADS)

Liu, Y.; Gupta, H.; Wagener, T.; Stewart, S.; Mahmoud, M.; Hartmann, H.; Springer, E.

2007-12-01

Some of the most challenging issues facing contemporary water resources management are those typified by complex coupled human-environmental systems with poorly characterized uncertainties. In other words, major decisions regarding water resources have to be made in the face of substantial uncertainty and complexity. It has been suggested that integrated models can be used to coherently assemble information from a broad set of domains, and can therefore serve as an effective means for tackling the complexity of environmental systems. Further, well-conceived scenarios can effectively inform decision making, particularly when high complexity and poorly characterized uncertainties make the problem intractable via traditional uncertainty analysis methods. This presentation discusses the integrated modeling framework adopted by SAHRA, an NSF Science & Technology Center, to investigate stakeholder-driven water sustainability issues within the semi-arid southwestern US. The multi-disciplinary, multi-resolution modeling framework incorporates a formal scenario approach to analyze the impacts of plausible (albeit uncertain) alternative futures to support adaptive management of water resources systems. Some of the major challenges involved in, and lessons learned from, this effort will be discussed.

Aeroservoelastic Uncertainty Model Identification from Flight Data

NASA Technical Reports Server (NTRS)

Brenner, Martin J.

2001-01-01

Uncertainty modeling is a critical element in the estimation of robust stability margins for stability boundary prediction and robust flight control system development. There has been a serious deficiency to date in aeroservoelastic data analysis with attention to uncertainty modeling. Uncertainty can be estimated from flight data using both parametric and nonparametric identification techniques. The model validation problem addressed in this paper is to identify aeroservoelastic models with associated uncertainty structures from a limited amount of controlled excitation inputs over an extensive flight envelope. The challenge to this problem is to update analytical models from flight data estimates while also deriving non-conservative uncertainty descriptions consistent with the flight data. Multisine control surface command inputs and control system feedbacks are used as signals in a wavelet-based modal parameter estimation procedure for model updates. Transfer function estimates are incorporated in a robust minimax estimation scheme to get input-output parameters and error bounds consistent with the data and model structure. Uncertainty estimates derived from the data in this manner provide an appropriate and relevant representation for model development and robust stability analysis. This model-plus-uncertainty identification procedure is applied to aeroservoelastic flight data from the NASA Dryden Flight Research Center F-18 Systems Research Aircraft.

Adaptive harvest management of North American waterfowl populations - recent successes and future prospects

USGS Publications Warehouse

Nichols, J.D.; Runge, M.C.; Johnson, F.A.; Williams, B.K.; Schodde, Richard; Hannon, Susan; Scheiffarth, Gregor; Bairlein, Franz

2006-01-01

The history of North American waterfowl harvest management has been characterized by attempts to use population monitoring data to make informed harvest management decisions. Early attempts can be characterized as intuitive decision processes, and later efforts were guided increasingly by population models and associated predictions. In 1995, a formal adaptive management process was implemented, and annual decisions about duck harvest regulations in the United States are still based on this process. This formal decision process is designed to deal appropriately with the various forms of uncertainty that characterize management decisions, environmental uncertainty, structural uncertainty, partial controllability and partial observability. The key components of the process are (1) objectives, (2) potential management actions, (3) model(s) of population response to management actions, (4) credibility measures for these models, and (5) a monitoring program. The operation of this iterative process is described, and a brief history of a decade of its use is presented. Future challenges range from social and political issues such as appropriate objectives and management actions, to technical issues such as multispecies management, geographic allocation of harvest, and incorporation of actions that include habitat acquisition and management.

Adaptive management for ecosystem services (j/a) | Science ...

EPA Pesticide Factsheets

Management of natural resources for the production of ecosystem services, which are vital for human well-being, is necessary even when there is uncertainty regarding system response to management action. This uncertainty is the result of incomplete controllability, complex internal feedbacks, and non-linearity that often interferes with desired management outcomes, and insufficient understanding of nature and people. Adaptive management was developed to reduce such uncertainty. We present a framework for the application of adaptive management for ecosystem services that explicitly accounts for cross-scale tradeoffs in the production of ecosystem services. Our framework focuses on identifying key spatiotemporal scales (plot, patch, ecosystem, landscape, and region) that encompass dominant structures and processes in the system, and includes within- and cross-scale dynamics, ecosystem service tradeoffs, and management controllability within and across scales. Resilience theory recognizes that a limited set of ecological processes in a given system regulate ecosystem services, yet our understanding of these processes is poorly understood. If management actions erode or remove these processes, the system may shift into an alternative state unlikely to support the production of desired services. Adaptive management provides a process to assess the underlying within and cross-scale tradeoffs associated with production of ecosystem services while proceeding with manage

When do business units benefit more from collective citizenship behavior of management teams? An upper echelons perspective.

PubMed

Liu, Wu; Gong, Yaping; Liu, Jun

2014-05-01

Drawing upon the notion of managerial discretion from upper echelons theory, we theorize which external contingencies moderate the relationship between collective organizational citizenship behavior (COCB) and unit performance. Focusing on business unit (BU) management teams, we hypothesize that COCB of BU management teams enhances BU performance and that this impact depends on environmental uncertainty and BU management-team decision latitude, 2 determinants of managerial discretion. In particular, the positive effect of COCB is stronger when environmental uncertainty or the BU management-team decision latitude is greater. Time-lagged data from 109 BUs of a telecommunications company support the hypotheses. Additional exploratory analysis shows that the positive moderating effect of environmental uncertainty is further amplified at higher levels of BU management-team decision latitude. Overall, this study extends the internally focused view in the micro OCB literature by introducing external contingencies for the COCB-unit-performance relationship. (c) 2014 APA, all rights reserved.

Analyzing Uncertainty and Risk in the Management of Water Resources in the State Of Texas

NASA Astrophysics Data System (ADS)

Singh, A.; Hauffpauir, R.; Mishra, S.; Lavenue, M.

2010-12-01

The State of Texas updates its state water plan every five years to determine the water demand required to meet its growing population. The plan compiles forecasts of water deficits from state-wide regional water planning groups as well as the water supply strategies to address these deficits. To date, the plan has adopted a deterministic framework, where reference values (e.g., best estimates, worst-case scenario) are used for key factors such as population growth, demand for water, severity of drought, water availability, etc. These key factors can, however, be affected by multiple sources of uncertainties such as - the impact of climate on surface water and groundwater availability, uncertainty in population projections, changes in sectoral composition of the economy, variability in water usage, feasibility of the permitting process, cost of implementation, etc. The objective of this study was to develop a generalized and scalable methodology for addressing uncertainty and risk in water resources management both at the regional and the local water planning level. The study proposes a framework defining the elements of an end-to-end system model that captures the key components of demand, supply and planning modules along with their associated uncertainties. The framework preserves the fundamental elements of the well-established planning process in the State of Texas, promoting an incremental and stakeholder-driven approach to adding different levels of uncertainty (and risk) into the decision-making environment. The uncertainty in the water planning process is broken down into two primary categories: demand uncertainty and supply uncertainty. Uncertainty in Demand is related to the uncertainty in population projections and the per-capita usage rates. Uncertainty in Supply, in turn, is dominated by the uncertainty in future climate conditions. Climate is represented in terms of time series of precipitation, temperature and/or surface evaporation flux for some future time period of interest, which can be obtained as outputs of global climate models (GCMs). These are then linked with hydrologic and water-availability models (WAMs) to estimate water availability for the worst drought conditions under each future climate scenario. Combining the demand scenarios with the water availability scenarios yields multiple scenarios for water shortage (or surplus). Given multiple shortage/surplus scenarios, various water management strategies can be assessed to evaluate the reliability of meeting projected deficits. These reliabilities are then used within a multi-criteria decision-framework to assess trade-offs between various water management objectives, thus helping to make more robust decisions while planning for the water needs of the future.

Interval-parameter semi-infinite fuzzy-stochastic mixed-integer programming approach for environmental management under multiple uncertainties.

PubMed

Guo, P; Huang, G H

2010-03-01

In this study, an interval-parameter semi-infinite fuzzy-chance-constrained mixed-integer linear programming (ISIFCIP) approach is developed for supporting long-term planning of waste-management systems under multiple uncertainties in the City of Regina, Canada. The method improves upon the existing interval-parameter semi-infinite programming (ISIP) and fuzzy-chance-constrained programming (FCCP) by incorporating uncertainties expressed as dual uncertainties of functional intervals and multiple uncertainties of distributions with fuzzy-interval admissible probability of violating constraint within a general optimization framework. The binary-variable solutions represent the decisions of waste-management-facility expansion, and the continuous ones are related to decisions of waste-flow allocation. The interval solutions can help decision-makers to obtain multiple decision alternatives, as well as provide bases for further analyses of tradeoffs between waste-management cost and system-failure risk. In the application to the City of Regina, Canada, two scenarios are considered. In Scenario 1, the City's waste-management practices would be based on the existing policy over the next 25 years. The total diversion rate for the residential waste would be approximately 14%. Scenario 2 is associated with a policy for waste minimization and diversion, where 35% diversion of residential waste should be achieved within 15 years, and 50% diversion over 25 years. In this scenario, not only landfill would be expanded, but also CF and MRF would be expanded. Through the scenario analyses, useful decision support for the City's solid-waste managers and decision-makers has been generated. Three special characteristics of the proposed method make it unique compared with other optimization techniques that deal with uncertainties. Firstly, it is useful for tackling multiple uncertainties expressed as intervals, functional intervals, probability distributions, fuzzy sets, and their combinations; secondly, it has capability in addressing the temporal variations of the functional intervals; thirdly, it can facilitate dynamic analysis for decisions of facility-expansion planning and waste-flow allocation within a multi-facility, multi-period and multi-option context. Copyright 2009 Elsevier Ltd. All rights reserved.

Climate change and European forests: what do we know, what are the uncertainties, and what are the implications for forest management?

PubMed

Lindner, Marcus; Fitzgerald, Joanne B; Zimmermann, Niklaus E; Reyer, Christopher; Delzon, Sylvain; van der Maaten, Ernst; Schelhaas, Mart-Jan; Lasch, Petra; Eggers, Jeannette; van der Maaten-Theunissen, Marieke; Suckow, Felicitas; Psomas, Achilleas; Poulter, Benjamin; Hanewinkel, Marc

2014-12-15

The knowledge about potential climate change impacts on forests is continuously expanding and some changes in growth, drought induced mortality and species distribution have been observed. However despite a significant body of research, a knowledge and communication gap exists between scientists and non-scientists as to how climate change impact scenarios can be interpreted and what they imply for European forests. It is still challenging to advise forest decision makers on how best to plan for climate change as many uncertainties and unknowns remain and it is difficult to communicate these to practitioners and other decision makers while retaining emphasis on the importance of planning for adaptation. In this paper, recent developments in climate change observations and projections, observed and projected impacts on European forests and the associated uncertainties are reviewed and synthesised with a view to understanding the implications for forest management. Current impact assessments with simulation models contain several simplifications, which explain the discrepancy between results of many simulation studies and the rapidly increasing body of evidence about already observed changes in forest productivity and species distribution. In simulation models uncertainties tend to cascade onto one another; from estimating what future societies will be like and general circulation models (GCMs) at the global level, down to forest models and forest management at the local level. Individual climate change impact studies should not be uncritically used for decision-making without reflection on possible shortcomings in system understanding, model accuracy and other assumptions made. It is important for decision makers in forest management to realise that they have to take long-lasting management decisions while uncertainty about climate change impacts are still large. We discuss how to communicate about uncertainty - which is imperative for decision making - without diluting the overall message. Considering the range of possible trends and uncertainties in adaptive forest management requires expert knowledge and enhanced efforts for providing science-based decision support. Copyright © 2014 Elsevier Ltd. All rights reserved.

Forest management under climatic and social uncertainty: trade-offs between reducing climate change impacts and fostering adaptive capacity.

PubMed

Seidl, Rupert; Lexer, Manfred J

2013-01-15

The unabated continuation of anthropogenic greenhouse gas emissions and the lack of an international consensus on a stringent climate change mitigation policy underscore the importance of adaptation for coping with the all but inevitable changes in the climate system. Adaptation measures in forestry have particularly long lead times. A timely implementation is thus crucial for reducing the considerable climate vulnerability of forest ecosystems. However, since future environmental conditions as well as future societal demands on forests are inherently uncertain, a core requirement for adaptation is robustness to a wide variety of possible futures. Here we explicitly address the roles of climatic and social uncertainty in forest management, and tackle the question of robustness of adaptation measures in the context of multi-objective sustainable forest management (SFM). We used the Austrian Federal Forests (AFF) as a case study, and employed a comprehensive vulnerability assessment framework based on ecosystem modeling, multi-criteria decision analysis, and practitioner participation. We explicitly considered climate uncertainty by means of three climate change scenarios, and accounted for uncertainty in future social demands by means of three societal preference scenarios regarding SFM indicators. We found that the effects of climatic and social uncertainty on the projected performance of management were in the same order of magnitude, underlining the notion that climate change adaptation requires an integrated social-ecological perspective. Furthermore, our analysis of adaptation measures revealed considerable trade-offs between reducing adverse impacts of climate change and facilitating adaptive capacity. This finding implies that prioritization between these two general aims of adaptation is necessary in management planning, which we suggest can draw on uncertainty analysis: Where the variation induced by social-ecological uncertainty renders measures aiming to reduce climate change impacts statistically insignificant (i.e., for approximately one third of the investigated management units of the AFF case study), fostering adaptive capacity is suggested as the preferred pathway for adaptation. We conclude that climate change adaptation needs to balance between anticipating expected future conditions and building the capacity to address unknowns and surprises. Copyright © 2012 Elsevier Ltd. All rights reserved.

Global sensitivity and uncertainty analysis of the nitrate leaching and crop yield simulation under different water and nitrogen management practices

USDA-ARS?s Scientific Manuscript database

Agricultural system models have become important tools in studying water and nitrogen (N) dynamics, as well as crop growth, under different management practices. Complexity in input parameters often leads to significant uncertainty when simulating dynamic processes such as nitrate leaching or crop y...

Uncertainty analysis of an irrigation scheduling model for water management in crop production

USDA-ARS?s Scientific Manuscript database

Irrigation scheduling tools are critical to allow producers to manage water resources for crop production in an accurate and timely manner. To be useful, these tools need to be accurate, complete, and relatively reliable. The current work presents the uncertainty analysis and its results for the Mis...

78 FR 25862 - Fisheries of the Northeastern United States; Final 2013-2015 Spiny Dogfish Fishery Specifications

Federal Register 2010, 2011, 2012, 2013, 2014

2013-05-03

... the status quo. The action is expected to maximize the profitability for the spiny dogfish fishery... possible commercial quotas by not making a deduction from the ACL accounting for management uncertainty...) in 2015; however, not accounting for management uncertainty would have increased the risk of...

Accepting uncertainty, assessing risk: decision quality in managing wildfire, forest resource values, and new technology

Treesearch

Jeffrey G. Borchers

2005-01-01

The risks, uncertainties, and social conflicts surrounding uncharacteristic wildfire and forest resource values have defied conventional approaches to planning and decision-making. Paradoxically, the adoption of technological innovations such as risk assessment, decision analysis, and landscape simulation models by land management organizations has been limited. The...

Predictive Uncertainty And Parameter Sensitivity Of A Sediment-Flux Model: Nitrogen Flux and Sediment Oxygen Demand

EPA Science Inventory

Estimating model predictive uncertainty is imperative to informed environmental decision making and management of water resources. This paper applies the Generalized Sensitivity Analysis (GSA) to examine parameter sensitivity and the Generalized Likelihood Uncertainty Estimation...

Making partnerships work: issues of risk, trust and control for managers and service providers.

PubMed

Walker, Rae; Smith, Penny; Adam, Jenny

2009-03-01

Trust is widely recognised is a core feature of partnership relationships and one that facilitates joint work. It is an issue that must be addressed if partnerships are to enhance service system integration. In recent literature trust has been linked to concepts of risk and control. In this study of trust within a Primary Care Partnership (PCP) in Australia the experiences of risk and uncertainty, and control, of participants in different structural positions, were explored in detail. The data used in this paper was qualitative, derived from 63 interviews with managers and service providers participating in committees of the PCP. This paper reports on the differences in the experience of risk and uncertainty, trust and control, of managers and service providers working as boundary spanners through the committees of a PCP. For managers there were significant risks and uncertainties, and trust and control were important. For service providers there were few risks and uncertainties, and trust and control were of much less importance. Some policy implications of the differences in perspective are discussed, as are important areas for further research.

Family support in the transition to adulthood in Portugal--its effects on identity capital development, uncertainty management and psychological well-being.

PubMed

Oliveira, José Egídio; Mendonça, Marina; Coimbra, Susana; Fontaine, Anne Marie

2014-12-01

In a familistic southern European society such as the Portuguese, the family has historically played a prominent role in supporting the negotiation of transition pathways into adulthood. The present study aimed at capturing (1) the relative weight of parental financial support and autonomy support in contributing to the youngsters' psychological well-being (PWB), and (2) the mediating role of identity capital and uncertainty management in this relationship. A total of 620 participants completed measures of parental support, identity capital, uncertainty management and PWB. Autonomy support was found to be the strongest predictor of PWB, both directly and indirectly through its effects on identity capital and the use of target focused uncertainty management strategies. Conversely, financial support evidenced only a minor indirect impact through the mediation of tangible identity capital. Autonomy stimulation may constitute one of the most developmentally determinant family challenges in assisting the process of coming of age in Portugal. Copyright © 2014 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

An inexact mixed risk-aversion two-stage stochastic programming model for water resources management under uncertainty.

PubMed

Li, W; Wang, B; Xie, Y L; Huang, G H; Liu, L

2015-02-01

Uncertainties exist in the water resources system, while traditional two-stage stochastic programming is risk-neutral and compares the random variables (e.g., total benefit) to identify the best decisions. To deal with the risk issues, a risk-aversion inexact two-stage stochastic programming model is developed for water resources management under uncertainty. The model was a hybrid methodology of interval-parameter programming, conditional value-at-risk measure, and a general two-stage stochastic programming framework. The method extends on the traditional two-stage stochastic programming method by enabling uncertainties presented as probability density functions and discrete intervals to be effectively incorporated within the optimization framework. It could not only provide information on the benefits of the allocation plan to the decision makers but also measure the extreme expected loss on the second-stage penalty cost. The developed model was applied to a hypothetical case of water resources management. Results showed that that could help managers generate feasible and balanced risk-aversion allocation plans, and analyze the trade-offs between system stability and economy.

Assessment of reservoir system variable forecasts

NASA Astrophysics Data System (ADS)

Kistenmacher, Martin; Georgakakos, Aris P.

2015-05-01

Forecast ensembles are a convenient means to model water resources uncertainties and to inform planning and management processes. For multipurpose reservoir systems, forecast types include (i) forecasts of upcoming inflows and (ii) forecasts of system variables and outputs such as reservoir levels, releases, flood damage risks, hydropower production, water supply withdrawals, water quality conditions, navigation opportunities, and environmental flows, among others. Forecasts of system variables and outputs are conditional on forecasted inflows as well as on specific management policies and can provide useful information for decision-making processes. Unlike inflow forecasts (in ensemble or other forms), which have been the subject of many previous studies, reservoir system variable and output forecasts are not formally assessed in water resources management theory or practice. This article addresses this gap and develops methods to rectify potential reservoir system forecast inconsistencies and improve the quality of management-relevant information provided to stakeholders and managers. The overarching conclusion is that system variable and output forecast consistency is critical for robust reservoir management and needs to be routinely assessed for any management model used to inform planning and management processes. The above are demonstrated through an application from the Sacramento-American-San Joaquin reservoir system in northern California.

Dream project: Applications of earth observations to disaster risk management

NASA Astrophysics Data System (ADS)

Dyke, G.; Gill, S.; Davies, R.; Betorz, F.; Andalsvik, Y.; Cackler, J.; Dos Santos, W.; Dunlop, K.; Ferreira, I.; Kebe, F.; Lamboglia, E.; Matsubara, Y.; Nikolaidis, V.; Ostoja-Starzewski, S.; Sakita, M.; Verstappen, N.

2011-01-01

The field of disaster risk management is relatively new and takes a structured approach to managing uncertainty related to the threat of natural and man-made disasters. Disaster risk management consists primarily of risk assessment and the development of strategies to mitigate disaster risk. This paper will discuss how increasing both Earth observation data and information technology capabilities can contribute to disaster risk management, particularly in Belize. The paper presents the results and recommendations of a project conducted by an international and interdisciplinary team of experts at the 2009 session of the International Space University in NASA Ames Research Center (California, USA). The aim is to explore the combination of current, planned and potential space-aided, airborne, and ground-based Earth observation tools, the emergence of powerful new web-based and mobile data management tools, and how this combination can support and improve the emerging field of disaster risk management. The starting point of the project was the World Bank's Comprehensive Approach to Probabilistic Risk Assessment (CAPRA) program, focused in Central America. This program was used as a test bed to analyze current space technologies used in risk management and develop new strategies and tools to be applied in other regions around the world.

Increased scientific rigor will improve reliability of research and effectiveness of management

USGS Publications Warehouse

Sells, Sarah N.; Bassing, Sarah B.; Barker, Kristin J.; Forshee, Shannon C.; Keever, Allison; Goerz, James W.; Mitchell, Michael S.

2018-01-01

Rigorous science that produces reliable knowledge is critical to wildlife management because it increases accurate understanding of the natural world and informs management decisions effectively. Application of a rigorous scientific method based on hypothesis testing minimizes unreliable knowledge produced by research. To evaluate the prevalence of scientific rigor in wildlife research, we examined 24 issues of the Journal of Wildlife Management from August 2013 through July 2016. We found 43.9% of studies did not state or imply a priori hypotheses, which are necessary to produce reliable knowledge. We posit that this is due, at least in part, to a lack of common understanding of what rigorous science entails, how it produces more reliable knowledge than other forms of interpreting observations, and how research should be designed to maximize inferential strength and usefulness of application. Current primary literature does not provide succinct explanations of the logic behind a rigorous scientific method or readily applicable guidance for employing it, particularly in wildlife biology; we therefore synthesized an overview of the history, philosophy, and logic that define scientific rigor for biological studies. A rigorous scientific method includes 1) generating a research question from theory and prior observations, 2) developing hypotheses (i.e., plausible biological answers to the question), 3) formulating predictions (i.e., facts that must be true if the hypothesis is true), 4) designing and implementing research to collect data potentially consistent with predictions, 5) evaluating whether predictions are consistent with collected data, and 6) drawing inferences based on the evaluation. Explicitly testing a priori hypotheses reduces overall uncertainty by reducing the number of plausible biological explanations to only those that are logically well supported. Such research also draws inferences that are robust to idiosyncratic observations and unavoidable human biases. Offering only post hoc interpretations of statistical patterns (i.e., a posteriorihypotheses) adds to uncertainty because it increases the number of plausible biological explanations without determining which have the greatest support. Further, post hocinterpretations are strongly subject to human biases. Testing hypotheses maximizes the credibility of research findings, makes the strongest contributions to theory and management, and improves reproducibility of research. Management decisions based on rigorous research are most likely to result in effective conservation of wildlife resources. 

Modeling framework for representing long-term effectiveness of best management practices in addressing hydrology and water quality problems: Framework development and demonstration using a Bayesian method

NASA Astrophysics Data System (ADS)

Liu, Yaoze; Engel, Bernard A.; Flanagan, Dennis C.; Gitau, Margaret W.; McMillan, Sara K.; Chaubey, Indrajeet; Singh, Shweta

2018-05-01

Best management practices (BMPs) are popular approaches used to improve hydrology and water quality. Uncertainties in BMP effectiveness over time may result in overestimating long-term efficiency in watershed planning strategies. To represent varying long-term BMP effectiveness in hydrologic/water quality models, a high level and forward-looking modeling framework was developed. The components in the framework consist of establishment period efficiency, starting efficiency, efficiency for each storm event, efficiency between maintenance, and efficiency over the life cycle. Combined, they represent long-term efficiency for a specific type of practice and specific environmental concern (runoff/pollutant). An approach for possible implementation of the framework was discussed. The long-term impacts of grass buffer strips (agricultural BMP) and bioretention systems (urban BMP) in reducing total phosphorus were simulated to demonstrate the framework. Data gaps were captured in estimating the long-term performance of the BMPs. A Bayesian method was used to match the simulated distribution of long-term BMP efficiencies with the observed distribution with the assumption that the observed data represented long-term BMP efficiencies. The simulated distribution matched the observed distribution well with only small total predictive uncertainties. With additional data, the same method can be used to further improve the simulation results. The modeling framework and results of this study, which can be adopted in hydrologic/water quality models to better represent long-term BMP effectiveness, can help improve decision support systems for creating long-term stormwater management strategies for watershed management projects.

Harnessing ecosystem models and multi-criteria decision analysis for the support of forest management.

PubMed

Wolfslehner, Bernhard; Seidl, Rupert

2010-12-01

The decision-making environment in forest management (FM) has changed drastically during the last decades. Forest management planning is facing increasing complexity due to a widening portfolio of forest goods and services, a societal demand for a rational, transparent decision process and rising uncertainties concerning future environmental conditions (e.g., climate change). Methodological responses to these challenges include an intensified use of ecosystem models to provide an enriched, quantitative information base for FM planning. Furthermore, multi-criteria methods are increasingly used to amalgamate information, preferences, expert judgments and value expressions, in support of the participatory and communicative dimensions of modern forestry. Although the potential of combining these two approaches has been demonstrated in a number of studies, methodological aspects in interfacing forest ecosystem models (FEM) and multi-criteria decision analysis (MCDA) are scarcely addressed explicitly. In this contribution we review the state of the art in FEM and MCDA in the context of FM planning and highlight some of the crucial issues when combining ecosystem and preference modeling. We discuss issues and requirements in selecting approaches suitable for supporting FM planning problems from the growing body of FEM and MCDA concepts. We furthermore identify two major challenges in a harmonized application of FEM-MCDA: (i) the design and implementation of an indicator-based analysis framework capturing ecological and social aspects and their interactions relevant for the decision process, and (ii) holistic information management that supports consistent use of different information sources, provides meta-information as well as information on uncertainties throughout the planning process.

Harnessing Ecosystem Models and Multi-Criteria Decision Analysis for the Support of Forest Management

NASA Astrophysics Data System (ADS)

Wolfslehner, Bernhard; Seidl, Rupert

2010-12-01

The decision-making environment in forest management (FM) has changed drastically during the last decades. Forest management planning is facing increasing complexity due to a widening portfolio of forest goods and services, a societal demand for a rational, transparent decision process and rising uncertainties concerning future environmental conditions (e.g., climate change). Methodological responses to these challenges include an intensified use of ecosystem models to provide an enriched, quantitative information base for FM planning. Furthermore, multi-criteria methods are increasingly used to amalgamate information, preferences, expert judgments and value expressions, in support of the participatory and communicative dimensions of modern forestry. Although the potential of combining these two approaches has been demonstrated in a number of studies, methodological aspects in interfacing forest ecosystem models (FEM) and multi-criteria decision analysis (MCDA) are scarcely addressed explicitly. In this contribution we review the state of the art in FEM and MCDA in the context of FM planning and highlight some of the crucial issues when combining ecosystem and preference modeling. We discuss issues and requirements in selecting approaches suitable for supporting FM planning problems from the growing body of FEM and MCDA concepts. We furthermore identify two major challenges in a harmonized application of FEM-MCDA: (i) the design and implementation of an indicator-based analysis framework capturing ecological and social aspects and their interactions relevant for the decision process, and (ii) holistic information management that supports consistent use of different information sources, provides meta-information as well as information on uncertainties throughout the planning process.

Evaluation of habitat suitability index models by global sensitivity and uncertainty analyses: a case study for submerged aquatic vegetation

USGS Publications Warehouse

Zajac, Zuzanna; Stith, Bradley M.; Bowling, Andrea C.; Langtimm, Catherine A.; Swain, Eric D.

2015-01-01

Habitat suitability index (HSI) models are commonly used to predict habitat quality and species distributions and are used to develop biological surveys, assess reserve and management priorities, and anticipate possible change under different management or climate change scenarios. Important management decisions may be based on model results, often without a clear understanding of the level of uncertainty associated with model outputs. We present an integrated methodology to assess the propagation of uncertainty from both inputs and structure of the HSI models on model outputs (uncertainty analysis: UA) and relative importance of uncertain model inputs and their interactions on the model output uncertainty (global sensitivity analysis: GSA). We illustrate the GSA/UA framework using simulated hydrology input data from a hydrodynamic model representing sea level changes and HSI models for two species of submerged aquatic vegetation (SAV) in southwest Everglades National Park: Vallisneria americana (tape grass) and Halodule wrightii (shoal grass). We found considerable spatial variation in uncertainty for both species, but distributions of HSI scores still allowed discrimination of sites with good versus poor conditions. Ranking of input parameter sensitivities also varied spatially for both species, with high habitat quality sites showing higher sensitivity to different parameters than low-quality sites. HSI models may be especially useful when species distribution data are unavailable, providing means of exploiting widely available environmental datasets to model past, current, and future habitat conditions. The GSA/UA approach provides a general method for better understanding HSI model dynamics, the spatial and temporal variation in uncertainties, and the parameters that contribute most to model uncertainty. Including an uncertainty and sensitivity analysis in modeling efforts as part of the decision-making framework will result in better-informed, more robust decisions.

A fuzzy stochastic framework for managing hydro-environmental and socio-economic interactions under uncertainty

NASA Astrophysics Data System (ADS)

Subagadis, Yohannes Hagos; Schütze, Niels; Grundmann, Jens

2014-05-01

An amplified interconnectedness between a hydro-environmental and socio-economic system brings about profound challenges of water management decision making. In this contribution, we present a fuzzy stochastic approach to solve a set of decision making problems, which involve hydrologically, environmentally, and socio-economically motivated criteria subjected to uncertainty and ambiguity. The proposed methodological framework combines objective and subjective criteria in a decision making procedure for obtaining an acceptable ranking in water resources management alternatives under different type of uncertainty (subjective/objective) and heterogeneous information (quantitative/qualitative) simultaneously. The first step of the proposed approach involves evaluating the performance of alternatives with respect to different types of criteria. The ratings of alternatives with respect to objective and subjective criteria are evaluated by simulation-based optimization and fuzzy linguistic quantifiers, respectively. Subjective and objective uncertainties related to the input information are handled through linking fuzziness and randomness together. Fuzzy decision making helps entail the linguistic uncertainty and a Monte Carlo simulation process is used to map stochastic uncertainty. With this framework, the overall performance of each alternative is calculated using an Order Weighted Averaging (OWA) aggregation operator accounting for decision makers' experience and opinions. Finally, ranking is achieved by conducting pair-wise comparison of management alternatives. This has been done on the basis of the risk defined by the probability of obtaining an acceptable ranking and mean difference in total performance for the pair of management alternatives. The proposed methodology is tested in a real-world hydrosystem, to find effective and robust intervention strategies for the management of a coastal aquifer system affected by saltwater intrusion due to excessive groundwater extraction for irrigated agriculture and municipal use. The results show that the approach gives useful support for robust decision-making and is sensitive to the decision makers' degree of optimism.

Self-completeness and the generalized uncertainty principle

NASA Astrophysics Data System (ADS)

Isi, Maximiliano; Mureika, Jonas; Nicolini, Piero

2014-03-01

The generalized uncertainty principle discloses a self-complete characteristic of gravity, namely the possibility of masking any curvature singularity behind an event horizon as a result of matter compression at the Planck scale. In this paper we extend the above reasoning in order to overcome some current limitations to the framework, including the absence of a consistent metric describing such Planck-scale black holes. We implement a minimum-size black hole in terms of the extremal configuration of a neutral non-rotating metric, which we derived by mimicking the effects of the generalized uncertainty principle via a short scale modified version of Einstein gravity. In such a way, we find a self- consistent scenario that reconciles the self-complete character of gravity and the generalized uncertainty principle.

Self-completeness and the generalized uncertainty principle

NASA Astrophysics Data System (ADS)

Isi, Maximiliano; Mureika, Jonas; Nicolini, Piero

2013-11-01

The generalized uncertainty principle discloses a self-complete characteristic of gravity, namely the possibility of masking any curvature singularity behind an event horizon as a result of matter compression at the Planck scale. In this paper we extend the above reasoning in order to overcome some current limitations to the framework, including the absence of a consistent metric describing such Planck-scale black holes. We implement a minimum-size black hole in terms of the extremal configuration of a neutral non-rotating metric, which we derived by mimicking the effects of the generalized uncertainty principle via a short scale modified version of Einstein gravity. In such a way, we find a self-consistent scenario that reconciles the self-complete character of gravity and the generalized uncertainty principle.

Adaptive resource management and the value of information

USGS Publications Warehouse

Williams, Byron K.; Eaton, Mitchell J.; Breininger, David R.

2011-01-01

The value of information is a general and broadly applicable concept that has been used for several decades to aid in making decisions in the face of uncertainty. Yet there are relatively few examples of its use in ecology and natural resources management, and almost none that are framed in terms of the future impacts of management decisions. In this paper we discuss the value of information in a context of adaptive management, in which actions are taken sequentially over a timeframe and both future resource conditions and residual uncertainties about resource responses are taken into account. Our objective is to derive the value of reducing or eliminating uncertainty in adaptive decision making. We describe several measures of the value of information, with each based on management objectives that are appropriate for adaptive management. We highlight some mathematical properties of these measures, discuss their geometries, and illustrate them with an example in natural resources management. Accounting for the value of information can help to inform decisions about whether and how much to monitor resource conditions through time.

Adaptive resource management and the value of information

USGS Publications Warehouse

Williams, B.K.; Eaton, M.J.; Breininger, D.R.

2011-01-01

The value of information is a general and broadly applicable concept that has been used for several decades to aid in making decisions in the face of uncertainty. Yet there are relatively few examples of its use in ecology and natural resources management, and almost none that are framed in terms of the future impacts of management decisions. In this paper we discuss the value of information in a context of adaptive management, in which actions are taken sequentially over a timeframe and both future resource conditions and residual uncertainties about resource responses are taken into account. Our objective is to derive the value of reducing or eliminating uncertainty in adaptive decision making. We describe several measures of the value of information, with each based on management objectives that are appropriate for adaptive management. We highlight some mathematical properties of these measures, discuss their geometries, and illustrate them with an example in natural resources management. Accounting for the value of information can help to inform decisions about whether and how much to monitor resource conditions through time. ?? 2011.

Flood resilience and uncertainty in flood risk assessment

NASA Astrophysics Data System (ADS)

Beven, K.; Leedal, D.; Neal, J.; Bates, P.; Hunter, N.; Lamb, R.; Keef, C.

2012-04-01

Flood risk assessments do not normally take account of the uncertainty in assessing flood risk. There is no requirement in the EU Floods Directive to do so. But given the generally short series (and potential non-stationarity) of flood discharges, the extrapolation to smaller exceedance potentials may be highly uncertain. This means that flood risk mapping may also be highly uncertainty, with additional uncertainties introduced by the representation of flood plain and channel geometry, conveyance and infrastructure. This suggests that decisions about flood plain management should be based on exceedance probability of risk rather than the deterministic hazard maps that are common in most EU countries. Some examples are given from 2 case studies in the UK where a framework for good practice in assessing uncertainty in flood risk mapping has been produced as part of the Flood Risk Management Research Consortium and Catchment Change Network Projects. This framework provides a structure for the communication and audit of assumptions about uncertainties.

Model parameter uncertainty analysis for an annual field-scale P loss model

NASA Astrophysics Data System (ADS)

Bolster, Carl H.; Vadas, Peter A.; Boykin, Debbie

2016-08-01

Phosphorous (P) fate and transport models are important tools for developing and evaluating conservation practices aimed at reducing P losses from agricultural fields. Because all models are simplifications of complex systems, there will exist an inherent amount of uncertainty associated with their predictions. It is therefore important that efforts be directed at identifying, quantifying, and communicating the different sources of model uncertainties. In this study, we conducted an uncertainty analysis with the Annual P Loss Estimator (APLE) model. Our analysis included calculating parameter uncertainties and confidence and prediction intervals for five internal regression equations in APLE. We also estimated uncertainties of the model input variables based on values reported in the literature. We then predicted P loss for a suite of fields under different management and climatic conditions while accounting for uncertainties in the model parameters and inputs and compared the relative contributions of these two sources of uncertainty to the overall uncertainty associated with predictions of P loss. Both the overall magnitude of the prediction uncertainties and the relative contributions of the two sources of uncertainty varied depending on management practices and field characteristics. This was due to differences in the number of model input variables and the uncertainties in the regression equations associated with each P loss pathway. Inspection of the uncertainties in the five regression equations brought attention to a previously unrecognized limitation with the equation used to partition surface-applied fertilizer P between leaching and runoff losses. As a result, an alternate equation was identified that provided similar predictions with much less uncertainty. Our results demonstrate how a thorough uncertainty and model residual analysis can be used to identify limitations with a model. Such insight can then be used to guide future data collection and model development and evaluation efforts.

SU-F-T-185: Study of the Robustness of a Proton Arc Technique Based On PBS Beams

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Z; Zheng, Y

Purpose: One potential technique to realize proton arc is through using PBS beams from many directions to form overlaid Bragg peak (OBP) spots and placing these OBP spots throughout the target volume to achieve desired dose distribution. In this study, we analyzed the robustness of this proton arc technique. Methods: We used a cylindrical water phantom of 20 cm in radius in our robustness analysis. To study the range uncertainty effect, we changed the density of the phantom by ±3%. To study the setup uncertainty effect, we shifted the phantom by 3 & 5 mm. We also combined the rangemore » and setup uncertainties (3mm/±3%). For each test plan, we performed dose calculation for the nominal and 6 disturbed scenarios. Two test plans were used, one with single OBP spot and the other consisting of 121 OBP spots covering a 10×10cm{sup 2} area. We compared the dose profiles between the nominal and disturbed scenarios to estimate the impact of the uncertainties. Dose calculation was performed with Gate/GEANT based Monte Carlo software in cloud computing environment. Results: For each of the 7 scenarios, we simulated 100k & 10M events for plans consisting of single OBP spot and 121 OBP spots respectively. For single OBP spot, the setup uncertainty had minimum impact on the spot’s dose profile while range uncertainty had significant impact on the dose profile. For plan consisting of 121 OBP spots, similar effect was observed but the extent of disturbance was much less compared to single OBP spot. Conclusion: For PBS arc technique, range uncertainty has significantly more impact than setup uncertainty. Although single OBP spot can be severely disturbed by the range uncertainty, the overall effect is much less when a large number of OBP spots are used. Robustness optimization for PBS arc technique should consider range uncertainty with priority.« less

Managing the risks of extreme events and disasters to advance climate change adaptation. Special report of the Intergovernmental Panel on Climate Change (IPCC)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Field, C.B.; Barros, V.; Stocker, T.F.

2012-07-01

This Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX) has been jointly coordinated by Working Groups I (WGI) and II (WGII) of the Intergovernmental Panel on Climate Change (IPCC). The report focuses on the relationship between climate change and extreme weather and climate events, the impacts of such events, and the strategies to manage the associated risks. This Special Report, in particular, contributes to frame the challenge of dealing with extreme weather and climate events as an issue in decision making under uncertainty, analyzing response in the context of risk management.more » The report consists of nine chapters, covering risk management; observed and projected changes in extreme weather and climate events; exposure and vulnerability to as well as losses resulting from such events; adaptation options from the local to the international scale; the role of sustainable development in modulating risks; and insights from specific case studies. (LN)« less

Rapid research and implementation priority setting for wound care uncertainties.

PubMed

Gray, Trish A; Dumville, Jo C; Christie, Janice; Cullum, Nicky A

2017-01-01

People with complex wounds are more likely to be elderly, living with multimorbidity and wound related symptoms. A variety of products are available for managing complex wounds and a range of healthcare professionals are involved in wound care, yet there is a lack of good evidence to guide practice and services. These factors create uncertainty for those who deliver and those who manage wound care. Formal priority setting for research and implementation topics is needed to more accurately target the gaps in treatment and services. We solicited practitioner and manager uncertainties in wound care and held a priority setting workshop to facilitate a collaborative approach to prioritising wound care-related uncertainties. We recruited healthcare professionals who regularly cared for patients with complex wounds, were wound care specialists or managed wound care services. Participants submitted up to five wound care uncertainties in consultation with their colleagues, via an on-line survey and attended a priority setting workshop. Submitted uncertainties were collated, sorted and categorised according professional group. On the day of the workshop, participants were divided into four groups depending on their profession. Uncertainties submitted by their professional group were viewed, discussed and amended, prior to the first of three individual voting rounds. Participants cast up to ten votes for the uncertainties they judged as being high priority. Continuing in the professional groups, the top 10 uncertainties from each group were displayed, and the process was repeated. Groups were then brought together for a plenary session in which the final priorities were individually scored on a scale of 0-10 by participants. Priorities were ranked and results presented. Nominal group technique was used for generating the final uncertainties, voting and discussions. Thirty-three participants attended the workshop comprising; 10 specialist nurses, 10 district nurses, seven podiatrists and six managers. Participants had been qualified for a mean of 20.7 years with a mean of 16.8 years of wound care experience. One hundred and thirty-nine uncertainties were submitted electronically and a further 20 were identified on the day of the workshop following lively, interactive group discussions. Twenty-five uncertainties from the total of 159 generated made it to the final prioritised list. These included six of the 20 new uncertainties. The uncertainties varied in focus, but could be broadly categorised into three themes: service delivery and organisation, patient centred care and treatment options. Specialist nurses were more likely to vote for service delivery and organisation topics, podiatrists for patient centred topics, district nurses for treatment options and operational leads for a broad range. This collaborative priority setting project is the first to engage front-line clinicians in prioritising research and implementation topics in wound care. We have shown that it is feasible to conduct topic prioritisation in a short time frame. This project has demonstrated that with careful planning and rigor, important questions that are raised in the course of clinicians' daily decision making can be translated into meaningful research and implementation initiatives that could make a difference to service delivery and patient care.

Rapid research and implementation priority setting for wound care uncertainties

PubMed Central

Dumville, Jo C.; Christie, Janice; Cullum, Nicky A.

2017-01-01

Introduction People with complex wounds are more likely to be elderly, living with multimorbidity and wound related symptoms. A variety of products are available for managing complex wounds and a range of healthcare professionals are involved in wound care, yet there is a lack of good evidence to guide practice and services. These factors create uncertainty for those who deliver and those who manage wound care. Formal priority setting for research and implementation topics is needed to more accurately target the gaps in treatment and services. We solicited practitioner and manager uncertainties in wound care and held a priority setting workshop to facilitate a collaborative approach to prioritising wound care-related uncertainties. Methods We recruited healthcare professionals who regularly cared for patients with complex wounds, were wound care specialists or managed wound care services. Participants submitted up to five wound care uncertainties in consultation with their colleagues, via an on-line survey and attended a priority setting workshop. Submitted uncertainties were collated, sorted and categorised according professional group. On the day of the workshop, participants were divided into four groups depending on their profession. Uncertainties submitted by their professional group were viewed, discussed and amended, prior to the first of three individual voting rounds. Participants cast up to ten votes for the uncertainties they judged as being high priority. Continuing in the professional groups, the top 10 uncertainties from each group were displayed, and the process was repeated. Groups were then brought together for a plenary session in which the final priorities were individually scored on a scale of 0–10 by participants. Priorities were ranked and results presented. Nominal group technique was used for generating the final uncertainties, voting and discussions. Results Thirty-three participants attended the workshop comprising; 10 specialist nurses, 10 district nurses, seven podiatrists and six managers. Participants had been qualified for a mean of 20.7 years with a mean of 16.8 years of wound care experience. One hundred and thirty-nine uncertainties were submitted electronically and a further 20 were identified on the day of the workshop following lively, interactive group discussions. Twenty-five uncertainties from the total of 159 generated made it to the final prioritised list. These included six of the 20 new uncertainties. The uncertainties varied in focus, but could be broadly categorised into three themes: service delivery and organisation, patient centred care and treatment options. Specialist nurses were more likely to vote for service delivery and organisation topics, podiatrists for patient centred topics, district nurses for treatment options and operational leads for a broad range. Conclusions This collaborative priority setting project is the first to engage front-line clinicians in prioritising research and implementation topics in wound care. We have shown that it is feasible to conduct topic prioritisation in a short time frame. This project has demonstrated that with careful planning and rigor, important questions that are raised in the course of clinicians’ daily decision making can be translated into meaningful research and implementation initiatives that could make a difference to service delivery and patient care. PMID:29206884

Mapping marine habitat suitability and uncertainty of Bayesian networks: a case study using Pacific benthic macrofauna

Treesearch

Andrea Havron; Chris Goldfinger; Sarah Henkel; Bruce G. Marcot; Chris Romsos; Lisa Gilbane

2017-01-01

Resource managers increasingly use habitat suitability map products to inform risk management and policy decisions. Modeling habitat suitability of data-poor species over large areas requires careful attention to assumptions and limitations. Resulting habitat suitability maps can harbor uncertainties from data collection and modeling processes; yet these limitations...

Decision making under uncertainty: Recommendations for the Wildland Fire Decision Support System (WFDSS)

Treesearch

Matthew P. Thompson

2015-01-01

The management of wildfire is a dynamic, complex, and fundamentally uncertain enterprise. Fire managers face uncertainties regarding fire weather and subsequent influence on fire behavior, the effects of fire on socioeconomic and ecological resources, and the efficacy of alternative suppression actions on fire outcomes. In these types of difficult decision environments...

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kastenberg, W.E.; Apostolakis, G.; Dhir, V.K.

Severe accident management can be defined as the use of existing and/or altemative resources, systems and actors to prevent or mitigate a core-melt accident. For each accident sequence and each combination of severe accident management strategies, there may be several options available to the operator, and each involves phenomenological and operational considerations regarding uncertainty. Operational uncertainties include operator, system and instrumentation behavior during an accident. A framework based on decision trees and influence diagrams has been developed which incorporates such criteria as feasibility, effectiveness, and adverse effects, for evaluating potential severe accident management strategies. The framework is also capable ofmore » propagating both data and model uncertainty. It is applied to several potential strategies including PWR cavity flooding, BWR drywell flooding, PWR depressurization and PWR feed and bleed.« less

Markov random fields and graphs for uncertainty management and symbolic data fusion in an urban scene interpretation

NASA Astrophysics Data System (ADS)

Moissinac, Henri; Maitre, Henri; Bloch, Isabelle

1995-11-01

An image interpretation method is presented for the automatic processing of aerial pictures of a urban landscape. In order to improve the picture analysis, some a priori knowledge extracted from a geographic map is introduced. A coherent graph-based model of the city is built, starting with the road network. A global uncertainty management scheme has been designed in order to evaluate the final confidence we can have in the final results. This model and the uncertainty management tend to reflect the hierarchy of the available data and the interpretation levels. The symbolic relationships linking the different kinds of elements are taken into account while propagating and combining the confidence measures along the interpretation process.

Barriers and Bridges to the Integration of Social-ecological Resilience and Law

EPA Science Inventory

There is a fundamental rift between how ecologists and lawyers view uncertainty. In the study of ecology, uncertainty is a catalyst for exploration. However, uncertainty is antithetical to the rule of law. This issue is particularly troubling in environmental management, where th...

A new statistic to express the uncertainty of kriging predictions for purposes of survey planning.

NASA Astrophysics Data System (ADS)

Lark, R. M.; Lapworth, D. J.

