Uncertainty in positioning ion chamber at reference depth for various water phantoms

Background

Uncertainty in the calibration of high-energy radiation sources is dependent on user and equipment type.

Probabilistic measures of climate change vulnerability,adaptation action benefits,and related uncertainty from maximum temperature metric selection

Predictions of the projected changes in species distributions and potential adaptation action benefits can help guide conservation actions. There is substantial uncertainty in projecting species distributions into an unknown future, however, which can undermine confidence in predictions or misdirect conservation actions if not properly considered. Recent studies have shown that the selection of alternative climate metrics describing very different climatic aspects (e.g., mean air temperature vs. mean precipitation) can be a substantial source of projection uncertainty. It is unclear, however, how much projection uncertainty might stem from selecting among highly correlated, ecologically similar climate metrics (e.g., maximum temperature in July, maximum 30‐day temperature) describing the same climatic aspect (e.g., maximum temperatures) known to limit a species’ distribution. It is also unclear how projection uncertainty might propagate into predictions of the potential benefits of adaptation actions that might lessen climate change effects. We provide probabilistic measures of climate change vulnerability, adaptation action benefits, and related uncertainty stemming from the selection of four maximum temperature metrics for brook trout (Salvelinus fontinalis), a cold‐water salmonid of conservation concern in the eastern United States. Projected losses in suitable stream length varied by as much as 20% among alternative maximum temperature metrics for mid‐century climate projections, which was similar to variation among three climate models. Similarly, the regional average predicted increase in brook trout occurrence probability under an adaptation action scenario of full riparian forest restoration varied by as much as .2 among metrics. Our use of Bayesian inference provides probabilistic measures of vulnerability and adaptation action benefits for individual stream reaches that properly address statistical uncertainty and can help guide conservation actions. Our study demonstrates that even relatively small differences in the definitions of climate metrics can result in very different projections and reveal high uncertainty in predicted climate change effects.     

Use of the reference dose in risk characterization of drinking water contaminants

Regulatory decisions should be made in the most expert and informed way since they are precipitated by real and perceived threats to public health, under the glare of public scrutiny. The development of environmental regulations require a three‐step paradigm, collectively called risk analysis. This paper will address the risk assessment practices required under the Safe Drinking Water Act (SDWA) Amendments of 1986 to determine a Maximum Contaminant Level Goal (MCLG, nonenforceable health goal) which should result in no known or anticipated health effects, and allows adequate margin of safety. The end product of this risk assessment, risk characterization, and risk management encompassing control options and nonrisk analysis are combined to derive the Maximum Contaminant Level (MCL, enforceable standard). Furthermore, this paper discusses the U.S. Environmental Protection Agency's efforts in exploring new and improved noncancer risk assessment approaches providing the basis for MCLGs for the protection of human health.     

Hydrologic sources of carbon cycling uncertainty throughout the terrestrial–aquatic continuum

Linking hydrologic interactions with global carbon cycling will reduce the uncertainty associated with scaling-up empirical studies and facilitate the incorporation of terrestrial–aquatic linkages within global and regional change models. Much of the uncertainty in estimates of carbon fluxes associated with precipitation and hydrologic transport results from the extensive spatial and temporal heterogeneity in both intrinsic functioning and anthropogenic modification of hydrological cycles. To better understand this variation we developed a landscape ecological approach to coupled hydrologic–carbon cycling that merges local mechanisms with multiple-scale spatial heterogeneity. This spatially explicit framework is applied to examine variability in hydrologic influences on carbon cycling along a continental scale water availability gradient with an explicit consideration of human sources of variability. Hydrologic variation is an important component of the uncertainty in carbon cycling; accounting for this variation will improve understanding of current conditions and projections of future ecosystem responses to global change.     

Model‐specification uncertainty in future forest pest outbreak

Climate change will modify forest pest outbreak characteristics, although there are disagreements regarding the specifics of these changes. A large part of this variability may be attributed to model specifications. As a case study, we developed a consensus model predicting spruce budworm (SBW, Choristoneura fumiferana [Clem.]) outbreak duration using two different predictor data sets and six different correlative methods. The model was used to project outbreak duration and the uncertainty associated with using different data sets and correlative methods (=model‐specification uncertainty) for 2011–2040, 2041–2070 and 2071–2100, according to three forcing scenarios (RCP 2.6, RCP 4.5 and RCP 8.5). The consensus model showed very high explanatory power and low bias. The model projected a more important northward shift and decrease in outbreak duration under the RCP 8.5 scenario. However, variation in single‐model projections increases with time, making future projections highly uncertain. Notably, the magnitude of the shifts in northward expansion, overall outbreak duration and the patterns of outbreaks duration at the southern edge were highly variable according to the predictor data set and correlative method used. We also demonstrated that variation in forcing scenarios contributed only slightly to the uncertainty of model projections compared with the two sources of model‐specification uncertainty. Our approach helped to quantify model‐specification uncertainty in future forest pest outbreak characteristics. It may contribute to sounder decision‐making by acknowledging the limits of the projections