2014-05-01

It is well-known that one advantage of kriging for spatial prediction is that, given the random effects model, the prediction error variance can be computed a priori for alternative sampling designs. This allows one to compare sampling schemes, in particular sampling at different densities, and so to decide on one which meets requirements in terms of the uncertainty of the resulting predictions. However, the planning of sampling schemes must account not only for statistical considerations, but also logistics and cost. This requires effective communication between statisticians, soil scientists and data users/sponsors such as managers, regulators or civil servants. In our experience the latter parties are not necessarily able to interpret the prediction error variance as a measure of uncertainty for decision making. In some contexts (particularly the solution of very specific problems at large cartographic scales, e.g. site remediation and precision farming) it is possible to translate uncertainty of predictions into a loss function directly comparable with the cost incurred in increasing precision. Often, however, sampling must be planned for more generic purposes (e.g. baseline or exploratory geochemical surveys). In this latter context the prediction error variance may be of limited value to a non-statistician who has to make a decision on sample intensity and associated cost. We propose an alternative criterion for these circumstances to aid communication between statisticians and data users about the uncertainty of geostatistical surveys based on different sampling intensities. The criterion is the consistency of estimates made from two non-coincident instantiations of a proposed sample design. We consider square sample grids, one instantiation is offset from the second by half the grid spacing along the rows and along the columns. If a sample grid is coarse relative to the important scales of variation in the target property then the consistency of predictions from two instantiations is expected to be small, and can be increased by reducing the grid spacing. The measure of consistency is the correlation between estimates from the two instantiations of the sample grid, averaged over a grid cell. We call this the offset correlation, it can be calculated from the variogram. We propose that this measure is easier to grasp intuitively than the prediction error variance, and has the advantage of having an upper bound (1.0) which will aid its interpretation. This quality measure is illustrated for some hypothetical examples, considering both ordinary kriging and factorial kriging of the variable of interest. It is also illustrated using data on metal concentrations in the soil of north-east England.

Combined effects of uncertainty and organizational justice on employee health: testing the uncertainty management model of fairness judgments among Finnish public sector employees.

PubMed

Elovainio, Marko; van den Bos, Kees; Linna, Anne; Kivimäki, Mika; Ala-Mursula, Leena; Pentti, Jaana; Vahtera, Jussi

2005-12-01

We examined whether the combination of uncertainty (lack of work-time control, and negative changes at work) and organizational justice (i.e., justice of decision-making procedures and interpersonal treatment at work) contributes to sickness absence. A total of 7083 male and 24,317 female Finnish public sector employees completed questionnaires designed to assess organizational justice, workload and other factors. Hierarchical regression showed that after adjustment for age, income, and health behaviors low procedural and interactional justice were related to long sickness absence spells. In accordance with the uncertainty management model, these associations were dependent on experienced work-time control and perceived changes at work.

Uncertainty and control in the context of a category-five tornado.

PubMed

Afifi, Walid A; Afifi, Tamara D; Merrill, Annie

2014-10-01

The purpose of this qualitative descriptive study was to illuminate the experience and management of uncertainty during a natural disaster. Interviews were conducted with 26 survivors of a category-five tornado that entirely demolished the small, rural town of Greensburg, Kansas. Three primary themes were found in the survivors' accounts. First, the survivors experienced rapidly shifting levels and kinds of uncertainty as they proceeded through the stages of the disaster. Second, the fluidity of much-needed information added to uncertainty. Third, the feeling of lack of control over outcomes of the disaster and its aftermath was pervasive and was often managed through reliance on communal coping. Recommendations for disaster-related intervention programs are suggested. © 2014 Wiley Periodicals, Inc.

Experiences of Uncertainty in Men With an Elevated PSA

PubMed Central

Biddle, Caitlin; Brasel, Alicia; Underwood, Willie; Orom, Heather

2016-01-01

A significant proportion of men, ages 50 to 70 years, have, and continue to receive prostate specific antigen (PSA) tests to screen for prostate cancer (PCa). Approximately 70% of men with an elevated PSA level will not subsequently be diagnosed with PCa. Semistructured interviews were conducted with 13 men with an elevated PSA level who had not been diagnosed with PCa. Uncertainty was prominent in men’s reactions to the PSA results, stemming from unanswered questions about the PSA test, PCa risk, and confusion about their management plan. Uncertainty was exacerbated or reduced depending on whether health care providers communicated in lay and empathetic ways, and provided opportunities for question asking. To manage uncertainty, men engaged in information and health care seeking, self-monitoring, and defensive cognition. Results inform strategies for meeting informational needs of men with an elevated PSA and confirm the primary importance of physician communication behavior for open information exchange and uncertainty reduction. PMID:25979635

Designing Flood Management Systems for Joint Economic and Ecological Robustness

NASA Astrophysics Data System (ADS)

Spence, C. M.; Grantham, T.; Brown, C. M.; Poff, N. L.

2015-12-01

Freshwater ecosystems across the United States are threatened by hydrologic change caused by water management operations and non-stationary climate trends. Nonstationary hydrology also threatens flood management systems' performance. Ecosystem managers and flood risk managers need tools to design systems that achieve flood risk reduction objectives while sustaining ecosystem functions and services in an uncertain hydrologic future. Robust optimization is used in water resources engineering to guide system design under climate change uncertainty. Using principles introduced by Eco-Engineering Decision Scaling (EEDS), we extend robust optimization techniques to design flood management systems that meet both economic and ecological goals simultaneously across a broad range of future climate conditions. We use three alternative robustness indices to identify flood risk management solutions that preserve critical ecosystem functions in a case study from the Iowa River, where recent severe flooding has tested the limits of the existing flood management system. We seek design modifications to the system that both reduce expected cost of flood damage while increasing ecologically beneficial inundation of riparian floodplains across a wide range of plausible climate futures. The first robustness index measures robustness as the fraction of potential climate scenarios in which both engineering and ecological performance goals are met, implicitly weighting each climate scenario equally. The second index builds on the first by using climate projections to weight each climate scenario, prioritizing acceptable performance in climate scenarios most consistent with climate projections. The last index measures robustness as mean performance across all climate scenarios, but penalizes scenarios with worse performance than average, rewarding consistency. Results stemming from alternate robustness indices reflect implicit assumptions about attitudes toward risk and reveal the tradeoffs between using structural and non-structural flood management strategies to ensure economic and ecological robustness.

Can we reliably estimate managed forest carbon dynamics using remotely sensed data?

NASA Astrophysics Data System (ADS)

Smallman, Thomas Luke; Exbrayat, Jean-Francois; Bloom, A. Anthony; Williams, Mathew

2015-04-01

Forests are an important part of the global carbon cycle, serving as both a large store of carbon and currently as a net sink of CO2. Forest biomass varies significantly in time and space, linked to climate, soils, natural disturbance and human impacts. This variation means that the global distribution of forest biomass and their dynamics are poorly quantified. Terrestrial ecosystem models (TEMs) are rarely evaluated for their predictions of forest carbon stocks and dynamics, due to a lack of knowledge on site specific factors such as disturbance dates and / or managed interventions. In this regard, managed forests present a valuable opportunity for model calibration and improvement. Spatially explicit datasets of planting dates, species and yield classification, in combination with remote sensing data and an appropriate data assimilation (DA) framework can reduce prediction uncertainty and error. We use a Baysian approach to calibrate the data assimilation linked ecosystem carbon (DALEC) model using a Metropolis Hastings-Markov Chain Monte Carlo (MH-MCMC) framework. Forest management information is incorporated into the data assimilation framework as part of ecological and dynamic constraints (EDCs). The key advantage here is that DALEC simulates a full carbon balance, not just the living biomass, and that both parameter and prediction uncertainties are estimated as part of the DA analysis. DALEC has been calibrated at two managed forests, in the USA (Pinus taeda; Duke Forest) and UK (Picea sitchensis; Griffin Forest). At each site DALEC is calibrated twice (exp1 & exp2). Both calibrations (exp1 & exp2) assimilated MODIS LAI and HWSD estimates of soil carbon stored in soil organic matter, in addition to common management information and prior knowledge included in parameter priors and the EDCs. Calibration exp1 also utilises multiple site level estimates of carbon storage in multiple pools. By comparing simulations we determine the impact of site-level observations on uncertainty and error on predictions, and which observations are key to constraining ecosystem processes. Preliminary simulations indicate that DALEC calibration exp1 accurately simulated the assimilated observations for forest and soil carbon stock estimates including, critically for forestry, standing wood stocks (R2 = 0.92, bias = -4.46 MgC ha-1, RMSE = 5.80 MgC ha-1). The results from exp1 indicate the model is able to find parameters that are both consistent with EDC and observations. In the absence of site-level stock observations (exp2) DALEC accurately estimates foliage and fine root pools, while the median estimate of above ground litter and wood stocks (R2 = 0.92, bias = -48.30 MgC ha-1, RMSE = 50.30 MgC ha-1) are over- and underestimated respectively, site-level observations are within model uncertainty. These results indicate that we can estimate managed forests dynamics using remotely sensed data, particularly as remotely sensed above ground biomass maps become available to provide constraint to correct biases in woody accumulation.

An integrated modeling approach to support management decisions of coupled groundwater-agricultural systems under multiple uncertainties

NASA Astrophysics Data System (ADS)

Hagos Subagadis, Yohannes; Schütze, Niels; Grundmann, Jens

2015-04-01

The planning and implementation of effective water resources management strategies need an assessment of multiple (physical, environmental, and socio-economic) issues, and often requires new research in which knowledge of diverse disciplines are combined in a unified methodological and operational frameworks. Such integrative research to link different knowledge domains faces several practical challenges. Such complexities are further compounded by multiple actors frequently with conflicting interests and multiple uncertainties about the consequences of potential management decisions. A fuzzy-stochastic multiple criteria decision analysis tool was developed in this study to systematically quantify both probabilistic and fuzzy uncertainties associated with complex hydrosystems management. It integrated physical process-based models, fuzzy logic, expert involvement and stochastic simulation within a general framework. Subsequently, the proposed new approach is applied to a water-scarce coastal arid region water management problem in northern Oman, where saltwater intrusion into a coastal aquifer due to excessive groundwater extraction for irrigated agriculture has affected the aquifer sustainability, endangering associated socio-economic conditions as well as traditional social structure. Results from the developed method have provided key decision alternatives which can serve as a platform for negotiation and further exploration. In addition, this approach has enabled to systematically quantify both probabilistic and fuzzy uncertainties associated with the decision problem. Sensitivity analysis applied within the developed tool has shown that the decision makers' risk aversion and risk taking attitude may yield in different ranking of decision alternatives. The developed approach can be applied to address the complexities and uncertainties inherent in water resources systems to support management decisions, while serving as a platform for stakeholder participation.

On the role of budget sufficiency, cost efficiency, and uncertainty in species management

USGS Publications Warehouse

van der Burg, Max Post; Bly, Bartholomew B.; Vercauteren, Tammy; Grand, James B.; Tyre, Andrew J.

2014-01-01

Many conservation planning frameworks rely on the assumption that one should prioritize locations for management actions based on the highest predicted conservation value (i.e., abundance, occupancy). This strategy may underperform relative to the expected outcome if one is working with a limited budget or the predicted responses are uncertain. Yet, cost and tolerance to uncertainty rarely become part of species management plans. We used field data and predictive models to simulate a decision problem involving western burrowing owls (Athene cunicularia hypugaea) using prairie dog colonies (Cynomys ludovicianus) in western Nebraska. We considered 2 species management strategies: one maximized abundance and the other maximized abundance in a cost-efficient way. We then used heuristic decision algorithms to compare the 2 strategies in terms of how well they met a hypothetical conservation objective. Finally, we performed an info-gap decision analysis to determine how these strategies performed under different budget constraints and uncertainty about owl response. Our results suggested that when budgets were sufficient to manage all sites, the maximizing strategy was optimal and suggested investing more in expensive actions. This pattern persisted for restricted budgets up to approximately 50% of the sufficient budget. Below this budget, the cost-efficient strategy was optimal and suggested investing in cheaper actions. When uncertainty in the expected responses was introduced, the strategy that maximized abundance remained robust under a sufficient budget. Reducing the budget induced a slight trade-off between expected performance and robustness, which suggested that the most robust strategy depended both on one's budget and tolerance to uncertainty. Our results suggest that wildlife managers should explicitly account for budget limitations and be realistic about their expected levels of performance.

Multi-year assessment of soil-vegetation-atmosphere transfer (SVAT) modeling uncertainties over a Mediterranean agricultural site

NASA Astrophysics Data System (ADS)

Garrigues, S.; Olioso, A.; Calvet, J.-C.; Lafont, S.; Martin, E.; Chanzy, A.; Marloie, O.; Bertrand, N.; Desfonds, V.; Renard, D.

2012-04-01

Vegetation productivity and water balance of Mediterranean regions will be particularly affected by climate and land-use changes. In order to analyze and predict these changes through land surface models, a critical step is to quantify the uncertainties associated with these models (processes, parameters) and their implementation over a long period of time. Besides, uncertainties attached to the data used to force these models (atmospheric forcing, vegetation and soil characteristics, crop management practices...) which are generally available at coarse spatial resolution (>1-10 km) and for a limited number of plant functional types, need to be evaluated. This paper aims at assessing the uncertainties in water (evapotranspiration) and energy fluxes estimated from a Soil Vegetation Atmosphere Transfer (SVAT) model over a Mediterranean agricultural site. While similar past studies focused on particular crop types and limited period of time, the originality of this paper consists in implementing the SVAT model and assessing its uncertainties over a long period of time (10 years), encompassing several cycles of distinct crops (wheat, sorghum, sunflower, peas). The impacts on the SVAT simulations of the following sources of uncertainties are characterized: - Uncertainties in atmospheric forcing are assessed comparing simulations forced with local meteorological measurements and simulations forced with re-analysis atmospheric dataset (SAFRAN database). - Uncertainties in key surface characteristics (soil, vegetation, crop management practises) are tested comparing simulations feeded with standard values from global database (e.g. ECOCLIMAP) and simulations based on in situ or site-calibrated values. - Uncertainties dues to the implementation of the SVAT model over a long period of time are analyzed with regards to crop rotation. The SVAT model being analyzed in this paper is ISBA in its a-gs version which simulates the photosynthesis and its coupling with the stomata conductance, as well as the time course of the plant biomass and the Leaf Area Index (LAI). The experiment was conducted at the INRA-Avignon (France) crop site (ICOS associated site), for which 10 years of energy and water eddy fluxes, soil moisture profiles, vegetation measurements, agricultural practises are available for distinct crop types. The uncertainties in evapotranspiration and energy flux estimates are quantified from both 10-year trend analysis and selected daily cycles spanning a range of atmospheric conditions and phenological stages. While the net radiation flux is correctly simulated, the cumulated latent heat flux is under-estimated. Daily plots indicate i) an overestimation of evapotranspiration over bare soil probably due to an overestimation of the soil water reservoir available for evaporation and ii) an under-estimation of transpiration for developed canopy. Uncertainties attached to the re-analysis atmospheric data show little influence on the cumulated values of evapotranspiration. Better performances are reached using in situ soil depths and site-calibrated photosynthesis parameters compared to the simulations based on the ECOCLIMAP standard values. Finally, this paper highlights the impact of the temporal succession of vegetation cover and bare soil on the simulation of soil moisture and evapotranspiration over a long period of time. Thus, solutions to account for crop rotation in the implementation of SVAT models are discussed.

Models of science-policy interaction: exploring approaches to Bisphenol A management in the EU.

PubMed

Udovyk, O

2014-07-01

This study investigated science-policy interaction models and their limitations under conditions of uncertainty. In detail, it looked at the management of the suspected endocrine-disrupting chemical Bisphenol A (BPA). Despite growing evidence that BPA is hazardous to human and environmental health, the level of scientific uncertainty is still high and, as a result, there is significant disagreement on the actual extent and type of risk. Analysis of decision-making processes at different regulatory levels (EU, Sweden, and the Swedish municipality of Gothenburg) exposed chemicals risk management and associated science-policy interaction under uncertainty. The results of the study show that chemicals management and associated science-policy interaction follow the modern model of science-policy interaction, where science is assumed to 'speak truth to policy' and highlights existing limitations of this model under conditions of uncertainty. The study not only explores alternative models (precautionary, consensus, science-policy demarcation. and extended participation) but also shows their limitations. The study concludes that all models come with their particular underlying assumptions, strengths, and limitations. At the same time, by exposing serious limitations of the modern model, the study calls for a rethinking of the relationship between science, policy, and management. Copyright © 2014 Elsevier B.V. All rights reserved.

DOE-EPSCOR SPONSORED PROJECT FINAL REPORT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhu, Jianting

Concern over the quality of environmental management and restoration has motivated the model development for predicting water and solute transport in the vadose zone. Soil hydraulic properties are required inputs to subsurface models of water flow and contaminant transport in the vadose zone. Computer models are now routinely used in research and management to predict the movement of water and solutes into and through the vadose zone of soils. Such models can be used successfully only if reliable estimates of the soil hydraulic parameters are available. The hydraulic parameters considered in this project consist of the saturated hydraulic conductivity andmore » four parameters of the water retention curves. To quantify hydraulic parameters for heterogeneous soils is both difficult and time consuming. The overall objective of this project was to better quantify soil hydraulic parameters which are critical in predicting water flows and contaminant transport in the vadose zone through a comprehensive and quantitative study to predict heterogeneous soil hydraulic properties and the associated uncertainties. Systematic and quantitative consideration of the parametric heterogeneity and uncertainty can properly address and further reduce predictive uncertainty for contamination characterization and environmental restoration at DOE-managed sites. We conducted a comprehensive study to assess soil hydraulic parameter heterogeneity and uncertainty. We have addressed a number of important issues related to the soil hydraulic property characterizations. The main focus centered on new methods to characterize anisotropy of unsaturated hydraulic property typical of layered soil formations, uncertainty updating method, and artificial neural network base pedo-transfer functions to predict hydraulic parameters from easily available data. The work also involved upscaling of hydraulic properties applicable to large scale flow and contaminant transport modeling in the vadose zone and geostatistical characterization of hydraulic parameter heterogeneity. The project also examined the validity of the some simple average schemes for unsaturated hydraulic properties widely used in previous studies. A new suite of pedo-transfer functions were developed to improve the predictability of hydraulic parameters. We also explored the concept of tension-dependent hydraulic conductivity anisotropy of unsaturated layered soils. This project strengthens collaboration between researchers at the Desert Research Institute in the EPSCoR State of Nevada and their colleagues at the Pacific Northwest National Laboratory. The results of numerical simulations of a field injection experiment at Hanford site in this project could be used to provide insights to the DOE mission of appropriate contamination characterization and environmental remediation.« less

Implementation uncertainty when using recreational hunting to manage carnivores

PubMed Central

Bischof, Richard; Nilsen, Erlend B; Brøseth, Henrik; Männil, Peep; Ozoliņš, Jaānis; Linnell, John D C; Bode, Michael

2012-01-01

1. Wildlife managers often rely on resource users, such as recreational or commercial hunters, to achieve management goals. The use of hunters to control wildlife populations is especially common for predators and ungulates, but managers cannot assume that hunters will always fill annual quotas set by the authorities. It has been advocated that resource management models should account for uncertainty in how harvest rules are realized, requiring that this implementation uncertainty be estimated. 2. We used a survival analysis framework and long-term harvest data from large carnivore management systems in three countries (Estonia, Latvia and Norway) involving four species (brown bear, grey wolf, Eurasian lynx and wolverine) to estimate the performance of hunters with respect to harvest goals set by managers. 3. Variation in hunter quota-filling performance was substantial, ranging from 40% for wolverine in Norway to nearly 100% for lynx in Latvia. Seasonal and regional variation was also high within country–species pairs. We detected a positive relationship between the instantaneous potential to fill a quota slot and the relative availability of the target species for both wolverine and lynx in Norway. 4. Survivor curves and hazards – with survival time measured as the time from the start of a season until a quota slot is filled – can indicate the extent to which managers can influence harvest through adjustments of season duration and quota limits. 5. Synthesis and applications. We investigated seven systems where authorities use recreational hunting to manage large carnivore populations. The variation and magnitude of deviation from harvest goals was substantial, underlining the need to incorporate implementation uncertainty into resource management models and decisions-making. We illustrate how survival analysis can be used by managers to estimate the performance of resource users with respect to achieving harvest goals set by managers. The findings in this study come at an opportune time given the growing popularity of management strategy evaluation (MSE) models in fisheries and a push towards incorporating MSE into terrestrial harvest management. PMID:23197878

Validity of Willingness to Pay Measures under Preference Uncertainty.

PubMed

Braun, Carola; Rehdanz, Katrin; Schmidt, Ulrich

2016-01-01

Recent studies in the marketing literature developed a new method for eliciting willingness to pay (WTP) with an open-ended elicitation format: the Range-WTP method. In contrast to the traditional approach of eliciting WTP as a single value (Point-WTP), Range-WTP explicitly allows for preference uncertainty in responses. The aim of this paper is to apply Range-WTP to the domain of contingent valuation and to test for its theoretical validity and robustness in comparison to the Point-WTP. Using data from two novel large-scale surveys on the perception of solar radiation management (SRM), a little-known technique for counteracting climate change, we compare the performance of both methods in the field. In addition to the theoretical validity (i.e. the degree to which WTP values are consistent with theoretical expectations), we analyse the test-retest reliability and stability of our results over time. Our evidence suggests that the Range-WTP method clearly outperforms the Point-WTP method.

US Power Production at Risk from Water Stress in a Changing Climate.

PubMed

Ganguli, Poulomi; Kumar, Devashish; Ganguly, Auroop R

2017-09-20

Thermoelectric power production in the United States primarily relies on wet-cooled plants, which in turn require water below prescribed design temperatures, both for cooling and operational efficiency. Thus, power production in US remains particularly vulnerable to water scarcity and rising stream temperatures under climate change and variability. Previous studies on the climate-water-energy nexus have primarily focused on mid- to end-century horizons and have not considered the full range of uncertainty in climate projections. Technology managers and energy policy makers are increasingly interested in the decadal time scales to understand adaptation challenges and investment strategies. Here we develop a new approach that relies on a novel multivariate water stress index, which considers the joint probability of warmer and scarcer water, and computes uncertainties arising from climate model imperfections and intrinsic variability. Our assessments over contiguous US suggest consistent increase in water stress for power production with about 27% of the production severely impacted by 2030s.

Validity of Willingness to Pay Measures under Preference Uncertainty

PubMed Central

Braun, Carola; Rehdanz, Katrin; Schmidt, Ulrich

2016-01-01

Recent studies in the marketing literature developed a new method for eliciting willingness to pay (WTP) with an open-ended elicitation format: the Range-WTP method. In contrast to the traditional approach of eliciting WTP as a single value (Point-WTP), Range-WTP explicitly allows for preference uncertainty in responses. The aim of this paper is to apply Range-WTP to the domain of contingent valuation and to test for its theoretical validity and robustness in comparison to the Point-WTP. Using data from two novel large-scale surveys on the perception of solar radiation management (SRM), a little-known technique for counteracting climate change, we compare the performance of both methods in the field. In addition to the theoretical validity (i.e. the degree to which WTP values are consistent with theoretical expectations), we analyse the test-retest reliability and stability of our results over time. Our evidence suggests that the Range-WTP method clearly outperforms the Point-WTP method. PMID:27096163

A Survey of Recent Advances in Particle Filters and Remaining Challenges for Multitarget Tracking

PubMed Central

Wang, Xuedong; Sun, Shudong; Corchado, Juan M.

2017-01-01

We review some advances of the particle filtering (PF) algorithm that have been achieved in the last decade in the context of target tracking, with regard to either a single target or multiple targets in the presence of false or missing data. The first part of our review is on remarkable achievements that have been made for the single-target PF from several aspects including importance proposal, computing efficiency, particle degeneracy/impoverishment and constrained/multi-modal systems. The second part of our review is on analyzing the intractable challenges raised within the general multitarget (multi-sensor) tracking due to random target birth and termination, false alarm, misdetection, measurement-to-track (M2T) uncertainty and track uncertainty. The mainstream multitarget PF approaches consist of two main classes, one based on M2T association approaches and the other not such as the finite set statistics-based PF. In either case, significant challenges remain due to unknown tracking scenarios and integrated tracking management. PMID:29168772

Benefits of the uncertainty management intervention for African American and White older breast cancer survivors: 20-month outcomes.

PubMed

Gil, Karen M; Mishel, Merle H; Belyea, Michael; Germino, Barbara; Porter, Laura S; Clayton, Margaret

2006-01-01

In a 2 x 2 randomized block repeated measure design, this study evaluated the follow-up efficacy of the uncertainty management intervention at 20 months. The sample included 483 recurrence-free women (342 White, 141 African American women; mean age = 64 years) who were 5-9 years posttreatment for breast cancer. Women were randomly assigned to either the intervention or usual care control condition. The intervention was delivered during 4 weekly telephone sessions in which survivors were guided in the use of audiotaped cognitive-behavioral strategies and a self-help manual. Repeated measures MANOVAs evaluating treatment group, ethnic group, and treatment by ethnic interaction effects at 20 months indicated that training in uncertainty management resulted in improvements in cognitive reframing, cancer knowledge, and a variety of coping skills. Importantly, the 20-month outcomes also demonstrated benefits for women in the intervention condition in terms of declines in illness uncertainty and stable effects in personal growth over time.

Uncertainty, learning, and the optimal management of wildlife

USGS Publications Warehouse

Williams, B.K.

2001-01-01

Wildlife management is limited by uncontrolled and often unrecognized environmental variation, by limited capabilities to observe and control animal populations, and by a lack of understanding about the biological processes driving population dynamics. In this paper I describe a comprehensive framework for management that includes multiple models and likelihood values to account for structural uncertainty, along with stochastic factors to account for environmental variation, random sampling, and partial controllability. Adaptive optimization is developed in terms of the optimal control of incompletely understood populations, with the expected value of perfect information measuring the potential for improving control through learning. The framework for optimal adaptive control is generalized by including partial observability and non-adaptive, sample-based updating of model likelihoods. Passive adaptive management is derived as a special case of constrained adaptive optimization, representing a potentially efficient suboptimal alternative that nonetheless accounts for structural uncertainty.

Unconscious vigilance: worldview defense without adaptations for terror, coalition, or uncertainty management.

PubMed

Holbrook, Colin; Sousa, Paulo; Hahn-Holbrook, Jennifer

2011-09-01

Individuals subtly reminded of death, coalitional challenges, or feelings of uncertainty display exaggerated preferences for affirmations and against criticisms of their cultural in-groups. Terror management, coalitional psychology, and uncertainty management theories postulate this "worldview defense" effect as the output of mechanisms evolved either to allay the fear of death, foster social support, or reduce anxiety by increasing adherence to cultural values. In 4 studies, we report evidence for an alternative perspective. We argue that worldview defense owes to unconscious vigilance, a state of accentuated reactivity to affective targets (which need not relate to cultural worldviews) that follows detection of subtle alarm cues (which need not pertain to death, coalitional challenges, or uncertainty). In Studies 1 and 2, death-primed participants produced exaggerated ratings of worldview-neutral affective targets. In Studies 3 and 4, subliminal threat manipulations unrelated to death, coalitional challenges, or uncertainty evoked worldview defense. These results are discussed as they inform evolutionary interpretations of worldview defense and future investigations of the influence of unconscious alarm on judgment. (PsycINFO Database Record (c) 2011 APA, all rights reserved). PsycINFO Database Record (c) 2011 APA, all rights reserved.

Adaptive management for ecosystem services.

PubMed

Birgé, Hannah E; Allen, Craig R; Garmestani, Ahjond S; Pope, Kevin L

2016-12-01

Management of natural resources for the production of ecosystem services, which are vital for human well-being, is necessary even when there is uncertainty regarding system response to management action. This uncertainty is the result of incomplete controllability, complex internal feedbacks, and non-linearity that often interferes with desired management outcomes, and insufficient understanding of nature and people. Adaptive management was developed to reduce such uncertainty. We present a framework for the application of adaptive management for ecosystem services that explicitly accounts for cross-scale tradeoffs in the production of ecosystem services. Our framework focuses on identifying key spatiotemporal scales (plot, patch, ecosystem, landscape, and region) that encompass dominant structures and processes in the system, and includes within- and cross-scale dynamics, ecosystem service tradeoffs, and management controllability within and across scales. Resilience theory recognizes that a limited set of ecological processes in a given system regulate ecosystem services, yet our understanding of these processes is poorly understood. If management actions erode or remove these processes, the system may shift into an alternative state unlikely to support the production of desired services. Adaptive management provides a process to assess the underlying within and cross-scale tradeoffs associated with production of ecosystem services while proceeding with management designed to meet the demands of a growing human population. Copyright © 2016 Elsevier Ltd. All rights reserved.

Not Normal: the uncertainties of scientific measurements

NASA Astrophysics Data System (ADS)

Bailey, David C.

2017-01-01

Judging the significance and reproducibility of quantitative research requires a good understanding of relevant uncertainties, but it is often unclear how well these have been evaluated and what they imply. Reported scientific uncertainties were studied by analysing 41 000 measurements of 3200 quantities from medicine, nuclear and particle physics, and interlaboratory comparisons ranging from chemistry to toxicology. Outliers are common, with 5σ disagreements up to five orders of magnitude more frequent than naively expected. Uncertainty-normalized differences between multiple measurements of the same quantity are consistent with heavy-tailed Student's t-distributions that are often almost Cauchy, far from a Gaussian Normal bell curve. Medical research uncertainties are generally as well evaluated as those in physics, but physics uncertainty improves more rapidly, making feasible simple significance criteria such as the 5σ discovery convention in particle physics. Contributions to measurement uncertainty from mistakes and unknown problems are not completely unpredictable. Such errors appear to have power-law distributions consistent with how designed complex systems fail, and how unknown systematic errors are constrained by researchers. This better understanding may help improve analysis and meta-analysis of data, and help scientists and the public have more realistic expectations of what scientific results imply.

Not Normal: the uncertainties of scientific measurements

PubMed Central

2017-01-01

Judging the significance and reproducibility of quantitative research requires a good understanding of relevant uncertainties, but it is often unclear how well these have been evaluated and what they imply. Reported scientific uncertainties were studied by analysing 41 000 measurements of 3200 quantities from medicine, nuclear and particle physics, and interlaboratory comparisons ranging from chemistry to toxicology. Outliers are common, with 5σ disagreements up to five orders of magnitude more frequent than naively expected. Uncertainty-normalized differences between multiple measurements of the same quantity are consistent with heavy-tailed Student’s t-distributions that are often almost Cauchy, far from a Gaussian Normal bell curve. Medical research uncertainties are generally as well evaluated as those in physics, but physics uncertainty improves more rapidly, making feasible simple significance criteria such as the 5σ discovery convention in particle physics. Contributions to measurement uncertainty from mistakes and unknown problems are not completely unpredictable. Such errors appear to have power-law distributions consistent with how designed complex systems fail, and how unknown systematic errors are constrained by researchers. This better understanding may help improve analysis and meta-analysis of data, and help scientists and the public have more realistic expectations of what scientific results imply. PMID:28280557

Pathology and failure in the design and implementation of adaptive management

USGS Publications Warehouse

Allen, Craig R.; Gunderson, Lance H.

2011-01-01

The conceptual underpinnings for adaptive management are simple; there will always be inherent uncertainty and unpredictability in the dynamics and behavior of complex ecological systems as a result non-linear interactions among components and emergence, yet management decisions must still be made. The strength of adaptive management is in the recognition and confrontation of such uncertainty. Rather than ignore uncertainty, or use it to preclude management actions, adaptive management can foster resilience and flexibility to cope with an uncertain future, and develop safe to fail management approaches that acknowledge inevitable changes and surprises. Since its initial introduction, adaptive management has been hailed as a solution to endless trial and error approaches to complex natural resource management challenges. However, its implementation has failed more often than not. It does not produce easy answers, and it is appropriate in only a subset of natural resource management problems. Clearly adaptive management has great potential when applied appropriately. Just as clearly adaptive management has seemingly failed to live up to its high expectations. Why? We outline nine pathologies and challenges that can lead to failure in adaptive management programs. We focus on general sources of failures in adaptive management, so that others can avoid these pitfalls in the future. Adaptive management can be a powerful and beneficial tool when applied correctly to appropriate management problems; the challenge is to keep the concept of adaptive management from being hijacked for inappropriate use.

Monte Carlo simulation for uncertainty estimation on structural data in implicit 3-D geological modeling, a guide for disturbance distribution selection and parameterization

NASA Astrophysics Data System (ADS)

Pakyuz-Charrier, Evren; Lindsay, Mark; Ogarko, Vitaliy; Giraud, Jeremie; Jessell, Mark

2018-04-01

Three-dimensional (3-D) geological structural modeling aims to determine geological information in a 3-D space using structural data (foliations and interfaces) and topological rules as inputs. This is necessary in any project in which the properties of the subsurface matters; they express our understanding of geometries in depth. For that reason, 3-D geological models have a wide range of practical applications including but not restricted to civil engineering, the oil and gas industry, the mining industry, and water management. These models, however, are fraught with uncertainties originating from the inherent flaws of the modeling engines (working hypotheses, interpolator's parameterization) and the inherent lack of knowledge in areas where there are no observations combined with input uncertainty (observational, conceptual and technical errors). Because 3-D geological models are often used for impactful decision-making it is critical that all 3-D geological models provide accurate estimates of uncertainty. This paper's focus is set on the effect of structural input data measurement uncertainty propagation in implicit 3-D geological modeling. This aim is achieved using Monte Carlo simulation for uncertainty estimation (MCUE), a stochastic method which samples from predefined disturbance probability distributions that represent the uncertainty of the original input data set. MCUE is used to produce hundreds to thousands of altered unique data sets. The altered data sets are used as inputs to produce a range of plausible 3-D models. The plausible models are then combined into a single probabilistic model as a means to propagate uncertainty from the input data to the final model. In this paper, several improved methods for MCUE are proposed. The methods pertain to distribution selection for input uncertainty, sample analysis and statistical consistency of the sampled distribution. Pole vector sampling is proposed as a more rigorous alternative than dip vector sampling for planar features and the use of a Bayesian approach to disturbance distribution parameterization is suggested. The influence of incorrect disturbance distributions is discussed and propositions are made and evaluated on synthetic and realistic cases to address the sighted issues. The distribution of the errors of the observed data (i.e., scedasticity) is shown to affect the quality of prior distributions for MCUE. Results demonstrate that the proposed workflows improve the reliability of uncertainty estimation and diminish the occurrence of artifacts.

Probabilistic Flood Maps to support decision-making: Mapping the Value of Information

NASA Astrophysics Data System (ADS)

Alfonso, L.; Mukolwe, M. M.; Di Baldassarre, G.

2016-02-01

Floods are one of the most frequent and disruptive natural hazards that affect man. Annually, significant flood damage is documented worldwide. Flood mapping is a common preimpact flood hazard mitigation measure, for which advanced methods and tools (such as flood inundation models) are used to estimate potential flood extent maps that are used in spatial planning. However, these tools are affected, largely to an unknown degree, by both epistemic and aleatory uncertainty. Over the past few years, advances in uncertainty analysis with respect to flood inundation modeling show that it is appropriate to adopt Probabilistic Flood Maps (PFM) to account for uncertainty. However, the following question arises; how can probabilistic flood hazard information be incorporated into spatial planning? Thus, a consistent framework to incorporate PFMs into the decision-making is required. In this paper, a novel methodology based on Decision-Making under Uncertainty theories, in particular Value of Information (VOI) is proposed. Specifically, the methodology entails the use of a PFM to generate a VOI map, which highlights floodplain locations where additional information is valuable with respect to available floodplain management actions and their potential consequences. The methodology is illustrated with a simplified example and also applied to a real case study in the South of France, where a VOI map is analyzed on the basis of historical land use change decisions over a period of 26 years. Results show that uncertain flood hazard information encapsulated in PFMs can aid decision-making in floodplain planning.

Uncertainty Representation and Interpretation in Model-Based Prognostics Algorithms Based on Kalman Filter Estimation

NASA Technical Reports Server (NTRS)

Galvan, Jose Ramon; Saxena, Abhinav; Goebel, Kai Frank

2012-01-01

This article discusses several aspects of uncertainty representation and management for model-based prognostics methodologies based on our experience with Kalman Filters when applied to prognostics for electronics components. In particular, it explores the implications of modeling remaining useful life prediction as a stochastic process, and how it relates to uncertainty representation, management and the role of prognostics in decision-making. A distinction between the interpretations of estimated remaining useful life probability density function is explained and a cautionary argument is provided against mixing interpretations for two while considering prognostics in making critical decisions.

Sunway Medical Laboratory Quality Control Plans Based on Six Sigma, Risk Management and Uncertainty.

PubMed

Jairaman, Jamuna; Sakiman, Zarinah; Li, Lee Suan

2017-03-01

Sunway Medical Centre (SunMed) implemented Six Sigma, measurement uncertainty, and risk management after the CLSI EP23 Individualized Quality Control Plan approach. Despite the differences in all three approaches, each implementation was beneficial to the laboratory, and none was in conflict with another approach. A synthesis of these approaches, built on a solid foundation of quality control planning, can help build a strong quality management system for the entire laboratory. Copyright © 2016 Elsevier Inc. All rights reserved.

Adaptive Management

EPA Science Inventory

Adaptive management is an approach to natural resource management that emphasizes learning through management where knowledge is incomplete, and when, despite inherent uncertainty, managers and policymakers must act. Unlike a traditional trial and error approach, adaptive managem...

Receiving water quality assessment: comparison between simplified and detailed integrated urban modelling approaches.

PubMed

Mannina, Giorgio; Viviani, Gaspare

2010-01-01

Urban water quality management often requires use of numerical models allowing the evaluation of the cause-effect relationship between the input(s) (i.e. rainfall, pollutant concentrations on catchment surface and in sewer system) and the resulting water quality response. The conventional approach to the system (i.e. sewer system, wastewater treatment plant and receiving water body), considering each component separately, does not enable optimisation of the whole system. However, recent gains in understanding and modelling make it possible to represent the system as a whole and optimise its overall performance. Indeed, integrated urban drainage modelling is of growing interest for tools to cope with Water Framework Directive requirements. Two different approaches can be employed for modelling the whole urban drainage system: detailed and simplified. Each has its advantages and disadvantages. Specifically, detailed approaches can offer a higher level of reliability in the model results, but can be very time consuming from the computational point of view. Simplified approaches are faster but may lead to greater model uncertainty due to an over-simplification. To gain insight into the above problem, two different modelling approaches have been compared with respect to their uncertainty. The first urban drainage integrated model approach uses the Saint-Venant equations and the 1D advection-dispersion equations, for the quantity and for the quality aspects, respectively. The second model approach consists of the simplified reservoir model. The analysis used a parsimonious bespoke model developed in previous studies. For the uncertainty analysis, the Generalised Likelihood Uncertainty Estimation (GLUE) procedure was used. Model reliability was evaluated on the basis of capacity of globally limiting the uncertainty. Both models have a good capability to fit the experimental data, suggesting that all adopted approaches are equivalent both for quantity and quality. The detailed model approach is more robust and presents less uncertainty in terms of uncertainty bands. On the other hand, the simplified river water quality model approach shows higher uncertainty and may be unsuitable for receiving water body quality assessment.

Uncertainty in prostate cancer. Ethnic and family patterns.

PubMed

Germino, B B; Mishel, M H; Belyea, M; Harris, L; Ware, A; Mohler, J

1998-01-01

Prostate cancer occurs 37% more often in African-American men than in white men. Patients and their family care providers (FCPs) may have different experiences of cancer and its treatment. This report addresses two questions: 1) What is the relationship of uncertainty to family coping, psychological adjustment to illness, and spiritual factors? and 2) Are these patterns of relationship similar for patients and their family care givers and for whites and African-Americans? A sample of white and African-American men and their family care givers (N = 403) was drawn from an ongoing study, testing the efficacy of an uncertainty management intervention with men with stage B prostate cancer. Data were collected at study entry, either 1 week after post-surgical catheter removal or at the beginning of primary radiation treatment. Measures of uncertainty, adult role behavior, problem solving, social support, importance of God in one's life, family coping, psychological adjustment to illness, and perceptions of health and illness met standard criteria for internal consistency. Analyses of baseline data using Pearson's product moment correlations were conducted to examine the relationships of person, disease, and contextual factors to uncertainty. For family coping, uncertainty was significantly and positively related to two domains in white family care providers only. In African-American and white family care providers, the more uncertainty experienced, the less positive they felt about treatment. Uncertainty for all care givers was related inversely to positive feelings about the patient recovering from the illness. For all patients and for white family members, uncertainty was related inversely to the quality of the domestic environment. For everyone, uncertainty was related inversely to psychological distress. Higher levels of uncertainty were related to a poorer social environment for African-American patients and for white family members. For white patients and their family members, higher levels of uncertainty were related to lower scores on adult role behavior (shopping, running errands). For white family members, higher levels of uncertainty were related to less active problem solving and less perceived social support. Finally, higher levels of uncertainty were related to the importance of God for white patients and family care providers. The clearest finding of the present study is that there are ethnic differences in the relationship of uncertainty to a number of quality-of-life and coping variables. This has immediate implications for the assessment of psychosocial responses to cancer and cancer treatment. Much of what is in curricula is based on clinical and research experience primarily with white individuals. The experience of uncertainty related to cancer and its treatment is influenced by the cultural perspectives of patients and their families. To assist patients and families with the inevitable uncertainties of the cancer experience, healthcare providers need to reconsider their ethnocentric assumptions and develop more skill in assessing patient and family beliefs, values, cultural perspectives, and the influence of these on patient and family uncertainties.

An overview of methods to identify and manage uncertainty for modelling problems in the water-environment-agriculture cross-sector

DOE PAGES

Jakeman, Anthony J.; Jakeman, John Davis

2018-03-14

Uncertainty pervades the representation of systems in the water–environment–agriculture cross-sector. Successful methods to address uncertainties have largely focused on standard mathematical formulations of biophysical processes in a single sector, such as partial or ordinary differential equations. More attention to integrated models of such systems is warranted. Model components representing the different sectors of an integrated model can have less standard, and different, formulations to one another, as well as different levels of epistemic knowledge and data informativeness. Thus, uncertainty is not only pervasive but also crosses boundaries and propagates between system components. Uncertainty assessment (UA) cries out for more eclecticmore » treatment in these circumstances, some of it being more qualitative and empirical. Here in this paper, we discuss the various sources of uncertainty in such a cross-sectoral setting and ways to assess and manage them. We have outlined a fast-growing set of methodologies, particularly in the computational mathematics literature on uncertainty quantification (UQ), that seem highly pertinent for uncertainty assessment. There appears to be considerable scope for advancing UA by integrating relevant UQ techniques into cross-sectoral problem applications. Of course this will entail considerable collaboration between domain specialists who often take first ownership of the problem and computational methods experts.« less

Uncertainties in Forecasting Streamflow using Entropy Theory

NASA Astrophysics Data System (ADS)

Cui, H.; Singh, V. P.

2017-12-01

Streamflow forecasting is essential in river restoration, reservoir operation, power generation, irrigation, navigation, and water management. However, there is always uncertainties accompanied in forecast, which may affect the forecasting results and lead to large variations. Therefore, uncertainties must be considered and be assessed properly when forecasting streamflow for water management. The aim of our work is to quantify the uncertainties involved in forecasting streamflow and provide reliable streamflow forecast. Despite that streamflow time series are stochastic, they exhibit seasonal and periodic patterns. Therefore, streamflow forecasting entails modeling seasonality, periodicity, and its correlation structure, and assessing uncertainties. This study applies entropy theory to forecast streamflow and measure uncertainties during the forecasting process. To apply entropy theory for streamflow forecasting, spectral analysis is combined to time series analysis, as spectral analysis can be employed to characterize patterns of streamflow variation and identify the periodicity of streamflow. That is, it permits to extract significant information for understanding the streamflow process and prediction thereof. Application of entropy theory for streamflow forecasting involves determination of spectral density, determination of parameters, and extension of autocorrelation function. The uncertainties brought by precipitation input, forecasting model and forecasted results are measured separately using entropy. With information theory, how these uncertainties transported and aggregated during these processes will be described.

An overview of methods to identify and manage uncertainty for modelling problems in the water-environment-agriculture cross-sector

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jakeman, Anthony J.; Jakeman, John Davis

Uncertainty pervades the representation of systems in the water–environment–agriculture cross-sector. Successful methods to address uncertainties have largely focused on standard mathematical formulations of biophysical processes in a single sector, such as partial or ordinary differential equations. More attention to integrated models of such systems is warranted. Model components representing the different sectors of an integrated model can have less standard, and different, formulations to one another, as well as different levels of epistemic knowledge and data informativeness. Thus, uncertainty is not only pervasive but also crosses boundaries and propagates between system components. Uncertainty assessment (UA) cries out for more eclecticmore » treatment in these circumstances, some of it being more qualitative and empirical. Here in this paper, we discuss the various sources of uncertainty in such a cross-sectoral setting and ways to assess and manage them. We have outlined a fast-growing set of methodologies, particularly in the computational mathematics literature on uncertainty quantification (UQ), that seem highly pertinent for uncertainty assessment. There appears to be considerable scope for advancing UA by integrating relevant UQ techniques into cross-sectoral problem applications. Of course this will entail considerable collaboration between domain specialists who often take first ownership of the problem and computational methods experts.« less

Uncertainty analysis in vulnerability estimations for elements at risk- a review of concepts and some examples on landslides

NASA Astrophysics Data System (ADS)

Ciurean, R. L.; Glade, T.

2012-04-01

Decision under uncertainty is a constant of everyday life and an important component of risk management and governance. Recently, experts have emphasized the importance of quantifying uncertainty in all phases of landslide risk analysis. Due to its multi-dimensional and dynamic nature, (physical) vulnerability is inherently complex and the "degree of loss" estimates imprecise and to some extent even subjective. Uncertainty analysis introduces quantitative modeling approaches that allow for a more explicitly objective output, improving the risk management process as well as enhancing communication between various stakeholders for better risk governance. This study presents a review of concepts for uncertainty analysis in vulnerability of elements at risk to landslides. Different semi-quantitative and quantitative methods are compared based on their feasibility in real-world situations, hazard dependency, process stage in vulnerability assessment (i.e. input data, model, output), and applicability within an integrated landslide hazard and risk framework. The resulted observations will help to identify current gaps and future needs in vulnerability assessment, including estimation of uncertainty propagation, transferability of the methods, development of visualization tools, but also address basic questions like what is uncertainty and how uncertainty can be quantified or treated in a reliable and reproducible way.

The integrated effects of future climate and hydrologic uncertainty on sustainable flood risk management

NASA Astrophysics Data System (ADS)

Steinschneider, S.; Wi, S.; Brown, C. M.

2013-12-01

Flood risk management performance is investigated within the context of integrated climate and hydrologic modeling uncertainty to explore system robustness. The research question investigated is whether structural and hydrologic parameterization uncertainties are significant relative to other uncertainties such as climate change when considering water resources system performance. Two hydrologic models are considered, a conceptual, lumped parameter model that preserves the water balance and a physically-based model that preserves both water and energy balances. In the conceptual model, parameter and structural uncertainties are quantified and propagated through the analysis using a Bayesian modeling framework with an innovative error model. Mean climate changes and internal climate variability are explored using an ensemble of simulations from a stochastic weather generator. The approach presented can be used to quantify the sensitivity of flood protection adequacy to different sources of uncertainty in the climate and hydrologic system, enabling the identification of robust projects that maintain adequate performance despite the uncertainties. The method is demonstrated in a case study for the Coralville Reservoir on the Iowa River, where increased flooding over the past several decades has raised questions about potential impacts of climate change on flood protection adequacy.

Bayesian calibration of mechanistic aquatic biogeochemical models and benefits for environmental management

NASA Astrophysics Data System (ADS)

Arhonditsis, George B.; Papantou, Dimitra; Zhang, Weitao; Perhar, Gurbir; Massos, Evangelia; Shi, Molu

2008-09-01

Aquatic biogeochemical models have been an indispensable tool for addressing pressing environmental issues, e.g., understanding oceanic response to climate change, elucidation of the interplay between plankton dynamics and atmospheric CO 2 levels, and examination of alternative management schemes for eutrophication control. Their ability to form the scientific basis for environmental management decisions can be undermined by the underlying structural and parametric uncertainty. In this study, we outline how we can attain realistic predictive links between management actions and ecosystem response through a probabilistic framework that accommodates rigorous uncertainty analysis of a variety of error sources, i.e., measurement error, parameter uncertainty, discrepancy between model and natural system. Because model uncertainty analysis essentially aims to quantify the joint probability distribution of model parameters and to make inference about this distribution, we believe that the iterative nature of Bayes' Theorem is a logical means to incorporate existing knowledge and update the joint distribution as new information becomes available. The statistical methodology begins with the characterization of parameter uncertainty in the form of probability distributions, then water quality data are used to update the distributions, and yield posterior parameter estimates along with predictive uncertainty bounds. Our illustration is based on a six state variable (nitrate, ammonium, dissolved organic nitrogen, phytoplankton, zooplankton, and bacteria) ecological model developed for gaining insight into the mechanisms that drive plankton dynamics in a coastal embayment; the Gulf of Gera, Island of Lesvos, Greece. The lack of analytical expressions for the posterior parameter distributions was overcome using Markov chain Monte Carlo simulations; a convenient way to obtain representative samples of parameter values. The Bayesian calibration resulted in realistic reproduction of the key temporal patterns of the system, offered insights into the degree of information the data contain about model inputs, and also allowed the quantification of the dependence structure among the parameter estimates. Finally, our study uses two synthetic datasets to examine the ability of the updated model to provide estimates of predictive uncertainty for water quality variables of environmental management interest.

Adaptive Management and the Value of Information: Learning Via Intervention in Epidemiology

PubMed Central

Shea, Katriona; Tildesley, Michael J.; Runge, Michael C.; Fonnesbeck, Christopher J.; Ferrari, Matthew J.

2014-01-01

Optimal intervention for disease outbreaks is often impeded by severe scientific uncertainty. Adaptive management (AM), long-used in natural resource management, is a structured decision-making approach to solving dynamic problems that accounts for the value of resolving uncertainty via real-time evaluation of alternative models. We propose an AM approach to design and evaluate intervention strategies in epidemiology, using real-time surveillance to resolve model uncertainty as management proceeds, with foot-and-mouth disease (FMD) culling and measles vaccination as case studies. We use simulations of alternative intervention strategies under competing models to quantify the effect of model uncertainty on decision making, in terms of the value of information, and quantify the benefit of adaptive versus static intervention strategies. Culling decisions during the 2001 UK FMD outbreak were contentious due to uncertainty about the spatial scale of transmission. The expected benefit of resolving this uncertainty prior to a new outbreak on a UK-like landscape would be £45–£60 million relative to the strategy that minimizes livestock losses averaged over alternate transmission models. AM during the outbreak would be expected to recover up to £20.1 million of this expected benefit. AM would also recommend a more conservative initial approach (culling of infected premises and dangerous contact farms) than would a fixed strategy (which would additionally require culling of contiguous premises). For optimal targeting of measles vaccination, based on an outbreak in Malawi in 2010, AM allows better distribution of resources across the affected region; its utility depends on uncertainty about both the at-risk population and logistical capacity. When daily vaccination rates are highly constrained, the optimal initial strategy is to conduct a small, quick campaign; a reduction in expected burden of approximately 10,000 cases could result if campaign targets can be updated on the basis of the true susceptible population. Formal incorporation of a policy to update future management actions in response to information gained in the course of an outbreak can change the optimal initial response and result in significant cost savings. AM provides a framework for using multiple models to facilitate public-health decision making and an objective basis for updating management actions in response to improved scientific understanding. PMID:25333371

Adaptive management and the value of information: learning via intervention in epidemiology

USGS Publications Warehouse

Shea, Katriona; Tildesley, Michael J.; Runge, Michael C.; Fonnesbeck, Christopher J.; Ferrari, Matthew J.

2014-01-01

Optimal intervention for disease outbreaks is often impeded by severe scientific uncertainty. Adaptive management (AM), long-used in natural resource management, is a structured decision-making approach to solving dynamic problems that accounts for the value of resolving uncertainty via real-time evaluation of alternative models. We propose an AM approach to design and evaluate intervention strategies in epidemiology, using real-time surveillance to resolve model uncertainty as management proceeds, with foot-and-mouth disease (FMD) culling and measles vaccination as case studies. We use simulations of alternative intervention strategies under competing models to quantify the effect of model uncertainty on decision making, in terms of the value of information, and quantify the benefit of adaptive versus static intervention strategies. Culling decisions during the 2001 UK FMD outbreak were contentious due to uncertainty about the spatial scale of transmission. The expected benefit of resolving this uncertainty prior to a new outbreak on a UK-like landscape would be £45–£60 million relative to the strategy that minimizes livestock losses averaged over alternate transmission models. AM during the outbreak would be expected to recover up to £20.1 million of this expected benefit. AM would also recommend a more conservative initial approach (culling of infected premises and dangerous contact farms) than would a fixed strategy (which would additionally require culling of contiguous premises). For optimal targeting of measles vaccination, based on an outbreak in Malawi in 2010, AM allows better distribution of resources across the affected region; its utility depends on uncertainty about both the at-risk population and logistical capacity. When daily vaccination rates are highly constrained, the optimal initial strategy is to conduct a small, quick campaign; a reduction in expected burden of approximately 10,000 cases could result if campaign targets can be updated on the basis of the true susceptible population. Formal incorporation of a policy to update future management actions in response to information gained in the course of an outbreak can change the optimal initial response and result in significant cost savings. AM provides a framework for using multiple models to facilitate public-health decision making and an objective basis for updating management actions in response to improved scientific understanding.

Ensembles modeling approach to study Climate Change impacts on Wheat

NASA Astrophysics Data System (ADS)

Ahmed, Mukhtar; Claudio, Stöckle O.; Nelson, Roger; Higgins, Stewart

2017-04-01

Simulations of crop yield under climate variability are subject to uncertainties, and quantification of such uncertainties is essential for effective use of projected results in adaptation and mitigation strategies. In this study we evaluated the uncertainties related to crop-climate models using five crop growth simulation models (CropSyst, APSIM, DSSAT, STICS and EPIC) and 14 general circulation models (GCMs) for 2 representative concentration pathways (RCP) of atmospheric CO2 (4.5 and 8.5 W m-2) in the Pacific Northwest (PNW), USA. The aim was to assess how different process-based crop models could be used accurately for estimation of winter wheat growth, development and yield. Firstly, all models were calibrated for high rainfall, medium rainfall, low rainfall and irrigated sites in the PNW using 1979-2010 as the baseline period. Response variables were related to farm management and soil properties, and included crop phenology, leaf area index (LAI), biomass and grain yield of winter wheat. All five models were run from 2000 to 2100 using the 14 GCMs and 2 RCPs to evaluate the effect of future climate (rainfall, temperature and CO2) on winter wheat phenology, LAI, biomass, grain yield and harvest index. Simulated time to flowering and maturity was reduced in all models except EPIC with some level of uncertainty. All models generally predicted an increase in biomass and grain yield under elevated CO2 but this effect was more prominent under rainfed conditions than irrigation. However, there was uncertainty in the simulation of crop phenology, biomass and grain yield under 14 GCMs during three prediction periods (2030, 2050 and 2070). We concluded that to improve accuracy and consistency in simulating wheat growth dynamics and yield under a changing climate, a multimodel ensemble approach should be used.

Uncertainties in mapping forest carbon in urban ecosystems.

PubMed

Chen, Gang; Ozelkan, Emre; Singh, Kunwar K; Zhou, Jun; Brown, Marilyn R; Meentemeyer, Ross K

2017-02-01

Spatially explicit urban forest carbon estimation provides a baseline map for understanding the variation in forest vertical structure, informing sustainable forest management and urban planning. While high-resolution remote sensing has proven promising for carbon mapping in highly fragmented urban landscapes, data cost and availability are the major obstacle prohibiting accurate, consistent, and repeated measurement of forest carbon pools in cities. This study aims to evaluate the uncertainties of forest carbon estimation in response to the combined impacts of remote sensing data resolution and neighborhood spatial patterns in Charlotte, North Carolina. The remote sensing data for carbon mapping were resampled to a range of resolutions, i.e., LiDAR point cloud density - 5.8, 4.6, 2.3, and 1.2 pt s/m 2 , aerial optical NAIP (National Agricultural Imagery Program) imagery - 1, 5, 10, and 20 m. Urban spatial patterns were extracted to represent area, shape complexity, dispersion/interspersion, diversity, and connectivity of landscape patches across the residential neighborhoods with built-up densities from low, medium-low, medium-high, to high. Through statistical analyses, we found that changing remote sensing data resolution introduced noticeable uncertainties (variation) in forest carbon estimation at the neighborhood level. Higher uncertainties were caused by the change of LiDAR point density (causing 8.7-11.0% of variation) than changing NAIP image resolution (causing 6.2-8.6% of variation). For both LiDAR and NAIP, urban neighborhoods with a higher degree of anthropogenic disturbance unveiled a higher level of uncertainty in carbon mapping. However, LiDAR-based results were more likely to be affected by landscape patch connectivity, and the NAIP-based estimation was found to be significantly influenced by the complexity of patch shape. Copyright © 2016 Elsevier Ltd. All rights reserved.

Mixing-controlled uncertainty in long-term predictions of acid rock drainage from heterogeneous waste-rock piles

NASA Astrophysics Data System (ADS)

Pedretti, D.; Beckie, R. D.; Mayer, K. U.

2015-12-01

The chemistry of drainage from waste-rock piles at mine sites is difficult to predict because of a number of uncertainties including heterogeneous reactive mineral content, distribution of minerals, weathering rates and physical flow properties. In this presentation, we examine the effects of mixing on drainage chemistry over timescales of 100s of years. We use a 1-D streamtube conceptualization of flow in waste rocks and multicomponent reactive transport modeling. We simplify the reactive system to consist of acid-producing sulfide minerals and acid-neutralizing carbonate minerals and secondary sulfate and iron oxide minerals. We create multiple realizations of waste-rock piles with distinct distributions of reactive minerals along each flow path and examine the uncertainty of drainage geochemistry through time. The limited mixing of streamtubes that is characteristic of the vertical unsaturated flow in many waste-rock piles, allows individual flowpaths to sustain acid or neutral conditions to the base of the pile, where the streamtubes mix. Consequently, mixing and the acidity/alkalinity balance of the streamtube waters, and not the overall acid- and base-producing mineral contents, control the instantaneous discharge chemistry. Our results show that the limited mixing implied by preferential flow and the heterogeneous distribution of mineral contents lead to large uncertainty in drainage chemistry over short and medium time scales. However, over longer timescales when one of either the acid-producing or neutralizing primary phases is depleted, the drainage chemistry becomes less controlled by mixing and in turn less uncertain. A correct understanding of the temporal variability of uncertainty is key to make informed long-term decisions in mining settings regarding the management of waste material.

Uncertainty and sensitivity assessment of flood risk assessments

NASA Astrophysics Data System (ADS)

de Moel, H.; Aerts, J. C.

2009-12-01

Floods are one of the most frequent and costly natural disasters. In order to protect human lifes and valuable assets from the effect of floods many defensive structures have been build. Despite these efforts economic losses due to catastrophic flood events have, however, risen substantially during the past couple of decades because of continuing economic developments in flood prone areas. On top of that, climate change is expected to affect the magnitude and frequency of flood events. Because these ongoing trends are expected to continue, a transition can be observed in various countries to move from a protective flood management approach to a more risk based flood management approach. In a risk based approach, flood risk assessments play an important role in supporting decision making. Most flood risk assessments assess flood risks in monetary terms (damage estimated for specific situations or expected annual damage) in order to feed cost-benefit analysis of management measures. Such flood risk assessments contain, however, considerable uncertainties. This is the result from uncertainties in the many different input parameters propagating through the risk assessment and accumulating in the final estimate. Whilst common in some other disciplines, as with integrated assessment models, full uncertainty and sensitivity analyses of flood risk assessments are not so common. Various studies have addressed uncertainties regarding flood risk assessments, but have mainly focussed on the hydrological conditions. However, uncertainties in other components of the risk assessment, like the relation between water depth and monetary damage, can be substantial as well. This research therefore tries to assess the uncertainties of all components of monetary flood risk assessments, using a Monte Carlo based approach. Furthermore, the total uncertainty will also be attributed to the different input parameters using a variance based sensitivity analysis. Assessing and visualizing the uncertainties of the final risk estimate will be helpful to decision makers to make better informed decisions and attributing this uncertainty to the input parameters helps to identify which parameters are most important when it comes to uncertainty in the final estimate and should therefore deserve additional attention in further research.

Process for estimating likelihood and confidence in post detonation nuclear forensics.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Darby, John L.; Craft, Charles M.

2014-07-01

Technical nuclear forensics (TNF) must provide answers to questions of concern to the broader community, including an estimate of uncertainty. There is significant uncertainty associated with post-detonation TNF. The uncertainty consists of a great deal of epistemic (state of knowledge) as well as aleatory (random) uncertainty, and many of the variables of interest are linguistic (words) and not numeric. We provide a process by which TNF experts can structure their process for answering questions and provide an estimate of uncertainty. The process uses belief and plausibility, fuzzy sets, and approximate reasoning.

Protocol and practice in the adaptive management of waterfowl harvests

USGS Publications Warehouse

Johnson, F.; Williams, K.

1999-01-01

Waterfowl harvest management in North America, for all its success, historically has had several shortcomings, including a lack of well-defined objectives, a failure to account for uncertain management outcomes, and inefficient use of harvest regulations to understand the effects of management. To address these and other concerns, the U.S. Fish and Wildlife Service began implementation of adaptive harvest management in 1995. Harvest policies are now developed using a Markov decision process in which there is an explicit accounting for uncontrolled environmental variation, partial controllability of harvest, and structural uncertainty in waterfowl population dynamics. Current policies are passively adaptive, in the sense that any reduction in structural uncertainty is an unplanned by-product of the regulatory process. A generalization of the Markov decision process permits the calculation of optimal actively adaptive policies, but it is not yet clear how state-specific harvest actions differ between passive and active approaches. The Markov decision process also provides managers the ability to explore optimal levels of aggregation or "management scale" for regulating harvests in a system that exhibits high temporal, spatial, and organizational variability. Progress in institutionalizing adaptive harvest management has been remarkable, but some managers still perceive the process as a panacea, while failing to appreciate the challenges presented by this more explicit and methodical approach to harvest regulation. Technical hurdles include the need to develop better linkages between population processes and the dynamics of landscapes, and to model the dynamics of structural uncertainty in a more comprehensive fashion. From an institutional perspective, agreement on how to value and allocate harvests continues to be elusive, and there is some evidence that waterfowl managers have overestimated the importance of achievement-oriented factors in setting hunting regulations. Indeed, it is these unresolved value judgements, and the lack of an effective structure for organizing debate, that present the greatest threat to adaptive harvest management as a viable means for coping with management uncertainty. Copyright ?? 1999 by The Resilience Alliance.

Multi-model comparison highlights consistency in predicted effect of warming on a semi-arid shrub

USGS Publications Warehouse

Renwick, Katherine M.; Curtis, Caroline; Kleinhesselink, Andrew R.; Schlaepfer, Daniel R.; Bradley, Bethany A.; Aldridge, Cameron L.; Poulter, Benjamin; Adler, Peter B.

2018-01-01

A number of modeling approaches have been developed to predict the impacts of climate change on species distributions, performance, and abundance. The stronger the agreement from models that represent different processes and are based on distinct and independent sources of information, the greater the confidence we can have in their predictions. Evaluating the level of confidence is particularly important when predictions are used to guide conservation or restoration decisions. We used a multi-model approach to predict climate change impacts on big sagebrush (Artemisia tridentata), the dominant plant species on roughly 43 million hectares in the western United States and a key resource for many endemic wildlife species. To evaluate the climate sensitivity of A. tridentata, we developed four predictive models, two based on empirically derived spatial and temporal relationships, and two that applied mechanistic approaches to simulate sagebrush recruitment and growth. This approach enabled us to produce an aggregate index of climate change vulnerability and uncertainty based on the level of agreement between models. Despite large differences in model structure, predictions of sagebrush response to climate change were largely consistent. Performance, as measured by change in cover, growth, or recruitment, was predicted to decrease at the warmest sites, but increase throughout the cooler portions of sagebrush's range. A sensitivity analysis indicated that sagebrush performance responds more strongly to changes in temperature than precipitation. Most of the uncertainty in model predictions reflected variation among the ecological models, raising questions about the reliability of forecasts based on a single modeling approach. Our results highlight the value of a multi-model approach in forecasting climate change impacts and uncertainties and should help land managers to maximize the value of conservation investments.

Crown fuel spatial variability and predictability of fire spread

Treesearch

Russell A. Parsons; Jeremy Sauer; Rodman R. Linn

2010-01-01

Fire behavior predictions, as well as measures of uncertainty in those predictions, are essential in operational and strategic fire management decisions. While it is becoming common practice to assess uncertainty in fire behavior predictions arising from variability in weather inputs, uncertainty arising from the fire models themselves is difficult to assess. This is...

Evaluating the impacts of agricultural land management practices on water resources: A probabilistic hydrologic modeling approach.

PubMed

Prada, A F; Chu, M L; Guzman, J A; Moriasi, D N

2017-05-15

Evaluating the effectiveness of agricultural land management practices in minimizing environmental impacts using models is challenged by the presence of inherent uncertainties during the model development stage. One issue faced during the model development stage is the uncertainty involved in model parameterization. Using a single optimized set of parameters (one snapshot) to represent baseline conditions of the system limits the applicability and robustness of the model to properly represent future or alternative scenarios. The objective of this study was to develop a framework that facilitates model parameter selection while evaluating uncertainty to assess the impacts of land management practices at the watershed scale. The model framework was applied to the Lake Creek watershed located in southwestern Oklahoma, USA. A two-step probabilistic approach was implemented to parameterize the Agricultural Policy/Environmental eXtender (APEX) model using global uncertainty and sensitivity analysis to estimate the full spectrum of total monthly water yield (WYLD) and total monthly Nitrogen loads (N) in the watershed under different land management practices. Twenty-seven models were found to represent the baseline scenario in which uncertainty of up to 29% and 400% in WYLD and N, respectively, is plausible. Changing the land cover to pasture manifested the highest decrease in N to up to 30% for a full pasture coverage while changing to full winter wheat cover can increase the N up to 11%. The methodology developed in this study was able to quantify the full spectrum of system responses, the uncertainty associated with them, and the most important parameters that drive their variability. Results from this study can be used to develop strategic decisions on the risks and tradeoffs associated with different management alternatives that aim to increase productivity while also minimizing their environmental impacts. Copyright © 2017 Elsevier Ltd. All rights reserved.

Illness Uncertainty and Posttraumatic Stress in Young Adults With Congenital Heart Disease.

PubMed

Moreland, Patricia; Santacroce, Sheila Judge

2018-03-29

Young adults with congenital heart disease (CHD) are at risk for chronic illness uncertainty in 4 domains: ambiguity about the state of their illness; lack of information about the disease, its treatment, and comorbidities; complexity of the healthcare system and relationship with healthcare providers; and unpredictability of the illness course and outcome. Chronic uncertainty has been associated with posttraumatic stress symptoms (PTSS) and posttraumatic stress disorder (PTSD). The aims of this study were to explore how young adults with CHD experience uncertainty and to describe the relationship between PTSS and the appraisal and management process. An exploratory, mixed methods design was used. Data were collected in person and via Skype from 25 participants (19-35 years old), who were diagnosed with CHD during childhood and able to read and write English. In-depth interviews and the University of California at Los Angeles Posttraumatic Stress Disorder Reaction Index were used to collect data. Qualitative data were analyzed using the constant comparative method. The 4 domains of uncertainty were evident in the narratives. The PTSD mean (SD) score was 31.3 (7.7). Six participants met criteria for PTSD. Narrative analysis revealed a relationship between severity of PTSS and the appraisal and management of uncertainty. Participants with PTSD used management strategies that included avoidance, reexperiencing, and hyperarousal. Young adults with CHD may be at risk for the development of long-term psychological stress and PTSD in the setting of chronic uncertainty. Regular monitoring to identify PTSS/PTSD may be a means to promote treatment adherence and participation in healthcare.

Acting on uncertainty in landscape management—options forestry.

Treesearch

Jonathan Thompson

2005-01-01

In response to the highly uncertain outcomes inherent in forest management, “options forestry” has been introduced as a novel approach that includes an honest appraisal of uncertainties and learning as a specific objective. The strategy is unique in that it uses a variety of management pathways, all designed to reach the same goal, and structures them in a rigorous...

Effects of a case management program on patients with oral precancerous lesions: a randomized controlled trial.

PubMed

Lin, Hsiu-Ying; Chen, Shu-Ching; Peng, Hsi-Ling; Chen, Mu-Kuan

2016-01-01

The aim of this study is to identify the effects of a case management program on knowledge about oral cancer, preventive behavior for oral cancer, and level of uncertainty for patients with oral precancerous lesions. A randomized controlled trial was conducted with two groups, using a pre- and posttest design. The experimental group received a case management program and telephone follow-up sessions; the control group received routine care. Patients were assessed at three time points: first visit to the otolaryngology clinic for biopsy examination (T0), and then at 2 weeks (T1) and 4 weeks (T2) after the biopsy examination. Patients in both groups had significantly higher levels of knowledge about oral cancer, preventive behavior for oral cancer, and lower level of uncertainty at T2 compared to T0. At T2, participants in the experimental group had significantly greater knowledge about oral cancer, more preventive behavior for oral cancer, and less uncertainty compared to those in the control group. The case management program with telephone counseling effectively improved knowledge about oral cancer, preventive behavior for oral cancer, and uncertainty levels in patients with oral precancerous lesions in the four weeks after receiving a biopsy examination. The case management program can be applied with positive results to patients receiving different types of cancer screening, including colorectal, breast, and cervical screening.

Accounting for complementarity to maximize monitoring power for species management.

PubMed

Tulloch, Ayesha I T; Chadès, Iadine; Possingham, Hugh P

2013-10-01

To choose among conservation actions that may benefit many species, managers need to monitor the consequences of those actions. Decisions about which species to monitor from a suite of different species being managed are hindered by natural variability in populations and uncertainty in several factors: the ability of the monitoring to detect a change, the likelihood of the management action being successful for a species, and how representative species are of one another. However, the literature provides little guidance about how to account for these uncertainties when deciding which species to monitor to determine whether the management actions are delivering outcomes. We devised an approach that applies decision science and selects the best complementary suite of species to monitor to meet specific conservation objectives. We created an index for indicator selection that accounts for the likelihood of successfully detecting a real trend due to a management action and whether that signal provides information about other species. We illustrated the benefit of our approach by analyzing a monitoring program for invasive predator management aimed at recovering 14 native Australian mammals of conservation concern. Our method selected the species that provided more monitoring power at lower cost relative to the current strategy and traditional approaches that consider only a subset of the important considerations. Our benefit function accounted for natural variability in species growth rates, uncertainty in the responses of species to the prescribed action, and how well species represent others. Monitoring programs that ignore uncertainty, likelihood of detecting change, and complementarity between species will be more costly and less efficient and may waste funding that could otherwise be used for management. © 2013 Society for Conservation Biology.

Nursing students' time management, reducing stress and gaining satisfaction: a grounded theory study.

PubMed

Mirzaei, Tayebeh; Oskouie, Fatemeh; Rafii, Forough

2012-03-01

In the course of their studies, nursing students must learn many skills and acquire the knowledge required for their future profession. This study investigates how Iranian nursing students manage their time according to the circumstances and obstacles of their academic field. Research was conducted using the grounded theory method. Twenty-one nursing students were purposefully chosen as participants. Data was collected through semi-structured interviews and analyzed using the method suggested by Corbin and Strauss. One of the three processes that the nursing students used was "unidirectional time management." This pattern consists of accepting the nursing field, overcoming uncertainty, assessing conditions, feeling stress, and trying to reduce stress and create satisfaction. It was found that students allotted most of their time to academic tasks in an attempt to overcome their stress. The findings of this study indicate the need for these students to have time for the extra-curricular activities and responsibilities that are appropriate to their age. © 2012 Blackwell Publishing Asia Pty Ltd.

Scientifically defensible fish conservation and recovery plans: Addressing diffuse threats and developing rigorous adaptive management plans

USGS Publications Warehouse

Maas-Hebner, Kathleen G.; Schreck, Carl B.; Hughes, Robert M.; Yeakley, Alan; Molina, Nancy

2016-01-01

We discuss the importance of addressing diffuse threats to long-term species and habitat viability in fish conservation and recovery planning. In the Pacific Northwest, USA, salmonid management plans have typically focused on degraded freshwater habitat, dams, fish passage, harvest rates, and hatchery releases. However, such plans inadequately address threats related to human population and economic growth, intra- and interspecific competition, and changes in climate, ocean, and estuarine conditions. Based on reviews conducted on eight conservation and/or recovery plans, we found that though threats resulting from such changes are difficult to model and/or predict, they are especially important for wide-ranging diadromous species. Adaptive management is also a critical but often inadequately constructed component of those plans. Adaptive management should be designed to respond to evolving knowledge about the fish and their supporting ecosystems; if done properly, it should help improve conservation efforts by decreasing uncertainty regarding known and diffuse threats. We conclude with a general call for environmental managers and planners to reinvigorate the adaptive management process in future management plans, including more explicitly identifying critical uncertainties, implementing monitoring programs to reduce those uncertainties, and explicitly stating what management actions will occur when pre-identified trigger points are reached.

Scenario-based fitted Q-iteration for adaptive control of water reservoir systems under uncertainty

NASA Astrophysics Data System (ADS)

Bertoni, Federica; Giuliani, Matteo; Castelletti, Andrea

2017-04-01

Over recent years, mathematical models have largely been used to support planning and management of water resources systems. Yet, the increasing uncertainties in their inputs - due to increased variability in the hydrological regimes - are a major challenge to the optimal operations of these systems. Such uncertainty, boosted by projected changing climate, violates the stationarity principle generally used for describing hydro-meteorological processes, which assumes time persisting statistical characteristics of a given variable as inferred by historical data. As this principle is unlikely to be valid in the future, the probability density function used for modeling stochastic disturbances (e.g., inflows) becomes an additional uncertain parameter of the problem, which can be described in a deterministic and set-membership based fashion. This study contributes a novel method for designing optimal, adaptive policies for controlling water reservoir systems under climate-related uncertainty. The proposed method, called scenario-based Fitted Q-Iteration (sFQI), extends the original Fitted Q-Iteration algorithm by enlarging the state space to include the space of the uncertain system's parameters (i.e., the uncertain climate scenarios). As a result, sFQI embeds the set-membership uncertainty of the future inflow scenarios in the action-value function and is able to approximate, with a single learning process, the optimal control policy associated to any scenario included in the uncertainty set. The method is demonstrated on a synthetic water system, consisting of a regulated lake operated for ensuring reliable water supply to downstream users. Numerical results show that the sFQI algorithm successfully identifies adaptive solutions to operate the system under different inflow scenarios, which outperform the control policy designed under historical conditions. Moreover, the sFQI policy generalizes over inflow scenarios not directly experienced during the policy design, thus alleviating the risk of mis-adaptation, namely the design of a solution fully adapted to a scenario that is different from the one that will actually realize.

Experiences of Uncertainty in Men With an Elevated PSA.

PubMed

Biddle, Caitlin; Brasel, Alicia; Underwood, Willie; Orom, Heather

2015-05-15

A significant proportion of men, ages 50 to 70 years, have, and continue to receive prostate specific antigen (PSA) tests to screen for prostate cancer (PCa). Approximately 70% of men with an elevated PSA level will not subsequently be diagnosed with PCa. Semistructured interviews were conducted with 13 men with an elevated PSA level who had not been diagnosed with PCa. Uncertainty was prominent in men's reactions to the PSA results, stemming from unanswered questions about the PSA test, PCa risk, and confusion about their management plan. Uncertainty was exacerbated or reduced depending on whether health care providers communicated in lay and empathetic ways, and provided opportunities for question asking. To manage uncertainty, men engaged in information and health care seeking, self-monitoring, and defensive cognition. Results inform strategies for meeting informational needs of men with an elevated PSA and confirm the primary importance of physician communication behavior for open information exchange and uncertainty reduction. © The Author(s) 2015.

From Executive Desks to the Arctic Ocean and Back: A Dynamic Framework for Natural Resource Management

NASA Astrophysics Data System (ADS)

Auad, G.

2017-12-01

The translating of observational evidence for decision- and policy-making needs to be placed within the context of specific organizational structures to achieve efficient and effective natural resource management. To reach that stage, these structures would consistently integrate governance, decision-making, and legislative and policy elements that, as a whole, can be harmoniously coupled to the natural system under consideration, while being aligned toward a high level management goal. Examples will be highlighted where communication structures found in nature connect hierarchical and spatial scales and are the core of effective living and physical systems. Based on these concepts, a framework will be described while linkages and tradeoffs will be established among the different components of the socio-ecological system being addressed. The importance for decision- and policy-makers to define a continuous learning dynamics will be highlighted as a way to ensure enhanced (scientific and traditional) knowledge over time and therefore reduced uncertainty at decision moment. The need for an overarching management goal will be addressed while its underpinnings will be described and conceptually linked through different internal and external communication models.

Taking Control: The Efficacy and Durability of a Peer-Led Uncertainty Management Intervention for People Recently Diagnosed With HIV.

PubMed

Brashers, Dale E; Basinger, Erin D; Rintamaki, Lance S; Caughlin, John P; Para, Michael

2017-01-01

HIV creates substantial uncertainty for people infected with the virus, which subsequently affects a host of psychosocial outcomes critical to successful management of the disease. This study assessed the efficacy and durability of a theoretically driven, one-on-one peer support intervention designed to facilitate uncertainty management and enhance psychosocial functioning for patients newly diagnosed with HIV. Using a pretest-posttest control group design, 98 participants received information and training in specific communication strategies (e.g., disclosing to friends and family, eliciting social support, talking to health care providers, using the Internet to gather information, and building social networks through AIDS service organizations). Participants in the experimental group attended six 1-hour sessions, whereas control participants received standard of care for 12 months (after which they received the intervention). Over time, participants in the intervention fared significantly better regarding (a) illness uncertainty, (b) depression, and (c) satisfaction with social support than did those in the control group. Given the utility and cost-effectiveness of this intervention and the uncertainty of a multitude of medical diagnoses and disease experiences, further work is indicated to determine how this program could be expanded to other illnesses and to address related factors, such as treatment adherence and clinical outcomes.

A review of uncertainty research in impact assessment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Leung, Wanda, E-mail: wanda.leung@usask.ca; Noble, Bram, E-mail: b.noble@usask.ca; Gunn, Jill, E-mail: jill.gunn@usask.ca

2015-01-15

This paper examines uncertainty research in Impact Assessment (IA) and the focus of attention of the IA scholarly literature. We do so by first exploring ‘outside’ the IA literature, identifying three main themes of uncertainty research, and then apply these themes to examine the focus of scholarly research on uncertainty ‘inside’ IA. Based on a search of the database Scopus, we identified 134 journal papers published between 1970 and 2013 that address uncertainty in IA, 75% of which were published since 2005. We found that 90% of IA research addressing uncertainty focused on uncertainty in the practice of IA, includingmore » uncertainty in impact predictions, models and managing environmental impacts. Notwithstanding early guidance on uncertainty treatment in IA from the 1980s, we found no common, underlying conceptual framework that was guiding research on uncertainty in IA practice. Considerably less attention, only 9% of papers, focused on uncertainty communication, disclosure and decision-making under uncertain conditions, the majority of which focused on the need to disclose uncertainties as opposed to providing guidance on how to do so and effectively use that information to inform decisions. Finally, research focused on theory building for explaining human behavior with respect to uncertainty avoidance constituted only 1% of the IA published literature. We suggest the need for further conceptual framework development for researchers focused on identifying and addressing uncertainty in IA practice; the need for guidance on how best to communicate uncertainties in practice, versus criticizing practitioners for not doing so; research that explores how best to interpret and use disclosures about uncertainty when making decisions about project approvals, and the implications of doing so; and academic theory building and exploring the utility of existing theories to better understand and explain uncertainty avoidance behavior in IA. - Highlights: • We identified three main themes of uncertainty research in 134 papers from the scholarly literature. • The majority of research has focused on better methods for managing uncertainty in predictions. • Uncertainty disclosure is demanded of practitioners, but there is little guidance on how to do so. • There is limited theoretical explanation as to why uncertainty is avoided or not disclosed. • Conceptual, practical and theoretical guidance are required for IA uncertainty consideration.« less

Optimization of monitoring networks based on uncertainty quantification of model predictions of contaminant transport

NASA Astrophysics Data System (ADS)

Vesselinov, V. V.; Harp, D.

2010-12-01

The process of decision making to protect groundwater resources requires a detailed estimation of uncertainties in model predictions. Various uncertainties associated with modeling a natural system, such as: (1) measurement and computational errors; (2) uncertainties in the conceptual model and model-parameter estimates; (3) simplifications in model setup and numerical representation of governing processes, contribute to the uncertainties in the model predictions. Due to this combination of factors, the sources of predictive uncertainties are generally difficult to quantify individually. Decision support related to optimal design of monitoring networks requires (1) detailed analyses of existing uncertainties related to model predictions of groundwater flow and contaminant transport, (2) optimization of the proposed monitoring network locations in terms of their efficiency to detect contaminants and provide early warning. We apply existing and newly-proposed methods to quantify predictive uncertainties and to optimize well locations. An important aspect of the analysis is the application of newly-developed optimization technique based on coupling of Particle Swarm and Levenberg-Marquardt optimization methods which proved to be robust and computationally efficient. These techniques and algorithms are bundled in a software package called MADS. MADS (Model Analyses for Decision Support) is an object-oriented code that is capable of performing various types of model analyses and supporting model-based decision making. The code can be executed under different computational modes, which include (1) sensitivity analyses (global and local), (2) Monte Carlo analysis, (3) model calibration, (4) parameter estimation, (5) uncertainty quantification, and (6) model selection. The code can be externally coupled with any existing model simulator through integrated modules that read/write input and output files using a set of template and instruction files (consistent with the PEST I/O protocol). MADS can also be internally coupled with a series of built-in analytical simulators. MADS provides functionality to work directly with existing control files developed for the code PEST (Doherty 2009). To perform the computational modes mentioned above, the code utilizes (1) advanced Latin-Hypercube sampling techniques (including Improved Distributed Sampling), (2) various gradient-based Levenberg-Marquardt optimization methods, (3) advanced global optimization methods (including Particle Swarm Optimization), and (4) a selection of alternative objective functions. The code has been successfully applied to perform various model analyses related to environmental management of real contamination sites. Examples include source identification problems, quantification of uncertainty, model calibration, and optimization of monitoring networks. The methodology and software codes are demonstrated using synthetic and real case studies where monitoring networks are optimized taking into account the uncertainty in model predictions of contaminant transport.

Development of robust building energy demand-side control strategy under uncertainty

NASA Astrophysics Data System (ADS)

Kim, Sean Hay

The potential of carbon emission regulations applied to an individual building will encourage building owners to purchase utility-provided green power or to employ onsite renewable energy generation. As both cases are based on intermittent renewable energy sources, demand side control is a fundamental precondition for maximizing the effectiveness of using renewable energy sources. Such control leads to a reduction in peak demand and/or in energy demand variability, therefore, such reduction in the demand profile eventually enhances the efficiency of an erratic supply of renewable energy. The combined operation of active thermal energy storage and passive building thermal mass has shown substantial improvement in demand-side control performance when compared to current state-of-the-art demand-side control measures. Specifically, "model-based" optimal control for this operation has the potential to significantly increase performance and bring economic advantages. However, due to the uncertainty in certain operating conditions in the field its control effectiveness could be diminished and/or seriously damaged, which results in poor performance. This dissertation pursues improvements of current demand-side controls under uncertainty by proposing a robust supervisory demand-side control strategy that is designed to be immune from uncertainty and perform consistently under uncertain conditions. Uniqueness and superiority of the proposed robust demand-side controls are found as below: a. It is developed based on fundamental studies about uncertainty and a systematic approach to uncertainty analysis. b. It reduces variability of performance under varied conditions, and thus avoids the worst case scenario. c. It is reactive in cases of critical "discrepancies" observed caused by the unpredictable uncertainty that typically scenario uncertainty imposes, and thus it increases control efficiency. This is obtainable by means of i) multi-source composition of weather forecasts including both historical archive and online sources and ii) adaptive Multiple model-based controls (MMC) to mitigate detrimental impacts of varying scenario uncertainties. The proposed robust demand-side control strategy verifies its outstanding demand-side control performance in varied and non-indigenous conditions compared to the existing control strategies including deterministic optimal controls. This result reemphasizes importance of the demand-side control for a building in the global carbon economy. It also demonstrates a capability of risk management of the proposed robust demand-side controls in highly uncertain situations, which eventually attains the maximum benefit in both theoretical and practical perspectives.

Interventions to Manage Uncertainty and Fear of Recurrence in Female Breast Cancer Survivors: A Review of the Literature.

PubMed

Dawson, Gretchen; Madsen, Lydia T; Dains, Joyce E

2016-12-01

Fear of cancer recurrence (FCR) is one of the largest unmet needs in the breast cancer survivor population. This review addresses this unmet need with the question. The purpose of this article is to better understand potential interventions to manage FCR when caring for breast cancer survivors. Databases used were PubMed, CINAHL®, Google Scholar, EMBASE, and Scopus. Articles published in English from 2009-2014 with female breast cancer survivors and interventions that address FCR as an endpoint or outcome measure or objectively illustrate an improvement in FCR were included. One hundred ninety-eight articles were initially identified in this literature review search. Upon detailed review of content for relevance, seven articles met criteria to be included in this review. This literature review provided current evidence of published interventions to manage uncertainty in the female breast cancer survivor population, as well as future research recommendations. Interventions surrounding being mindful, managing uncertainty, having more effective patient-provider communication, and handling stress through counseling are options for managing FCR.

Optimal infrastructure maintenance scheduling problem under budget uncertainty.

DOT National Transportation Integrated Search

2010-05-01

This research addresses a general class of infrastructure asset management problems. Infrastructure : agencies usually face budget uncertainties that will eventually lead to suboptimal planning if : maintenance decisions are made without taking the u...

Structured decision making as a framework for large-scale wildlife harvest management decisions

USGS Publications Warehouse

Robinson, Kelly F.; Fuller, Angela K.; Hurst, Jeremy E.; Swift, Bryan L.; Kirsch, Arthur; Farquhar, James F.; Decker, Daniel J.; Siemer, William F.

2016-01-01

Fish and wildlife harvest management at large spatial scales often involves making complex decisions with multiple objectives and difficult tradeoffs, population demographics that vary spatially, competing stakeholder values, and uncertainties that might affect management decisions. Structured decision making (SDM) provides a formal decision analytic framework for evaluating difficult decisions by breaking decisions into component parts and separating the values of stakeholders from the scientific evaluation of management actions and uncertainty. The result is a rigorous, transparent, and values-driven process. This decision-aiding process provides the decision maker with a more complete understanding of the problem and the effects of potential management actions on stakeholder values, as well as how key uncertainties can affect the decision. We use a case study to illustrate how SDM can be used as a decision-aiding tool for management decision making at large scales. We evaluated alternative white-tailed deer (Odocoileus virginianus) buck-harvest regulations in New York designed to reduce harvest of yearling bucks, taking into consideration the values of the state wildlife agency responsible for managing deer, as well as deer hunters. We incorporated tradeoffs about social, ecological, and economic management concerns throughout the state. Based on the outcomes of predictive models, expert elicitation, and hunter surveys, the SDM process identified management alternatives that optimized competing objectives. The SDM process provided biologists and managers insight about aspects of the buck-harvest decision that helped them adopt a management strategy most compatible with diverse hunter values and management concerns.

Water shortage risk assessment considering large-scale regional transfers: a copula-based uncertainty case study in Lunan, China.

PubMed

Gao, Xueping; Liu, Yinzhu; Sun, Bowen

2018-06-05

The risk of water shortage caused by uncertainties, such as frequent drought, varied precipitation, multiple water resources, and different water demands, brings new challenges to the water transfer projects. Uncertainties exist for transferring water and local surface water; therefore, the relationship between them should be thoroughly studied to prevent water shortage. For more effective water management, an uncertainty-based water shortage risk assessment model (UWSRAM) is developed to study the combined effect of multiple water resources and analyze the shortage degree under uncertainty. The UWSRAM combines copula-based Monte Carlo stochastic simulation and the chance-constrained programming-stochastic multiobjective optimization model, using the Lunan water-receiving area in China as an example. Statistical copula functions are employed to estimate the joint probability of available transferring water and local surface water and sampling from the multivariate probability distribution, which are used as inputs for the optimization model. The approach reveals the distribution of water shortage and is able to emphasize the importance of improving and updating transferring water and local surface water management, and examine their combined influence on water shortage risk assessment. The possible available water and shortages can be calculated applying the UWSRAM, also with the corresponding allocation measures under different water availability levels and violating probabilities. The UWSRAM is valuable for mastering the overall multi-water resource and water shortage degree, adapting to the uncertainty surrounding water resources, establishing effective water resource planning policies for managers and achieving sustainable development.

Defining and Measuring Diagnostic Uncertainty in Medicine: A Systematic Review.

PubMed

Bhise, Viraj; Rajan, Suja S; Sittig, Dean F; Morgan, Robert O; Chaudhary, Pooja; Singh, Hardeep

2018-01-01

Physicians routinely encounter diagnostic uncertainty in practice. Despite its impact on health care utilization, costs and error, measurement of diagnostic uncertainty is poorly understood. We conducted a systematic review to describe how diagnostic uncertainty is defined and measured in medical practice. We searched OVID Medline and PsycINFO databases from inception until May 2017 using a combination of keywords and Medical Subject Headings (MeSH). Additional search strategies included manual review of references identified in the primary search, use of a topic-specific database (AHRQ-PSNet) and expert input. We specifically focused on articles that (1) defined diagnostic uncertainty; (2) conceptualized diagnostic uncertainty in terms of its sources, complexity of its attributes or strategies for managing it; or (3) attempted to measure diagnostic uncertainty. We identified 123 articles for full review, none of which defined diagnostic uncertainty. Three attributes of diagnostic uncertainty were relevant for measurement: (1) it is a subjective perception experienced by the clinician; (2) it has the potential to impact diagnostic evaluation-for example, when inappropriately managed, it can lead to diagnostic delays; and (3) it is dynamic in nature, changing with time. Current methods for measuring diagnostic uncertainty in medical practice include: (1) asking clinicians about their perception of uncertainty (surveys and qualitative interviews), (2) evaluating the patient-clinician encounter (such as by reviews of medical records, transcripts of patient-clinician communication and observation), and (3) experimental techniques (patient vignette studies). The term "diagnostic uncertainty" lacks a clear definition, and there is no comprehensive framework for its measurement in medical practice. Based on review findings, we propose that diagnostic uncertainty be defined as a "subjective perception of an inability to provide an accurate explanation of the patient's health problem." Methodological advancements in measuring diagnostic uncertainty can improve our understanding of diagnostic decision-making and inform interventions to reduce diagnostic errors and overuse of health care resources.

"It's not if I get cancer, it's when I get cancer": BRCA-positive patients' (un)certain health experiences regarding hereditary breast and ovarian cancer risk.

PubMed

Dean, Marleah

2016-08-01

Women with a harmful mutation in the BReast CAncer (BRCA) gene are at significantly increased risk of developing hereditary breast and ovarian cancer (HBOC) during their lifetime, compared to those without. Such patients-with a genetic predisposition to develop cancer but who have not yet been diagnosed with cancer-live in a constant state of uncertainty and wonder not if they might get cancer but when. Framed by uncertainty management theory, the purpose of this study was to explore BRCA-positive patients' health experiences after testing positive for the BRCA genetic mutation, specifically identifying their sources of uncertainty. Thirty-four, qualitative interviews were conducted with female patients. Participants responded to online research postings on the non-profit organization Facing Our Risk of Cancer Empowered's (FORCE) message board and social media pages as well as HBOC-specific Facebook groups. The interview data were coded using the constant comparison method. Two major themes representing BRCA-positive patients' sources of uncertainty regarding their genetic predisposition and health experiences emerged from the data. Medical uncertainty included the following three subthemes: the unknown future, medical appointments, and personal cancer scares. Familial uncertainty encompassed the subthemes traumatic family cancer memories and motherhood. Overall, the study supports and extends existing research on uncertainty-revealing uncertainty is inherent in BRCA-positive patients' health experiences-and offers new insight regarding uncertainty management and HBOC risk. Copyright © 2016 Elsevier Ltd. All rights reserved.

A fuzzy chance-constrained programming model with type 1 and type 2 fuzzy sets for solid waste management under uncertainty

NASA Astrophysics Data System (ADS)

Ma, Xiaolin; Ma, Chi; Wan, Zhifang; Wang, Kewei

2017-06-01

Effective management of municipal solid waste (MSW) is critical for urban planning and development. This study aims to develop an integrated type 1 and type 2 fuzzy sets chance-constrained programming (ITFCCP) model for tackling regional MSW management problem under a fuzzy environment, where waste generation amounts are supposed to be type 2 fuzzy variables and treated capacities of facilities are assumed to be type 1 fuzzy variables. The evaluation and expression of uncertainty overcome the drawbacks in describing fuzzy possibility distributions as oversimplified forms. The fuzzy constraints are converted to their crisp equivalents through chance-constrained programming under the same or different confidence levels. Regional waste management of the City of Dalian, China, was used as a case study for demonstration. The solutions under various confidence levels reflect the trade-off between system economy and reliability. It is concluded that the ITFCCP model is capable of helping decision makers to generate reasonable waste-allocation alternatives under uncertainties.

Sustainable water management under future uncertainty with eco-engineering decision scaling

NASA Astrophysics Data System (ADS)

Poff, N. Leroy; Brown, Casey M.; Grantham, Theodore E.; Matthews, John H.; Palmer, Margaret A.; Spence, Caitlin M.; Wilby, Robert L.; Haasnoot, Marjolijn; Mendoza, Guillermo F.; Dominique, Kathleen C.; Baeza, Andres

2016-01-01

Managing freshwater resources sustainably under future climatic and hydrological uncertainty poses novel challenges. Rehabilitation of ageing infrastructure and construction of new dams are widely viewed as solutions to diminish climate risk, but attaining the broad goal of freshwater sustainability will require expansion of the prevailing water resources management paradigm beyond narrow economic criteria to include socially valued ecosystem functions and services. We introduce a new decision framework, eco-engineering decision scaling (EEDS), that explicitly and quantitatively explores trade-offs in stakeholder-defined engineering and ecological performance metrics across a range of possible management actions under unknown future hydrological and climate states. We illustrate its potential application through a hypothetical case study of the Iowa River, USA. EEDS holds promise as a powerful framework for operationalizing freshwater sustainability under future hydrological uncertainty by fostering collaboration across historically conflicting perspectives of water resource engineering and river conservation ecology to design and operate water infrastructure for social and environmental benefits.

Sustainable water management under future uncertainty with eco-engineering decision scaling

USGS Publications Warehouse

Poff, N LeRoy; Brown, Casey M; Grantham, Theodore E.; Matthews, John H; Palmer, Margaret A.; Spence, Caitlin M; Wilby, Robert L.; Haasnoot, Marjolijn; Mendoza, Guillermo F; Dominique, Kathleen C; Baeza, Andres

2015-01-01

Managing freshwater resources sustainably under future climatic and hydrological uncertainty poses novel challenges. Rehabilitation of ageing infrastructure and construction of new dams are widely viewed as solutions to diminish climate risk, but attaining the broad goal of freshwater sustainability will require expansion of the prevailing water resources management paradigm beyond narrow economic criteria to include socially valued ecosystem functions and services. We introduce a new decision framework, eco-engineering decision scaling (EEDS), that explicitly and quantitatively explores trade-offs in stakeholder-defined engineering and ecological performance metrics across a range of possible management actions under unknown future hydrological and climate states. We illustrate its potential application through a hypothetical case study of the Iowa River, USA. EEDS holds promise as a powerful framework for operationalizing freshwater sustainability under future hydrological uncertainty by fostering collaboration across historically conflicting perspectives of water resource engineering and river conservation ecology to design and operate water infrastructure for social and environmental benefits.

Risk analysis under uncertainty, the precautionary principle, and the new EU chemicals strategy.

PubMed

Rogers, Michael D

2003-06-01

Three categories of uncertainty in relation to risk assessment are defined; uncertainty in effect, uncertainty in cause, and uncertainty in the relationship between a hypothesised cause and effect. The Precautionary Principle (PP) relates to the third type of uncertainty. Three broad descriptions of the PP are set out, uncertainty justifies action, uncertainty requires action, and uncertainty requires a reversal of the burden of proof for risk assessments. The application of the PP is controversial but what matters in practise is the precautionary action (PA) that follows. The criteria by which the PAs should be judged are detailed. This framework for risk assessment and management under uncertainty is then applied to the envisaged European system for the regulation of chemicals. A new EU regulatory system has been proposed which shifts the burden of proof concerning risk assessments from the regulator to the producer, and embodies the PP in all three of its main regulatory stages. The proposals are critically discussed in relation to three chemicals, namely, atrazine (an endocrine disrupter), cadmium (toxic and possibly carcinogenic), and hydrogen fluoride (a toxic, high-production-volume chemical). Reversing the burden of proof will speed up the regulatory process but the examples demonstrate that applying the PP appropriately, and balancing the countervailing risks and the socio-economic benefits, will continue to be a difficult task for the regulator. The paper concludes with a discussion of the role of precaution in the management of change and of the importance of trust in the effective regulation of uncertain risks.

An adaptive-management framework for optimal control of hiking near golden eagle nests in Denali National Park

USGS Publications Warehouse

Martin, Julien; Fackler, Paul L.; Nichols, James D.; Runge, Michael C.; McIntyre, Carol L.; Lubow, Bruce L.; McCluskie, Maggie C.; Schmutz, Joel A.

2011-01-01

Unintended effects of recreational activities in protected areas are of growing concern. We used an adaptive-management framework to develop guidelines for optimally managing hiking activities to maintain desired levels of territory occupancy and reproductive success of Golden Eagles (Aquila chrysaetos) in Denali National Park (Alaska, U.S.A.). The management decision was to restrict human access (hikers) to particular nesting territories to reduce disturbance. The management objective was to minimize restrictions on hikers while maintaining reproductive performance of eagles above some specified level. We based our decision analysis on predictive models of site occupancy of eagles developed using a combination of expert opinion and data collected from 93 eagle territories over 20 years. The best predictive model showed that restricting human access to eagle territories had little effect on occupancy dynamics. However, when considering important sources of uncertainty in the models, including environmental stochasticity, imperfect detection of hares on which eagles prey, and model uncertainty, restricting access of territories to hikers improved eagle reproduction substantially. An adaptive management framework such as ours may help reduce uncertainty of the effects of hiking activities on Golden Eagles

Isohaline position as a habitat indicator for estuarine populations

USGS Publications Warehouse

Jassby, Alan D.; Kimmerer, W.J.; Monismith, Stephen G.; Armor, C.; Cloern, James E.; Powell, T.M.; Vedlinski, Timothy J.

1995-01-01

The striped bass survival data were also used to illustrate a related important point: incorporating additionalexplanatory variables may decrease the prediction error for a population or process, but it can increase theuncertainty in parameter estimates and management strategies based on these estimates. Even in cases wherethe uncertainty is currently too large to guide management decisions, an uncertainty analysis can identify themost practical direction for future data acquisition.

Estimating Uncertainties in the Multi-Instrument SBUV Profile Ozone Merged Data Set

NASA Technical Reports Server (NTRS)

Frith, Stacey; Stolarski, Richard

2015-01-01

The MOD data set is uniquely qualified for use in long-term ozone analysis because of its long record, high spatial coverage, and consistent instrument design and algorithm. The estimated MOD uncertainty term significantly increases the uncertainty over the statistical error alone. Trends in the post-2000 period are generally positive in the upper stratosphere, but only significant at 1-1.6 hPa. Remaining uncertainties not yet included in the Monte Carlo model are Smoothing Error ( 1 from 10 to 1 hPa) Relative calibration uncertainty between N11 and N17Seasonal cycle differences between SBUV records.

Uncertainty in projected climate change arising from uncertain fossil-fuel emission factors

NASA Astrophysics Data System (ADS)

Quilcaille, Y.; Gasser, T.; Ciais, P.; Lecocq, F.; Janssens-Maenhout, G.; Mohr, S.

2018-04-01

Emission inventories are widely used by the climate community, but their uncertainties are rarely accounted for. In this study, we evaluate the uncertainty in projected climate change induced by uncertainties in fossil-fuel emissions, accounting for non-CO2 species co-emitted with the combustion of fossil-fuels and their use in industrial processes. Using consistent historical reconstructions and three contrasted future projections of fossil-fuel extraction from Mohr et al we calculate CO2 emissions and their uncertainties stemming from estimates of fuel carbon content, net calorific value and oxidation fraction. Our historical reconstructions of fossil-fuel CO2 emissions are consistent with other inventories in terms of average and range. The uncertainties sum up to a ±15% relative uncertainty in cumulative CO2 emissions by 2300. Uncertainties in the emissions of non-CO2 species associated with the use of fossil fuels are estimated using co-emission ratios varying with time. Using these inputs, we use the compact Earth system model OSCAR v2.2 and a Monte Carlo setup, in order to attribute the uncertainty in projected global surface temperature change (ΔT) to three sources of uncertainty, namely on the Earth system’s response, on fossil-fuel CO2 emission and on non-CO2 co-emissions. Under the three future fuel extraction scenarios, we simulate the median ΔT to be 1.9, 2.7 or 4.0 °C in 2300, with an associated 90% confidence interval of about 65%, 52% and 42%. We show that virtually all of the total uncertainty is attributable to the uncertainty in the future Earth system’s response to the anthropogenic perturbation. We conclude that the uncertainty in emission estimates can be neglected for global temperature projections in the face of the large uncertainty in the Earth system response to the forcing of emissions. We show that this result does not hold for all variables of the climate system, such as the atmospheric partial pressure of CO2 and the radiative forcing of tropospheric ozone, that have an emissions-induced uncertainty representing more than 40% of the uncertainty in the Earth system’s response.

Combinations of Earth Orientation Measurements: SPACE2001, COMB2001, and POLE2001

NASA Technical Reports Server (NTRS)

Gross, Richard S.

2002-01-01

Independent Earth-orientation measurements taken by the space-geodetic techniques of lunar and satellite laser ranging, very long baseline interferometry, and the global positioning system have been combined using a Kalman filter. The resulting combined Earth-orientation series, SPACE2001, consists of values and uncertainties for Universal Time, polar motion, and their rates that span from September 28.0, 1976 to January 19.0, 2002 at daily intervals. The space-geodetic measurements used to generate SPACE2001 have been combined with optical astrometric measurements to form two additional combined Earth-orientation series: (1) COMB2001, consisting of values and uncertainties for Universal Time, polar motion, and their rates that span from January 20.0, 1962 to January 15.0, 2002 at five-day intervals, and (2) POLE2001, consisting of values and uncertainties for polar motion and its rates that span from January 20, 1900 to December 21, 2001 at 30.4375-day intervals.

Adaptive Management for Urban Watersheds: The Slavic Village Pilot Project

EPA Science Inventory

Adaptive management is an environmental management strategy that uses an iterative process of decision-making to reduce the uncertainty in environmental management via system monitoring. A central tenet of adaptive management is that management involves a learning process that ca...

Robust Decision Making to Support Water Quality Climate Adaptation: a Case Study in the Chesapeake Bay Watershed

NASA Astrophysics Data System (ADS)

Fischbach, J. R.; Lempert, R. J.; Molina-Perez, E.

2017-12-01

The U.S. Environmental Protection Agency (USEPA), together with state and local partners, develops watershed implementation plans designed to meet water quality standards. Climate uncertainty, along with uncertainty about future land use changes or the performance of water quality best management practices (BMPs), may make it difficult for these implementation plans to meet water quality goals. In this effort, we explored how decision making under deep uncertainty (DMDU) methods such as Robust Decision Making (RDM) could help USEPA and its partners develop implementation plans that are more robust to future uncertainty. The study focuses on one part of the Chesapeake Bay watershed, the Patuxent River, which is 2,479 sq km in area, highly urbanized, and has a rapidly growing population. We simulated the contribution of stormwater contaminants from the Patuxent to the overall Total Maximum Daily Load (TMDL) for the Chesapeake Bay under multiple scenarios reflecting climate and other uncertainties. Contaminants considered included nitrogen, phosphorus, and sediment loads. The assessment included a large set of scenario simulations using the USEPA Chesapeake Bay Program's Phase V watershed model. Uncertainties represented in the analysis included 18 downscaled climate projections (based on 6 general circulation models and 3 emissions pathways), 12 land use scenarios with different population projections and development patterns, and alternative assumptions about BMP performance standards and efficiencies associated with different suites of stormwater BMPs. Finally, we developed cost estimates for each of the performance standards and compared cost to TMDL performance as a key tradeoff for future water quality management decisions. In this talk, we describe how this research can help inform climate-related decision support at USEPA's Chesapeake Bay Program, and more generally how RDM and other DMDU methods can support improved water quality management under climate uncertainty.

Sources of uncertanity as a basis to fill the information gap in a response to flood

NASA Astrophysics Data System (ADS)

Kekez, Toni; Knezic, Snjezana

2016-04-01

Taking into account uncertainties in flood risk management remains a challenge due to difficulties in choosing adequate structural and/or non-structural risk management options. Despite stated measures wrong decisions are often being made when flood occurs. Parameter and structural uncertainties which include model and observation errors as well as lack of knowledge about system characteristics are the main considerations. Real time flood risk assessment methods are predominantly based on measured water level values and vulnerability as well as other relevant characteristics of flood affected area. The goal of this research is to identify sources of uncertainties and to minimize information gap between the point where the water level is measured and the affected area, taking into consideration main uncertainties that can affect risk value at the observed point or section of the river. Sources of uncertainties are identified and determined using system analysis approach and relevant uncertainties are included in the risk assessment model. With such methodological approach it is possible to increase response time with more effective risk assessment which includes uncertainty propagation model. Response phase could be better planned with adequate early warning systems resulting in more time and less costs to help affected areas and save human lives. Reliable and precise information is necessary to raise emergency operability level in order to enhance safety of citizens and reducing possible damage. The results of the EPISECC (EU funded FP7) project are used to validate potential benefits of this research in order to improve flood risk management and response methods. EPISECC aims at developing a concept of a common European Information Space for disaster response which, among other disasters, considers the floods.

Translating Radiometric Requirements for Satellite Sensors to Match International Standards.

PubMed

Pearlman, Aaron; Datla, Raju; Kacker, Raghu; Cao, Changyong

2014-01-01

International scientific standards organizations created standards on evaluating uncertainty in the early 1990s. Although scientists from many fields use these standards, they are not consistently implemented in the remote sensing community, where traditional error analysis framework persists. For a satellite instrument under development, this can create confusion in showing whether requirements are met. We aim to create a methodology for translating requirements from the error analysis framework to the modern uncertainty approach using the product level requirements of the Advanced Baseline Imager (ABI) that will fly on the Geostationary Operational Environmental Satellite R-Series (GOES-R). In this paper we prescribe a method to combine several measurement performance requirements, written using a traditional error analysis framework, into a single specification using the propagation of uncertainties formula. By using this approach, scientists can communicate requirements in a consistent uncertainty framework leading to uniform interpretation throughout the development and operation of any satellite instrument.

Translating Radiometric Requirements for Satellite Sensors to Match International Standards

PubMed Central

Pearlman, Aaron; Datla, Raju; Kacker, Raghu; Cao, Changyong

2014-01-01

International scientific standards organizations created standards on evaluating uncertainty in the early 1990s. Although scientists from many fields use these standards, they are not consistently implemented in the remote sensing community, where traditional error analysis framework persists. For a satellite instrument under development, this can create confusion in showing whether requirements are met. We aim to create a methodology for translating requirements from the error analysis framework to the modern uncertainty approach using the product level requirements of the Advanced Baseline Imager (ABI) that will fly on the Geostationary Operational Environmental Satellite R-Series (GOES-R). In this paper we prescribe a method to combine several measurement performance requirements, written using a traditional error analysis framework, into a single specification using the propagation of uncertainties formula. By using this approach, scientists can communicate requirements in a consistent uncertainty framework leading to uniform interpretation throughout the development and operation of any satellite instrument. PMID:26601032

Chronic Wasting Disease: Transmission Mechanisms and the Possibility of Harvest Management

PubMed Central

Potapov, Alex; Merrill, Evelyn; Pybus, Margo; Lewis, Mark A.

2016-01-01

We develop a model of CWD management by nonselective deer harvest, currently the most feasible approach available for managing CWD in wild populations. We use the model to explore the effects of 6 common harvest strategies on disease prevalence and to identify potential optimal harvest policies for reducing disease prevalence without population collapse. The model includes 4 deer categories (juveniles, adult females, younger adult males, older adult males) that may be harvested at different rates, a food-based carrying capacity, which influences juvenile survival but not adult reproduction or survival, and seasonal force of infection terms for each deer category under differing frequency-dependent transmission dynamics resulting from environmental and direct contact mechanisms. Numerical experiments show that the interval of transmission coefficients β where the disease can be controlled is generally narrow and efficiency of a harvest policy to reduce disease prevalence depends crucially on the details of the disease transmission mechanism, in particular on the intensity of disease transmission to juveniles and the potential differences in the behavior of older and younger males that influence contact rates. Optimal harvest policy to minimize disease prevalence for each of the assumed transmission mechanisms is shown to depend on harvest intensity. Across mechanisms, a harvest that focuses on antlered deer, without distinguishing between age classes reduces disease prevalence most consistently, whereas distinguishing between young and older antlered deer produces higher uncertainty in the harvest effects on disease prevalence. Our results show that, despite uncertainties, a modelling approach can determine classes of harvest strategy that are most likely to be effective in combatting CWD. PMID:26963921

A dominance-based approach to map risks of ecological invasions in the presence of severe uncertainty

Treesearch

Denys Yemshanov; Frank H. Koch; D. Barry Lyons; Mark Ducey; Klaus Koehler

2012-01-01

Aim Uncertainty has been widely recognized as one of the most critical issues in predicting the expansion of ecological invasions. The uncertainty associated with the introduction and spread of invasive organisms influences how pest management decision makers respond to expanding incursions. We present a model-based approach to map risk of ecological invasions that...

Application of Risk Management and Uncertainty Concepts and Methods for Ecosystem Restoration: Principles and Best Practice

DTIC Science & Technology

2012-08-01

habitats for specific species of trout . The report noted that these uncertainties — and the SMEs, who had past experience in such topic areas — were...reduce uncertainty in HREP projects is reflected in the completion of the Pool 11 Islands (UMRS RM 583-593) HREP in 2003. In 1989 the Browns Lake

Managing Uncertainty during a Corporate Acquisition: A Longitudinal Study of Communication During an Airline Acquisition

ERIC Educational Resources Information Center

Kramer, Michael W.; Dougherty, Debbie S.; Pierce, Tamyra A.

2004-01-01

This study examined pilots' (N at T1 = 140; N at T2 = 126; N at T3 = 104) reactions to communication and uncertainty during the acquisition of their airline by another airline. Quantitative results indicate that communication helped to reduce uncertainty and was predictive of affective responses to the acquisition. However, contrary to…

Evaluating critical uncertainty thresholds in a spatial model of forest pest invasion risk

Treesearch

Frank H. Koch; Denys Yemshanov; Daniel W. McKenney; William D. Smith

2009-01-01

Pest risk maps can provide useful decision support in invasive species management, but most do not adequately consider the uncertainty associated with predicted risk values. This study explores how increased uncertainty in a risk model’s numeric assumptions might affect the resultant risk map. We used a spatial stochastic model, integrating components for...

Uncertainty representation of grey numbers and grey sets.

PubMed

Yang, Yingjie; Liu, Sifeng; John, Robert

2014-09-01

In the literature, there is a presumption that a grey set and an interval-valued fuzzy set are equivalent. This presumption ignores the existence of discrete components in a grey number. In this paper, new measurements of uncertainties of grey numbers and grey sets, consisting of both absolute and relative uncertainties, are defined to give a comprehensive representation of uncertainties in a grey number and a grey set. Some simple examples are provided to illustrate that the proposed uncertainty measurement can give an effective representation of both absolute and relative uncertainties in a grey number and a grey set. The relationships between grey sets and interval-valued fuzzy sets are also analyzed from the point of view of the proposed uncertainty representation. The analysis demonstrates that grey sets and interval-valued fuzzy sets provide different but overlapping models for uncertainty representation in sets.

Accounting for Uncertainty and Time Lags in Equivalency Calculations for Offsetting in Aquatic Resources Management Programs

NASA Astrophysics Data System (ADS)

Bradford, Michael J.

2017-10-01

Biodiversity offset programs attempt to minimize unavoidable environmental impacts of anthropogenic activities by requiring offsetting measures in sufficient quantity to counterbalance losses due to the activity. Multipliers, or offsetting ratios, have been used to increase the amount of offsets to account for uncertainty but those ratios have generally been derived from theoretical or ad-hoc considerations. I analyzed uncertainty in the offsetting process in the context of offsetting for impacts to freshwater fisheries productivity. For aquatic habitats I demonstrate that an empirical risk-based approach for evaluating prediction uncertainty is feasible, and if data are available appropriate adjustments to offset requirements can be estimated. For two data-rich examples I estimate multipliers in the range of 1.5:1 - 2.5:1 are sufficient to account for the uncertainty in the prediction of gains and losses. For aquatic habitats adjustments for time delays in the delivery of offset benefits can also be calculated and are likely smaller than those for prediction uncertainty. However, the success of a biodiversity offsetting program will also depend on the management of the other components of risk not addressed by these adjustments.

How Environmental Uncertainty Moderates the Effect of Relative Advantage and Perceived Credibility on the Adoption of Mobile Health Services by Chinese Organizations in the Big Data Era.

PubMed

Chen, Xing; Zhang, Xing

2016-01-01

Despite the importance of adoption of mobile health services by an organization on the diffusion of mobile technology in the big data era, it has received minimal attention in literature. This study investigates how relative advantage and perceived credibility affect an organization's adoption of mobile health services, as well as how environmental uncertainty changes the relationship of relative advantage and perceived credibility with adoption. A research model that integrates relative advantage, perceived credibility, environmental uncertainty, and an organization's intention to use mobile health service is developed. Quantitative data are collected from senior managers and information systems managers in 320 Chinese healthcare organizations. The empirical findings show that while relative advantage and perceived credibility both have positive effects on an organization's intention to use mobile health services, relative advantage plays a more important role than perceived credibility. Moreover, environmental uncertainty positively moderates the effect of relative advantage on an organization's adoption of mobile health services. Thus, mobile health services in environments characterized with high levels of uncertainty are more likely to be adopted because of relative advantage than in environments with low levels of uncertainty.

Accounting for Uncertainty and Time Lags in Equivalency Calculations for Offsetting in Aquatic Resources Management Programs.

PubMed

Bradford, Michael J

2017-10-01

Biodiversity offset programs attempt to minimize unavoidable environmental impacts of anthropogenic activities by requiring offsetting measures in sufficient quantity to counterbalance losses due to the activity. Multipliers, or offsetting ratios, have been used to increase the amount of offsets to account for uncertainty but those ratios have generally been derived from theoretical or ad-hoc considerations. I analyzed uncertainty in the offsetting process in the context of offsetting for impacts to freshwater fisheries productivity. For aquatic habitats I demonstrate that an empirical risk-based approach for evaluating prediction uncertainty is feasible, and if data are available appropriate adjustments to offset requirements can be estimated. For two data-rich examples I estimate multipliers in the range of 1.5:1 - 2.5:1 are sufficient to account for the uncertainty in the prediction of gains and losses. For aquatic habitats adjustments for time delays in the delivery of offset benefits can also be calculated and are likely smaller than those for prediction uncertainty. However, the success of a biodiversity offsetting program will also depend on the management of the other components of risk not addressed by these adjustments.

How Environmental Uncertainty Moderates the Effect of Relative Advantage and Perceived Credibility on the Adoption of Mobile Health Services by Chinese Organizations in the Big Data Era

PubMed Central

2016-01-01

Despite the importance of adoption of mobile health services by an organization on the diffusion of mobile technology in the big data era, it has received minimal attention in literature. This study investigates how relative advantage and perceived credibility affect an organization's adoption of mobile health services, as well as how environmental uncertainty changes the relationship of relative advantage and perceived credibility with adoption. A research model that integrates relative advantage, perceived credibility, environmental uncertainty, and an organization's intention to use mobile health service is developed. Quantitative data are collected from senior managers and information systems managers in 320 Chinese healthcare organizations. The empirical findings show that while relative advantage and perceived credibility both have positive effects on an organization's intention to use mobile health services, relative advantage plays a more important role than perceived credibility. Moreover, environmental uncertainty positively moderates the effect of relative advantage on an organization's adoption of mobile health services. Thus, mobile health services in environments characterized with high levels of uncertainty are more likely to be adopted because of relative advantage than in environments with low levels of uncertainty. PMID:28115932

Evaluation of Global LAI/FPAR Products from VIIRS and MODIS: Spatiotemporal Consistency and Uncertainty

NASA Astrophysics Data System (ADS)

Xu, B.; Park, T.; Yan, K.; Chen, C.; Jing, L.; Qinhuo, L.; Song, W.; Knyazikhin, Y.; Myneni, R.

2017-12-01

The operational EOS MODIS LAI/FPAR algorithm has been successfully transitioned to Suomi-NPP VIIRS by optimizing a small set of configurable parameters in Look-Up-Tables (LUTs). Our preliminary evaluation results show a reasonable agreement between VIIRS and MODIS LAI/FPAR retrievals. However, we still need more comprehensive investigations to assure the continuity of multi-sensor based global LAI/FPAR time series, as the preliminary evaluation was spatiotemporally limited. Here, we used a multi-year (2012-2016) global LAI/FPAR product generated from VIIRS Version 1 and MODIS Collection 6 to evaluate their spatiotemporal consistency at global and site scales. We also quantified the uncertainty of the product by defining and measuring theoretical and physical terms. For both consistency and uncertainty evaluation, we accounted varying biome types and temporal resolutions (i.e., 8-day, seasonal and annual steps). A newly developed approach (a.k.a., Grading and Upscaling Ground Measurements, GUGM) generating accurate validation datasets was implemented to help validating both products. Our results clearly indicate that the LAI/FPAR retrievals from VIIRS and MODIS are quite consistent at different spatio- (i.e., global and site) and temporal- (i.e., 8-day, seasonal and annual) scales. It is also worthy to note that the rate of retrievals from the radiative transfer based main algorithm is also comparable. However, we also saw a relatively larger LAI/FPAR discrepancy over highly dense tropical forests and a slightly less retrieval rate (main algorithm) from VIIRS over high latitude regions. For the uncertainty assessment, the theoretical uncertainty of VIIRS LAI (FPAR) is less than 0.2 (0.06) for non-forest and 0.9 (0.08) for forest, which is nearly identical to those of MODIS. The physical uncertainties of VIIRS and MODIS LAI (FPAR) products assessed by comparing to ground measurements are estimated by 0.60 (0.10) and 0.55 (0.11), respectively. All of the results presented here imbue confidence in assuring the consistency between VIIRS and MODIS LAI/FPAR retrievals, and the feasibility of generating long-term multi-sensor LAI/FPAR time series.

Adaptive management for a turbulent future

USGS Publications Warehouse

Allen, Craig R.; Fontaine, J.J.; Pope, K.L.; Garmestani, A.S.

2011-01-01

The challenges that face humanity today differ from the past because as the scale of human influence has increased, our biggest challenges have become global in nature, and formerly local problems that could be addressed by shifting populations or switching resources, now aggregate (i.e., "scale up") limiting potential management options. Adaptive management is an approach to natural resource management that emphasizes learning through management based on the philosophy that knowledge is incomplete and much of what we think we know is actually wrong. Adaptive management has explicit structure, including careful elucidation of goals, identification of alternative management objectives and hypotheses of causation, and procedures for the collection of data followed by evaluation and reiteration. It is evident that adaptive management has matured, but it has also reached a crossroads. Practitioners and scientists have developed adaptive management and structured decision making techniques, and mathematicians have developed methods to reduce the uncertainties encountered in resource management, yet there continues to be misapplication of the method and misunderstanding of its purpose. Ironically, the confusion over the term "adaptive management" may stem from the flexibility inherent in the approach, which has resulted in multiple interpretations of "adaptive management" that fall along a continuum of complexity and a priori design. Adaptive management is not a panacea for the navigation of 'wicked problems' as it does not produce easy answers, and is only appropriate in a subset of natural resource management problems where both uncertainty and controllability are high. Nonetheless, the conceptual underpinnings of adaptive management are simple; there will always be inherent uncertainty and unpredictability in the dynamics and behavior of complex social-ecological systems, but management decisions must still be made, and whenever possible, we should incorporate learning into management. ?? 2010 .

Adaptive Management for a Turbulent Future

USGS Publications Warehouse

Allen, Craig R.; Fontaine, Joseph J.; Pope, Kevin L.; Garmestani, Ahjond S.

2011-01-01

The challenges that face humanity today differ from the past because as the scale of human influence has increased, our biggest challenges have become global in nature, and formerly local problems that could be addressed by shifting populations or switching resources, now aggregate (i.e., "scale up") limiting potential management options. Adaptive management is an approach to natural resource management that emphasizes learning through management based on the philosophy that knowledge is incomplete and much of what we think we know is actually wrong. Adaptive management has explicit structure, including careful elucidation of goals, identification of alternative management objectives and hypotheses of causation, and procedures for the collection of data followed by evaluation and reiteration. It is evident that adaptive management has matured, but it has also reached a crossroads. Practitioners and scientists have developed adaptive management and structured decision making techniques, and mathematicians have developed methods to reduce the uncertainties encountered in resource management, yet there continues to be misapplication of the method and misunderstanding of its purpose. Ironically, the confusion over the term "adaptive management" may stem from the flexibility inherent in the approach, which has resulted in multiple interpretations of "adaptive management" that fall along a continuum of complexity and a priori design. Adaptive management is not a panacea for the navigation of 'wicked problems' as it does not produce easy answers, and is only appropriate in a subset of natural resource management problems where both uncertainty and controllability are high. Nonetheless, the conceptual underpinnings of adaptive management are simple; there will always be inherent uncertainty and unpredictability in the dynamics and behavior of complex social-ecological systems, but management decisions must still be made, and whenever possible, we should incorporate learning into management. Published by Elsevier Ltd.

Improving the precision of lake ecosystem metabolism estimates by identifying predictors of model uncertainty

USGS Publications Warehouse

Rose, Kevin C.; Winslow, Luke A.; Read, Jordan S.; Read, Emily K.; Solomon, Christopher T.; Adrian, Rita; Hanson, Paul C.

2014-01-01

Diel changes in dissolved oxygen are often used to estimate gross primary production (GPP) and ecosystem respiration (ER) in aquatic ecosystems. Despite the widespread use of this approach to understand ecosystem metabolism, we are only beginning to understand the degree and underlying causes of uncertainty for metabolism model parameter estimates. Here, we present a novel approach to improve the precision and accuracy of ecosystem metabolism estimates by identifying physical metrics that indicate when metabolism estimates are highly uncertain. Using datasets from seventeen instrumented GLEON (Global Lake Ecological Observatory Network) lakes, we discovered that many physical characteristics correlated with uncertainty, including PAR (photosynthetically active radiation, 400-700 nm), daily variance in Schmidt stability, and wind speed. Low PAR was a consistent predictor of high variance in GPP model parameters, but also corresponded with low ER model parameter variance. We identified a threshold (30% of clear sky PAR) below which GPP parameter variance increased rapidly and was significantly greater in nearly all lakes compared with variance on days with PAR levels above this threshold. The relationship between daily variance in Schmidt stability and GPP model parameter variance depended on trophic status, whereas daily variance in Schmidt stability was consistently positively related to ER model parameter variance. Wind speeds in the range of ~0.8-3 m s–1 were consistent predictors of high variance for both GPP and ER model parameters, with greater uncertainty in eutrophic lakes. Our findings can be used to reduce ecosystem metabolism model parameter uncertainty and identify potential sources of that uncertainty.

Geostatistical estimation of forest biomass in interior Alaska combining Landsat-derived tree cover, sampled airborne lidar and field observations

NASA Astrophysics Data System (ADS)

Babcock, Chad; Finley, Andrew O.; Andersen, Hans-Erik; Pattison, Robert; Cook, Bruce D.; Morton, Douglas C.; Alonzo, Michael; Nelson, Ross; Gregoire, Timothy; Ene, Liviu; Gobakken, Terje; Næsset, Erik

2018-06-01

The goal of this research was to develop and examine the performance of a geostatistical coregionalization modeling approach for combining field inventory measurements, strip samples of airborne lidar and Landsat-based remote sensing data products to predict aboveground biomass (AGB) in interior Alaska's Tanana Valley. The proposed modeling strategy facilitates pixel-level mapping of AGB density predictions across the entire spatial domain. Additionally, the coregionalization framework allows for statistically sound estimation of total AGB for arbitrary areal units within the study area---a key advance to support diverse management objectives in interior Alaska. This research focuses on appropriate characterization of prediction uncertainty in the form of posterior predictive coverage intervals and standard deviations. Using the framework detailed here, it is possible to quantify estimation uncertainty for any spatial extent, ranging from pixel-level predictions of AGB density to estimates of AGB stocks for the full domain. The lidar-informed coregionalization models consistently outperformed their counterpart lidar-free models in terms of point-level predictive performance and total AGB precision. Additionally, the inclusion of Landsat-derived forest cover as a covariate further improved estimation precision in regions with lower lidar sampling intensity. Our findings also demonstrate that model-based approaches that do not explicitly account for residual spatial dependence can grossly underestimate uncertainty, resulting in falsely precise estimates of AGB. On the other hand, in a geostatistical setting, residual spatial structure can be modeled within a Bayesian hierarchical framework to obtain statistically defensible assessments of uncertainty for AGB estimates.

Design of a Multi-mode Flight Deck Decision Support System for Airborne Conflict Management

NASA Technical Reports Server (NTRS)

Barhydt, Richard; Krishnamurthy, Karthik

2004-01-01

NASA Langley has developed a multi-mode decision support system for pilots operating in a Distributed Air-Ground Traffic Management (DAG-TM) environment. An Autonomous Operations Planner (AOP) assists pilots in performing separation assurance functions, including conflict detection, prevention, and resolution. Ongoing AOP design has been based on a comprehensive human factors analysis and evaluation results from previous human-in-the-loop experiments with airline pilot test subjects. AOP considers complex flight mode interactions and provides flight guidance to pilots consistent with the current aircraft control state. Pilots communicate goals to AOP by setting system preferences and actively probing potential trajectories for conflicts. To minimize training requirements and improve operational use, AOP design leverages existing alerting philosophies, displays, and crew interfaces common on commercial aircraft. Future work will consider trajectory prediction uncertainties, integration with the TCAS collision avoidance system, and will incorporate enhancements based on an upcoming air-ground coordination experiment.

A facility location model for municipal solid waste management system under uncertain environment.

PubMed

Yadav, Vinay; Bhurjee, A K; Karmakar, Subhankar; Dikshit, A K

2017-12-15

In municipal solid waste management system, decision makers have to develop an insight into the processes namely, waste generation, collection, transportation, processing, and disposal methods. Many parameters (e.g., waste generation rate, functioning costs of facilities, transportation cost, and revenues) in this system are associated with uncertainties. Often, these uncertainties of parameters need to be modeled under a situation of data scarcity for generating probability distribution function or membership function for stochastic mathematical programming or fuzzy mathematical programming respectively, with only information of extreme variations. Moreover, if uncertainties are ignored, then the problems like insufficient capacities of waste management facilities or improper utilization of available funds may be raised. To tackle uncertainties of these parameters in a more efficient manner an algorithm, based on interval analysis, has been developed. This algorithm is applied to find optimal solutions for a facility location model, which is formulated to select economically best locations of transfer stations in a hypothetical urban center. Transfer stations are an integral part of contemporary municipal solid waste management systems, and economic siting of transfer stations ensures financial sustainability of this system. The model is written in a mathematical programming language AMPL with KNITRO as a solver. The developed model selects five economically best locations out of ten potential locations with an optimum overall cost of [394,836, 757,440] Rs. 1 /day ([5906, 11,331] USD/day) approximately. Further, the requirement of uncertainty modeling is explained based on the results of sensitivity analysis. Copyright © 2017 Elsevier B.V. All rights reserved.

Socializing Identity Through Practice: A Mixed Methods Approach to Family Medicine Resident Perspectives on Uncertainty.

PubMed

Ledford, Christy J W; Cafferty, Lauren A; Seehusen, Dean A

2015-01-01

Uncertainty is a central theme in the practice of medicine and particularly primary care. This study explored how family medicine resident physicians react to uncertainty in their practice. This study incorporated a two-phase mixed methods approach, including semi-structured personal interviews (n=21) and longitudinal self-report surveys (n=21) with family medicine residents. Qualitative analysis showed that though residents described uncertainty as an implicit part of their identity, they still developed tactics to minimize or manage uncertainty in their practice. Residents described increasing comfort with uncertainty the longer they practiced and anticipated that growth continuing throughout their careers. Quantitative surveys showed that reactions to uncertainty were more positive over time; however, the difference was not statistically significant. Qualitative and quantitative results show that as family medicine residents practice medicine their perception of uncertainty changes. To reduce uncertainty, residents use relational information-seeking strategies. From a broader view of practice, residents describe uncertainty neutrally, asserting that uncertainty is simply part of the practice of family medicine.

Experimental and modeling uncertainties in the validation of lower hybrid current drive

DOE Office of Scientific and Technical Information (OSTI.GOV)

Poli, F. M.; Bonoli, P. T.; Chilenski, M.

Our work discusses sources of uncertainty in the validation of lower hybrid wave current drive simulations against experiments, by evolving self-consistently the magnetic equilibrium and the heating and current drive profiles, calculated with a combined toroidal ray tracing code and 3D Fokker–Planck solver. The simulations indicate a complex interplay of elements, where uncertainties in the input plasma parameters, in the models and in the transport solver combine and compensate each other, at times. It is concluded that ray-tracing calculations should include a realistic representation of the density and temperature in the region between the confined plasma and the wall, whichmore » is especially important in regimes where the LH waves are weakly damped and undergo multiple reflections from the plasma boundary. Uncertainties introduced in the processing of diagnostic data as well as uncertainties introduced by model approximations are assessed. We show that, by comparing the evolution of the plasma parameters in self-consistent simulations with available data, inconsistencies can be identified and limitations in the models or in the experimental data assessed.« less

Experimental and modeling uncertainties in the validation of lower hybrid current drive

DOE PAGES

Poli, F. M.; Bonoli, P. T.; Chilenski, M.; ...

2016-07-28

Our work discusses sources of uncertainty in the validation of lower hybrid wave current drive simulations against experiments, by evolving self-consistently the magnetic equilibrium and the heating and current drive profiles, calculated with a combined toroidal ray tracing code and 3D Fokker–Planck solver. The simulations indicate a complex interplay of elements, where uncertainties in the input plasma parameters, in the models and in the transport solver combine and compensate each other, at times. It is concluded that ray-tracing calculations should include a realistic representation of the density and temperature in the region between the confined plasma and the wall, whichmore » is especially important in regimes where the LH waves are weakly damped and undergo multiple reflections from the plasma boundary. Uncertainties introduced in the processing of diagnostic data as well as uncertainties introduced by model approximations are assessed. We show that, by comparing the evolution of the plasma parameters in self-consistent simulations with available data, inconsistencies can be identified and limitations in the models or in the experimental data assessed.« less

Many-Objective Robust Decision Making: Managing Water in a Deeply Uncertain World of Change (Invited)

NASA Astrophysics Data System (ADS)

Reed, P. M.

2013-12-01

Water resources planning and management has always required the consideration of uncertainties and the associated system vulnerabilities that they may cause. Despite the long legacy of these issues, our decision support frameworks that have dominated the literature over the past 50 years have struggled with the strongly multiobjective and deeply uncertain nature of water resources systems. The term deep uncertainty (or Knightian uncertainty) refers to factors in planning that strongly shape system risks that maybe unknown and even if known there is a strong lack of consensus on their likelihoods over decadal planning horizons (population growth, financial stability, valuation of resources, ecosystem requirements, evolving water institutions, regulations, etc). In this presentation, I will propose and demonstrate the many-objective robust decision making (MORDM) framework for water resources management under deep uncertainty. The MORDM framework will be demonstrated using an urban water portfolio management test case. In the test case, a city in the Lower Rio Grande Valley managing population and drought pressures must cost effectively maintain the reliability of its water supply by blending permanent rights to reservoir inflows with alternative strategies for purchasing water within the region's water market. The case study illustrates the significant potential pitfalls in the classic Cost-Reliability conception of the problem. Moreover, the proposed MORDM framework exploits recent advances in multiobjective search, visualization, and sensitivity analysis to better expose these pitfalls en route to identifying highly robust water planning alternatives.

An inexact multistage fuzzy-stochastic programming for regional electric power system management constrained by environmental quality.

PubMed

Fu, Zhenghui; Wang, Han; Lu, Wentao; Guo, Huaicheng; Li, Wei

2017-12-01

Electric power system involves different fields and disciplines which addressed the economic system, energy system, and environment system. Inner uncertainty of this compound system would be an inevitable problem. Therefore, an inexact multistage fuzzy-stochastic programming (IMFSP) was developed for regional electric power system management constrained by environmental quality. A model which concluded interval-parameter programming, multistage stochastic programming, and fuzzy probability distribution was built to reflect the uncertain information and dynamic variation in the case study, and the scenarios under different credibility degrees were considered. For all scenarios under consideration, corrective actions were allowed to be taken dynamically in accordance with the pre-regulated policies and the uncertainties in reality. The results suggest that the methodology is applicable to handle the uncertainty of regional electric power management systems and help the decision makers to establish an effective development plan.

Uncertainty and Surprise: Ideas from the Open Discussion

NASA Astrophysics Data System (ADS)

Jordan, Michelle E.

Approximately one hundred participants met for three days at a conference entitled "Uncertainty and Surprise: Questions on Working with the Unexpected and Unknowable." There were a diversity of conference participants ranging from researchers in the natural sciences and researchers in the social sciences (business professors, physicists, ethnographers, nursing school deans) to practitioners and executives in public policy and management (business owners, health care managers, high tech executives), all of whom had varying levels of experience and expertise in dealing with uncertainty and surprise. One group held the traditional, statistical view that uncertainty comes from variance and events that are described by usually unimodal probability law. A second group was comfortable on the one hand with phase diagrams and the phase transitions that come from systems with multi-modal distributions, and on the other hand, with deterministic chaos. A third group was comfortable with the emergent events from evolutionary processes that may not have any probability laws at all.

A Discussion on Uncertainty Representation and Interpretation in Model-Based Prognostics Algorithms based on Kalman Filter Estimation Applied to Prognostics of Electronics Components

NASA Technical Reports Server (NTRS)

Celaya, Jose R.; Saxen, Abhinav; Goebel, Kai

2012-01-01

This article discusses several aspects of uncertainty representation and management for model-based prognostics methodologies based on our experience with Kalman Filters when applied to prognostics for electronics components. In particular, it explores the implications of modeling remaining useful life prediction as a stochastic process and how it relates to uncertainty representation, management, and the role of prognostics in decision-making. A distinction between the interpretations of estimated remaining useful life probability density function and the true remaining useful life probability density function is explained and a cautionary argument is provided against mixing interpretations for the two while considering prognostics in making critical decisions.

Consumer responses to communication about food risk management.

PubMed

van Dijk, Heleen; Houghton, Julie; van Kleef, Ellen; van der Lans, Ivo; Rowe, Gene; Frewer, Lynn

2008-01-01

Recent emphasis within policy circles has been on transparent communication with consumers about food risk management decisions and practices. As a consequence, it is important to develop best practice regarding communication with the public about how food risks are managed. In the current study, the provision of information about regulatory enforcement, proactive risk management, scientific uncertainty and risk variability were manipulated in an experiment designed to examine their impact on consumer perceptions of food risk management quality. In order to compare consumer reactions across different cases, three food hazards were selected (mycotoxins on organically grown food, pesticide residues, and a genetically modified potato). Data were collected from representative samples of consumers in Germany, Greece, Norway and the UK. Scores on the "perceived food risk management quality" scale were subjected to a repeated-measures mixed linear model. Analysis points to a number of important findings, including the existence of cultural variation regarding the impact of risk communication strategies-something which has obvious implications for pan-European risk communication approaches. For example, while communication of uncertainty had a positive impact in Germany, it had a negative impact in the UK and Norway. Results also indicate that food risk managers should inform the public about enforcement of safety laws when communicating scientific uncertainty associated with risks. This has implications for the coordination of risk communication strategies between risk assessment and risk management organizations.

Uncertainty after treatment for prostate cancer: definition, assessment, and management.

PubMed

Yu Ko, Wellam F; Degner, Lesley F

2008-10-01

Prostate cancer is the second most common type of cancer in men living in the United States and the most common type of malignancy in Canadian men, accounting for 186,320 new cases in the United States and 24,700 in Canada in 2008. Uncertainty, a component of all illness experiences, influences how men perceive the processes of treatment and adaptation. The Reconceptualized Uncertainty in Illness Theory explains the chronic nature of uncertainty in cancer survivorship by describing a shift from an emergent acute phase of uncertainty in survivors to a new level of uncertainty that is no longer acute and becomes a part of daily life. Proper assessment of certainty and uncertainty may allow nurses to maximize the effectiveness of patient-provider communication, cognitive reframing, and problem-solving interventions to reduce uncertainty after cancer treatment.

What are the dietary treatment research priorities for inflammatory bowel disease? A short report based on a priority setting partnership with the James Lind Alliance.

PubMed

Lomer, M C; Hart, A L; Verjee, A; Daly, A; Solomon, J; Mclaughlin, J

2017-12-01

Treatment of inflammatory bowel disease (IBD) involves a multidisciplinary approach comprising medical management and sometimes surgery. Although diet is central to IBD management, the optimal diet for patients with IBD is uncertain. A UK collaborative partnership within the James Lind Alliance was set up between patients, clinicians and other stakeholders to develop research priorities in IBD. The aim of this short report is to provide a comprehensive summary of the research priority findings relating to diet in the treatment of IBD. The James Lind Alliance Priority Setting Partnership process was used to develop research priorities in IBD. In brief, patients, clinicians and other stakeholders were invited to provide up to five treatment uncertainties in IBD. These uncertainties were collated, revised and ranked, leading to a final top 10 research questions in IBD. A total of 1671 uncertainties from 531 participants were collected and refined to exclude duplicates leaving 1253 uncertainties. Of these, 348 were categorised as diet-related and grouped according to topic. There were 206 uncertainties related to how diet can be used to treat IBD or alleviate symptoms. Seventy-two percent of diet-related questions came from patients. One broadly diet-related and two diet-specific treatment uncertainties were included in the top 10 research priorities for IBD. Dietary treatment options in the management of IBD are important research priorities. Almost three-quarters of diet related questions came from patients, who were particularly interested in how diet can impact disease activity and symptom control. © 2017 The British Dietetic Association Ltd.

Collaborative decision-analytic framework to maximize resilience of tidal marshes to climate change

USGS Publications Warehouse

Thorne, Karen M.; Mattsson, Brady J.; Takekawa, John Y.; Cummings, Jonathan; Crouse, Debby; Block, Giselle; Bloom, Valary; Gerhart, Matt; Goldbeck, Steve; Huning, Beth; Sloop, Christina; Stewart, Mendel; Taylor, Karen; Valoppi, Laura

2015-01-01

Decision makers that are responsible for stewardship of natural resources face many challenges, which are complicated by uncertainty about impacts from climate change, expanding human development, and intensifying land uses. A systematic process for evaluating the social and ecological risks, trade-offs, and cobenefits associated with future changes is critical to maximize resilience and conserve ecosystem services. This is particularly true in coastal areas where human populations and landscape conversion are increasing, and where intensifying storms and sea-level rise pose unprecedented threats to coastal ecosystems. We applied collaborative decision analysis with a diverse team of stakeholders who preserve, manage, or restore tidal marshes across the San Francisco Bay estuary, California, USA, as a case study. Specifically, we followed a structured decision-making approach, and we using expert judgment developed alternative management strategies to increase the capacity and adaptability to manage tidal marsh resilience while considering uncertainties through 2050. Because sea-level rise projections are relatively confident to 2050, we focused on uncertainties regarding intensity and frequency of storms and funding. Elicitation methods allowed us to make predictions in the absence of fully compatible models and to assess short- and long-term trade-offs. Specifically we addressed two questions. (1) Can collaborative decision analysis lead to consensus among a diverse set of decision makers responsible for environmental stewardship and faced with uncertainties about climate change, funding, and stakeholder values? (2) What is an optimal strategy for the conservation of tidal marshes, and what strategy is robust to the aforementioned uncertainties? We found that when taking this approach, consensus was reached among the stakeholders about the best management strategies to maintain tidal marsh integrity. A Bayesian decision network revealed that a strategy considering sea-level rise and storms explicitly in wetland restoration planning and designs was optimal, and it was robust to uncertainties about management effectiveness and budgets. We found that strategies that avoided explicitly accounting for future climate change had the lowest expected performance based on input from the team. Our decision-analytic framework is sufficiently general to offer an adaptable template, which can be modified for use in other areas that include a diverse and engaged stakeholder group.

BAYESIAN METHODS FOR REGIONAL-SCALE EUTROPHICATION MODELS. (R830887)

EPA Science Inventory

We demonstrate a Bayesian classification and regression tree (CART) approach to link multiple environmental stressors to biological responses and quantify uncertainty in model predictions. Such an approach can: (1) report prediction uncertainty, (2) be consistent with the amou...

Transportation risk management : international practices for program development and project delivery.

DOT National Transportation Integrated Search

2012-08-01

Managing transportation networks, including agency : management, program development, and project : delivery, is extremely complex and fraught with : uncertainty. Administrators, planners, and engineers : coordinate a multitude of organizational and ...

Latent dimensions of social anxiety disorder: A re-evaluation of the Social Phobia Inventory (SPIN).

PubMed

Campbell-Sills, Laura; Espejo, Emmanuel; Ayers, Catherine R; Roy-Byrne, Peter; Stein, Murray B

2015-12-01

The Social Phobia Inventory (SPIN; Connor et al., 2000) is a well-validated instrument for assessing severity of social anxiety disorder (SAD). However, evaluations of its factor structure have produced inconsistent results and this aspect of the scale requires further study. Primary care patients with SAD (N=397) completed the SPIN as part of baseline assessment for the Coordinated Anxiety Learning and Management study (Roy-Byrne et al., 2010). These data were used for exploratory and confirmatory factor analysis of the SPIN. A 3-factor model provided the best fit for the data and factors were interpreted as Fear of Negative Evaluation, Fear of Physical Symptoms, and Fear of Uncertainty in Social Situations. Tests of a second-order model showed that the three factors loaded strongly on a single higher-order factor that was labeled Social Anxiety. Findings are consistent with theories identifying Fear of Negative Evaluation as the core feature of SAD, and with evidence that anxiety sensitivity and intolerance of uncertainty further contribute to SAD severity. Copyright © 2015 Elsevier Ltd. All rights reserved.

Where do uncertainties reside within environmental risk assessments? Expert opinion on uncertainty distributions for pesticide risks to surface water organisms.

PubMed

Skinner, Daniel J C; Rocks, Sophie A; Pollard, Simon J T

2016-12-01

A reliable characterisation of uncertainties can aid uncertainty identification during environmental risk assessments (ERAs). However, typologies can be implemented inconsistently, causing uncertainties to go unidentified. We present an approach based on nine structured elicitations, in which subject-matter experts, for pesticide risks to surface water organisms, validate and assess three dimensions of uncertainty: its level (the severity of uncertainty, ranging from determinism to ignorance); nature (whether the uncertainty is epistemic or aleatory); and location (the data source or area in which the uncertainty arises). Risk characterisation contains the highest median levels of uncertainty, associated with estimating, aggregating and evaluating the magnitude of risks. Regarding the locations in which uncertainty is manifest, data uncertainty is dominant in problem formulation, exposure assessment and effects assessment. The comprehensive description of uncertainty described will enable risk analysts to prioritise the required phases, groups of tasks, or individual tasks within a risk analysis according to the highest levels of uncertainty, the potential for uncertainty to be reduced or quantified, or the types of location-based uncertainty, thus aiding uncertainty prioritisation during environmental risk assessments. In turn, it is expected to inform investment in uncertainty reduction or targeted risk management action. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Identifying and assessing critical uncertainty thresholds in a forest pest risk model

Treesearch

Frank H. Koch; Denys Yemshanov

2015-01-01

Pest risk maps can provide helpful decision support for invasive alien species management, but often fail to address adequately the uncertainty associated with their predicted risk values. Th is chapter explores how increased uncertainty in a risk model’s numeric assumptions (i.e. its principal parameters) might aff ect the resulting risk map. We used a spatial...

Uncertainties in Past and Future Global Water Availability

NASA Astrophysics Data System (ADS)

Sheffield, J.; Kam, J.

2014-12-01

Understanding how water availability changes on inter-annual to decadal time scales and how it may change in the future under climate change are a key part of understanding future stresses on water and food security. Historic evaluations of water availability on regional to global scales are generally based on large-scale model simulations with their associated uncertainties, in particular for long-term changes. Uncertainties are due to model errors and missing processes, parameter uncertainty, and errors in meteorological forcing data. Recent multi-model inter-comparisons and impact studies have highlighted large differences for past reconstructions, due to different simplifying assumptions in the models or the inclusion of physical processes such as CO2 fertilization. Modeling of direct anthropogenic factors such as water and land management also carry large uncertainties in their physical representation and from lack of socio-economic data. Furthermore, there is little understanding of the impact of uncertainties in the meteorological forcings that underpin these historic simulations. Similarly, future changes in water availability are highly uncertain due to climate model diversity, natural variability and scenario uncertainty, each of which dominates at different time scales. In particular, natural climate variability is expected to dominate any externally forced signal over the next several decades. We present results from multi-land surface model simulations of the historic global availability of water in the context of natural variability (droughts) and long-term changes (drying). The simulations take into account the impact of uncertainties in the meteorological forcings and the incorporation of water management in the form of reservoirs and irrigation. The results indicate that model uncertainty is important for short-term drought events, and forcing uncertainty is particularly important for long-term changes, especially uncertainty in precipitation due to reduced gauge density in recent years. We also discuss uncertainties in future projections from these models as driven by bias-corrected and downscaled CMIP5 climate projections, in the context of the balance between climate model robustness and climate model diversity.

Model structures amplify uncertainty in predicted soil carbon responses to climate change.

PubMed

Shi, Zheng; Crowell, Sean; Luo, Yiqi; Moore, Berrien

2018-06-04

Large model uncertainty in projected future soil carbon (C) dynamics has been well documented. However, our understanding of the sources of this uncertainty is limited. Here we quantify the uncertainties arising from model parameters, structures and their interactions, and how those uncertainties propagate through different models to projections of future soil carbon stocks. Both the vertically resolved model and the microbial explicit model project much greater uncertainties to climate change than the conventional soil C model, with both positive and negative C-climate feedbacks, whereas the conventional model consistently predicts positive soil C-climate feedback. Our findings suggest that diverse model structures are necessary to increase confidence in soil C projection. However, the larger uncertainty in the complex models also suggests that we need to strike a balance between model complexity and the need to include diverse model structures in order to forecast soil C dynamics with high confidence and low uncertainty.

Study of Uncertainties of Predicting Space Shuttle Thermal Environment. [impact of heating rate prediction errors on weight of thermal protection system

NASA Technical Reports Server (NTRS)

Fehrman, A. L.; Masek, R. V.

1972-01-01

Quantitative estimates of the uncertainty in predicting aerodynamic heating rates for a fully reusable space shuttle system are developed and the impact of these uncertainties on Thermal Protection System (TPS) weight are discussed. The study approach consisted of statistical evaluations of the scatter of heating data on shuttle configurations about state-of-the-art heating prediction methods to define the uncertainty in these heating predictions. The uncertainties were then applied as heating rate increments to the nominal predicted heating rate to define the uncertainty in TPS weight. Separate evaluations were made for the booster and orbiter, for trajectories which included boost through reentry and touchdown. For purposes of analysis, the vehicle configuration is divided into areas in which a given prediction method is expected to apply, and separate uncertainty factors and corresponding uncertainty in TPS weight derived for each area.

Estimating discharge measurement uncertainty using the interpolated variance estimator

USGS Publications Warehouse

Cohn, T.; Kiang, J.; Mason, R.

2012-01-01

Methods for quantifying the uncertainty in discharge measurements typically identify various sources of uncertainty and then estimate the uncertainty from each of these sources by applying the results of empirical or laboratory studies. If actual measurement conditions are not consistent with those encountered in the empirical or laboratory studies, these methods may give poor estimates of discharge uncertainty. This paper presents an alternative method for estimating discharge measurement uncertainty that uses statistical techniques and at-site observations. This Interpolated Variance Estimator (IVE) estimates uncertainty based on the data collected during the streamflow measurement and therefore reflects the conditions encountered at the site. The IVE has the additional advantage of capturing all sources of random uncertainty in the velocity and depth measurements. It can be applied to velocity-area discharge measurements that use a velocity meter to measure point velocities at multiple vertical sections in a channel cross section.

Conceptual, Methodological, and Ethical Problems in Communicating Uncertainty in Clinical Evidence

PubMed Central

Han, Paul K. J.

2014-01-01

The communication of uncertainty in clinical evidence is an important endeavor that poses difficult conceptual, methodological, and ethical problems. Conceptual problems include logical paradoxes in the meaning of probability and “ambiguity”— second-order uncertainty arising from the lack of reliability, credibility, or adequacy of probability information. Methodological problems include questions about optimal methods for representing fundamental uncertainties and for communicating these uncertainties in clinical practice. Ethical problems include questions about whether communicating uncertainty enhances or diminishes patient autonomy and produces net benefits or harms. This article reviews the limited but growing literature on these problems and efforts to address them and identifies key areas of focus for future research. It is argued that the critical need moving forward is for greater conceptual clarity and consistent representational methods that make the meaning of various uncertainties understandable, and for clinical interventions to support patients in coping with uncertainty in decision making. PMID:23132891

Adaptive management for soil ecosystem services

USGS Publications Warehouse

Birge, Hannah E.; Bevans, Rebecca A.; Allen, Craig R.; Angeler, David G.; Baer, Sara G.; Wall, Diana H.

2016-01-01

Ecosystem services provided by soil include regulation of the atmosphere and climate, primary (including agricultural) production, waste processing, decomposition, nutrient conservation, water purification, erosion control, medical resources, pest control, and disease mitigation. The simultaneous production of these multiple services arises from complex interactions among diverse aboveground and belowground communities across multiple scales. When a system is mismanaged, non-linear and persistent losses in ecosystem services can arise. Adaptive management is an approach to management designed to reduce uncertainty as management proceeds. By developing alternative hypotheses, testing these hypotheses and adjusting management in response to outcomes, managers can probe dynamic mechanistic relationships among aboveground and belowground soil system components. In doing so, soil ecosystem services can be preserved and critical ecological thresholds avoided. Here, we present an adaptive management framework designed to reduce uncertainty surrounding the soil system, even when soil ecosystem services production is not the explicit management objective, so that managers can reach their management goals without undermining soil multifunctionality or contributing to an irreversible loss of soil ecosystem services.

Translating reference doses into allergen management practice: challenges for stakeholders.

PubMed

Crevel, René W R; Baumert, Joseph L; Luccioli, Stefano; Baka, Athanasia; Hattersley, Sue; Hourihane, Jonathan O'B; Ronsmans, Stefan; Timmermans, Frans; Ward, Rachel; Chung, Yong-joo

2014-05-01

Risk assessment describes the impact of a particular hazard as a function of dose and exposure. It forms the foundation of risk management and contributes to the overall decision-making process, but is not its endpoint. This paper outlines a risk analysis framework to underpin decision-making in the area of allergen cross-contact. Specifically, it identifies challenges relevant to each component of the risk analysis: risk assessment (data gaps and output interpretation); risk management (clear and realistic objectives); and risk communication (clear articulation of risk and benefit). Translation of the outputs from risk assessment models into risk management measures must be informed by a clear understanding of the model outputs and their limitations. This will lead to feasible and achievable risk management objectives, grounded in a level of risk accepted by the different stakeholders, thereby avoiding potential unintended detrimental consequences. Clear, consistent and trustworthy communications actively involving all stakeholders underpin these objectives. The conclusions, integrating the perspectives of different stakeholders, offer a vision where clear, science-based benchmarks form the basis of allergen management and labelling, cutting through the current confusion and uncertainty. Finally, the paper recognises that the proposed framework must be adaptable to new and emerging evidence. Copyright © 2014 ILSI Europe. Published by Elsevier Ltd.. All rights reserved.

Integrated planning for regional development planning and water resources management under uncertainty: A case study of Xining, China

NASA Astrophysics Data System (ADS)

Fu, Z. H.; Zhao, H. J.; Wang, H.; Lu, W. T.; Wang, J.; Guo, H. C.

2017-11-01

Economic restructuring, water resources management, population planning and environmental protection are subjects to inner uncertainties of a compound system with objectives which are competitive alternatives. Optimization model and water quality model are usually used to solve problems in a certain aspect. To overcome the uncertainty and coupling in reginal planning management, an interval fuzzy program combined with water quality model for regional planning and management has been developed to obtain the absolutely ;optimal; solution in this study. The model is a hybrid methodology of interval parameter programming (IPP), fuzzy programing (FP), and a general one-dimensional water quality model. The method extends on the traditional interval parameter fuzzy programming method by integrating water quality model into the optimization framework. Meanwhile, as an abstract concept, water resources carrying capacity has been transformed into specific and calculable index. Besides, unlike many of the past studies about water resource management, population as a significant factor has been considered. The results suggested that the methodology was applicable for reflecting the complexities of the regional planning and management systems within the planning period. The government policy makers could establish effective industrial structure, water resources utilization patterns and population planning, and to better understand the tradeoffs among economic, water resources, population and environmental objectives.

Uncertainty management in intelligent design aiding systems

NASA Technical Reports Server (NTRS)

Brown, Donald E.; Gabbert, Paula S.

1988-01-01

A novel approach to uncertainty management which is particularly effective in intelligent design aiding systems for large-scale systems is presented. The use of this approach in the materials handling system design domain is discussed. It is noted that, during any point in the design process, a point value can be obtained for the evaluation of feasible designs; however, the techniques described provide unique solutions for these point values using only the current information about the design environment.

Waste management with recourse: an inexact dynamic programming model containing fuzzy boundary intervals in objectives and constraints.

PubMed

Tan, Q; Huang, G H; Cai, Y P

2010-09-01

The existing inexact optimization methods based on interval-parameter linear programming can hardly address problems where coefficients in objective functions are subject to dual uncertainties. In this study, a superiority-inferiority-based inexact fuzzy two-stage mixed-integer linear programming (SI-IFTMILP) model was developed for supporting municipal solid waste management under uncertainty. The developed SI-IFTMILP approach is capable of tackling dual uncertainties presented as fuzzy boundary intervals (FuBIs) in not only constraints, but also objective functions. Uncertainties expressed as a combination of intervals and random variables could also be explicitly reflected. An algorithm with high computational efficiency was provided to solve SI-IFTMILP. SI-IFTMILP was then applied to a long-term waste management case to demonstrate its applicability. Useful interval solutions were obtained. SI-IFTMILP could help generate dynamic facility-expansion and waste-allocation plans, as well as provide corrective actions when anticipated waste management plans are violated. It could also greatly reduce system-violation risk and enhance system robustness through examining two sets of penalties resulting from variations in fuzziness and randomness. Moreover, four possible alternative models were formulated to solve the same problem; solutions from them were then compared with those from SI-IFTMILP. The results indicate that SI-IFTMILP could provide more reliable solutions than the alternatives. 2010 Elsevier Ltd. All rights reserved.

Uncertainty Assessment of Synthetic Design Hydrographs for Gauged and Ungauged Catchments

NASA Astrophysics Data System (ADS)

Brunner, Manuela I.; Sikorska, Anna E.; Furrer, Reinhard; Favre, Anne-Catherine

2018-03-01

Design hydrographs described by peak discharge, hydrograph volume, and hydrograph shape are essential for engineering tasks involving storage. Such design hydrographs are inherently uncertain as are classical flood estimates focusing on peak discharge only. Various sources of uncertainty contribute to the total uncertainty of synthetic design hydrographs for gauged and ungauged catchments. These comprise model uncertainties, sampling uncertainty, and uncertainty due to the choice of a regionalization method. A quantification of the uncertainties associated with flood estimates is essential for reliable decision making and allows for the identification of important uncertainty sources. We therefore propose an uncertainty assessment framework for the quantification of the uncertainty associated with synthetic design hydrographs. The framework is based on bootstrap simulations and consists of three levels of complexity. On the first level, we assess the uncertainty due to individual uncertainty sources. On the second level, we quantify the total uncertainty of design hydrographs for gauged catchments and the total uncertainty of regionalizing them to ungauged catchments but independently from the construction uncertainty. On the third level, we assess the coupled uncertainty of synthetic design hydrographs in ungauged catchments, jointly considering construction and regionalization uncertainty. We find that the most important sources of uncertainty in design hydrograph construction are the record length and the choice of the flood sampling strategy. The total uncertainty of design hydrographs in ungauged catchments depends on the catchment properties and is not negligible in our case.

Logical hypothesis: Low FODMAP diet to prevent diverticulitis

PubMed Central

Uno, Yoshiharu; van Velkinburgh, Jennifer C

2016-01-01

Despite little evidence for the therapeutic benefits of a high-fiber diet for diverticulitis, it is commonly recommended as part of the clinical management. The ongoing uncertainty of the cause(s) of diverticulitis confounds attempts to determine the validity of this therapy. However, the features of a high-fiber diet represent a logical contradiction for colon diverticulitis. Considering that Bernoulli’s principle, by which enlarged diameter of the lumen leads to increased pressure and decreased fluid velocity, might contribute to development of the diverticulum. Thus, theoretically, prevention of high pressure in the colon would be important and adoption of a low FODMAP diet (consisting of fermentable oligosaccharides, disaccharides, monosaccharides, and polyols) may help prevent recurrence of diverticulitis. PMID:27867683

Uncertainty analysis and robust trajectory linearization control of a flexible air-breathing hypersonic vehicle

NASA Astrophysics Data System (ADS)

Pu, Zhiqiang; Tan, Xiangmin; Fan, Guoliang; Yi, Jianqiang

2014-08-01

Flexible air-breathing hypersonic vehicles feature significant uncertainties which pose huge challenges to robust controller designs. In this paper, four major categories of uncertainties are analyzed, that is, uncertainties associated with flexible effects, aerodynamic parameter variations, external environmental disturbances, and control-oriented modeling errors. A uniform nonlinear uncertainty model is explored for the first three uncertainties which lumps all uncertainties together and consequently is beneficial for controller synthesis. The fourth uncertainty is additionally considered in stability analysis. Based on these analyses, the starting point of the control design is to decompose the vehicle dynamics into five functional subsystems. Then a robust trajectory linearization control (TLC) scheme consisting of five robust subsystem controllers is proposed. In each subsystem controller, TLC is combined with the extended state observer (ESO) technique for uncertainty compensation. The stability of the overall closed-loop system with the four aforementioned uncertainties and additional singular perturbations is analyzed. Particularly, the stability of nonlinear ESO is also discussed from a Liénard system perspective. At last, simulations demonstrate the great control performance and the uncertainty rejection ability of the robust scheme.

[Air contamination in the Autonomous City of Buenos Aires: the current risk or future climate change, a false option].

PubMed

Abrutzky, Rosana; Dawidowski, Laura; Murgida, Ana; Natenzon, Claudia Eleonor

2014-09-01

Based on the theoretical framework of environmental risk, this article discusses the management of air quality in the Autonomous City of Buenos Aires in relation to current and potential impacts of toxic gases and global climate change on the health of the population. Information on historical and current management of the air was linked to the results of the South American Emissions, Megacities and Climate research project to assess danger, exposure, vulnerability and uncertainty as the dimensions of risk. By contextualizing public policies developed in recent decades on this subject, it was possible to identify emerging configurations of risk and uncertainties as accelerators of social vulnerability. On the one hand, the fact that there is a positive correlation between mortality, changes in temperature and air pollution was confirmed. On the other hand, it became clear that there is a disconnect between air quality management and health care management, while limitations were found in the proposed mitigation measures relating to emissions of greenhouse gases produced by fuel, revealing uncertainties regarding their efficacy.

The Effect of Forest Management Strategy on Carbon Storage and Revenue in Western Washington: A Probabilistic Simulation of Tradeoffs.

PubMed

Fischer, Paul W; Cullen, Alison C; Ettl, Gregory J

2017-01-01

The objectives of this study are to understand tradeoffs between forest carbon and timber values, and evaluate the impact of uncertainty in improved forest management (IFM) carbon offset projects to improve forest management decisions. The study uses probabilistic simulation of uncertainty in financial risk for three management scenarios (clearcutting in 45- and 65-year rotations and no harvest) under three carbon price schemes (historic voluntary market prices, cap and trade, and carbon prices set to equal net present value (NPV) from timber-oriented management). Uncertainty is modeled for value and amount of carbon credits and wood products, the accuracy of forest growth model forecasts, and four other variables relevant to American Carbon Registry methodology. Calculations use forest inventory data from a 1,740 ha forest in western Washington State, using the Forest Vegetation Simulator (FVS) growth model. Sensitivity analysis shows that FVS model uncertainty contributes more than 70% to overall NPV variance, followed in importance by variability in inventory sample (3-14%), and short-term prices for timber products (8%), while variability in carbon credit price has little influence (1.1%). At regional average land-holding costs, a no-harvest management scenario would become revenue-positive at a carbon credit break-point price of $14.17/Mg carbon dioxide equivalent (CO 2 e). IFM carbon projects are associated with a greater chance of both large payouts and large losses to landowners. These results inform policymakers and forest owners of the carbon credit price necessary for IFM approaches to equal or better the business-as-usual strategy, while highlighting the magnitude of financial risk and reward through probabilistic simulation. © 2016 Society for Risk Analysis.

Communication of uncertainty regarding individualized cancer risk estimates: effects and influential factors.

PubMed

Han, Paul K J; Klein, William M P; Lehman, Tom; Killam, Bill; Massett, Holly; Freedman, Andrew N

2011-01-01

To examine the effects of communicating uncertainty regarding individualized colorectal cancer risk estimates and to identify factors that influence these effects. Two Web-based experiments were conducted, in which adults aged 40 years and older were provided with hypothetical individualized colorectal cancer risk estimates differing in the extent and representation of expressed uncertainty. The uncertainty consisted of imprecision (otherwise known as "ambiguity") of the risk estimates and was communicated using different representations of confidence intervals. Experiment 1 (n = 240) tested the effects of ambiguity (confidence interval v. point estimate) and representational format (textual v. visual) on cancer risk perceptions and worry. Potential effect modifiers, including personality type (optimism), numeracy, and the information's perceived credibility, were examined, along with the influence of communicating uncertainty on responses to comparative risk information. Experiment 2 (n = 135) tested enhanced representations of ambiguity that incorporated supplemental textual and visual depictions. Communicating uncertainty led to heightened cancer-related worry in participants, exemplifying the phenomenon of "ambiguity aversion." This effect was moderated by representational format and dispositional optimism; textual (v. visual) format and low (v. high) optimism were associated with greater ambiguity aversion. However, when enhanced representations were used to communicate uncertainty, textual and visual formats showed similar effects. Both the communication of uncertainty and use of the visual format diminished the influence of comparative risk information on risk perceptions. The communication of uncertainty regarding cancer risk estimates has complex effects, which include heightening cancer-related worry-consistent with ambiguity aversion-and diminishing the influence of comparative risk information on risk perceptions. These responses are influenced by representational format and personality type, and the influence of format appears to be modifiable and content dependent.

Uncertainty in hydrological signatures

NASA Astrophysics Data System (ADS)

McMillan, Hilary; Westerberg, Ida

2015-04-01

Information that summarises the hydrological behaviour or flow regime of a catchment is essential for comparing responses of different catchments to understand catchment organisation and similarity, and for many other modelling and water-management applications. Such information types derived as an index value from observed data are known as hydrological signatures, and can include descriptors of high flows (e.g. mean annual flood), low flows (e.g. mean annual low flow, recession shape), the flow variability, flow duration curve, and runoff ratio. Because the hydrological signatures are calculated from observed data such as rainfall and flow records, they are affected by uncertainty in those data. Subjective choices in the method used to calculate the signatures create a further source of uncertainty. Uncertainties in the signatures may affect our ability to compare different locations, to detect changes, or to compare future water resource management scenarios. The aim of this study was to contribute to the hydrological community's awareness and knowledge of data uncertainty in hydrological signatures, including typical sources, magnitude and methods for its assessment. We proposed a generally applicable method to calculate these uncertainties based on Monte Carlo sampling and demonstrated it for a variety of commonly used signatures. The study was made for two data rich catchments, the 50 km2 Mahurangi catchment in New Zealand and the 135 km2 Brue catchment in the UK. For rainfall data the uncertainty sources included point measurement uncertainty, the number of gauges used in calculation of the catchment spatial average, and uncertainties relating to lack of quality control. For flow data the uncertainty sources included uncertainties in stage/discharge measurement and in the approximation of the true stage-discharge relation by a rating curve. The resulting uncertainties were compared across the different signatures and catchments, to quantify uncertainty magnitude and bias, and to test how uncertainty depended on the density of the raingauge network and flow gauging station characteristics. The uncertainties were sometimes large (i.e. typical intervals of ±10-40% relative uncertainty) and highly variable between signatures. Uncertainty in the mean discharge was around ±10% for both catchments, while signatures describing the flow variability had much higher uncertainties in the Mahurangi where there was a fast rainfall-runoff response and greater high-flow rating uncertainty. Event and total runoff ratios had uncertainties from ±10% to ±15% depending on the number of rain gauges used; precipitation uncertainty was related to interpolation rather than point uncertainty. Uncertainty distributions in these signatures were skewed, and meant that differences in signature values between these catchments were often not significant. We hope that this study encourages others to use signatures in a way that is robust to data uncertainty.

Stochastic Coastal/Regional Uncertainty Modelling: a Copernicus marine research project in the framework of Service Evolution

NASA Astrophysics Data System (ADS)

Vervatis, Vassilios; De Mey, Pierre; Ayoub, Nadia; Kailas, Marios; Sofianos, Sarantis

2017-04-01

The project entitled Stochastic Coastal/Regional Uncertainty Modelling (SCRUM) aims at strengthening CMEMS in the areas of ocean uncertainty quantification, ensemble consistency verification and ensemble data assimilation. The project has been initiated by the University of Athens and LEGOS/CNRS research teams, in the framework of CMEMS Service Evolution. The work is based on stochastic modelling of ocean physics and biogeochemistry in the Bay of Biscay, on an identical sub-grid configuration of the IBI-MFC system in its latest CMEMS operational version V2. In a first step, we use a perturbed tendencies scheme to generate ensembles describing uncertainties in open ocean and on the shelf, focusing on upper ocean processes. In a second step, we introduce two methodologies (i.e. rank histograms and array modes) aimed at checking the consistency of the above ensembles with respect to TAC data and arrays. Preliminary results highlight that wind uncertainties dominate all other atmosphere-ocean sources of model errors. The ensemble spread in medium-range ensembles is approximately 0.01 m for SSH and 0.15 °C for SST, though these values vary depending on season and cross shelf regions. Ecosystem model uncertainties emerging from perturbations in physics appear to be moderately larger than those perturbing the concentration of the biogeochemical compartments, resulting in total chlorophyll spread at about 0.01 mg.m-3. First consistency results show that the model ensemble and the pseudo-ensemble of OSTIA (L4) observation SSTs appear to exhibit nonzero joint probabilities with each other since error vicinities overlap. Rank histograms show that the model ensemble is initially under-dispersive, though results improve in the context of seasonal-range ensembles.

Probabilistic economic frameworks for disaster risk management

NASA Astrophysics Data System (ADS)

Dulac, Guillaume; Forni, Marc

2013-04-01

Starting from the general concept of risk, we set up an economic analysis framework for Disaster Risk Management (DRM) investment. It builds on uncertainty management techniques - notably Monte Carlo simulations - and includes both a risk and performance metrics adapted to recurring issues in disaster risk management as entertained by governments and international organisations. This type of framework proves to be enlightening in several regards, and is thought to ease the promotion of DRM projects as "investments" rather than "costs to be born" and allow for meaningful comparison between DRM and other sectors. We then look at the specificities of disaster risk investments of medium to large scales through this framework, where some "invariants" can be identified, notably: (i) it makes more sense to perform analysis over long-term horizons -space and time scales are somewhat linked; (ii) profiling of the fluctuations of the gains and losses of DRM investments over long periods requires the ability to handle possibly highly volatile variables; (iii) complexity increases with the scale which results in a higher sensitivity of the analytic framework on the results; (iv) as the perimeter of analysis (time, theme and space-wise) is widened, intrinsic parameters of the project tend to weight lighter. This puts DRM in a very different perspective from traditional modelling, which usually builds on more intrinsic features of the disaster as it relates to the scientific knowledge about hazard(s). As models hardly accommodate for such complexity or "data entropy" (they require highly structured inputs), there is a need for a complementary approach to understand risk at global scale. The proposed framework suggests opting for flexible ad hoc modelling of specific issues consistent with one's objective, risk and performance metrics. Such tailored solutions are strongly context-dependant (time and budget, sensitivity of the studied variable in the economic framework) and can range from simple elicitation of data from a subject matter expert to calibrate a probability distribution to more advanced stochastic modelling. This approach can be referred to more as a proficiency in the language of uncertainty rather than modelling per se in the sense that it allows for greater flexibility to adapt a given context. In a real decision making context, one seldom has neither time nor budget resources to investigate all of these variables thoroughly, hence the importance of being able to prioritize the level of effort among them. Under the proposed framework, this can be done in an optimised fashion. The point here consists in applying probabilistic sensitivity analysis together with the fundamentals of the economic value of information; the framework as built is well suited to such considerations, and variables can be ranked according to their contribution to risk understanding. Efforts to deal with second order uncertainties on variables prove to be valuable when dealing with the economic value of sample information.

Computer-Based Model Calibration and Uncertainty Analysis: Terms and Concepts

DTIC Science & Technology

2015-07-01

uncertainty analyses throughout the lifecycle of planning, designing, and operating of Civil Works flood risk management projects as described in...value 95% of the time. In the frequentist approach to PE, model parameters area regarded as having true values, and their estimate is based on the...in catchment models. 1. Evaluating parameter uncertainty. Water Resources Research 19(5):1151–1172. Lee, P. M. 2012. Bayesian statistics: An

Quantifying the Value of Perfect Information in Emergency Vaccination Campaigns.

PubMed

Bradbury, Naomi V; Probert, William J M; Shea, Katriona; Runge, Michael C; Fonnesbeck, Christopher J; Keeling, Matt J; Ferrari, Matthew J; Tildesley, Michael J

2017-02-01

Foot-and-mouth disease outbreaks in non-endemic countries can lead to large economic costs and livestock losses but the use of vaccination has been contentious, partly due to uncertainty about emergency FMD vaccination. Value of information methods can be applied to disease outbreak problems such as FMD in order to investigate the performance improvement from resolving uncertainties. Here we calculate the expected value of resolving uncertainty about vaccine efficacy, time delay to immunity after vaccination and daily vaccination capacity for a hypothetical FMD outbreak in the UK. If it were possible to resolve all uncertainty prior to the introduction of control, we could expect savings of £55 million in outbreak cost, 221,900 livestock culled and 4.3 days of outbreak duration. All vaccination strategies were found to be preferable to a culling only strategy. However, the optimal vaccination radius was found to be highly dependent upon vaccination capacity for all management objectives. We calculate that by resolving the uncertainty surrounding vaccination capacity we would expect to return over 85% of the above savings, regardless of management objective. It may be possible to resolve uncertainty about daily vaccination capacity before an outbreak, and this would enable decision makers to select the optimal control action via careful contingency planning.

What healthcare students do with what they don't know: the socializing power of 'uncertainty' in the case presentation.

PubMed

Spafford, Marlee M; Schryer, Catherine F; Lingard, Lorelei; Hrynchak, Patricia K

2006-01-01

Healthcare students learn to manage clinical uncertainty amid the tensions that emerge between clinical omniscience and the 'truth for now' realities of the knowledge explosion in healthcare. The case presentation provides a portal to viewing the practitioner's ability to manage uncertainty. We examined the communicative features of uncertainty in 31 novice optometry case presentations and considered how these features contributed to the development of professional identity in optometry students. We also reflected on how these features compared with our earlier study of medical students' case presentations. Optometry students, like their counterparts in medicine, displayed a novice rhetoric of uncertainty that focused on personal deficits in knowledge. While optometry and medical students shared aspects of this rhetoric (seeking guidance and deflecting criticism), optometry students displayed instances of owning limits while medical students displayed instances of proving competence. We found that the nature of this novice rhetoric was shaped by professional identity (a tendency to assume an attitude of moral authority or defer to a higher authority) and the clinical setting (inpatient versus outpatient settings). More explicit discussions regarding uncertainty may help the novice unlock the code of contextual forces that cue the savvy member of the community to sanctioned discursive strategies.

Quantifying the Value of Perfect Information in Emergency Vaccination Campaigns

PubMed Central

Probert, William J. M.; Shea, Katriona; Fonnesbeck, Christopher J.; Ferrari, Matthew J.; Tildesley, Michael J.

2017-01-01

Foot-and-mouth disease outbreaks in non-endemic countries can lead to large economic costs and livestock losses but the use of vaccination has been contentious, partly due to uncertainty about emergency FMD vaccination. Value of information methods can be applied to disease outbreak problems such as FMD in order to investigate the performance improvement from resolving uncertainties. Here we calculate the expected value of resolving uncertainty about vaccine efficacy, time delay to immunity after vaccination and daily vaccination capacity for a hypothetical FMD outbreak in the UK. If it were possible to resolve all uncertainty prior to the introduction of control, we could expect savings of £55 million in outbreak cost, 221,900 livestock culled and 4.3 days of outbreak duration. All vaccination strategies were found to be preferable to a culling only strategy. However, the optimal vaccination radius was found to be highly dependent upon vaccination capacity for all management objectives. We calculate that by resolving the uncertainty surrounding vaccination capacity we would expect to return over 85% of the above savings, regardless of management objective. It may be possible to resolve uncertainty about daily vaccination capacity before an outbreak, and this would enable decision makers to select the optimal control action via careful contingency planning. PMID:28207777

Epistemic uncertainty in the location and magnitude of earthquakes in Italy from Macroseismic data

USGS Publications Warehouse

Bakun, W.H.; Gomez, Capera A.; Stucchi, M.

2011-01-01

Three independent techniques (Bakun and Wentworth, 1997; Boxer from Gasperini et al., 1999; and Macroseismic Estimation of Earthquake Parameters [MEEP; see Data and Resources section, deliverable D3] from R.M.W. Musson and M.J. Jimenez) have been proposed for estimating an earthquake location and magnitude from intensity data alone. The locations and magnitudes obtained for a given set of intensity data are almost always different, and no one technique is consistently best at matching instrumental locations and magnitudes of recent well-recorded earthquakes in Italy. Rather than attempting to select one of the three solutions as best, we use all three techniques to estimate the location and the magnitude and the epistemic uncertainties among them. The estimates are calculated using bootstrap resampled data sets with Monte Carlo sampling of a decision tree. The decision-tree branch weights are based on goodness-of-fit measures of location and magnitude for recent earthquakes. The location estimates are based on the spatial distribution of locations calculated from the bootstrap resampled data. The preferred source location is the locus of the maximum bootstrap location spatial density. The location uncertainty is obtained from contours of the bootstrap spatial density: 68% of the bootstrap locations are within the 68% confidence region, and so on. For large earthquakes, our preferred location is not associated with the epicenter but with a location on the extended rupture surface. For small earthquakes, the epicenters are generally consistent with the location uncertainties inferred from the intensity data if an epicenter inaccuracy of 2-3 km is allowed. The preferred magnitude is the median of the distribution of bootstrap magnitudes. As with location uncertainties, the uncertainties in magnitude are obtained from the distribution of bootstrap magnitudes: the bounds of the 68% uncertainty range enclose 68% of the bootstrap magnitudes, and so on. The instrumental magnitudes for large and small earthquakes are generally consistent with the confidence intervals inferred from the distribution of bootstrap resampled magnitudes.

Evaluation of harvest and information needs for North American sea ducks

USGS Publications Warehouse

Koneff, Mark D.; Zimmerman, Guthrie S.; Dwyer, Chris P.; Fleming, Kathleen K.; Padding, Paul I.; Devers, Patrick K.; Johnson, Fred A.; Runge, Michael C.; Roberts, Anthony J.

2017-01-01

Wildlife managers routinely seek to establish sustainable limits of sport harvest or other regulated forms of take while confronted with considerable uncertainty. A growing body of ecological research focuses on methods to describe and account for uncertainty in management decision-making and to prioritize research and monitoring investments to reduce the most influential uncertainties. We used simulation methods incorporating measures of demographic uncertainty to evaluate risk of overharvest and prioritize information needs for North American sea ducks (Tribe Mergini). Sea ducks are popular game birds in North America, yet they are poorly monitored and their population dynamics are poorly understood relative to other North American waterfowl. There have been few attempts to assess the sustainability of harvest of North American sea ducks, and no formal harvest strategy exists in the U.S. or Canada to guide management. The popularity of sea duck hunting, extended hunting opportunity for some populations (i.e., special seasons and/or bag limits), and population declines have led to concern about potential overharvest. We used Monte Carlo simulation to contrast estimates of allowable harvest and observed harvest and assess risk of overharvest for 7 populations of North American sea ducks: the American subspecies of common eider (Somateria mollissima dresseri), eastern and western populations of black scoter (Melanitta americana) and surf scoter (M. perspicillata), and continental populations of white-winged scoter (M. fusca) and long-tailed duck (Clangula hyemalis). We combined information from empirical studies and the opinions of experts through formal elicitation to create probability distributions reflecting uncertainty in the individual demographic parameters used in this assessment. Estimates of maximum growth (rmax), and therefore of allowable harvest, were highly uncertain for all populations. Long-tailed duck and American common eider appeared to be at high risk of overharvest (i.e., observed harvest < allowable harvest in 5–7% and 19–26% of simulations, respectively depending on the functional form of density dependence), whereas the other populations appeared to be at moderate risk to low risk (observed harvest < allowable harvest in 22–68% of simulations, again conditional on the form of density dependence). We also evaluated the sensitivity of the difference between allowable and observed harvest estimates to uncertainty in individual demographic parameters to prioritize information needs. We found that uncertainty in overall fecundity had more influence on comparisons of allowable and observed harvest than adult survival or observed harvest for all species except long-tailed duck. Although adult survival was characterized by less uncertainty than individual components of fecundity, it was identified as a high priority information need given the sensitivity of growth rate and allowable harvest to this parameter. Uncertainty about population size was influential in the comparison of observed and allowable harvest for 5 of the 6 populations where it factored into the assessment. While this assessment highlights a high degree of uncertainty in allowable harvest, it provides a framework for integration of improved data from future research and monitoring. It could also serve as the basis for harvest strategy development as management objectives and regulatory alternatives are specified by the management community.

Evaluation of harvest and information needs for North American sea ducks.

PubMed

Koneff, Mark D; Zimmerman, Guthrie S; Dwyer, Chris P; Fleming, Kathleen K; Padding, Paul I; Devers, Patrick K; Johnson, Fred A; Runge, Michael C; Roberts, Anthony J

2017-01-01

Wildlife managers routinely seek to establish sustainable limits of sport harvest or other regulated forms of take while confronted with considerable uncertainty. A growing body of ecological research focuses on methods to describe and account for uncertainty in management decision-making and to prioritize research and monitoring investments to reduce the most influential uncertainties. We used simulation methods incorporating measures of demographic uncertainty to evaluate risk of overharvest and prioritize information needs for North American sea ducks (Tribe Mergini). Sea ducks are popular game birds in North America, yet they are poorly monitored and their population dynamics are poorly understood relative to other North American waterfowl. There have been few attempts to assess the sustainability of harvest of North American sea ducks, and no formal harvest strategy exists in the U.S. or Canada to guide management. The popularity of sea duck hunting, extended hunting opportunity for some populations (i.e., special seasons and/or bag limits), and population declines have led to concern about potential overharvest. We used Monte Carlo simulation to contrast estimates of allowable harvest and observed harvest and assess risk of overharvest for 7 populations of North American sea ducks: the American subspecies of common eider (Somateria mollissima dresseri), eastern and western populations of black scoter (Melanitta americana) and surf scoter (M. perspicillata), and continental populations of white-winged scoter (M. fusca) and long-tailed duck (Clangula hyemalis). We combined information from empirical studies and the opinions of experts through formal elicitation to create probability distributions reflecting uncertainty in the individual demographic parameters used in this assessment. Estimates of maximum growth (rmax), and therefore of allowable harvest, were highly uncertain for all populations. Long-tailed duck and American common eider appeared to be at high risk of overharvest (i.e., observed harvest < allowable harvest in 5-7% and 19-26% of simulations, respectively depending on the functional form of density dependence), whereas the other populations appeared to be at moderate risk to low risk (observed harvest < allowable harvest in 22-68% of simulations, again conditional on the form of density dependence). We also evaluated the sensitivity of the difference between allowable and observed harvest estimates to uncertainty in individual demographic parameters to prioritize information needs. We found that uncertainty in overall fecundity had more influence on comparisons of allowable and observed harvest than adult survival or observed harvest for all species except long-tailed duck. Although adult survival was characterized by less uncertainty than individual components of fecundity, it was identified as a high priority information need given the sensitivity of growth rate and allowable harvest to this parameter. Uncertainty about population size was influential in the comparison of observed and allowable harvest for 5 of the 6 populations where it factored into the assessment. While this assessment highlights a high degree of uncertainty in allowable harvest, it provides a framework for integration of improved data from future research and monitoring. It could also serve as the basis for harvest strategy development as management objectives and regulatory alternatives are specified by the management community.

Evaluation of harvest and information needs for North American sea ducks

PubMed Central

Dwyer, Chris P.; Fleming, Kathleen K.; Padding, Paul I.; Devers, Patrick K.; Johnson, Fred A.; Runge, Michael C.; Roberts, Anthony J.

2017-01-01

Wildlife managers routinely seek to establish sustainable limits of sport harvest or other regulated forms of take while confronted with considerable uncertainty. A growing body of ecological research focuses on methods to describe and account for uncertainty in management decision-making and to prioritize research and monitoring investments to reduce the most influential uncertainties. We used simulation methods incorporating measures of demographic uncertainty to evaluate risk of overharvest and prioritize information needs for North American sea ducks (Tribe Mergini). Sea ducks are popular game birds in North America, yet they are poorly monitored and their population dynamics are poorly understood relative to other North American waterfowl. There have been few attempts to assess the sustainability of harvest of North American sea ducks, and no formal harvest strategy exists in the U.S. or Canada to guide management. The popularity of sea duck hunting, extended hunting opportunity for some populations (i.e., special seasons and/or bag limits), and population declines have led to concern about potential overharvest. We used Monte Carlo simulation to contrast estimates of allowable harvest and observed harvest and assess risk of overharvest for 7 populations of North American sea ducks: the American subspecies of common eider (Somateria mollissima dresseri), eastern and western populations of black scoter (Melanitta americana) and surf scoter (M. perspicillata), and continental populations of white-winged scoter (M. fusca) and long-tailed duck (Clangula hyemalis). We combined information from empirical studies and the opinions of experts through formal elicitation to create probability distributions reflecting uncertainty in the individual demographic parameters used in this assessment. Estimates of maximum growth (rmax), and therefore of allowable harvest, were highly uncertain for all populations. Long-tailed duck and American common eider appeared to be at high risk of overharvest (i.e., observed harvest < allowable harvest in 5–7% and 19–26% of simulations, respectively depending on the functional form of density dependence), whereas the other populations appeared to be at moderate risk to low risk (observed harvest < allowable harvest in 22–68% of simulations, again conditional on the form of density dependence). We also evaluated the sensitivity of the difference between allowable and observed harvest estimates to uncertainty in individual demographic parameters to prioritize information needs. We found that uncertainty in overall fecundity had more influence on comparisons of allowable and observed harvest than adult survival or observed harvest for all species except long-tailed duck. Although adult survival was characterized by less uncertainty than individual components of fecundity, it was identified as a high priority information need given the sensitivity of growth rate and allowable harvest to this parameter. Uncertainty about population size was influential in the comparison of observed and allowable harvest for 5 of the 6 populations where it factored into the assessment. While this assessment highlights a high degree of uncertainty in allowable harvest, it provides a framework for integration of improved data from future research and monitoring. It could also serve as the basis for harvest strategy development as management objectives and regulatory alternatives are specified by the management community. PMID:28419113

Quantifying parametric uncertainty in the Rothermel model

Treesearch

S. Goodrick

2008-01-01

The purpose of the present work is to quantify parametric uncertainty in the Rothermel wildland fire spreadmodel (implemented in software such as fire spread models in the United States. This model consists of a non-linear system of equations that relates environmentalvariables (input parameter groups...

Prediction uncertainty and data worth assessment for groundwater transport times in an agricultural catchment

NASA Astrophysics Data System (ADS)

Zell, Wesley O.; Culver, Teresa B.; Sanford, Ward E.

2018-06-01

Uncertainties about the age of base-flow discharge can have serious implications for the management of degraded environmental systems where subsurface pathways, and the ongoing release of pollutants that accumulated in the subsurface during past decades, dominate the water quality signal. Numerical groundwater models may be used to estimate groundwater return times and base-flow ages and thus predict the time required for stakeholders to see the results of improved agricultural management practices. However, the uncertainty inherent in the relationship between (i) the observations of atmospherically-derived tracers that are required to calibrate such models and (ii) the predictions of system age that the observations inform have not been investigated. For example, few if any studies have assessed the uncertainty of numerically-simulated system ages or evaluated the uncertainty reductions that may result from the expense of collecting additional subsurface tracer data. In this study we combine numerical flow and transport modeling of atmospherically-derived tracers with prediction uncertainty methods to accomplish four objectives. First, we show the relative importance of head, discharge, and tracer information for characterizing response times in a uniquely data rich catchment that includes 266 age-tracer measurements (SF6, CFCs, and 3H) in addition to long term monitoring of water levels and stream discharge. Second, we calculate uncertainty intervals for model-simulated base-flow ages using both linear and non-linear methods, and find that the prediction sensitivity vector used by linear first-order second-moment methods results in much larger uncertainties than non-linear Monte Carlo methods operating on the same parameter uncertainty. Third, by combining prediction uncertainty analysis with multiple models of the system, we show that data-worth calculations and monitoring network design are sensitive to variations in the amount of water leaving the system via stream discharge and irrigation withdrawals. Finally, we demonstrate a novel model-averaged computation of potential data worth that can account for these uncertainties in model structure.

When 1+1 can be >2: Uncertainties compound when simulating climate, fisheries and marine ecosystems

NASA Astrophysics Data System (ADS)

Evans, Karen; Brown, Jaclyn N.; Sen Gupta, Alex; Nicol, Simon J.; Hoyle, Simon; Matear, Richard; Arrizabalaga, Haritz

2015-03-01

Multi-disciplinary approaches that combine oceanographic, biogeochemical, ecosystem, fisheries population and socio-economic models are vital tools for modelling whole ecosystems. Interpreting the outputs from such complex models requires an appreciation of the many different types of modelling frameworks being used and their associated limitations and uncertainties. Both users and developers of particular model components will often have little involvement or understanding of other components within such modelling frameworks. Failure to recognise limitations and uncertainties associated with components and how these uncertainties might propagate throughout modelling frameworks can potentially result in poor advice for resource management. Unfortunately, many of the current integrative frameworks do not propagate the uncertainties of their constituent parts. In this review, we outline the major components of a generic whole of ecosystem modelling framework incorporating the external pressures of climate and fishing. We discuss the limitations and uncertainties associated with each component of such a modelling system, along with key research gaps. Major uncertainties in modelling frameworks are broadly categorised into those associated with (i) deficient knowledge in the interactions of climate and ocean dynamics with marine organisms and ecosystems; (ii) lack of observations to assess and advance modelling efforts and (iii) an inability to predict with confidence natural ecosystem variability and longer term changes as a result of external drivers (e.g. greenhouse gases, fishing effort) and the consequences for marine ecosystems. As a result of these uncertainties and intrinsic differences in the structure and parameterisation of models, users are faced with considerable challenges associated with making appropriate choices on which models to use. We suggest research directions required to address these uncertainties, and caution against overconfident predictions. Understanding the full impact of uncertainty makes it clear that full comprehension and robust certainty about the systems themselves are not feasible. A key research direction is the development of management systems that are robust to this unavoidable uncertainty.

Parameter uncertainty and nonstationarity in regional extreme rainfall frequency analysis in Qu River Basin, East China

NASA Astrophysics Data System (ADS)

Zhu, Q.; Xu, Y. P.; Gu, H.

2014-12-01

Traditionally, regional frequency analysis methods were developed for stationary environmental conditions. Nevertheless, recent studies have identified significant changes in hydrological records, leading to the 'death' of stationarity. Besides, uncertainty in hydrological frequency analysis is persistent. This study aims to investigate the impact of one of the most important uncertainty sources, parameter uncertainty, together with nonstationarity, on design rainfall depth in Qu River Basin, East China. A spatial bootstrap is first proposed to analyze the uncertainty of design rainfall depth estimated by regional frequency analysis based on L-moments and estimated on at-site scale. Meanwhile, a method combining the generalized additive models with 30-year moving window is employed to analyze non-stationarity existed in the extreme rainfall regime. The results show that the uncertainties of design rainfall depth with 100-year return period under stationary conditions estimated by regional spatial bootstrap can reach 15.07% and 12.22% with GEV and PE3 respectively. On at-site scale, the uncertainties can reach 17.18% and 15.44% with GEV and PE3 respectively. In non-stationary conditions, the uncertainties of maximum rainfall depth (corresponding to design rainfall depth) with 0.01 annual exceedance probability (corresponding to 100-year return period) are 23.09% and 13.83% with GEV and PE3 respectively. Comparing the 90% confidence interval, the uncertainty of design rainfall depth resulted from parameter uncertainty is less than that from non-stationarity frequency analysis with GEV, however, slightly larger with PE3. This study indicates that the spatial bootstrap can be successfully applied to analyze the uncertainty of design rainfall depth on both regional and at-site scales. And the non-stationary analysis shows that the differences between non-stationary quantiles and their stationary equivalents are important for decision makes of water resources management and risk management.

A METHODOLOGY FOR ESTIMATING UNCERTAINTY OF A DISTRIBUTED HYDROLOGIC MODEL: APPLICATION TO POCONO CREEK WATERSHED

EPA Science Inventory

Utility of distributed hydrologic and water quality models for watershed management and sustainability studies should be accompanied by rigorous model uncertainty analysis. However, the use of complex watershed models primarily follows the traditional {calibrate/validate/predict}...

Mindful Organizing as a Paradigm to Develop Managers

ERIC Educational Resources Information Center

Gebauer, Annette

2013-01-01

How can managers prepare for extreme but exceptional events and for the challenge of managing complexity and uncertainty in their daily business? Confronted with the challenge of achieving high and reliable performance in risk-prone, fast-paced, and unpredictable environments, managers and management scholars can learn a lot from the organizing…

Policy, practice and decision making for zoonotic disease management: water and Cryptosporidium.

PubMed

Austin, Zoë; Alcock, Ruth E; Christley, Robert M; Haygarth, Philip M; Heathwaite, A Louise; Latham, Sophia M; Mort, Maggie; Oliver, David M; Pickup, Roger; Wastling, Jonathan M; Wynne, Brian

2012-04-01

Decision making for zoonotic disease management should be based on many forms of appropriate data and sources of evidence. However, the criteria and timing for policy response and the resulting management decisions are often altered when a disease outbreak occurs and captures full media attention. In the case of waterborne disease, such as the robust protozoa, Cryptosporidium spp, exposure can cause significant human health risks and preventing exposure by maintaining high standards of biological and chemical water quality remains a priority for water companies in the UK. Little has been documented on how knowledge and information is translated between the many stakeholders involved in the management of Cryptosporidium, which is surprising given the different drivers that have shaped management decisions. Such information, coupled with the uncertainties that surround these data is essential for improving future management strategies that minimise disease outbreaks. Here, we examine the interplay between scientific information, the media, and emergent government and company policies to examine these issues using qualitative and quantitative data relating to Cryptosporidium management decisions by a water company in the North West of England. Our results show that political and media influences are powerful drivers of management decisions if fuelled by high profile outbreaks. Furthermore, the strength of the scientific evidence is often constrained by uncertainties in the data, and in the way knowledge is translated between policy levels during established risk management procedures. In particular, under or over-estimating risk during risk assessment procedures together with uncertainty regarding risk factors within the wider environment, was found to restrict the knowledge-base for decision-making in Cryptosporidium management. Our findings highlight some key current and future challenges facing the management of such diseases that are widely applicable to other risk management situations. Copyright © 2011 Elsevier Ltd. All rights reserved.

Frequency Comparison of [Formula: see text] Ion Optical Clocks at PTB and NPL via GPS PPP.

PubMed

Leute, J; Huntemann, N; Lipphardt, B; Tamm, Christian; Nisbet-Jones, P B R; King, S A; Godun, R M; Jones, J M; Margolis, H S; Whibberley, P B; Wallin, A; Merimaa, M; Gill, P; Peik, E

2016-07-01

We used precise point positioning, a well-established GPS carrier-phase frequency transfer method to perform a direct remote comparison of two optical frequency standards based on single laser-cooled [Formula: see text] ions operated at the National Physical Laboratory (NPL), U.K. and the Physikalisch-Technische Bundesanstalt (PTB), Germany. At both institutes, an active hydrogen maser serves as a flywheel oscillator which is connected to a GPS receiver as an external frequency reference and compared simultaneously to a realization of the unperturbed frequency of the (2)S1/2(F=0)-(2)D3/2(F=2) electric quadrupole transition in [Formula: see text] via an optical femtosecond frequency comb. To profit from long coherent GPS-link measurements, we extrapolate the fractional frequency difference over the various data gaps in the optical clock to maser comparisons which introduces maser noise to the frequency comparison but improves the uncertainty from the GPS-link instability. We determined the total statistical uncertainty consisting of the GPS-link uncertainty and the extrapolation uncertainties for several extrapolation schemes. Using the extrapolation scheme with the smallest combined uncertainty, we find a fractional frequency difference [Formula: see text] of -1.3×10(-15) with a combined uncertainty of 1.2×10(-15) for a total measurement time of 67 h. This result is consistent with an agreement of the frequencies realized by both optical clocks and with recent absolute frequency measurements against caesium fountain clocks within the corresponding uncertainties.

Convergence and dissonance: evolution in private-sector approaches to disease management and care coordination.

PubMed

Mays, Glen P; Au, Melanie; Claxton, Gary

2007-01-01

Disease management (DM) approaches survived the 1990s backlash against managed care because of their potential for consumer-friendly cost containment, but purchasers have been cautious about investing heavily in them because of uncertainty about return on investment. This study examines how private-sector approaches to DM have evolved over the past two years in the midst of the movement toward consumer-driven health care. Findings indicate that these programs have become standard features of health plan design, despite a thin evidence base concerning their effectiveness. Uncertainties remain regarding how well these programs will function within benefit designs that require higher consumer cost sharing.

Markov decision processes in natural resources management: observability and uncertainty

USGS Publications Warehouse

Williams, Byron K.

2015-01-01

The breadth and complexity of stochastic decision processes in natural resources presents a challenge to analysts who need to understand and use these approaches. The objective of this paper is to describe a class of decision processes that are germane to natural resources conservation and management, namely Markov decision processes, and to discuss applications and computing algorithms under different conditions of observability and uncertainty. A number of important similarities are developed in the framing and evaluation of different decision processes, which can be useful in their applications in natural resources management. The challenges attendant to partial observability are highlighted, and possible approaches for dealing with it are discussed.

Five reasons not to use numerical models in water resource management (Arne Richter Award Lecture for OYS)

NASA Astrophysics Data System (ADS)

Pianosi, Francesca

2015-04-01

Sustainable water resource management in a quickly changing world poses new challenges to hydrology and decision sciences. Systems analysis can contribute to promote sustainable practices by providing the theoretical background and the operational tools for an objective and transparent appraisal of policy options for water resource systems (WRS) management. Traditionally, limited availability of data and computing resources imposed to use oversimplified WRS models, with little consideration of modeling uncertainties and of the non-stationarity and feedbacks between WRS drivers, and a priori aggregation of costs and benefits. Nowadays we increasingly recognize the inadequacy of these simplifications, and consider them among the reasons for the limited use of model-generated information in actual decision-making processes. On the other hand, fast-growing availability of data and computing resources are opening up unprecedented possibilities in the way we build and apply numerical models. In this talk I will discuss my experiences and ideas on how we can exploit this potential to improve model-informed decision-making while facing the challenges of uncertainty, non-stationarity, feedbacks and conflicting objectives. In particular, through practical examples of WRS design and operation problems, my talk will aim at stimulating discussion about the impact of uncertainty on decisions: can inaccurate and imprecise predictions still carry valuable information for decision-making? Does uncertainty in predictions necessarily limit our ability to make 'good' decisions? Or can uncertainty even be of help for decision-making, for instance by reducing the projected conflict between competing water use? Finally, I will also discuss how the traditionally separate disciplines of numerical modelling, optimization, and uncertainty and sensitivity analysis have in my experience been just different facets of the same 'systems approach'.

Full uncertainty quantification of N2O and NO emissions using the biogeochemical model LandscapeDNDC on site and regional scale

NASA Astrophysics Data System (ADS)

Haas, Edwin; Santabarbara, Ignacio; Kiese, Ralf; Butterbach-Bahl, Klaus

2017-04-01

Numerical simulation models are increasingly used to estimate greenhouse gas emissions at site to regional / national scale and are outlined as the most advanced methodology (Tier 3) in the framework of UNFCCC reporting. Process-based models incorporate the major processes of the carbon and nitrogen cycle of terrestrial ecosystems and are thus thought to be widely applicable at various conditions and spatial scales. Process based modelling requires high spatial resolution input data on soil properties, climate drivers and management information. The acceptance of model based inventory calculations depends on the assessment of the inventory's uncertainty (model, input data and parameter induced uncertainties). In this study we fully quantify the uncertainty in modelling soil N2O and NO emissions from arable, grassland and forest soils using the biogeochemical model LandscapeDNDC. We address model induced uncertainty (MU) by contrasting two different soil biogeochemistry modules within LandscapeDNDC. The parameter induced uncertainty (PU) was assessed by using joint parameter distributions for key parameters describing microbial C and N turnover processes as obtained by different Bayesian calibration studies for each model configuration. Input data induced uncertainty (DU) was addressed by Bayesian calibration of soil properties, climate drivers and agricultural management practices data. For the MU, DU and PU we performed several hundred simulations each to contribute to the individual uncertainty assessment. For the overall uncertainty quantification we assessed the model prediction probability, followed by sampled sets of input datasets and parameter distributions. Statistical analysis of the simulation results have been used to quantify the overall full uncertainty of the modelling approach. With this study we can contrast the variation in model results to the different sources of uncertainties for each ecosystem. Further we have been able to perform a fully uncertainty analysis for modelling N2O and NO emissions from arable, grassland and forest soils necessary for the comprehensibility of modelling results. We have applied the methodology to a regional inventory to assess the overall modelling uncertainty for a regional N2O and NO emissions inventory for the state of Saxony, Germany.

Convergence in parameters and predictions using computational experimental design.

PubMed

Hagen, David R; White, Jacob K; Tidor, Bruce

2013-08-06

Typically, biological models fitted to experimental data suffer from significant parameter uncertainty, which can lead to inaccurate or uncertain predictions. One school of thought holds that accurate estimation of the true parameters of a biological system is inherently problematic. Recent work, however, suggests that optimal experimental design techniques can select sets of experiments whose members probe complementary aspects of a biochemical network that together can account for its full behaviour. Here, we implemented an experimental design approach for selecting sets of experiments that constrain parameter uncertainty. We demonstrated with a model of the epidermal growth factor-nerve growth factor pathway that, after synthetically performing a handful of optimal experiments, the uncertainty in all 48 parameters converged below 10 per cent. Furthermore, the fitted parameters converged to their true values with a small error consistent with the residual uncertainty. When untested experimental conditions were simulated with the fitted models, the predicted species concentrations converged to their true values with errors that were consistent with the residual uncertainty. This paper suggests that accurate parameter estimation is achievable with complementary experiments specifically designed for the task, and that the resulting parametrized models are capable of accurate predictions.

Methods for handling uncertainty within pharmaceutical funding decisions

NASA Astrophysics Data System (ADS)

Stevenson, Matt; Tappenden, Paul; Squires, Hazel

2014-01-01

This article provides a position statement regarding decision making under uncertainty within the economic evaluation of pharmaceuticals, with a particular focus upon the National Institute for Health and Clinical Excellence context within England and Wales. This area is of importance as funding agencies have a finite budget from which to purchase a selection of competing health care interventions. The objective function generally used is that of maximising societal health with an explicit acknowledgement that there will be opportunity costs associated with purchasing a particular intervention. Three components of uncertainty are discussed within a pharmaceutical funding perspective: methodological uncertainty, parameter uncertainty and structural uncertainty, alongside a discussion of challenges that are particularly pertinent to health economic evaluation. The discipline has focused primarily on handling methodological and parameter uncertainty and a clear reference case has been developed for consistency across evaluations. However, uncertainties still remain. Less attention has been given to methods for handling structural uncertainty. The lack of adequate methods to explicitly incorporate this aspect of model development may result in the true uncertainty surrounding health care investment decisions being underestimated. Research in this area is ongoing as we review.

Optimal clinical management of children receiving dietary therapies for epilepsy: Updated recommendations of the International Ketogenic Diet Study Group.

PubMed

Kossoff, Eric H; Zupec-Kania, Beth A; Auvin, Stéphane; Ballaban-Gil, Karen R; Christina Bergqvist, A G; Blackford, Robyn; Buchhalter, Jeffrey R; Caraballo, Roberto H; Cross, J Helen; Dahlin, Maria G; Donner, Elizabeth J; Guzel, Orkide; Jehle, Rana S; Klepper, Joerg; Kang, Hoon-Chul; Lambrechts, Danielle A; Liu, Y M Christiana; Nathan, Janak K; Nordli, Douglas R; Pfeifer, Heidi H; Rho, Jong M; Scheffer, Ingrid E; Sharma, Suvasini; Stafstrom, Carl E; Thiele, Elizabeth A; Turner, Zahava; Vaccarezza, Maria M; van der Louw, Elles J T M; Veggiotti, Pierangelo; Wheless, James W; Wirrell, Elaine C

2018-06-01

Ketogenic dietary therapies (KDTs) are established, effective nonpharmacologic treatments for intractable childhood epilepsy. For many years KDTs were implemented differently throughout the world due to lack of consistent protocols. In 2009, an expert consensus guideline for the management of children on KDT was published, focusing on topics of patient selection, pre-KDT counseling and evaluation, diet choice and attributes, implementation, supplementation, follow-up, side events, and KDT discontinuation. It has been helpful in outlining a state-of-the-art protocol, standardizing KDT for multicenter clinical trials, and identifying areas of controversy and uncertainty for future research. Now one decade later, the organizers and authors of this guideline present a revised version with additional authors, in order to include recent research, especially regarding other dietary treatments, clarifying indications for use, side effects during initiation and ongoing use, value of supplements, and methods of KDT discontinuation. In addition, authors completed a survey of their institution's practices, which was compared to responses from the original consensus survey, to show trends in management over the last 10 years.

Inexact fuzzy-stochastic mixed-integer programming approach for long-term planning of waste management--Part A: methodology.

PubMed

Guo, P; Huang, G H

2009-01-01

In this study, an inexact fuzzy chance-constrained two-stage mixed-integer linear programming (IFCTIP) approach is proposed for supporting long-term planning of waste-management systems under multiple uncertainties in the City of Regina, Canada. The method improves upon the existing inexact two-stage programming and mixed-integer linear programming techniques by incorporating uncertainties expressed as multiple uncertainties of intervals and dual probability distributions within a general optimization framework. The developed method can provide an effective linkage between the predefined environmental policies and the associated economic implications. Four special characteristics of the proposed method make it unique compared with other optimization techniques that deal with uncertainties. Firstly, it provides a linkage to predefined policies that have to be respected when a modeling effort is undertaken; secondly, it is useful for tackling uncertainties presented as intervals, probabilities, fuzzy sets and their incorporation; thirdly, it facilitates dynamic analysis for decisions of facility-expansion planning and waste-flow allocation within a multi-facility, multi-period, multi-level, and multi-option context; fourthly, the penalties are exercised with recourse against any infeasibility, which permits in-depth analyses of various policy scenarios that are associated with different levels of economic consequences when the promised solid waste-generation rates are violated. In a companion paper, the developed method is applied to a real case for the long-term planning of waste management in the City of Regina, Canada.

Diagnosing Geospatial Uncertainty Visualization Challenges in Seasonal Temperature and Precipitation Forecasts

NASA Astrophysics Data System (ADS)

Speciale, A.; Kenney, M. A.; Gerst, M.; Baer, A. E.; DeWitt, D.; Gottschalk, J.; Handel, S.

2017-12-01

The uncertainty of future weather and climate conditions is important for many decisions made in communities and economic sectors. One tool that decision-makers use in gauging this uncertainty is forecasts, especially maps (or visualizations) of probabilistic forecast results. However, visualizing geospatial uncertainty is challenging because including probability introduces an extra variable to represent and probability is often poorly understood by users. Using focus group and survey methods, this study seeks to understand the barriers to using probabilistic temperature and precipitation visualizations for specific decisions in the agriculture, energy, emergency management, and water resource sectors. Preliminary results shown here focus on findings of emergency manager needs. Our experimental design uses National Oceanic and Atmospheric Administration (NOAA's) Climate Prediction Center (CPC) climate outlooks, which produce probabilistic temperature and precipitation forecast visualizations at the 6-10 day, 8-14 day, 3-4 week, and 1 and 3 month timeframes. Users were asked to complete questions related to how they use weather information, how uncertainty is represented, and design elements (e.g., color, contour lines) of the visualizations. Preliminary results from the emergency management sector indicate there is significant confusion on how "normal" weather is defined, boundaries between probability ranges, and meaning of the contour lines. After a complete understandability diagnosis is made using results from all sectors, we will collaborate with CPC to suggest modifications to the climate outlook visualizations. These modifications will then be retested in similar focus groups and web-based surveys to confirm they better meet the needs of users.

Accounting for Age Uncertainty in Growth Modeling, the Case Study of Yellowfin Tuna (Thunnus albacares) of the Indian Ocean

PubMed Central

Dortel, Emmanuelle; Massiot-Granier, Félix; Rivot, Etienne; Million, Julien; Hallier, Jean-Pierre; Morize, Eric; Munaron, Jean-Marie; Bousquet, Nicolas; Chassot, Emmanuel

2013-01-01

Age estimates, typically determined by counting periodic growth increments in calcified structures of vertebrates, are the basis of population dynamics models used for managing exploited or threatened species. In fisheries research, the use of otolith growth rings as an indicator of fish age has increased considerably in recent decades. However, otolith readings include various sources of uncertainty. Current ageing methods, which converts an average count of rings into age, only provide periodic age estimates in which the range of uncertainty is fully ignored. In this study, we describe a hierarchical model for estimating individual ages from repeated otolith readings. The model was developed within a Bayesian framework to explicitly represent the sources of uncertainty associated with age estimation, to allow for individual variations and to include knowledge on parameters from expertise. The performance of the proposed model was examined through simulations, and then it was coupled to a two-stanza somatic growth model to evaluate the impact of the age estimation method on the age composition of commercial fisheries catches. We illustrate our approach using the saggital otoliths of yellowfin tuna of the Indian Ocean collected through large-scale mark-recapture experiments. The simulation performance suggested that the ageing error model was able to estimate the ageing biases and provide accurate age estimates, regardless of the age of the fish. Coupled with the growth model, this approach appeared suitable for modeling the growth of Indian Ocean yellowfin and is consistent with findings of previous studies. The simulations showed that the choice of the ageing method can strongly affect growth estimates with subsequent implications for age-structured data used as inputs for population models. Finally, our modeling approach revealed particularly useful to reflect uncertainty around age estimates into the process of growth estimation and it can be applied to any study relying on age estimation. PMID:23637773

Genetic concepts and uncertainties in restoring fish populations and species

USGS Publications Warehouse

Reisenbichler, R.R.; Utter, F.M.; Krueger, C.C.

2003-01-01

Genetic considerations can be crucially important to the success of reintroductions of lotic species. Current paradigms for conservation and population genetics provide guidance for reducing uncertainties in genetic issues and for increasing the likelihood of achieving restoration. Effective restoration is facilitated through specific goals and objectives developed from the definition that a restored or healthy population is (i) genetically adapted to the local environment, (ii) self-sustaining at abundances consistent with the carrying capacity of the river system, (iii) genetically compatible with neighboring populations so that substantial outbreeding depression does not result from straying and interbreeding between populations, and (iv) sufficiently diverse genetically to accommodate environmental variability over many decades. Genetic principles reveal the importance of describing and adhering to the ancestral lineages for the species to be restored and enabling genetic processes to maintain diversity and fitness in the populations under restoration. Newly established populations should be protected from unnecessary human sources of mortality, gene flow from maladapted (e.g., hatchery) or exotic populations, and inadvertent selection by fisheries or other human activities. Such protection facilitates initial, rapid adaptation of the population to its environment and should enhance the chances for persistence. Various uncertainties about specific restoration actions must be addressed on a case-by-case basis. Such uncertainties include whether to allow natural colonization or to introduce fish, which populations are suitable as sources for reintroduction, appropriate levels of gene flow from other populations, appropriate levels of artificial production, appropriate minimum numbers of individuals released or maintained in the population, and the best developmental stages for releasing fish into the restored stream. Rigorous evaluation or experimental management is necessary to reduce uncertainty in our knowledge so that future conservation and restoration activities can be more effective.

Numerical modelling of instantaneous plate tectonics

NASA Technical Reports Server (NTRS)

Minster, J. B.; Haines, E.; Jordan, T. H.; Molnar, P.

1974-01-01

Assuming lithospheric plates to be rigid, 68 spreading rates, 62 fracture zones trends, and 106 earthquake slip vectors are systematically inverted to obtain a self-consistent model of instantaneous relative motions for eleven major plates. The inverse problem is linearized and solved iteratively by a maximum-likelihood procedure. Because the uncertainties in the data are small, Gaussian statistics are shown to be adequate. The use of a linear theory permits (1) the calculation of the uncertainties in the various angular velocity vectors caused by uncertainties in the data, and (2) quantitative examination of the distribution of information within the data set. The existence of a self-consistent model satisfying all the data is strong justification of the rigid plate assumption. Slow movement between North and South America is shown to be resolvable.

An inexact reverse logistics model for municipal solid waste management systems.

PubMed

Zhang, Yi Mei; Huang, Guo He; He, Li

2011-03-01

This paper proposed an inexact reverse logistics model for municipal solid waste management systems (IRWM). Waste managers, suppliers, industries and distributors were involved in strategic planning and operational execution through reverse logistics management. All the parameters were assumed to be intervals to quantify the uncertainties in the optimization process and solutions in IRWM. To solve this model, a piecewise interval programming was developed to deal with Min-Min functions in both objectives and constraints. The application of the model was illustrated through a classical municipal solid waste management case. With different cost parameters for landfill and the WTE, two scenarios were analyzed. The IRWM could reflect the dynamic and uncertain characteristics of MSW management systems, and could facilitate the generation of desired management plans. The model could be further advanced through incorporating methods of stochastic or fuzzy parameters into its framework. Design of multi-waste, multi-echelon, multi-uncertainty reverse logistics model for waste management network would also be preferred. Copyright © 2010 Elsevier Ltd. All rights reserved.

Energy management and control of active distribution systems

NASA Astrophysics Data System (ADS)

Shariatzadeh, Farshid

Advancements in the communication, control, computation and information technologies have driven the transition to the next generation active power distribution systems. Novel control techniques and management strategies are required to achieve the efficient, economic and reliable grid. The focus of this work is energy management and control of active distribution systems (ADS) with integrated renewable energy sources (RESs) and demand response (DR). Here, ADS mean automated distribution system with remotely operated controllers and distributed energy resources (DERs). DER as active part of the next generation future distribution system includes: distributed generations (DGs), RESs, energy storage system (ESS), plug-in hybrid electric vehicles (PHEV) and DR. Integration of DR and RESs into ADS is critical to realize the vision of sustainability. The objective of this dissertation is the development of management architecture to control and operate ADS in the presence of DR and RES. One of the most challenging issues for operating ADS is the inherent uncertainty of DR and RES as well as conflicting objective of DER and electric utilities. ADS can consist of different layers such as system layer and building layer and coordination between these layers is essential. In order to address these challenges, multi-layer energy management and control architecture is proposed with robust algorithms in this work. First layer of proposed multi-layer architecture have been implemented at the system layer. Developed AC optimal power flow (AC-OPF) generates fair price for all DR and non-DR loads which is used as a control signal for second layer. Second layer controls DR load at buildings using a developed look-ahead robust controller. Load aggregator collects information from all buildings and send aggregated load to the system optimizer. Due to the different time scale at these two management layers, time coordination scheme is developed. Robust and deterministic controllers are developed to maximize the energy usage from rooftop photovoltaic (PV) generation locally and minimize heat-ventilation and air conditioning (HVAC) consumption while maintaining inside temperature within comfort zone. The performance of the developed multi-layer architecture has been analyzed using test case studies and results show the robustness of developed controller in the presence of uncertainty.

The Glen Canyon Dam Adaptive Management Program: An experiment in science-based resource management

NASA Astrophysics Data System (ADS)

kaplinski, m

2001-12-01

In 1996, Glen Canyon Dam Adaptive Management (GCDAMP) program was established to provide input on Glen Canyon Dam operations and their affect on the Colorado Ecosystem in Grand Canyon. The GCDAMP is a bold experiment in federal resource management that features a governing partnership with all relevant stakeholders sitting at the same table. It is a complicated, difficult process where stakeholder-derived management actions must balance resource protection with water and power delivery compacts, the Endangered Species Act, the National Historical Preservation Act, the Grand Canyon Protection Act, National Park Service Policy, and other stakeholder concerns. The program consists of four entities: the Adaptive Management Workgroup (AMWG), the Technical Workgroup (TWG), the Grand Canyon Monitoring and Research Center (GCMRC), and independent review panels. The AMWG and TWG are federal advisory committees that consists of federal and state resource managers, Native American tribes, power, environmental and recreation interests. The AMWG is develops, evaluates and recommends alternative dam operations to the Secretary. The TWG translates AMWG policy and goals into management objectives and information needs, provides questions that serve as the basis for long-term monitoring and research activities, interprets research results from the GCMRC, and prepares reports as required for the AMWG. The GCMRC is an independent science center that is responsible for all GCDAMP monitoring and research activities. The GCMRC utilizes proposal requests with external peer review and an in-house staff that directs and synthesizes monitoring and research results. The GCMRC meets regularly with the TWG and AMWG and provides scientific information on the consequences of GCDAMP actions. Independent review panels consist of external peer review panels that provide reviews of scientific activities and the program in general, technical advice to the GCMRC, TWG and AMWG, and play a critical balancing role to ensure overall scientific credibility to the program. Many lessons have been learned and many challenges remain. Incorporation of social issues such as recreation experience and non-use economic valuation has been especially difficult and not entirely satisfactory to many stakeholders. Adaptive management can be frustrating and a fragile spirit of cooperation exists between stakeholders with opposing interests. Communication and flexibility is the key to program success. Scientific results need to be clearly stated to keep results relevant to managers, and managers must embrace the uncertainty of scientific endeavors. Managers must remain flexible in creating and revising program goals and objectives to incorporate new scientific results into meaningful management strategies.

A continental-scale hydrology and water quality model for Europe: Calibration and uncertainty of a high-resolution large-scale SWAT model

NASA Astrophysics Data System (ADS)

Abbaspour, K. C.; Rouholahnejad, E.; Vaghefi, S.; Srinivasan, R.; Yang, H.; Kløve, B.

2015-05-01

A combination of driving forces are increasing pressure on local, national, and regional water supplies needed for irrigation, energy production, industrial uses, domestic purposes, and the environment. In many parts of Europe groundwater quantity, and in particular quality, have come under sever degradation and water levels have decreased resulting in negative environmental impacts. Rapid improvements in the economy of the eastern European block of countries and uncertainties with regard to freshwater availability create challenges for water managers. At the same time, climate change adds a new level of uncertainty with regard to freshwater supplies. In this research we build and calibrate an integrated hydrological model of Europe using the Soil and Water Assessment Tool (SWAT) program. Different components of water resources are simulated and crop yield and water quality are considered at the Hydrological Response Unit (HRU) level. The water resources are quantified at subbasin level with monthly time intervals. Leaching of nitrate into groundwater is also simulated at a finer spatial level (HRU). The use of large-scale, high-resolution water resources models enables consistent and comprehensive examination of integrated system behavior through physically-based, data-driven simulation. In this article we discuss issues with data availability, calibration of large-scale distributed models, and outline procedures for model calibration and uncertainty analysis. The calibrated model and results provide information support to the European Water Framework Directive and lay the basis for further assessment of the impact of climate change on water availability and quality. The approach and methods developed are general and can be applied to any large region around the world.

Natural recharge estimation and uncertainty analysis of an adjudicated groundwater basin using a regional-scale flow and subsidence model (Antelope Valley, California, USA)

USGS Publications Warehouse

Siade, Adam J.; Nishikawa, Tracy; Martin, Peter

2015-01-01

Groundwater has provided 50–90 % of the total water supply in Antelope Valley, California (USA). The associated groundwater-level declines have led the Los Angeles County Superior Court of California to recently rule that the Antelope Valley groundwater basin is in overdraft, i.e., annual pumpage exceeds annual recharge. Natural recharge consists primarily of mountain-front recharge and is an important component of the total groundwater budget in Antelope Valley. Therefore, natural recharge plays a major role in the Court’s decision. The exact quantity and distribution of natural recharge is uncertain, with total estimates from previous studies ranging from 37 to 200 gigaliters per year (GL/year). In order to better understand the uncertainty associated with natural recharge and to provide a tool for groundwater management, a numerical model of groundwater flow and land subsidence was developed. The transient model was calibrated using PEST with water-level and subsidence data; prior information was incorporated through the use of Tikhonov regularization. The calibrated estimate of natural recharge was 36 GL/year, which is appreciably less than the value used by the court (74 GL/year). The effect of parameter uncertainty on the estimation of natural recharge was addressed using the Null-Space Monte Carlo method. A Pareto trade-off method was also used to portray the reasonableness of larger natural recharge rates. The reasonableness of the 74 GL/year value and the effect of uncertain pumpage rates were also evaluated. The uncertainty analyses indicate that the total natural recharge likely ranges between 34.5 and 54.3 GL/year.

Consistency assessment of rating curve data in various locations using Bidirectional Reach (BReach)

NASA Astrophysics Data System (ADS)

Van Eerdenbrugh, Katrien; Van Hoey, Stijn; Coxon, Gemma; Freer, Jim; Verhoest, Niko E. C.

2017-10-01

When estimating discharges through rating curves, temporal data consistency is a critical issue. In this research, consistency in stage-discharge data is investigated using a methodology called Bidirectional Reach (BReach), which departs from a (in operational hydrology) commonly used definition of consistency. A period is considered to be consistent if no consecutive and systematic deviations from a current situation occur that exceed observational uncertainty. Therefore, the capability of a rating curve model to describe a subset of the (chronologically sorted) data is assessed in each observation by indicating the outermost data points for which the rating curve model behaves satisfactorily. These points are called the maximum left or right reach, depending on the direction of the investigation. This temporal reach should not be confused with a spatial reach (indicating a part of a river). Changes in these reaches throughout the data series indicate possible changes in data consistency and if not resolved could introduce additional errors and biases. In this research, various measurement stations in the UK, New Zealand and Belgium are selected based on their significant historical ratings information and their specific characteristics related to data consistency. For each country, regional information is maximally used to estimate observational uncertainty. Based on this uncertainty, a BReach analysis is performed and, subsequently, results are validated against available knowledge about the history and behavior of the site. For all investigated cases, the methodology provides results that appear to be consistent with this knowledge of historical changes and thus facilitates a reliable assessment of (in)consistent periods in stage-discharge measurements. This assessment is not only useful for the analysis and determination of discharge time series, but also to enhance applications based on these data (e.g., by informing hydrological and hydraulic model evaluation design about consistent time periods to analyze).

A Data Cleaning Method for Big Trace Data Using Movement Consistency

PubMed Central

Tang, Luliang; Zhang, Xia; Li, Qingquan

2018-01-01

Given the popularization of GPS technologies, the massive amount of spatiotemporal GPS traces collected by vehicles are becoming a new kind of big data source for urban geographic information extraction. The growing volume of the dataset, however, creates processing and management difficulties, while the low quality generates uncertainties when investigating human activities. Based on the conception of the error distribution law and position accuracy of the GPS data, we propose in this paper a data cleaning method for this kind of spatial big data using movement consistency. First, a trajectory is partitioned into a set of sub-trajectories using the movement characteristic points. In this process, GPS points indicate that the motion status of the vehicle has transformed from one state into another, and are regarded as the movement characteristic points. Then, GPS data are cleaned based on the similarities of GPS points and the movement consistency model of the sub-trajectory. The movement consistency model is built using the random sample consensus algorithm based on the high spatial consistency of high-quality GPS data. The proposed method is evaluated based on extensive experiments, using GPS trajectories generated by a sample of vehicles over a 7-day period in Wuhan city, China. The results show the effectiveness and efficiency of the proposed method. PMID:29522456

The role of future scenarios to understand deep uncertainty in air quality management

EPA Science Inventory

The environment and it’s interaction with human systems (economic, social and political) is complex and dynamic. Key drivers may disrupt system dynamics in unforeseen ways, making it difficult to predict future conditions precisely. This kind of deep uncertainty presents ...

The role of future scenarios to understand deep uncertainty for air quality management.

EPA Science Inventory

The environment and its interaction with human systems (economic, social and political) is complex and dynamic. Key drivers may disrupt systemdynamics in unforeseen ways, making it difficult to predict future conditions precisely. This kind of deep uncertainty presents a challeng...

Detectability and Interpretational Uncertainties: Considerations in Gauging the Impacts of Land Disturbance on Streamflow

EPA Science Inventory

Hydrologic impacts of land disturbance and management can be confounded by rainfall variability. As a consequence, attempts to gauge and quantify these effects through streamflow monitoring are typically subject to uncertainties. This paper addresses the quantification and deline...

Uncertainty Analysis of Ozone Formation and Response to Emission Controls Using Higher-Order Sensitivities

EPA Science Inventory

Understanding ozone response to its precursor emissions is crucial for effective air quality management practices. This nonlinear response is usually simulated using chemical transport models, and the modeling results are affected by uncertainties in emissions inputs. In this stu...

Listening beyond the Self: How Organizations Create Direct Involvement

ERIC Educational Resources Information Center

Michel, Alexandra; Wortham, Stanton

2007-01-01

A two-year ethnographic study examines how two U.S. investment banks managed bankers' uncertainty differently and achieved distinct forms of participant transformation, including listening outcomes. People Bank reduced uncertainty by conveying abstract concepts. Socialized bankers exhibited a preferential orientation toward abstractions, including…

The impacts of uncertainty and variability in groundwater-driven health risk assessment. (Invited)

NASA Astrophysics Data System (ADS)

Maxwell, R. M.

2010-12-01

Potential human health risk from contaminated groundwater is becoming an important, quantitative measure used in management decisions in a range of applications from Superfund to CO2 sequestration. Quantitatively assessing the potential human health risks from contaminated groundwater is challenging due to the many coupled processes, uncertainty in transport parameters and the variability in individual physiology and behavior. Perspective on human health risk assessment techniques will be presented and a framework used to predict potential, increased human health risk from contaminated groundwater will be discussed. This framework incorporates transport of contaminants through the subsurface from source to receptor and health risks to individuals via household exposure pathways. The subsurface is shown subject to both physical and chemical heterogeneity which affects downstream concentrations at receptors. Cases are presented where hydraulic conductivity can exhibit both uncertainty and spatial variability in addition to situations where hydraulic conductivity is the dominant source of uncertainty in risk assessment. Management implications, such as characterization and remediation will also be discussed.

Top 10 research priorities in head and neck cancer: Results of an Alberta priority setting partnership of patients, caregivers, family members, and clinicians.

PubMed

Lechelt, Leah A; Rieger, Jana M; Cowan, Katherine; Debenham, Brock J; Krewski, Bernie; Nayar, Suresh; Regunathan, Akhila; Seikaly, Hadi; Singh, Ameeta E; Laupacis, Andreas

2018-03-01

The epidemiology, etiology, and management of head and neck cancer are evolving. Understanding the perspectives and priorities of nonresearchers regarding treatment uncertainties is important to inform future research. Using the James Lind Alliance approach, patients, caregivers, and clinicians responded to a survey regarding their unanswered questions about treating and managing head and neck cancer. Distinct uncertainties were extracted from responses and sorted into themes. Uncertainties already answered in the literature were removed. Those remaining were ranked by patients and clinicians to develop a short list of priorities, which were discussed at a workshop and reduced to the top 10. One hundred sixty-one respondents posed 818 uncertainties, culminating in 77 for interim ranking and 27 for discussion at a workshop. Participants reached consensus on the top 10, which included questions on prevention, screening, treatment, and quality of life. Nonresearchers can effectively collaborate to establish priorities for future research in head and neck cancer. © 2017 Wiley Periodicals, Inc.

Integrated Modeling and Assessment of Climate Change Mitigation in North America: Lessons learned from Mexico

NASA Astrophysics Data System (ADS)

Olguin-Alvarez, M. I.; Kurz, W. A.; Wayson, C.; Birdsey, R.; Richardson, K.; Angeles, G.; Vargas, B.; Corral, J.; Magnan, M.; Fellows, M.; Morken, S.; Maldonado, V.; Mascorro, V.; Meneses, C.; Galicia, G.; Serrano, E.

2016-12-01

The Government of Mexico has recently designed a system of measurement, reporting and verification (MRV) to account for the emissions and removals of greenhouse gases (GHG) associated with the country's forest sector. This system reports national-scale GHG emissions based on the "stock-difference" approach combining information from two sets of measurements from the national forest inventory and remote sensing data. However, consistent with the commitments made by the country to the United Nations Framework Convention on Climate Change (UNFCCC), the MRV system must strive to reduce, as far as practicable, the uncertainties associated with national estimates on GHG fluxes. Since 2012, the Mexican government through its National Forestry Commission, with support from the North America Commission of Environmental Cooperation, the Forest Services of Canada and USA, the SilvaCarbon Program and research institutes in Mexico, has made progress towards the use of carbon dynamics models ("gain-loss" approach) to reduce uncertainty of the GHG estimates in strategic landscapes. In Mexico, most of the forests are under social tenure where management includes a wide array of activities (e.g. selective harvesting, firewood collection). Altering these diverse management activities (REDD+ strategies as well as harvested wood products), can augment their mitigation potential. Here we present the main steps conducted to compile and integrate information from forest inventories, remote sensing, disturbance data and ecosystem carbon transfers to generate inputs required to calibrate these models and validate their outputs. The analyses are supported by the use of the CBM-CFS3 model with the appropriate modification of the model parameters and input data according to the 2006 guidelines of the Intergovernmental Panel on Climate Change (IPCC) for preparing Tier 3-GHG inventories. The ultimate goal of this tri-national effort is to show how the data and tools developed for carbon assessment in strategic landscapes in North America can help estimate the impact of several mitigation options consistent with national goals of GHG emission reductions.

A Model-Based Prognostics Approach Applied to Pneumatic Valves

NASA Technical Reports Server (NTRS)

Daigle, Matthew J.; Goebel, Kai

2011-01-01

Within the area of systems health management, the task of prognostics centers on predicting when components will fail. Model-based prognostics exploits domain knowledge of the system, its components, and how they fail by casting the underlying physical phenomena in a physics-based model that is derived from first principles. Uncertainty cannot be avoided in prediction, therefore, algorithms are employed that help in managing these uncertainties. The particle filtering algorithm has become a popular choice for model-based prognostics due to its wide applicability, ease of implementation, and support for uncertainty management. We develop a general model-based prognostics methodology within a robust probabilistic framework using particle filters. As a case study, we consider a pneumatic valve from the Space Shuttle cryogenic refueling system. We develop a detailed physics-based model of the pneumatic valve, and perform comprehensive simulation experiments to illustrate our prognostics approach and evaluate its effectiveness and robustness. The approach is demonstrated using historical pneumatic valve data from the refueling system.

Management of hyperglycemia in type 2 diabetes: evidence and uncertainty.

PubMed

Esposito, Katherine; Gentile, Sandro; Candido, Riccardo; De Micheli, Alberto; Gallo, Marco; Medea, Gerardo; Ceriello, Antonio

2013-05-30

The panoply of treatment algorithms, periodically released to improve guidance, is one mean to face therapeutic uncertainty in pharmacological management of hyperglycemia in type 2 diabetes, especially after metformin failure. Failure of recent guidelines to give advice on the use of specific antidiabetic drugs in patients with co-morbidity may generate further uncertainty, given the frequent association of type 2 diabetes with common comorbidity, including, although not limited to obesity, cardiovascular disease, impaired renal function, and frailty. The Italian Association of Diabetologists (Associazione Medici Diabetologi, AMD) recognized the need to develop personalized treatment plans for people with type 2 diabetes, taking into account the patients' individual profile (phenotype), with the objective of the safest possible glycemic control. As not every subject with type 2 diabetes benefits from intensive glycemic control, flexible regimens of treatment with diabetes drugs (including insulin) are needed for reaching individualized glycemic goals. Whether personalized diabetology will improve the quality healthcare practice of diabetes management is unknown, but specific research has been launched.

Interval-parameter chance-constraint programming model for end-of-life vehicles management under rigorous environmental regulations.

PubMed

Simic, Vladimir

2016-06-01

As the number of end-of-life vehicles (ELVs) is estimated to increase to 79.3 million units per year by 2020 (e.g., 40 million units were generated in 2010), there is strong motivation to effectively manage this fast-growing waste flow. Intensive work on management of ELVs is necessary in order to more successfully tackle this important environmental challenge. This paper proposes an interval-parameter chance-constraint programming model for end-of-life vehicles management under rigorous environmental regulations. The proposed model can incorporate various uncertainty information in the modeling process. The complex relationships between different ELV management sub-systems are successfully addressed. Particularly, the formulated model can help identify optimal patterns of procurement from multiple sources of ELV supply, production and inventory planning in multiple vehicle recycling factories, and allocation of sorted material flows to multiple final destinations under rigorous environmental regulations. A case study is conducted in order to demonstrate the potentials and applicability of the proposed model. Various constraint-violation probability levels are examined in detail. Influences of parameter uncertainty on model solutions are thoroughly investigated. Useful solutions for the management of ELVs are obtained under different probabilities of violating system constraints. The formulated model is able to tackle a hard, uncertainty existing ELV management problem. The presented model has advantages in providing bases for determining long-term ELV management plans with desired compromises between economic efficiency of vehicle recycling system and system-reliability considerations. The results are helpful for supporting generation and improvement of ELV management plans. Copyright © 2016 Elsevier Ltd. All rights reserved.

Can climate variability information constrain a hydrological model for an ungauged Costa Rican catchment?

NASA Astrophysics Data System (ADS)

Quesada-Montano, Beatriz; Westerberg, Ida K.; Fuentes-Andino, Diana; Hidalgo-Leon, Hugo; Halldin, Sven

2017-04-01

Long-term hydrological data are key to understanding catchment behaviour and for decision making within water management and planning. Given the lack of observed data in many regions worldwide, hydrological models are an alternative for reproducing historical streamflow series. Additional types of information - to locally observed discharge - can be used to constrain model parameter uncertainty for ungauged catchments. Climate variability exerts a strong influence on streamflow variability on long and short time scales, in particular in the Central-American region. We therefore explored the use of climate variability knowledge to constrain the simulated discharge uncertainty of a conceptual hydrological model applied to a Costa Rican catchment, assumed to be ungauged. To reduce model uncertainty we first rejected parameter relationships that disagreed with our understanding of the system. We then assessed how well climate-based constraints applied at long-term, inter-annual and intra-annual time scales could constrain model uncertainty. Finally, we compared the climate-based constraints to a constraint on low-flow statistics based on information obtained from global maps. We evaluated our method in terms of the ability of the model to reproduce the observed hydrograph and the active catchment processes in terms of two efficiency measures, a statistical consistency measure, a spread measure and 17 hydrological signatures. We found that climate variability knowledge was useful for reducing model uncertainty, in particular, unrealistic representation of deep groundwater processes. The constraints based on global maps of low-flow statistics provided more constraining information than those based on climate variability, but the latter rejected slow rainfall-runoff representations that the low flow statistics did not reject. The use of such knowledge, together with information on low-flow statistics and constraints on parameter relationships showed to be useful to constrain model uncertainty for an - assumed to be - ungauged basin. This shows that our method is promising for reconstructing long-term flow data for ungauged catchments on the Pacific side of Central America, and that similar methods can be developed for ungauged basins in other regions where climate variability exerts a strong control on streamflow variability.

A qualitative investigation of the impact of asthma and self-management strategies among older adults.

PubMed

O'Conor, Rachel; Martynenko, Melissa; Gagnon, Monica; Hauser, Diane; Young, Edwin; Lurio, Joseph; Wisnivesky, Juan P; Wolf, Michael S; Federman, Alex D

2017-01-02

 We sought feedback from elderly patients living with asthma to understand their experience with assuming self-management roles for their asthma in order to inform the design and implementation of a primary care-based strategy that could best support their asthma control. We held six focus groups with a total of 31 English- and Spanish-speaking older adults with a current diagnosis of asthma. Focus groups addressed the effect of asthma on patients' lives and self-management strategies. Transcripts were analyzed using constant comparative techniques. Asthma exerted a consistent effect on patients' physical and psychological well-being. Common barriers to self-care included misuse of controller medications and uncertainty whether shortness of breath, fatigue, and cough were due to their asthma or some other chronic illness. Patients developed coping strategies to continue with daily activities even when experiencing symptoms, but did not recognize attainable asthma quality of life. Asthma had a distinct impact on elderly adults' quality of life; due to their longstanding history with this condition, many patients had accepted these symptoms as a "new normal." Developing strategies to reorient patients' perceptions of the possibilities for managing their illness will be critical to the success of asthma self-management support programs specific to older adults.

Ways forward in quantifying data uncertainty in geological databases

NASA Astrophysics Data System (ADS)

Kint, Lars; Chademenos, Vasileios; De Mol, Robin; Kapel, Michel; Lagring, Ruth; Stafleu, Jan; van Heteren, Sytze; Van Lancker, Vera

2017-04-01

Issues of compatibility of geological data resulting from the merging of many different data sources and time periods may jeopardize harmonization of data products. Important progress has been made due to increasing data standardization, e.g., at a European scale through the SeaDataNet and Geo-Seas data management infrastructures. Common geological data standards are unambiguously defined, avoiding semantic overlap in geological data and associated metadata. Quality flagging is also applied increasingly, though ways in further propagating this information in data products is still at its infancy. For the Belgian and southern Netherlands part of the North Sea, databases are now rigorously re-analyzed in view of quantifying quality flags in terms of uncertainty to be propagated through a 3D voxel model of the subsurface (https://odnature.naturalsciences.be/tiles/). An approach is worked out to consistently account for differences in positioning, sampling gear, analysis procedures and vintage. The flag scaling is used in the interpolation process of geological data, but will also be used when visualizing the suitability of geological resources in a decision support system. Expert knowledge is systematically revisited as to avoid totally inappropriate use of the flag scaling process. The quality flagging is also important when communicating results to end-users. Therefore, an open data policy in combination with several processing tools will be at the heart of a new Belgian geological data portal as a platform for knowledge building (KB) and knowledge management (KM) serving the marine geoscience, the policy community and the public at large.

Assessment of Satellite Ocean Colour Radiometry and Derived Geophysical Products. Chapter 6.1

NASA Technical Reports Server (NTRS)

Melin, Frederic; Franz, Bryan A.

2014-01-01

Standardization of methods to assess and assign quality metrics to satellite ocean color radiometry and derived geophysical products has become paramount with the inclusion of the marine reflectance and chlorophyll-a concentration (Chla) as essential climate variables (ECV; [1]) and the recognition that optical remote sensing of the oceans can only contribute to climate research if and when a continuous succession of satellite missions can be shown to collectively provide a consistent, long-term record with known uncertainties. In 20 years, the community has made significant advancements toward that objective, but providing a complete uncertainty budget for all products and for all conditions remains a daunting task. In the retrieval of marine water-leaving radiance from observed top-of-atmosphere radiance, the sources of uncertainties include those associated with propagation of sensor noise and radiometric calibration and characterization errors, as well as a multitude of uncertainties associated with the modeling and removal of effects from the atmosphere and sea surface. This chapter describes some common approaches used to assess quality and consistency of ocean color satellite products and reviews the current status of uncertainty quantification in the field. Its focus is on the primary ocean color product, the spectrum of marine reflectance Rrs, but uncertainties in some derived products such as the Chla or inherent optical properties (IOPs) will also be considered.

The method of belief scales as a means for dealing with uncertainty in tough regulatory decisions.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pilch, Martin M.

Modeling and simulation is playing an increasing role in supporting tough regulatory decisions, which are typically characterized by variabilities and uncertainties in the scenarios, input conditions, failure criteria, model parameters, and even model form. Variability exists when there is a statistically significant database that is fully relevant to the application. Uncertainty, on the other hand, is characterized by some degree of ignorance. A simple algebraic problem was used to illustrate how various risk methodologies address variability and uncertainty in a regulatory context. These traditional risk methodologies include probabilistic methods (including frequensic and Bayesian perspectives) and second-order methods where variabilities andmore » uncertainties are treated separately. Representing uncertainties with (subjective) probability distributions and using probabilistic methods to propagate subjective distributions can lead to results that are not logically consistent with available knowledge and that may not be conservative. The Method of Belief Scales (MBS) is developed as a means to logically aggregate uncertain input information and to propagate that information through the model to a set of results that are scrutable, easily interpretable by the nonexpert, and logically consistent with the available input information. The MBS, particularly in conjunction with sensitivity analyses, has the potential to be more computationally efficient than other risk methodologies. The regulatory language must be tailored to the specific risk methodology if ambiguity and conflict are to be avoided.« less

Uncertainty in flood damage estimates and its potential effect on investment decisions

NASA Astrophysics Data System (ADS)

Wagenaar, Dennis; de Bruijn, Karin; Bouwer, Laurens; de Moel, Hans

2015-04-01

This paper addresses the large differences that are found between damage estimates of different flood damage models. It explains how implicit assumptions in flood damage models can lead to large uncertainties in flood damage estimates. This explanation is used to quantify this uncertainty with a Monte Carlo Analysis. This Monte Carlo analysis uses a damage function library with 272 functions from 7 different flood damage models. This results in uncertainties in the order of magnitude of a factor 2 to 5. This uncertainty is typically larger for small water depths and for smaller flood events. The implications of the uncertainty in damage estimates for flood risk management are illustrated by a case study in which the economic optimal investment strategy for a dike segment in the Netherlands is determined. The case study shows that the uncertainty in flood damage estimates can lead to significant over- or under-investments.

Uncertainty in flood damage estimates and its potential effect on investment decisions

NASA Astrophysics Data System (ADS)

Wagenaar, D. J.; de Bruijn, K. M.; Bouwer, L. M.; De Moel, H.

2015-01-01

This paper addresses the large differences that are found between damage estimates of different flood damage models. It explains how implicit assumptions in flood damage models can lead to large uncertainties in flood damage estimates. This explanation is used to quantify this uncertainty with a Monte Carlo Analysis. As input the Monte Carlo analysis uses a damage function library with 272 functions from 7 different flood damage models. This results in uncertainties in the order of magnitude of a factor 2 to 5. The resulting uncertainty is typically larger for small water depths and for smaller flood events. The implications of the uncertainty in damage estimates for flood risk management are illustrated by a case study in which the economic optimal investment strategy for a dike segment in the Netherlands is determined. The case study shows that the uncertainty in flood damage estimates can lead to significant over- or under-investments.

An empirical application of transaction-costs theory to organizational design characteristics.

PubMed

Williams, S

2000-01-01

The environmental uncertainty component of transaction-costs theory was used to predict the organizational structural characteristics of size (number of employees) and horizontal differentiation (number of vice presidents) using financial and management information from the COMPACT DISCLOSURE data base (which contains the most recent annual and periodic reports for more than 12,000 public companies). Organizations were categorized as low- or high-uncertainty industries according to Dess and Beard's (1984) Dynamism Scale, and net sales volume was controlled. As predicted, high-uncertainty companies had significantly higher horizontal differentiation than low-uncertainty firms, a finding that supports the transaction-costs expectation that organizations may require more departments or personnel to cope with increasing uncertainty. Surprisingly, low-uncertainty firms were found to have significantly more employees than high-uncertainty organizations, which is the opposite of what transaction-costs theory predicts. Possible explanations for this unexpected finding and further potential limitations are discussed.

Decision making with epistemic uncertainty under safety constraints: An application to seismic design

USGS Publications Warehouse

Veneziano, D.; Agarwal, A.; Karaca, E.

2009-01-01

The problem of accounting for epistemic uncertainty in risk management decisions is conceptually straightforward, but is riddled with practical difficulties. Simple approximations are often used whereby future variations in epistemic uncertainty are ignored or worst-case scenarios are postulated. These strategies tend to produce sub-optimal decisions. We develop a general framework based on Bayesian decision theory and exemplify it for the case of seismic design of buildings. When temporal fluctuations of the epistemic uncertainties and regulatory safety constraints are included, the optimal level of seismic protection exceeds the normative level at the time of construction. Optimal Bayesian decisions do not depend on the aleatory or epistemic nature of the uncertainties, but only on the total (epistemic plus aleatory) uncertainty and how that total uncertainty varies randomly during the lifetime of the project. ?? 2009 Elsevier Ltd. All rights reserved.

Combination of uncertainty theories and decision-aiding methods for natural risk management in a context of imperfect information

NASA Astrophysics Data System (ADS)

Tacnet, Jean-Marc; Dupouy, Guillaume; Carladous, Simon; Dezert, Jean; Batton-Hubert, Mireille

2017-04-01

In mountain areas, natural phenomena such as snow avalanches, debris-flows and rock-falls, put people and objects at risk with sometimes dramatic consequences. Risk is classically considered as a combination of hazard, the combination of the intensity and frequency of the phenomenon, and vulnerability which corresponds to the consequences of the phenomenon on exposed people and material assets. Risk management consists in identifying the risk level as well as choosing the best strategies for risk prevention, i.e. mitigation. In the context of natural phenomena in mountainous areas, technical and scientific knowledge is often lacking. Risk management decisions are therefore based on imperfect information. This information comes from more or less reliable sources ranging from historical data, expert assessments, numerical simulations etc. Finally, risk management decisions are the result of complex knowledge management and reasoning processes. Tracing the information and propagating information quality from data acquisition to decisions are therefore important steps in the decision-making process. One major goal today is therefore to assist decision-making while considering the availability, quality and reliability of information content and sources. A global integrated framework is proposed to improve the risk management process in a context of information imperfection provided by more or less reliable sources: uncertainty as well as imprecision, inconsistency and incompleteness are considered. Several methods are used and associated in an original way: sequential decision context description, development of specific multi-criteria decision-making methods, imperfection propagation in numerical modeling and information fusion. This framework not only assists in decision-making but also traces the process and evaluates the impact of information quality on decision-making. We focus and present two main developments. The first one relates to uncertainty and imprecision propagation in numerical modeling using both classical Monte-Carlo probabilistic approach and also so-called Hybrid approach using possibility theory. Second approach deals with new multi-criteria decision-making methods which consider information imperfection, source reliability, importance and conflict, using fuzzy sets as well as possibility and belief function theories. Implemented methods consider information imperfection propagation and information fusion in total aggregation methods such as AHP (Saaty, 1980) or partial aggregation methods such as the Electre outranking method (see Soft Electre Tri ) or decisions in certain but also risky or uncertain contexts (see new COWA-ER and FOWA-ER- Cautious and Fuzzy Ordered Weighted Averaging-Evidential Reasoning). For example, the ER-MCDA methodology considers expert assessment as a multi-criteria decision process based on imperfect information provided by more or less heterogeneous, reliable and conflicting sources: it mixes AHP, fuzzy sets theory, possibility theory and belief function theory using DSmT (Dezert-Smarandache Theory) framework which provides powerful fusion rules.

Overview of Management Theory

DTIC Science & Technology

1991-02-01

theory orients command leadership for the enormous task of managing organizations in our environment fraught with volatility, uncertainty...performance and organizational ethics. A THEORY OF MANAGEMENT BACKGROUND BASIC MANAGEMENT BEHAVIORAL Definitions FUNCTIONS ASPECTS History Planning Leadership ...the best way to manage in their theory of managerial leadership . To them, the 9,9 position on their model, "is acknowledged by managers as the

Merging Methods to Manage Uncertainty: Combining Simulation Modeling and Scenario Planning to Inform Resource Management Under Climate Change

NASA Astrophysics Data System (ADS)

Miller, B. W.; Schuurman, G. W.; Symstad, A.; Fisichelli, N. A.; Frid, L.

2017-12-01

Managing natural resources in this era of anthropogenic climate change is fraught with uncertainties around how ecosystems will respond to management actions and a changing climate. Scenario planning (oftentimes implemented as a qualitative, participatory exercise for exploring multiple possible futures) is a valuable tool for addressing this challenge. However, this approach may face limits in resolving responses of complex systems to altered climate and management conditions, and may not provide the scientific credibility that managers often require to support actions that depart from current practice. Quantitative information on projected climate changes and ecological responses is rapidly growing and evolving, but this information is often not at a scale or in a form that is `actionable' for resource managers. We describe a project that sought to create usable information for resource managers in the northern Great Plains by combining qualitative and quantitative methods. In particular, researchers, resource managers, and climate adaptation specialists co-produced a simulation model in conjunction with scenario planning workshops to inform natural resource management in southwest South Dakota. Scenario planning for a wide range of resources facilitated open-minded thinking about a set of divergent and challenging, yet relevant and plausible, climate scenarios and management alternatives that could be implemented in the simulation. With stakeholder input throughout the process, we built a simulation of key vegetation types, grazing, exotic plants, fire, and the effects of climate and management on rangeland productivity and composition. By simulating multiple land management jurisdictions, climate scenarios, and management alternatives, the model highlighted important tradeoffs between herd sizes and vegetation composition, and between the short- versus long-term costs of invasive species management. It also identified impactful uncertainties related to the effects of fire and grazing on vegetation. Ultimately, this integrative and iterative approach yielded counter-intuitive and surprising findings, and resulted in a more tractable set of possible futures for resource management planning.

The carbon cycle revisited

NASA Technical Reports Server (NTRS)

Bolin, Bert; Fung, Inez

1992-01-01

Discussions during the Global Change Institute indicated a need to present, in some detail and as accurately as possible, our present knowledge about the carbon cycle, the uncertainties in this knowledge, and the reasons for these uncertainties. We discuss basic issues of internal consistency within the carbon cycle, and end by summarizing the key unknowns.

Bayesian Model Averaging for Propensity Score Analysis

ERIC Educational Resources Information Center

Kaplan, David; Chen, Jianshen

2013-01-01

The purpose of this study is to explore Bayesian model averaging in the propensity score context. Previous research on Bayesian propensity score analysis does not take into account model uncertainty. In this regard, an internally consistent Bayesian framework for model building and estimation must also account for model uncertainty. The…

An integrated bi-level optimization model for air quality management of Beijing's energy system under uncertainty.

PubMed

Jin, S W; Li, Y P; Nie, S

2018-05-15

In this study, an interval chance-constrained bi-level programming (ICBP) method is developed for air quality management of municipal energy system under uncertainty. ICBP can deal with uncertainties presented as interval values and probability distributions as well as examine the risk of violating constraints. Besides, a leader-follower decision strategy is incorporated into the optimization process where two decision makers with different goals and preferences are involved. To solve the proposed model, a bi-level interactive algorithm based on satisfactory degree is introduced into the decision-making processes. Then, an ICBP based energy and environmental systems (ICBP-EES) model is formulated for Beijing, in which air quality index (AQI) is used for evaluating the integrated air quality of multiple pollutants. Result analysis can help different stakeholders adjust their tolerances to achieve the overall satisfaction of EES planning for the study city. Results reveal that natural gas is the main source for electricity-generation and heating that could lead to a potentially increment of imported energy for Beijing in future. Results also disclose that PM 10 is the major contributor to AQI. These findings can help decision makers to identify desired alternatives for EES planning and provide useful information for regional air quality management under uncertainty. Copyright © 2018 Elsevier B.V. All rights reserved.

Optimal regeneration planning for old-growth forest: addressing scientific uncertainty in endangered species recovery through adaptive management

USGS Publications Warehouse

Moore, C.T.; Conroy, M.J.

2006-01-01

Stochastic and structural uncertainties about forest dynamics present challenges in the management of ephemeral habitat conditions for endangered forest species. Maintaining critical foraging and breeding habitat for the endangered red-cockaded woodpecker (Picoides borealis) requires an uninterrupted supply of old-growth forest. We constructed and optimized a dynamic forest growth model for the Piedmont National Wildlife Refuge (Georgia, USA) with the objective of perpetuating a maximum stream of old-growth forest habitat. Our model accommodates stochastic disturbances and hardwood succession rates, and uncertainty about model structure. We produced a regeneration policy that was indexed by current forest state and by current weight of evidence among alternative model forms. We used adaptive stochastic dynamic programming, which anticipates that model probabilities, as well as forest states, may change through time, with consequent evolution of the optimal decision for any given forest state. In light of considerable uncertainty about forest dynamics, we analyzed a set of competing models incorporating extreme, but plausible, parameter values. Under any of these models, forest silviculture practices currently recommended for the creation of woodpecker habitat are suboptimal. We endorse fully adaptive approaches to the management of endangered species habitats in which predictive modeling, monitoring, and assessment are tightly linked.

Adaptive management for soil ecosystem services.

PubMed

Birgé, Hannah E; Bevans, Rebecca A; Allen, Craig R; Angeler, David G; Baer, Sara G; Wall, Diana H

2016-12-01

Ecosystem services provided by soil include regulation of the atmosphere and climate, primary (including agricultural) production, waste processing, decomposition, nutrient conservation, water purification, erosion control, medical resources, pest control, and disease mitigation. The simultaneous production of these multiple services arises from complex interactions among diverse aboveground and belowground communities across multiple scales. When a system is mismanaged, non-linear and persistent losses in ecosystem services can arise. Adaptive management is an approach to management designed to reduce uncertainty as management proceeds. By developing alternative hypotheses, testing these hypotheses and adjusting management in response to outcomes, managers can probe dynamic mechanistic relationships among aboveground and belowground soil system components. In doing so, soil ecosystem services can be preserved and critical ecological thresholds avoided. Here, we present an adaptive management framework designed to reduce uncertainty surrounding the soil system, even when soil ecosystem services production is not the explicit management objective, so that managers can reach their management goals without undermining soil multifunctionality or contributing to an irreversible loss of soil ecosystem services. Copyright © 2016. Published by Elsevier Ltd.

Managing Uncertainty: Environmental Analysis/Forecasting in Academic Planning.

ERIC Educational Resources Information Center

Morrison, James L.; Mecca, Thomas V.

An approach to environmental analysis and forecasting that educational policymakers can employ in dealing with the level of uncertainty in strategic decision making is presented. Traditional planning models are weak in identifying environmental changes and assessing their organizational impact. The proposed approach does not lead decision makers…

Development of risk management strategies for state DOTs to effectively deal with volatile prices of transportation construction materials.

DOT National Transportation Integrated Search

2014-06-01

Volatility in price of critical materials used in transportation projects, such as asphalt cement, leads to : considerable uncertainty about project cost. This uncertainty may lead to price speculation and inflated : bid prices submitted by highway c...

Role of future scenarios in understanding deep uncertainty in long-term air quality management

EPA Science Inventory

The environment and its interactions with human systems, whether economic, social or political, are complex. Relevant drivers may disrupt system dynamics in unforeseen ways, making it difficult to predict future conditions. This kind of deep uncertainty presents a challenge to ...

Two-Stage Fracturing Wastewater Management in Shale Gas Development

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Xiaodong; Sun, Alexander Y.; Duncan, Ian J.

Here, management of shale gas wastewater treatment, disposal, and reuse has become a significant environmental challenge, driven by an ongoing boom in development of U.S. shale gas reservoirs. Systems-analysis based decision support is helpful for effective management of wastewater, and provision of cost-effective decision alternatives from a whole-system perspective. Uncertainties are inherent in many modeling parameters, affecting the generated decisions. In order to effectively deal with the recourse issue in decision making, in this work a two-stage stochastic fracturing wastewater management model, named TSWM, is developed to provide decision support for wastewater management planning in shale plays. Using the TSWMmore » model, probabilistic and nonprobabilistic uncertainties are effectively handled. The TSWM model provides flexibility in generating shale gas wastewater management strategies, in which the first-stage decision predefined by decision makers before uncertainties are unfolded is corrected in the second stage to achieve the whole-system’s optimality. Application of the TSWM model to a comprehensive synthetic example demonstrates its practical applicability and feasibility. Optimal results are generated for allowable wastewater quantities, excess wastewater, and capacity expansions of hazardous wastewater treatment plants to achieve the minimized total system cost. The obtained interval solutions encompass both optimistic and conservative decisions. Trade-offs between economic and environmental objectives are made depending on decision makers’ knowledge and judgment, as well as site-specific information. In conclusion, the proposed model is helpful in forming informed decisions for wastewater management associated with shale gas development.« less

Two-Stage Fracturing Wastewater Management in Shale Gas Development

DOE PAGES

Zhang, Xiaodong; Sun, Alexander Y.; Duncan, Ian J.; ...

2017-01-19

Here, management of shale gas wastewater treatment, disposal, and reuse has become a significant environmental challenge, driven by an ongoing boom in development of U.S. shale gas reservoirs. Systems-analysis based decision support is helpful for effective management of wastewater, and provision of cost-effective decision alternatives from a whole-system perspective. Uncertainties are inherent in many modeling parameters, affecting the generated decisions. In order to effectively deal with the recourse issue in decision making, in this work a two-stage stochastic fracturing wastewater management model, named TSWM, is developed to provide decision support for wastewater management planning in shale plays. Using the TSWMmore » model, probabilistic and nonprobabilistic uncertainties are effectively handled. The TSWM model provides flexibility in generating shale gas wastewater management strategies, in which the first-stage decision predefined by decision makers before uncertainties are unfolded is corrected in the second stage to achieve the whole-system’s optimality. Application of the TSWM model to a comprehensive synthetic example demonstrates its practical applicability and feasibility. Optimal results are generated for allowable wastewater quantities, excess wastewater, and capacity expansions of hazardous wastewater treatment plants to achieve the minimized total system cost. The obtained interval solutions encompass both optimistic and conservative decisions. Trade-offs between economic and environmental objectives are made depending on decision makers’ knowledge and judgment, as well as site-specific information. In conclusion, the proposed model is helpful in forming informed decisions for wastewater management associated with shale gas development.« less

Participatory Scenario Planning for the Cienega Watershed: Embracing Uncertainty in Public Lands Management in the U.S. Southwest

NASA Astrophysics Data System (ADS)

Hartmann, H.; Morino, K.; Bodner, G.; Markstein, A.; McFarlin, S.

2013-12-01

Land managers and communities struggle to sustain natural landscapes and the benefits they provide--especially in an era of rapid and unpredictable changes being driven by shifts in climate and other drivers that are largely outside the control of local managers and residents. The Cienega Watershed Partnership (CWP) is a long-standing multi-agency partnership involved in managing lands and resources over about 700,000 acres in southeast Arizona, surrounding the Bureau of Land Management's Las Cienegas National Conservation Area. The region forms a vital wildlife corridor connecting the diverse ecosystems of the Sonoran and Chihuahuan deserts and grasslands with the Sierra Madrean and Rocky Mountain forests and woodlands. The CWP has long-standing forums and relationships for considering complex issues and novel approaches for management, including practical implementation of adaptive management, development of monitoring programs and protocols, and the use of nested objectives to adjust management targets. However, current plans have objectives and strategies based on what is known or likely to become known about natural and socio-cultural systems; they do not incorporate uncertainties related to rapid changes in climate or have well developed feedback mechanisms for routinely reconsidering climate information. Since 2011, more than 50 individuals from over 20 federal and local governments, non-governmental organizations, and private landowners have participated in scenario planning for the Cienega Watershed. Scenario planning is an important tool for (1) managing risks in the face of high volatility, uncertainty, complexity, and ambiguity; (2) integrating quantitative climate projections, trend and impact assessments, and local expertise to develop qualitative scenario narratives that can inform decisions even by simply provoking insights; and (3) engaging jurisdictions having different missions, objectives, and planning processes. Participants are helping to extend and refine participatory scenario planning methods from the development of regional qualitative narratives to (1) development of scenario narratives that are relevant at the local management level, (2) creation and evaluation of portfolios of management options that can accommodate changes in management objectives, connect to formal agency planning processes, and that can be adjusted as the future evolves, and (3) explicit identification of the data and information that link qualitative narratives to quantitative scenario and adaptation assessments, which can be used to drive the timing and implementation of activities within the adaptation portfolios, and to prioritize monitoring and research activities to resolve near-term uncertainties. Project tasks are structured around four resource teams that focus on their specific management concerns (Montane, Riparian, Upland and Cultural), but that come together periodically to consider interaction and conflict among their scenarios or prospective adaptation. Participants are finding that embracing uncertainty enables them to approach climate change with a sense of empowerment rather than a sense of reacting to crises, and they appreciate the methods and opportunities for thinking differently and crossing boundaries that the scenario planning exercises provide.

An Uncertainty Quantification Framework for Prognostics and Condition-Based Monitoring

NASA Technical Reports Server (NTRS)

Sankararaman, Shankar; Goebel, Kai

2014-01-01

This paper presents a computational framework for uncertainty quantification in prognostics in the context of condition-based monitoring of aerospace systems. The different sources of uncertainty and the various uncertainty quantification activities in condition-based prognostics are outlined in detail, and it is demonstrated that the Bayesian subjective approach is suitable for interpreting uncertainty in online monitoring. A state-space model-based framework for prognostics, that can rigorously account for the various sources of uncertainty, is presented. Prognostics consists of two important steps. First, the state of the system is estimated using Bayesian tracking, and then, the future states of the system are predicted until failure, thereby computing the remaining useful life of the system. The proposed framework is illustrated using the power system of a planetary rover test-bed, which is being developed and studied at NASA Ames Research Center.

Building a geological reference platform using sequence stratigraphy combined with geostatistical tools

NASA Astrophysics Data System (ADS)

Bourgine, Bernard; Lasseur, Éric; Leynet, Aurélien; Badinier, Guillaume; Ortega, Carole; Issautier, Benoit; Bouchet, Valentin

2015-04-01

In 2012 BRGM launched an extensive program to build the new French Geological Reference platform (RGF). Among the objectives of this program is to provide the public with validated, reliable and 3D-consistent geological data, with estimation of uncertainty. Approx. 100,000 boreholes over the whole French national territory provide a preliminary interpretation in terms of depths of main geological interfaces, but with an unchecked, unknown and often low reliability. The aim of this paper is to present the procedure that has been tested on two areas in France, in order to validate (or not) these boreholes, with the aim of being generalized as much as possible to the nearly 100,000 boreholes waiting for validation. The approach is based on the following steps, and includes the management of uncertainty at different steps: (a) Selection of a loose network of boreholes owning a logging or coring information enabling a reliable interpretation. This first interpretation is based on the correlation of well log data and allows defining 3D sequence stratigraphic framework identifying isochronous surfaces. A litho-stratigraphic interpretation is also performed. Be "A" the collection of all boreholes used for this step (typically 3 % of the total number of holes to be validated) and "B" the other boreholes to validate, (b) Geostatistical analysis of characteristic geological interfaces. The analysis is carried out firstly on the "A" type data (to validate the variogram model), then on the "B" type data and at last on "B" knowing "A". It is based on cross-validation tests and evaluation of the uncertainty associated to each geological interface. In this step, we take into account inequality constraints provided by boreholes that do not intersect all interfaces, as well as the "litho-stratigraphic pile" defining the formations and their relationships (depositing surfaces or erosion). The goal is to identify quickly and semi-automatically potential errors among the data, up to the geologist to check and correct the anomalies, (c) Consistency tests are also used to verify the appropriateness of interpretations towards other constraints (geological map, maximal formation extension limits, digital terrain model ...), (d) Construction of a 3D geological model from "A"+ "B" boreholes: continuous surfaces representation makes it possible to assess the overall consistency and to validate or invalidate interpretations. Standard-deviation maps allow visualizing areas where data from available but not yet validated boreholes could be added to reduce uncertainty. Maps of absolute or relative errors help to quantify and visualize model uncertainty. This procedure helps to quickly identify the main errors in the data. It guarantees rationalization, reproducibility and traceability of the various stages of validation. Automation aspect is obviously important when it comes to dealing with datasets that can contain tens of thousands of surveys. For this, specific tools have been developed by BRGM (GDM/ MultiLayer software, R scripts, GIS tools).

Investigating uncertainty and emotions in conversations about family health history: a test of the theory of motivated information management.

PubMed

Rauscher, Emily A; Hesse, Colin

2014-01-01

Although the importance of being knowledgeable of one's family health history is widely known, very little research has investigated how families communicate about this important topic. This study investigated how young adults seek information from parents about family health history. The authors used the Theory of Motivated Information Management as a framework to understand the process of uncertainty discrepancy and emotion in seeking information about family health history. Results of this study show the Theory of Motivated Information Management to be a good model to explain the process young adults go through in deciding to seek information from parents about family health history. Results also show that emotions other than anxiety can be used with success in the Theory of Motivated Information Management framework.

Monitoring in the context of structured decision-making and adaptive management

USGS Publications Warehouse

Lyons, J.E.; Runge, M.C.; Laskowski, H.P.; Kendall, W.L.

2008-01-01

In a natural resource management setting, monitoring is a crucial component of an informed process for making decisions, and monitoring design should be driven by the decision context and associated uncertainties. Monitoring itself can play >3 roles. First, it is important for state-dependent decision-making, as when managers need to know the system state before deciding on the appropriate course of action during the ensuing management cycle. Second, monitoring is critical for evaluating the effectiveness of management actions relative to objectives. Third, in an adaptive management setting, monitoring provides the feedback loop for learning about the system; learning is sought not for its own sake but primarily to better achieve management objectives. In this case, monitoring should be designed to reduce the critical uncertainties in models of the managed system. The United States Geological Survey and United States Fish and Wildlife Service are conducting a large-scale management experiment on 23 National Wildlife Refuges across the Northeast and Midwest Regions. The primary management objective is to provide habitat for migratory waterbirds, particularly during migration, using water-level manipulations in managed wetlands. Key uncertainties are related to the potential trade-offs created by management for a specific waterbird guild (e.g., migratory shorebirds) and the response of waterbirds, plant communities, and invertebrates to specific experimental hydroperiods. We reviewed the monitoring program associated with this study, and the ways that specific observations fill >1 of the roles identified above. We used observations from our monitoring to improve state-dependent decisions to control undesired plants, to evaluate management performance relative to shallow-water habitat objectives, and to evaluate potential trade-offs between waterfowl and shorebird habitat management. With limited staff and budgets, management agencies need efficient monitoring programs that are used for decision-making, not comprehensive studies that elucidate all manner of ecological relationships.

Everglades Collaborative Adaptive Management Program Progress

EPA Science Inventory

When the Comprehensive Everglades Restoration Plan (CERP) was authorized in 2000, adaptive management (AM) was recognized as a necessary tool to address uncertainty in achieving the broad goals and objectives for restoring a highly managed system. The Everglades covers18,000 squ...

Economic analysis of fuel treatments

Treesearch

D. Evan Mercer; Jeffrey P. Prestemon

2012-01-01

The economics of wildfire is complicated because wildfire behavior depends on the spatial and temporal scale at which management decisions made, and because of uncertainties surrounding the results of management actions. Like the wildfire processes they seek to manage, interventions through fire prevention programs, suppression, and fuels management are scale dependent...

Designing monitoring programs in an adaptive management context for regional multiple species conservation plans

USGS Publications Warehouse

Atkinson, A.J.; Trenham, P.C.; Fisher, R.N.; Hathaway, S.A.; Johnson, B.S.; Torres, S.G.; Moore, Y.C.

2004-01-01

critical management uncertainties; and 3) implementing long-term monitoring and adaptive management. Ultimately, the success of regional conservation planning depends on the ability of monitoring programs to confront the challenges of adaptively managing and monitoring complex ecosystems and diverse arrays of sensitive species.

Capacity planning for electronic waste management facilities under uncertainty: multi-objective multi-time-step model development.

PubMed

Poonam Khanijo Ahluwalia; Nema, Arvind K

2011-07-01

Selection of optimum locations for locating new facilities and decision regarding capacities at the proposed facilities is a major concern for municipal authorities/managers. The decision as to whether a single facility is preferred over multiple facilities of smaller capacities would vary with varying priorities to cost and associated risks such as environmental or health risk or risk perceived by the society. Currently management of waste streams such as that of computer waste is being done using rudimentary practices and is flourishing as an unorganized sector, mainly as backyard workshops in many cities of developing nations such as India. Uncertainty in the quantification of computer waste generation is another major concern due to the informal setup of present computer waste management scenario. Hence, there is a need to simultaneously address uncertainty in waste generation quantities while analyzing the tradeoffs between cost and associated risks. The present study aimed to address the above-mentioned issues in a multi-time-step, multi-objective decision-support model, which can address multiple objectives of cost, environmental risk, socially perceived risk and health risk, while selecting the optimum configuration of existing and proposed facilities (location and capacities).

Using real options analysis to support strategic management decisions

NASA Astrophysics Data System (ADS)

Kabaivanov, Stanimir; Markovska, Veneta; Milev, Mariyan

2013-12-01

Decision making is a complex process that requires taking into consideration multiple heterogeneous sources of uncertainty. Standard valuation and financial analysis techniques often fail to properly account for all these sources of risk as well as for all sources of additional flexibility. In this paper we explore applications of a modified binomial tree method for real options analysis (ROA) in an effort to improve decision making process. Usual cases of use of real options are analyzed with elaborate study on the applications and advantages that company management can derive from their application. A numeric results based on extending simple binomial tree approach for multiple sources of uncertainty are provided to demonstrate the improvement effects on management decisions.

Slepton pair production at the LHC in NLO+NLL with resummation-improved parton densities

NASA Astrophysics Data System (ADS)

Fiaschi, Juri; Klasen, Michael

2018-03-01

Novel PDFs taking into account resummation-improved matrix elements, albeit only in the fit of a reduced data set, allow for consistent NLO+NLL calculations of slepton pair production at the LHC. We apply a factorisation method to this process that minimises the effect of the data set reduction, avoids the problem of outlier replicas in the NNPDF method for PDF uncertainties and preserves the reduction of the scale uncertainty. For Run II of the LHC, left-handed selectron/smuon, right-handed and maximally mixed stau production, we confirm that the consistent use of threshold-improved PDFs partially compensates the resummation contributions in the matrix elements. Together with the reduction of the scale uncertainty at NLO+NLL, the described method further increases the reliability of slepton pair production cross sections at the LHC.

Info-gap management of public health Policy for TB with HIV-prevalence and epidemiological uncertainty

PubMed Central

2012-01-01

Background Formulation and evaluation of public health policy commonly employs science-based mathematical models. For instance, epidemiological dynamics of TB is dominated, in general, by flow between actively and latently infected populations. Thus modelling is central in planning public health intervention. However, models are highly uncertain because they are based on observations that are geographically and temporally distinct from the population to which they are applied. Aims We aim to demonstrate the advantages of info-gap theory, a non-probabilistic approach to severe uncertainty when worst cases cannot be reliably identified and probability distributions are unreliable or unavailable. Info-gap is applied here to mathematical modelling of epidemics and analysis of public health decision-making. Methods Applying info-gap robustness analysis to tuberculosis/HIV (TB/HIV) epidemics, we illustrate the critical role of incorporating uncertainty in formulating recommendations for interventions. Robustness is assessed as the magnitude of uncertainty that can be tolerated by a given intervention. We illustrate the methodology by exploring interventions that alter the rates of diagnosis, cure, relapse and HIV infection. Results We demonstrate several policy implications. Equivalence among alternative rates of diagnosis and relapse are identified. The impact of initial TB and HIV prevalence on the robustness to uncertainty is quantified. In some configurations, increased aggressiveness of intervention improves the predicted outcome but also reduces the robustness to uncertainty. Similarly, predicted outcomes may be better at larger target times, but may also be more vulnerable to model error. Conclusions The info-gap framework is useful for managing model uncertainty and is attractive when uncertainties on model parameters are extreme. When a public health model underlies guidelines, info-gap decision theory provides valuable insight into the confidence of achieving agreed-upon goals. PMID:23249291

Info-gap management of public health Policy for TB with HIV-prevalence and epidemiological uncertainty.

PubMed

Ben-Haim, Yakov; Dacso, Clifford C; Zetola, Nicola M

2012-12-19

Formulation and evaluation of public health policy commonly employs science-based mathematical models. For instance, epidemiological dynamics of TB is dominated, in general, by flow between actively and latently infected populations. Thus modelling is central in planning public health intervention. However, models are highly uncertain because they are based on observations that are geographically and temporally distinct from the population to which they are applied. We aim to demonstrate the advantages of info-gap theory, a non-probabilistic approach to severe uncertainty when worst cases cannot be reliably identified and probability distributions are unreliable or unavailable. Info-gap is applied here to mathematical modelling of epidemics and analysis of public health decision-making. Applying info-gap robustness analysis to tuberculosis/HIV (TB/HIV) epidemics, we illustrate the critical role of incorporating uncertainty in formulating recommendations for interventions. Robustness is assessed as the magnitude of uncertainty that can be tolerated by a given intervention. We illustrate the methodology by exploring interventions that alter the rates of diagnosis, cure, relapse and HIV infection. We demonstrate several policy implications. Equivalence among alternative rates of diagnosis and relapse are identified. The impact of initial TB and HIV prevalence on the robustness to uncertainty is quantified. In some configurations, increased aggressiveness of intervention improves the predicted outcome but also reduces the robustness to uncertainty. Similarly, predicted outcomes may be better at larger target times, but may also be more vulnerable to model error. The info-gap framework is useful for managing model uncertainty and is attractive when uncertainties on model parameters are extreme. When a public health model underlies guidelines, info-gap decision theory provides valuable insight into the confidence of achieving agreed-upon goals.

[Uncertainty of cross calibration-applied beam quality conversion factor for the Japan Society of Medical Physics 12].

PubMed

Kinoshita, Naoki; Kita, Akinobu; Takemura, Akihiro; Nishimoto, Yasuhiro; Adachi, Toshiki

2014-09-01

The uncertainty of the beam quality conversion factor (k(Q,Q0)) of standard dosimetry of absorbed dose to water in external beam radiotherapy 12 (JSMP12) is determined by combining the uncertainty of each beam quality conversion factor calculated for each type of ionization chamber. However, there is no guarantee that ionization chambers of the same type have the same structure and thickness, so there may be individual variations. We evaluated the uncertainty of k(Q,Q0) for JSMP12 using an ionization chamber dosimeter and linear accelerator without a specific device or technique in consideration of the individual variation of ionization chambers and in clinical radiation field. The cross calibration formula was modified and the beam quality conversion factor for the experimental values [(k(Q,Q0))field] determined using the modified formula. It's uncertainty was calculated to be 1.9%. The differences between (k(Q,Q0))field of experimental values and k(Q,Q0) for Japan Society of Medical Physics 12 (JSMP12) were 0.73% and 0.88% for 6- and 10-MV photon beams, respectively, remaining within ± 1.9%. This showed k(Q,Q0) for JSMP12 to be consistent with (k(Q,Q0))field of experimental values within the estimated uncertainty range. Although inter-individual differences may be generated, even when the same type of ionized chamber is used, k(Q,Q0) for JSMP12 appears to be consistent within the estimated uncertainty range of (k(Q,Q0)field.

Stakeholder views of management and decision support tools to integrate climate change into Great Lakes Lake Whitefish management

USGS Publications Warehouse

Lynch, Abigail J.; Taylor, William W.; McCright, Aaron M.

2016-01-01

Decision support tools can aid decision making by systematically incorporating information, accounting for uncertainties, and facilitating evaluation between alternatives. Without user buy-in, however, decision support tools can fail to influence decision-making processes. We surveyed fishery researchers, managers, and fishers affiliated with the Lake Whitefish Coregonus clupeaformis fishery in the 1836 Treaty Waters of Lakes Huron, Michigan, and Superior to assess opinions of current and future management needs to identify barriers to, and opportunities for, developing a decision support tool based on Lake Whitefish recruitment projections with climate change. Approximately 64% of 39 respondents were satisfied with current management, and nearly 85% agreed that science was well integrated into management programs. Though decision support tools can facilitate science integration into management, respondents suggest that they face significant implementation barriers, including lack of political will to change management and perceived uncertainty in decision support outputs. Recommendations from this survey can inform development of decision support tools for fishery management in the Great Lakes and other regions.

Rethinking 'risk' and self-management for chronic illness.

PubMed

Morden, Andrew; Jinks, Clare; Ong, Bie Nio

2012-02-01

Self-management for chronic illness is a current high profile UK healthcare policy. Policy and clinical recommendations relating to chronic illnesses are framed within a language of lifestyle risk management. This article argues the enactment of risk within current UK self-management policy is intimately related to neo-liberal ideology and is geared towards population governance. The approach that dominates policy perspectives to 'risk' management is critiqued for positioning people as rational subjects who calculate risk probabilities and act upon them. Furthermore this perspective fails to understand the lay person's construction and enactment of risk, their agenda and contextual needs when living with chronic illness. Of everyday relevance to lay people is the management of risk and uncertainty relating to social roles and obligations, the emotions involved when encountering the risk and uncertainty in chronic illness, and the challenges posed by social structural factors and social environments that have to be managed. Thus, clinical enactments of self-management policy would benefit from taking a more holistic view to patient need and seek to avoid solely communicating lifestyle risk factors to be self-managed.

Beyond optimality: Multistakeholder robustness tradeoffs for regional water portfolio planning under deep uncertainty

NASA Astrophysics Data System (ADS)

Herman, Jonathan D.; Zeff, Harrison B.; Reed, Patrick M.; Characklis, Gregory W.

2014-10-01

While optimality is a foundational mathematical concept in water resources planning and management, "optimal" solutions may be vulnerable to failure if deeply uncertain future conditions deviate from those assumed during optimization. These vulnerabilities may produce severely asymmetric impacts across a region, making it vital to evaluate the robustness of management strategies as well as their impacts for regional stakeholders. In this study, we contribute a multistakeholder many-objective robust decision making (MORDM) framework that blends many-objective search and uncertainty analysis tools to discover key tradeoffs between water supply alternatives and their robustness to deep uncertainties (e.g., population pressures, climate change, and financial risks). The proposed framework is demonstrated for four interconnected water utilities representing major stakeholders in the "Research Triangle" region of North Carolina, U.S. The utilities supply well over one million customers and have the ability to collectively manage drought via transfer agreements and shared infrastructure. We show that water portfolios for this region that compose optimal tradeoffs (i.e., Pareto-approximate solutions) under expected future conditions may suffer significantly degraded performance with only modest changes in deeply uncertain hydrologic and economic factors. We then use the Patient Rule Induction Method (PRIM) to identify which uncertain factors drive the individual and collective vulnerabilities for the four cooperating utilities. Our framework identifies key stakeholder dependencies and robustness tradeoffs associated with cooperative regional planning, which are critical to understanding the tensions between individual versus regional water supply goals. Cooperative demand management was found to be the key factor controlling the robustness of regional water supply planning, dominating other hydroclimatic and economic uncertainties through the 2025 planning horizon. Results suggest that a modest reduction in the projected rate of demand growth (from approximately 3% per year to 2.4%) will substantially improve the utilities' robustness to future uncertainty and reduce the potential for regional tensions. The proposed multistakeholder MORDM framework offers critical insights into the risks and challenges posed by rising water demands and hydrological uncertainties, providing a planning template for regions now forced to confront rapidly evolving water scarcity risks.

Uncertainties in climate data sets

NASA Technical Reports Server (NTRS)

Mcguirk, James P.

1992-01-01

Climate diagnostics are constructed from either analyzed fields or from observational data sets. Those that have been commonly used are normally considered ground truth. However, in most of these collections, errors and uncertainties exist which are generally ignored due to the consistency of usage over time. Examples of uncertainties and errors are described in NMC and ECMWF analyses and in satellite observational sets-OLR, TOVS, and SMMR. It is suggested that these errors can be large, systematic, and not negligible in climate analysis.

Communicating Geographical Risks in Crisis Management: The Need for Research.

PubMed

French, Simon; Argyris, Nikolaos; Haywood, Stephanie M; Hort, Matthew C; Smith, Jim Q

2017-10-23

In any crisis, there is a great deal of uncertainty, often geographical uncertainty or, more precisely, spatiotemporal uncertainty. Examples include the spread of contamination from an industrial accident, drifting volcanic ash, and the path of a hurricane. Estimating spatiotemporal probabilities is usually a difficult task, but that is not our primary concern. Rather, we ask how analysts can communicate spatiotemporal uncertainty to those handling the crisis. We comment on the somewhat limited literature on the representation of spatial uncertainty on maps. We note that many cognitive issues arise and that the potential for confusion is high. We note that in the early stages of handling a crisis, the uncertainties involved may be deep, i.e., difficult or impossible to quantify in the time available. In such circumstance, we suggest the idea of presenting multiple scenarios. © 2017 Society for Risk Analysis.

Collaborative Research for Water Resource Management under Climate Change Conditions

NASA Astrophysics Data System (ADS)

Brundiers, K.; Garfin, G. M.; Gober, P.; Basile, G.; Bark, R. H.

2010-12-01

We present an ongoing project to co-produce science and policy called Collaborative Planning for Climate Change: An Integrated Approach to Water-Planning, Climate Downscaling, and Robust Decision-Making. The project responds to motivations related to dealing with sustainability challenges in research and practice: (a) state and municipal water managers seek research that addresses their planning needs; (b) the scientific literature and funding agencies call for more meaningful engagement between science and policy communities, in ways that address user needs, while advancing basic research; and (c) empirical research contributes to methods for the design and implementation of collaborative projects. To understand how climate change might impact water resources and management in the Southwest US, our project convenes local, state, and federal water management practitioners with climate-, hydrology-, policy-, and decision scientists. Three areas of research inform this collaboration: (a) the role of paleo-hydrology in water resources scenario construction; (b) the types of uncertainties that impact decision-making beyond climate and modeling uncertainty; and (c) basin-scale statistical and dynamical downscaling of climate models to generate hydrologic projections for regional water resources planning. The project engages all participants in the research process, from research design to workshops that build capacity for understanding data generation and sources of uncertainty to the discussion of water management decision contexts. A team of “science-practice translators” facilitates the collaboration between academic and professional communities. In this presentation we contextualize the challenges and opportunities of use-inspired science-policy research collaborations by contrasting the initial project design with the process of implementation. We draw from two sources to derive lessons learned: literature on collaborative research, and evaluations provided by participating scientists and water managers throughout the process. Lessons learned include: RESULTS: The research process needs to generate academic (peer-reviewed publications, grant proposals) and applied (usable dataset, communication support) products. Additionally, the project also strives for intangible products, e.g., the research currently continues to support efforts to predict future regional hydroclimatology, whereas management requires a paradigm shift toward anticipation of needs for adapting to multiple possible futures. APPROACH: Collaborative research is not a one-off event or consultation, but a process of mutual engagement that needs to allow for adaptive evolution of the project and its organization. TOPICS: With the acceptance of hydroclimatic non-stationarity, the focus of water managers shifts from reducing scientific uncertainty to enhancing their ability to present academically and politically defensible scenarios to their constituencies. This requires addressing the related need for exploring how to deal with political and institutional uncertainties in decision-making.

Structured decision making for managing pneumonia epizootics in bighorn sheep

USGS Publications Warehouse

Sells, Sarah N.; Mitchell, Michael S.; Edwards, Victoria L.; Gude, Justin A.; Anderson, Neil J.

2016-01-01

Good decision-making is essential to conserving wildlife populations. Although there may be multiple ways to address a problem, perfect solutions rarely exist. Managers are therefore tasked with identifying decisions that will best achieve desired outcomes. Structured decision making (SDM) is a method of decision analysis used to identify the most effective, efficient, and realistic decisions while accounting for values and priorities of the decision maker. The stepwise process includes identifying the management problem, defining objectives for solving the problem, developing alternative approaches to achieve the objectives, and formally evaluating which alternative is most likely to accomplish the objectives. The SDM process can be more effective than informal decision-making because it provides a transparent way to quantitatively evaluate decisions for addressing multiple management objectives while incorporating science, uncertainty, and risk tolerance. To illustrate the application of this process to a management need, we present an SDM-based decision tool developed to identify optimal decisions for proactively managing risk of pneumonia epizootics in bighorn sheep (Ovis canadensis) in Montana. Pneumonia epizootics are a major challenge for managers due to long-term impacts to herds, epistemic uncertainty in timing and location of future epizootics, and consequent difficulty knowing how or when to manage risk. The decision tool facilitates analysis of alternative decisions for how to manage herds based on predictions from a risk model, herd-specific objectives, and predicted costs and benefits of each alternative. Decision analyses for 2 example herds revealed that meeting management objectives necessitates specific approaches unique to each herd. The analyses showed how and under what circumstances the alternatives are optimal compared to other approaches and current management. Managers can be confident that these decisions are effective, efficient, and realistic because they explicitly account for important considerations managers implicitly weigh when making decisions, including competing management objectives, uncertainty in potential outcomes, and risk tolerance.

Climate change and observed climate trends in the fort cobb experimental watershed.

PubMed

Garbrecht, J D; Zhang, X C; Steiner, J L

2014-07-01

Recurring droughts in the Southern Great Plains of the United States are stressing the landscape, increasing uncertainty and risk in agricultural production, and impeding optimal agronomic management of crop, pasture, and grazing systems. The distinct possibility that the severity of recent droughts may be related to a greenhouse-gas induced climate change introduces new challenges for water resources managers because the intensification of droughts could represent a permanent feature of the future climate. Climate records of the Fort Cobb watershed in central Oklahoma were analyzed to determine if recent decade-long trends in precipitation and air temperature were consistent with climate change projections for central Oklahoma. The historical precipitation record did not reveal any compelling evidence that the recent 20-yr-long decline in precipitation was related to climate change. Also, precipitation projections by global circulation models (GCMs) displayed a flat pattern through the end of the 21st century. Neither observed nor projected precipitation displayed a multidecadal monotonic rising or declining trend consistent with an ongoing warming climate. The recent trend in observed annual precipitation was probably a decade-scale variation not directly related to the warming climate. On the other hand, the observed monotonic warming trend of 0.34°C decade that started around 1978 is consistent with GCM projections of increasing temperature for central Oklahoma. Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.

A management study template for learning about postwildfire management.

Treesearch

B.T. Bormann; J.A. Laurence; K. Shimamoto; J. Thrailkill; J. Lehmkuhl; G. Reeves; A. Markus; D.W. Peterson; E. Forsman

2008-01-01

The concept of management studies--implemented by managers as normal business to meet priority learning needs--is applied to a priority regional question: how to manage after a large wildfire to better meet preexisting or new societal needs. Because of a lack of knowledge and studies, deciding how to manage after wildfire is fraught with uncertainty. We have developed...

Interdependency Management in Universities: A Case Study

ERIC Educational Resources Information Center

Braun, Dietmar; Benninghoff, Martin; Ramuz, Raphaël; Gorga, Adriana

2015-01-01

There remains uncertainty in scientific discussions regarding the governance of universities in new public management regimes in terms of who actually "rules" in the university. Apparently, a strengthened management leadership is confronted with continuing elements of academic self-regulation and professional autonomy in knowledge…

ENVIRONMENTAL SYSTEMS MANAGEMENT, SUSTAINABILITY THEORY, AND THE CHALLENGE OF UNCERTAINTY

EPA Science Inventory

Environmental Systems Management is the management of environmental problems at the systems level fully accounting fo rthe multi-dimensional nature of the environment. This includes socio-economic dimensions as well s the usual physical and life science aspects. This is important...

Novel GIS approaches to watershed science and management: Description, prediction, and integration

EPA Science Inventory

Spatial data and geographic information systems (GIS) are playing an increasingly important role in watershed science and management, particularly in the face of increasing climate uncertainty and demand for water resources. Concomitantly, scientists and managers are presented wi...

The Effect of Uncertainty Management Program on Quality of Life Among Vietnamese Women at 3 Weeks Postmastectomy.

PubMed

Ha, Xuan Thi Nhu; Thanasilp, Sureeporn; Thato, Ratsiri

2018-05-10

In Vietnam, breast cancer is a top contributor to cancer-related deaths in women. Evidence shows that, after mastectomy, women in Vietnam have a lower quality of life than women in other countries. In addition, high uncertainty is a predictor of low quality of life postmastectomy. Therefore, if nurses can manage uncertainty, the quality of life postmastectomy can improve. This study examined the effect of the Uncertainty Management Program (UMP) on quality of life at 3 weeks postmastectomy in Vietnamese women. This research was a quasi-experimental study using a "posttest only with control group" design. There were 115 subjects assigned to either the experimental group (n = 57), who participated in the UMP and routine care, or the control group (n = 58), who received only routine care. Participants were assessed 2 times postmastectomy using the modified Quality of Life Index Scale-Vietnamese version. The experimental group exhibited low uncertainty before discharge and significantly higher quality of life than the control group at 1 and 3 weeks postmastectomy, respectively (P < .05). Women's physical well-being, psychological well-being, body image concerns, and social concerns were significantly increased with UMP. The UMP was considered as a promising program that might benefit the QoL of women with breast cancer 3 weeks postmastectomy. The UMP appears feasible to apply for women with breast cancer to improve their QoL postmastectomy in various settings. Nurses can flexibility instruct women in their holistic care attention both in the hospital and at home.

The Years of Uncertainty: Eighth Grade Family Life Education.

ERIC Educational Resources Information Center

Carson, Mary, Ed.; And Others

The family life sex education unit for eighth graders, "The Years of Uncertainty," consists of a series of daily lesson plans that span a 29-day period of one-hour class sessions. Topics covered are: problem solving, knowledge and attitudes, male and female reproductive systems, conception, pregnancy, birth, birth defects, venereal…

Contributions to the revision of the 'Guide to the expression of uncertainty in measurement'

NASA Astrophysics Data System (ADS)

Kyriazis, G. A.

2015-01-01

Some inconsistencies of the current version of the 'Guide to the expression of uncertainty in measurement' are discussed and suggestions to make this document consistent are commented. The paper is written taking into account the terminology of the third version of the 'International vocabulary of metrology'.

Stomaching uncertainty: Relationships among intolerance of uncertainty, eating disorder pathology, and comorbid emotional symptoms.

PubMed

Renjan, Vidhya; McEvoy, Peter M; Handley, Alicia K; Fursland, Anthea

2016-06-01

Intolerance of uncertainty (IU) is proposed to be a transdiagnostic vulnerability factor for various emotional disorders. There is robust evidence for the role of IU in anxiety and depressive disorders, but a paucity of evidence in eating disorders (ED). This study evaluated the factorial validity, internal consistency, and convergent validity of the Intolerance of Uncertainty Scale-Short Form (IUS-12; Carleton, Norton, & Asmundson, 2007), and examined whether IU is associated with ED pathology and comorbid emotional symptoms, in a clinical sample with EDs (N=134). A unitary factor solution provided the best fit. The IUS-12 showed excellent internal consistency, and good convergent validity. IU had an indirect effect on dietary restraint, purging, and emotional symptoms via overvaluation of eating, weight, and shape. The indirect effect was not significant for bingeing. Findings provide partial support for the notion that IU is a vulnerability factor for ED pathology and support the notion that IU is a transdiagnostic vulnerability factor for emotional symptoms. Limitations, research implications, and future directions for research are discussed. Copyright © 2016 Elsevier Ltd. All rights reserved.

Near-Term Actions to Address Long-Term Climate Risk

NASA Astrophysics Data System (ADS)

Lempert, R. J.

2014-12-01

Addressing climate change requires effective long-term policy making, which occurs when reflecting on potential events decades or more in the future causes policy makers to choose near-term actions different than those they would otherwise pursue. Contrary to some expectations, policy makers do sometimes make such long-term decisions, but not as commonly and successfully as climate change may require. In recent years however, the new capabilities of analytic decision support tools, combined with improved understanding of cognitive and organizational behaviors, has significantly improved the methods available for organizations to manage longer-term climate risks. In particular, these tools allow decision makers to understand what near-term actions consistently contribute to achieving both short- and long-term societal goals, even in the face of deep uncertainty regarding the long-term future. This talk will describe applications of these approaches for infrastructure, water, and flood risk management planning, as well as studies of how near-term choices about policy architectures can affect long-term greenhouse gas emission reduction pathways.

Combinations of Earth Orientation Measurements: SPACE2005, COMB2005, and POLE2005

NASA Technical Reports Server (NTRS)

Gross, Richard S.

2006-01-01

Independent Earth orientation measurements taken by the space-geodetic techniques of lunar and satellite laser ranging, by very long baseline interferometry, and by the Global Positioning System have been combined using a Kalman filter. The resulting combined Earth orientation series, SPACE2005, consists of values and uncertainties for Universal Time, polar motion, and their rates that span from September 28, 1976, to January 7, 2006, at daily intervals and is available in versions whose epochs are given at either midnight or noon. The space-geodetic measurements used to generate SPACE2005 have then been combined with optical astrometric measurements to form two additional combined Earth orientation series: (1) COMB2005, consisting of values and uncertainties for Universal Time, polar motion, and their rates that span from January 20, 1962, to January 7, 2006, at daily intervals and which is also available in versions whose epochs are given at either midnight or noon; and (2) POLE2005, consisting of values and uncertainties for polar motion and its rate that span from January 20, 1900, to December 21, 2005, at 30.4375-day intervals.

Combinations of Earth Orientation Measurements: SPACE2003, COMB2003, and POLE2003

NASA Technical Reports Server (NTRS)

Gross, Richard S.

2004-01-01

Independent Earth orientation measurements taken by the space-geodetic techniques of lunar and satellite laser ranging, very long baseline interferometry, and the global positioning system have been combined using a Kalman filter. The resulting combined Earth orientation series, SPACE2003, consists of values and uncertainties for Universal Time, polar motion, and their rates that span from September 28.0, 1976 to January 31.0, 2004 at daily intervals and is available in versions whose epochs are given at either midnight or noon. The space-geodetic measurements used to generate SPACE2003 have then been combined with optical astrometric measurements to form two additional combined Earth orientation series: (1) COMB2003, consisting of values and uncertainties for Universal Time, polar motion, and their rates that span from January 20.0, 1962 to January 31.0, 2004 at daily intervals and which is also available in versions whose epochs are given at either midnight or noon, and (2) POLE2003, consisting of values and uncertainties for polar motion and its rate that span from January 20, 1900 to January 21,2004 at 30.4375-day intervals.

Combinations of Earth Orientation Measurements: SPACE2004, COMB2004, and POLE2004

NASA Technical Reports Server (NTRS)

Gross, Richard R.

2005-01-01

Independent Earth orientation measurements taken by the space-geodetic techniques of lunar and satellite laser ranging, very long baseline interferometry, and the global positioning system have been combined using a Kalman filter. The resulting combined Earth orientation series, SPACE2004, consists of values and uncertainties for Universal Time, polar motion, and their rates that span from September 28, 1976, to January 22, 2005, at daily intervals and is available in versions whose epochs are given at either midnight or noon. The space-geodetic measurements used to generate SPACE2004 have then been combined with optical astrometric measurements to form two additional combined Earth orientation series: (1) COMB2004, consisting of values and uncertainties for Universal Time, polar motion, and their rates that span from January 20, 1962, to January 22, 2005, at daily intervals and which is also available in versions whose epochs are given at either midnight or noon, and (2) POLE2004, consisting of values and uncertainties for polar motion and its rate that span from January 20, 1900, to January 20, 2005, at 30.4375-day intervals.

Combinations of Earth Orientation Measurements: SPACE2014, COMB2014, and POLE2014

NASA Technical Reports Server (NTRS)

Ratcliff, J. T.; Gross, R. S.

2015-01-01

Independent Earth orientation measurements taken by the space-geodetic techniques of lunar and satellite laser ranging, very long baseline interferometry, and the Global Positioning System have been combined using a Kalman filter. The resulting combined Earth orientation series, SPACE2013, consists of values and uncertainties for Universal Time, polar motion, and their rates that span from September 28, 1976, to June 30, 2014, at daily intervals and is available in versions with epochs given at either midnight or noon. The space-geodetic measurements used to generate SPACE2013 have then been combined with optical astrometric measurements to form two additional combined Earth orientation series: (1) COMB2013, consisting of values and uncertainties for Universal Time, polar motion, and their rates that span from January 20, 1962, to June 30, 2014, at daily intervals and which are also available in versions with epochs given at either midnight or noon; and (2) POLE2013, consisting of values and uncertainties for polar motion and its rate that span from January 20, 1900, to June 22, 2014, at 30.4375-day intervals.

Combinations of Earth Orientation Measurements: SPACE2011, COMB2011, and POLE2011

NASA Technical Reports Server (NTRS)

Ratcliff, J. T.; Gross, R. S.

2013-01-01

Independent Earth orientation measurements taken by the space-geodetic techniques of lunar and satellite laser ranging, very long baseline interferometry, and the Global Positioning System have been combined using a Kalman filter. The resulting combined Earth orientation series, SPACE2011, consists of values and uncertainties for Universal Time, polar motion, and their rates that span from September 28, 1976, to July 13, 2012, at daily intervals and is available in versions with epochs given at either midnight or noon. The space-geodetic measurements used to generate SPACE2011 have then been combined with optical astrometric measurements to form two additional combined Earth orientation series: (1) COMB2011, consisting of values and uncertainties for Universal Time, polar motion, and their rates that span from January 20, 1962, to July 13, 2012, at daily intervals and which are also available in versions with epochs given at either midnight or noon; and (2) POLE2011, consisting of values and uncertainties for polar motion and its rate that span from January 20, 1900, to June 21, 2012, at 30.4375-day intervals.

Combinations of Earth Orientation Measurements: SPACE2013, COMB2013, and POLE2013

NASA Technical Reports Server (NTRS)

Ratcliff, J. T.; Gross, R. S.

2015-01-01

Independent Earth orientation measurements taken by the space-geodetic techniques of lunar and satellite laser ranging, very long baseline interferometry, and the Global Positioning System have been combined using a Kalman filter. The resulting combined Earth orientation series, SPACE2013, consists of values and uncertainties for Universal Time, polar motion, and their rates that span from September 28, 1976, to June 30, 2014, at daily intervals and is available in versions with epochs given at either midnight or noon. The space-geodetic measurements used to generate SPACE2013 have then been combined with optical astrometric measurements to form two additional combined Earth orientation series: (1) COMB2013, consisting of values and uncertainties for Universal Time, polar motion, and their rates that span from January 20, 1962, to June 30, 2014, at daily intervals and which are also available in versions with epochs given at either midnight or noon; and (2) POLE2013, consisting of values and uncertainties for polar motion and its rate that span from January 20, 1900, to June 22, 2014, at 30.4375-day intervals.

Combinations of Earth Orientation Measurements: SPACE2016, COMB2016, and POLE2016

NASA Technical Reports Server (NTRS)

Ratcliff, J. T.; Gross, R. S.

2017-01-01

Independent Earth orientation measurements taken by the space-geodetic techniques of lunar and satellite laser ranging, very long baseline interferometry, and the Global Positioning System have been combined using a Kalman filter. The resulting combined Earth orientation series, SPACE2016, consists of values and uncertainties for Universal Time, polar motion, and their rates that span from September 28, 1976, to June 30, 2017, at daily intervals and is available in versions with epochs given at either midnight or noon. The space-geodetic measurements used to generate SPACE2016 have then been combined with optical astrometric measurements to form two additional combined Earth orientation series: (1) COMB2016, consisting of values and uncertainties for Universal Time, polar motion, and their rates that span from January 20, 1962, to June 30, 2017, at daily intervals and which are also available in versions with epochs given at either midnight or noon; and (2) POLE2016, consisting of values and uncertainties for polar motion and its rate that span from January 20, 1900, to June 22, 2017, at 30.4375-day intervals.

Combinations of Earth Orientation Measurements: SPACE2012, COMB2012, and POLE2012

NASA Technical Reports Server (NTRS)

Ratcliff, J. T.; Gross, R. S.

2013-01-01

Independent Earth orientation measurements taken by the space-geodetic techniques of lunar and satellite laser ranging, very long baseline interferometry, and the Global Positioning System have been combined using a Kalman filter. The resulting combined Earth orientation series, SPACE2012, consists of values and uncertainties for Universal Time, polar motion, and their rates that span from September 28, 1976, to April 26, 2013, at daily intervals and is available in versions with epochs given at either midnight or noon. The space-geodetic measurements used to generate SPACE2012 have then been combined with optical astrometric measurements to form two additional combined Earth orientation series: (1) COMB2012, consisting of values and uncertainties for Universal Time, polar motion, and their rates that span from January 20, 1962, to April 26, 2013, at daily intervals and which are also available in versions with epochs given at either midnight or noon; and (2) POLE2012, consisting of values and uncertainties for polar motion and its rate that span from January 20, 1900, to May 22, 2013, at 30.4375-day intervals.

Hotspots of uncertainty in land-use and land-cover change projections: A global-scale model comparison

DOE PAGES

Prestele, Reinhard; Alexander, Peter; Rounsevell, Mark D. A.; ...

2016-05-02

Model-based global projections of future land use and land cover (LULC) change are frequently used in environmental assessments to study the impact of LULC change on environmental services and to provide decision support for policy. These projections are characterized by a high uncertainty in terms of quantity and allocation of projected changes, which can severely impact the results of environmental assessments. In this study, we identify hotspots of uncertainty, based on 43 simulations from 11 global-scale LULC change models representing a wide range of assumptions of future biophysical and socio-economic conditions. We attribute components of uncertainty to input data, modelmore » structure, scenario storyline and a residual term, based on a regression analysis and analysis of variance. From this diverse set of models and scenarios we find that the uncertainty varies, depending on the region and the LULC type under consideration. Hotspots of uncertainty appear mainly at the edges of globally important biomes (e.g. boreal and tropical forests). Our results indicate that an important source of uncertainty in forest and pasture areas originates from different input data applied in the models. Cropland, in contrast, is more consistent among the starting conditions, while variation in the projections gradually increases over time due to diverse scenario assumptions and different modeling approaches. Comparisons at the grid cell level indicate that disagreement is mainly related to LULC type definitions and the individual model allocation schemes. We conclude that improving the quality and consistency of observational data utilized in the modeling process as well as improving the allocation mechanisms of LULC change models remain important challenges. Furthermore, current LULC representation in environmental assessments might miss the uncertainty arising from the diversity of LULC change modeling approaches and many studies ignore the uncertainty in LULC projections in assessments of LULC change impacts on climate, water resources or biodiversity.« less

Hotspots of uncertainty in land-use and land-cover change projections: a global-scale model comparison.

PubMed

Prestele, Reinhard; Alexander, Peter; Rounsevell, Mark D A; Arneth, Almut; Calvin, Katherine; Doelman, Jonathan; Eitelberg, David A; Engström, Kerstin; Fujimori, Shinichiro; Hasegawa, Tomoko; Havlik, Petr; Humpenöder, Florian; Jain, Atul K; Krisztin, Tamás; Kyle, Page; Meiyappan, Prasanth; Popp, Alexander; Sands, Ronald D; Schaldach, Rüdiger; Schüngel, Jan; Stehfest, Elke; Tabeau, Andrzej; Van Meijl, Hans; Van Vliet, Jasper; Verburg, Peter H

2016-12-01

Model-based global projections of future land-use and land-cover (LULC) change are frequently used in environmental assessments to study the impact of LULC change on environmental services and to provide decision support for policy. These projections are characterized by a high uncertainty in terms of quantity and allocation of projected changes, which can severely impact the results of environmental assessments. In this study, we identify hotspots of uncertainty, based on 43 simulations from 11 global-scale LULC change models representing a wide range of assumptions of future biophysical and socioeconomic conditions. We attribute components of uncertainty to input data, model structure, scenario storyline and a residual term, based on a regression analysis and analysis of variance. From this diverse set of models and scenarios, we find that the uncertainty varies, depending on the region and the LULC type under consideration. Hotspots of uncertainty appear mainly at the edges of globally important biomes (e.g., boreal and tropical forests). Our results indicate that an important source of uncertainty in forest and pasture areas originates from different input data applied in the models. Cropland, in contrast, is more consistent among the starting conditions, while variation in the projections gradually increases over time due to diverse scenario assumptions and different modeling approaches. Comparisons at the grid cell level indicate that disagreement is mainly related to LULC type definitions and the individual model allocation schemes. We conclude that improving the quality and consistency of observational data utilized in the modeling process and improving the allocation mechanisms of LULC change models remain important challenges. Current LULC representation in environmental assessments might miss the uncertainty arising from the diversity of LULC change modeling approaches, and many studies ignore the uncertainty in LULC projections in assessments of LULC change impacts on climate, water resources or biodiversity. © 2016 The Authors. Global Change Biology Published by John Wiley & Sons Ltd.

Quantification of uncertainty in first-principles predicted mechanical properties of solids: Application to solid ion conductors

NASA Astrophysics Data System (ADS)

Ahmad, Zeeshan; Viswanathan, Venkatasubramanian

2016-08-01

Computationally-guided material discovery is being increasingly employed using a descriptor-based screening through the calculation of a few properties of interest. A precise understanding of the uncertainty associated with first-principles density functional theory calculated property values is important for the success of descriptor-based screening. The Bayesian error estimation approach has been built in to several recently developed exchange-correlation functionals, which allows an estimate of the uncertainty associated with properties related to the ground state energy, for example, adsorption energies. Here, we propose a robust and computationally efficient method for quantifying uncertainty in mechanical properties, which depend on the derivatives of the energy. The procedure involves calculating energies around the equilibrium cell volume with different strains and fitting the obtained energies to the corresponding energy-strain relationship. At each strain, we use instead of a single energy, an ensemble of energies, giving us an ensemble of fits and thereby, an ensemble of mechanical properties associated with each fit, whose spread can be used to quantify its uncertainty. The generation of ensemble of energies is only a post-processing step involving a perturbation of parameters of the exchange-correlation functional and solving for the energy non-self-consistently. The proposed method is computationally efficient and provides a more robust uncertainty estimate compared to the approach of self-consistent calculations employing several different exchange-correlation functionals. We demonstrate the method by calculating the uncertainty bounds for several materials belonging to different classes and having different structures using the developed method. We show that the calculated uncertainty bounds the property values obtained using three different GGA functionals: PBE, PBEsol, and RPBE. Finally, we apply the approach to calculate the uncertainty associated with the DFT-calculated elastic properties of solid state Li-ion and Na-ion conductors.

Hotspots of uncertainty in land-use and land-cover change projections: A global-scale model comparison

DOE Office of Scientific and Technical Information (OSTI.GOV)

Prestele, Reinhard; Alexander, Peter; Rounsevell, Mark D. A.

Model-based global projections of future land use and land cover (LULC) change are frequently used in environmental assessments to study the impact of LULC change on environmental services and to provide decision support for policy. These projections are characterized by a high uncertainty in terms of quantity and allocation of projected changes, which can severely impact the results of environmental assessments. In this study, we identify hotspots of uncertainty, based on 43 simulations from 11 global-scale LULC change models representing a wide range of assumptions of future biophysical and socio-economic conditions. We attribute components of uncertainty to input data, modelmore » structure, scenario storyline and a residual term, based on a regression analysis and analysis of variance. From this diverse set of models and scenarios we find that the uncertainty varies, depending on the region and the LULC type under consideration. Hotspots of uncertainty appear mainly at the edges of globally important biomes (e.g. boreal and tropical forests). Our results indicate that an important source of uncertainty in forest and pasture areas originates from different input data applied in the models. Cropland, in contrast, is more consistent among the starting conditions, while variation in the projections gradually increases over time due to diverse scenario assumptions and different modeling approaches. Comparisons at the grid cell level indicate that disagreement is mainly related to LULC type definitions and the individual model allocation schemes. We conclude that improving the quality and consistency of observational data utilized in the modeling process as well as improving the allocation mechanisms of LULC change models remain important challenges. Furthermore, current LULC representation in environmental assessments might miss the uncertainty arising from the diversity of LULC change modeling approaches and many studies ignore the uncertainty in LULC projections in assessments of LULC change impacts on climate, water resources or biodiversity.« less

Psychometric properties of the parent́s perception uncertainty in illness scale, spanish version.

PubMed

Suarez-Acuña, C E; Carvajal-Carrascal, G; Serrano-Gómez, M E

2018-03-27

To analyze the psychometric properties of the Parents' Perception of Uncertainty in Illness Scale, parents/children, adapted to Spanish. A descriptive methodological study involving the translation into Spanish of the Parents' Perception of Uncertainty in Illness Scale, parents/children, and analysis of their face validity, content validity, construct validity and internal consistency. The original version of the scale in English was translated into Spanish, and approved by its author. Six face validity items with comprehension difficulty were reported; which were reviewed and adapted, keeping its structure. The global content validity index with expert appraisal was 0.94. In the exploratory analysis of factors, 3 dimensions were identified: ambiguity and lack of information, unpredictability and lack of clarity, with a KMO=0.846, which accumulated 91.5% of the explained variance. The internal consistency of the scale yielded a Cronbach alpha of 0.86 demonstrating a good level of correlation between items. The Spanish version of "Parent's Perception of Uncertainty in Illness Scale" is a valid and reliable tool that can be used to determine the level of uncertainty of parents facing the illness of their children. Copyright © 2018 Sociedad Española de Enfermería Intensiva y Unidades Coronarias (SEEIUC). Publicado por Elsevier España, S.L.U. All rights reserved.

How much can we trust a geological model underlying a subsurface hydrological investigation?

NASA Astrophysics Data System (ADS)

Wellmann, Florian; de la Varga, Miguel; Schaaf, Alexander; Burs, David

2017-04-01

Geological models often provide an important basis for subsequent hydrological investigations. As these models are generally built with a limited amount of information, they can contain significant uncertainties - and it is reasonable to assume that these uncertainties can potentially influence subsequent hydrological simulations. However, the investigation of uncertainties in geological models is not straightforward - and, even though recent advances have been made in the field, there is no out-of-the-box implementation to analyze uncertainties in a standard geological modeling package. We present here results of recent developments to address this problem with an efficient implementation of a geological modeling method for complex structural models, integrated in a Bayesian inference framework. The implemented geological modeling approach is based on a full 3-D implicit interpolation that directly respects interface positions and orientation measurements, as well as the influence of faults. In combination, the approach allows us to generate ensembles of geological model realizations, constrained by additional information in the form of likelihood functions to ensure consistency with additional geological aspects (e.g. sequence continuity, topology, fault network consistency), and we demonstrate the potential of the method in an exemplified case study. With this approach, we aim to contribute to a better understanding of the influence of geological uncertainties on subsurface hydrological investigations.

A data-driven approach for modeling post-fire debris-flow volumes and their uncertainty

USGS Publications Warehouse

Friedel, Michael J.

2011-01-01

This study demonstrates the novel application of genetic programming to evolve nonlinear post-fire debris-flow volume equations from variables associated with a data-driven conceptual model of the western United States. The search space is constrained using a multi-component objective function that simultaneously minimizes root-mean squared and unit errors for the evolution of fittest equations. An optimization technique is then used to estimate the limits of nonlinear prediction uncertainty associated with the debris-flow equations. In contrast to a published multiple linear regression three-variable equation, linking basin area with slopes greater or equal to 30 percent, burn severity characterized as area burned moderate plus high, and total storm rainfall, the data-driven approach discovers many nonlinear and several dimensionally consistent equations that are unbiased and have less prediction uncertainty. Of the nonlinear equations, the best performance (lowest prediction uncertainty) is achieved when using three variables: average basin slope, total burned area, and total storm rainfall. Further reduction in uncertainty is possible for the nonlinear equations when dimensional consistency is not a priority and by subsequently applying a gradient solver to the fittest solutions. The data-driven modeling approach can be applied to nonlinear multivariate problems in all fields of study.

Analysis of the uncertainty associated with national fossil fuel CO2 emissions datasets for use in the global Fossil Fuel Data Assimilation System (FFDAS) and carbon budgets

NASA Astrophysics Data System (ADS)

Song, Y.; Gurney, K. R.; Rayner, P. J.; Asefi-Najafabady, S.

2012-12-01

High resolution quantification of global fossil fuel CO2 emissions has become essential in research aimed at understanding the global carbon cycle and supporting the verification of international agreements on greenhouse gas emission reductions. The Fossil Fuel Data Assimilation System (FFDAS) was used to estimate global fossil fuel carbon emissions at 0.25 degree from 1992 to 2010. FFDAS quantifies CO2 emissions based on areal population density, per capita economic activity, energy intensity and carbon intensity. A critical constraint to this system is the estimation of national-scale fossil fuel CO2 emissions disaggregated into economic sectors. Furthermore, prior uncertainty estimation is an important aspect of the FFDAS. Objective techniques to quantify uncertainty for the national emissions are essential. There are several institutional datasets that quantify national carbon emissions, including British Petroleum (BP), the International Energy Agency (IEA), the Energy Information Administration (EIA), and the Carbon Dioxide Information and Analysis Center (CDIAC). These four datasets have been "harmonized" by Jordan Macknick for inter-comparison purposes (Macknick, Carbon Management, 2011). The harmonization attempted to generate consistency among the different institutional datasets via a variety of techniques such as reclassifying into consistent emitting categories, recalculating based on consistent emission factors, and converting into consistent units. These harmonized data form the basis of our uncertainty estimation. We summarized the maximum, minimum and mean national carbon emissions for all the datasets from 1992 to 2010. We calculated key statistics highlighting the remaining differences among the harmonized datasets. We combine the span (max - min) of datasets for each country and year with the standard deviation of the national spans over time. We utilize the economic sectoral definitions from IEA to disaggregate the national total emission into specific sectors required by FFDAS. Our results indicated that although the harmonization performed by Macknick generates better agreement among datasets, significant differences remain at national total level. For example, the CO2 emission span for most countries range from 10% to 12%; BP is generally the highest of the four datasets while IEA is typically the lowest; The US and China had the highest absolute span values but lower percentage span values compared to other countries. However, the US and China make up nearly one-half of the total global absolute span quantity. The absolute span value for the summation of national differences approaches 1 GtC/year in 2007, almost one-half of the biological "missing sink". The span value is used as a potential bias in a recalculation of global and regional carbon budgets to highlight the importance of fossil fuel CO2 emissions in calculating the missing sink. We conclude that if the harmonized span represents potential bias, calculations of the missing sink through forward budget or inverse approaches may be biased by nearly a factor of two.

Robustness of risk maps and survey networks to knowledge gaps about a new invasive pest.

PubMed

Yemshanov, Denys; Koch, Frank H; Ben-Haim, Yakov; Smith, William D

2010-02-01

In pest risk assessment it is frequently necessary to make management decisions regarding emerging threats under severe uncertainty. Although risk maps provide useful decision support for invasive alien species, they rarely address knowledge gaps associated with the underlying risk model or how they may change the risk estimates. Failure to recognize uncertainty leads to risk-ignorant decisions and miscalculation of expected impacts as well as the costs required to minimize these impacts. Here we use the information gap concept to evaluate the robustness of risk maps to uncertainties in key assumptions about an invading organism. We generate risk maps with a spatial model of invasion that simulates potential entries of an invasive pest via international marine shipments, their spread through a landscape, and establishment on a susceptible host. In particular, we focus on the question of how much uncertainty in risk model assumptions can be tolerated before the risk map loses its value. We outline this approach with an example of a forest pest recently detected in North America, Sirex noctilio Fabricius. The results provide a spatial representation of the robustness of predictions of S. noctilio invasion risk to uncertainty and show major geographic hotspots where the consideration of uncertainty in model parameters may change management decisions about a new invasive pest. We then illustrate how the dependency between the extent of uncertainties and the degree of robustness of a risk map can be used to select a surveillance network design that is most robust to knowledge gaps about the pest.

Managing Uncertainty in Water Infrastructure Design Using Info-gap Robustness

NASA Astrophysics Data System (ADS)

Irias, X.; Cicala, D.

2013-12-01

Info-gap theory, a tool for managing deep uncertainty, can be of tremendous value for design of water systems in areas of high seismic risk. Maintaining reliable water service in those areas is subject to significant uncertainties including uncertainty of seismic loading, unknown seismic performance of infrastructure, uncertain costs of innovative seismic-resistant construction, unknown costs to repair seismic damage, unknown societal impacts from downtime, and more. Practically every major earthquake that strikes a population center reveals additional knowledge gaps. In situations of such deep uncertainty, info-gap can offer advantages over traditional approaches, whether deterministic approaches that use empirical safety factors to address the uncertainties involved, or probabilistic methods that attempt to characterize various stochastic properties and target a compromise between cost and reliability. The reason is that in situations of deep uncertainty, it may not be clear what safety factor would be reasonable, or even if any safety factor is sufficient to address the uncertainties, and we may lack data to characterize the situation probabilistically. Info-gap is a tool that recognizes up front that our best projection of the future may be wrong. Thus, rather than seeking a solution that is optimal for that projection, info-gap seeks a solution that works reasonably well for all plausible conditions. In other words, info-gap seeks solutions that are robust in the face of uncertainty. Info-gap has been used successfully across a wide range of disciplines including climate change science, project management, and structural design. EBMUD is currently using info-gap to help it gain insight into possible solutions for providing reliable water service to an island community within its service area. The island, containing about 75,000 customers, is particularly vulnerable to water supply disruption from earthquakes, since it has negligible water storage and is entirely dependent on four potentially fragile water transmission mains for its day-to-day water supply. Using info-gap analysis, EBMUD is evaluating competing strategies for providing water supply to the island, for example submarine pipelines versus tunnels. The analysis considers not only the likely or 'average' results for each strategy, but also the worst-case performance of each strategy under varying levels of uncertainty. This analysis is improving the quality of the planning process, since it can identify strategies that ensure minimal disruption of water supply following a major earthquake, even if the earthquake and resulting damage fail to conform to our expectations. Results to date are presented, including a discussion of how info-gap analysis complements existing tools for comparing alternative strategies, and how info-gap improves our ability to quantify our tolerance for uncertainty.

Bridging the gap between uncertainty analysis for complex watershed models and decision-making for watershed-scale water management

NASA Astrophysics Data System (ADS)

Zheng, Y.; Han, F.; Wu, B.

2013-12-01

Process-based, spatially distributed and dynamic models provide desirable resolutions to watershed-scale water management. However, their reliability in solving real management problems has been seriously questioned, since the model simulation usually involves significant uncertainty with complicated origins. Uncertainty analysis (UA) for complex hydrological models has been a hot topic in the past decade, and a variety of UA approaches have been developed, but mostly in a theoretical setting. Whether and how a UA could benefit real management decisions remains to be critical questions. We have conducted a series of studies to investigate the applicability of classic approaches, such as GLUE and Markov Chain Monte Carlo (MCMC) methods, in real management settings, unravel the difficulties encountered by such methods, and tailor the methods to better serve the management. Frameworks and new algorithms, such as Probabilistic Collocation Method (PCM)-based approaches, were also proposed for specific management issues. This presentation summarize our past and ongoing studies on the role of UA in real water management. Challenges and potential strategies to bridge the gap between UA for complex models and decision-making for management will be discussed. Future directions for the research in this field will also be suggested. Two common water management settings were examined. One is the Total Maximum Daily Loads (TMDLs) management for surface water quality protection. The other is integrated water resources management for watershed sustainability. For the first setting, nutrients and pesticides TMDLs in the Newport Bay Watershed (Orange Country, California, USA) were discussed. It is a highly urbanized region with a semi-arid Mediterranean climate, typical of the western U.S. For the second setting, the water resources management in the Zhangye Basin (the midstream part of Heihe Baisn, China), where the famous 'Silk Road' came through, was investigated. The Zhangye Basin has a Gobi-oasis system typical of the western China, with extensive agriculture in its oasis.

Have GRACE Satellites Overestimated Groundwater Depletion in the Northwest India Aquifer?

NASA Technical Reports Server (NTRS)

Long, Di; Chen, Xi; Scanlon, Bridget R.; Wada, Yoshihide; Hong, Yang; Singh, Vijay P.; Chen, Yaning; Wang, Cunguang; Han, Zhongying; Yang, Wenting

2016-01-01

The Northwest India Aquifer (NWIA) has been shown to have the highest groundwater depletion (GWD) rate globally, threatening crop production and sustainability of groundwater resources. Gravity Recovery and Climate Experiment (GRACE) satellites have been emerging as a powerful tool to evaluate GWD with ancillary data. Accurate GWD estimation is, however, challenging because of uncertainties in GRACE data processing. We evaluated GWD rates over the NWIA using a variety of approaches, including newly developed constrained forward modeling resulting in a GWD rate of 3.1 plus or minus 0.1 centimeters per acre (or 14 plus or minus 0.4 cubic kilometers per acre) for Jan 2005-Dec 2010, consistent with the GWD rate (2.8 centimeters per acre or 12.3 cubic kilometers per acre) from groundwater-level monitoring data. Published studies (e.g., 4 plus or minus 1 centimeter per acre or 18 plus or minus 4.4 cubic kilometers per acre) may overestimate GWD over this region. This study highlights uncertainties in GWD estimates and the importance of incorporating a priori information to refine spatial patterns of GRACE signals that could be more useful in groundwater resource management and need to be paid more attention in future studies.

Have GRACE satellites overestimated groundwater depletion in the Northwest India Aquifer?

PubMed Central

Long, Di; Chen, Xi; Scanlon, Bridget R.; Wada, Yoshihide; Hong, Yang; Singh, Vijay P.; Chen, Yaning; Wang, Cunguang; Han, Zhongying; Yang, Wenting

2016-01-01

The Northwest India Aquifer (NWIA) has been shown to have the highest groundwater depletion (GWD) rate globally, threatening crop production and sustainability of groundwater resources. Gravity Recovery and Climate Experiment (GRACE) satellites have been emerging as a powerful tool to evaluate GWD with ancillary data. Accurate GWD estimation is, however, challenging because of uncertainties in GRACE data processing. We evaluated GWD rates over the NWIA using a variety of approaches, including newly developed constrained forward modeling resulting in a GWD rate of 3.1 ± 0.1 cm/a (or 14 ± 0.4 km3/a) for Jan 2005–Dec 2010, consistent with the GWD rate (2.8 cm/a or 12.3 km3/a) from groundwater-level monitoring data. Published studies (e.g., 4 ± 1 cm/a or 18 ± 4.4 km3/a) may overestimate GWD over this region. This study highlights uncertainties in GWD estimates and the importance of incorporating a priori information to refine spatial patterns of GRACE signals that could be more useful in groundwater resource management and need to be paid more attention in future studies. PMID:27075595

Heat transfer in a compact heat exchanger containing rectangular channels and using helium gas

NASA Technical Reports Server (NTRS)

Olson, D. A.

1991-01-01

Development of a National Aerospace Plane (NASP), which will fly at hypersonic speeds, require novel cooling techniques to manage the anticipated high heat fluxes on various components. A compact heat exchanger was constructed consisting of 12 parallel, rectangular channels in a flat piece of commercially pure nickel. The channel specimen was radiatively heated on the top side at heat fluxes of up to 77 W/sq cm, insulated on the back side, and cooled with helium gas flowing in the channels at 3.5 to 7.0 MPa and Reynolds numbers of 1400 to 28,000. The measured friction factor was lower than that of the accepted correlation for fully developed turbulent flow, although the uncertainty was high due to uncertainty in the channel height and a high ratio of dynamic pressure to pressure drop. The measured Nusselt number, when modified to account for differences in fluid properties between the wall and the cooling fluid, agreed with past correlations for fully developed turbulent flow in channels. Flow nonuniformity from channel-to-channel was as high as 12 pct above and 19 pct below the mean flow.

An Integrated Gate Turnaround Management Concept Leveraging Big Data/Analytics for NAS Performance Improvements

NASA Technical Reports Server (NTRS)

Chung, William; Chachad, Girish; Hochstetler, Ronald

2016-01-01

The Integrated Gate Turnaround Management (IGTM) concept was developed to improve the gate turnaround performance at the airport by leveraging relevant historical data to support optimization of airport gate operations, which include: taxi to the gate, gate services, push back, taxi to the runway, and takeoff, based on available resources, constraints, and uncertainties. By analyzing events of gate operations, primary performance dependent attributes of these events were identified for the historical data analysis such that performance models can be developed based on uncertainties to support descriptive, predictive, and prescriptive functions. A system architecture was developed to examine system requirements in support of such a concept. An IGTM prototype was developed to demonstrate the concept using a distributed network and collaborative decision tools for stakeholders to meet on time pushback performance under uncertainties.

Communicating Uncertain News in Cancer Consultations.

PubMed

Alby, Francesca; Zucchermaglio, Cristina; Fatigante, Marilena

2017-12-01

In cancer communication, most of the literature is in the realm of delivering bad news while much less attention has been given to the communication of uncertain news around the diagnosis and the possible outcomes of the illness. Drawing on video-recorded cancer consultations collected in two Italian hospitals, this article analyzes three communication practices used by oncologists to interactionally manage the uncertainty during the visit: alternating between uncertain bad news and certain good news, anticipating scenarios, and guessing test results. Both diagnostic and personal uncertainties are not hidden to the patient, yet they are reduced through these practices. Such communication practices are present in 32 % of the visits in the data set, indicating that the interactional management of uncertainty is a relevant phenomenon in oncological encounters. Further studies are needed to improve both its understanding and its teaching.

Cost-effectiveness of alternative strategies for the initial medical management of non-ST elevation acute coronary syndrome: systematic review and decision-analytical modelling.

PubMed

Robinson, M; Palmer, S; Sculpher, M; Philips, Z; Ginnelly, L; Bowens, A; Golder, S; Alfakih, K; Bakhai, A; Packham, C; Cooper, N; Abrams, K; Eastwood, A; Pearman, A; Flather, M; Gray, D; Hall, A

2005-07-01

To identify and prioritise key areas of clinical uncertainty regarding the medical management of non-ST elevation acute coronary syndrome (ACS) in current UK practice. Electronic databases. Consultations with clinical advisors. Postal survey of cardiologists. Potential areas of important uncertainty were identified and 'decision problems' prioritised. A systematic literature review was carried out using standard methods. The constructed decision model consisted of a short-term phase that applied the results of the systematic review and a long-term phase that included relevant information from a UK observational study to extrapolate estimated costs and effects. Sensitivity analyses were undertaken to examine the dependence of the results on baseline parameters, using alternative data sources. Expected value of information analysis was undertaken to estimate the expected value of perfect information associated with the decision problem. This provided an upper bound on the monetary value associated with additional research in the area. Seven current areas of clinical uncertainty (decision problems) in the drug treatment of unstable angina patients were identified. The agents concerned were clopidogrel, low molecular weight heparin, hirudin and intravenous glycoprotein antagonists (GPAs). Twelve published clinical guidelines for unstable angina or non-ST elevation ACS were identified, but few contained recommendations about the specified decision problems. The postal survey of clinicians showed that the greatest disagreement existed for the use of small molecule GPAs, and the greatest uncertainty existed for decisions relating to the use of abciximab (a large molecule GPA). Overall, decision problems concerning the GPA class of drugs were considered to be the highest priority for further study. Selected papers describing the clinical efficacy of treatment were divided into three groups, each representing an alternative strategy. The strategy involving the use of GPAs as part of the initial medical management of all non-ST elevation ACS was the optimal choice, with an incremental cost-effectiveness ratio (ICER) of 5738 pounds per quality-adjusted life-year (QALY) compared with no use of GPAs. Stochastic analysis showed that if the health service is willing to pay 10,000 pounds per additional QALY, the probability of this strategy being cost-effective was around 82%, increasing to 95% at a threshold of 50,000 pounds per QALY. A sensitivity analysis including an additional strategy of using GPAs as part of initial medical management only in patients at particular high risk (as defined by age, ST depression or diabetes) showed that this additional strategy was yet more cost-effective, with an ICER of 3996 pounds per QALY compared with no treatment with GPA. Value of information analysis suggested that there was considerable merit in additional research to reduce the level of uncertainty in the optimal decision. At a threshold of 10,000 pounds per QALY, the maximum potential value of such research in the base case was calculated as 12.7 million pounds per annum for the UK as a whole. Taking account of the greater uncertainty in the sensitivity analyses including clopidogrel, this figure was increased to approximately 50 million pounds. This study suggests the use of GPAs in all non-ST elevation ACS patients as part of their initial medical management. Sensitivity analysis showed that virtually all of the benefit could be realised by treating only high-risk patients. Further clarification of the optimum role of GPAs in the UK NHS depends on the availability of further high-quality observational and trial data. Value of information analysis derived from the model suggests that a relatively large investment in such research may be worthwhile. Further research should focus on the identification of the characteristics of patients who benefit most from GPAs as part of medical management, the comparison of GPAs with clopidogrel as an adjunct to standard care, follow-up cohort studies of the costs and outcomes of high-risk non-ST elevation ACS over several years, and exploring how clinicians' decisions combine a normative evidence-based decision model with their own personal behavioural perspective.

Decision making under uncertainty, therapeutic inertia, and physicians' risk preferences in the management of multiple sclerosis (DIScUTIR MS).

PubMed

Saposnik, Gustavo; Sempere, Angel Perez; Raptis, Roula; Prefasi, Daniel; Selchen, Daniel; Maurino, Jorge

2016-05-04

The management of multiple sclerosis (MS) is rapidly changing by the introduction of new and more effective disease-modifying agents. The importance of risk stratification was confirmed by results on disease progression predicted by different risk score systems. Despite these advances, we know very little about medical decisions under uncertainty in the management of MS. The goal of this study is to i) identify whether overconfidence, tolerance to risk/uncertainty, herding influence medical decisions, and ii) to evaluate the frequency of therapeutic inertia (defined as lack of treatment initiation or intensification in patients not at goals of care) and its predisposing factors in the management of MS. This is a prospective study comprising a combination of case-vignettes and surveys and experiments from Neuroeconomics/behavioral economics to identify cognitive distortions associated with medical decisions and therapeutic inertia. Participants include MS fellows and MS experts from across Spain. Each participant will receive an individual link using Qualtrics platform(©) that includes 20 case-vignettes, 3 surveys, and 4 behavioral experiments. The total time for completing the study is approximately 30-35 min. Case vignettes were selected to be representative of common clinical encounters in MS practice. Surveys and experiments include standardized test to measure overconfidence, aversion to risk and ambiguity, herding (following colleague's suggestions even when not supported by the evidence), physicians' reactions to uncertainty, and questions from the Socio-Economic Panel Study (SOEP) related to risk preferences in different domains. By applying three different MS score criteria (modified Rio, EMA, Prosperini's scheme) we take into account physicians' differences in escalating therapy when evaluating medical decisions across case-vignettes. The present study applies an innovative approach by combining tools to assess medical decisions with experiments from Neuroeconomics that applies to common scenarios in MS care. Our results will help advance the field by providing a better understanding on the influence of cognitive factors (e.g., overconfidence, aversion to risk and uncertainty, herding) on medical decisions and therapeutic inertia in the management of MS which could lead to better outcomes.

Tsunami hazard assessments with consideration of uncertain earthquakes characteristics

NASA Astrophysics Data System (ADS)

Sepulveda, I.; Liu, P. L. F.; Grigoriu, M. D.; Pritchard, M. E.

2017-12-01

The uncertainty quantification of tsunami assessments due to uncertain earthquake characteristics faces important challenges. First, the generated earthquake samples must be consistent with the properties observed in past events. Second, it must adopt an uncertainty propagation method to determine tsunami uncertainties with a feasible computational cost. In this study we propose a new methodology, which improves the existing tsunami uncertainty assessment methods. The methodology considers two uncertain earthquake characteristics, the slip distribution and location. First, the methodology considers the generation of consistent earthquake slip samples by means of a Karhunen Loeve (K-L) expansion and a translation process (Grigoriu, 2012), applicable to any non-rectangular rupture area and marginal probability distribution. The K-L expansion was recently applied by Le Veque et al. (2016). We have extended the methodology by analyzing accuracy criteria in terms of the tsunami initial conditions. Furthermore, and unlike this reference, we preserve the original probability properties of the slip distribution, by avoiding post sampling treatments such as earthquake slip scaling. Our approach is analyzed and justified in the framework of the present study. Second, the methodology uses a Stochastic Reduced Order model (SROM) (Grigoriu, 2009) instead of a classic Monte Carlo simulation, which reduces the computational cost of the uncertainty propagation. The methodology is applied on a real case. We study tsunamis generated at the site of the 2014 Chilean earthquake. We generate earthquake samples with expected magnitude Mw 8. We first demonstrate that the stochastic approach of our study generates consistent earthquake samples with respect to the target probability laws. We also show that the results obtained from SROM are more accurate than classic Monte Carlo simulations. We finally validate the methodology by comparing the simulated tsunamis and the tsunami records for the 2014 Chilean earthquake. Results show that leading wave measurements fall within the tsunami sample space. At later times, however, there are mismatches between measured data and the simulated results, suggesting that other sources of uncertainty are as relevant as the uncertainty of the studied earthquake characteristics.

ISO/GUM UNCERTAINTIES AND CIAAW (UNCERTAINTY TREATMENT FOR RECOMMENDED ATOMIC WEIGHTS AND ISOTOPIC ABUNDANCES)

DOE Office of Scientific and Technical Information (OSTI.GOV)

HOLDEN,N.E.

2007-07-23

The International Organization for Standardization (ISO) has published a Guide to the expression of Uncertainty in Measurement (GUM). The IUPAC Commission on Isotopic Abundance and Atomic Weight (CIAAW) began attaching uncertainty limits to their recommended values about forty years ago. CIAAW's method for determining and assigning uncertainties has evolved over time. We trace this evolution to their present method and their effort to incorporate the basic ISO/GUM procedures into evaluations of these uncertainties. We discuss some dilemma the CIAAW faces in their present method and whether it is consistent with the application of the ISO/GUM rules. We discuss the attemptmore » to incorporate variations in measured isotope ratios, due to natural fractionation, into the ISO/GUM system. We make some observations about the inconsistent treatment in the incorporation of natural variations into recommended data and uncertainties. A recommendation for expressing atomic weight values using a tabulated range of values for various chemical elements is discussed.« less

48 CFR 215.404-71-2 - Performance risk.

Code of Federal Regulations, 2010 CFR

2010-10-01

...—the technical uncertainties of performance. (2) Management/cost control—the degree of management... Technical (1) (2) N/A N/A 22 Management/Cost Control (1) (2) N/A N/A 23 Performance Risk (Composite) N/A (3...(percent) Assignedvalue (percent) Weightedvalue (percent) Technical 60 5.0 3.0 Management/Cost Control 40 4...

The science of decisionmaking: applications for sustainable forest and grassland management in the National Forest System

Treesearch

Matthew P. Thompson; Bruce G. Marcot; Frank R. Thompson; Steven McNulty; Larry A. Fisher; Michael C. Runge; David Cleaves; Monica Tomosy

2013-01-01

Sustainable management of national forests and grasslands within the National Forest System (NFS) often requires managers to make tough decisions under considerable uncertainty, complexity, and potential conflict. Resource decisionmakers must weigh a variety of risks, stressors, and challenges to sustainable management, including climate change, wildland fire, invasive...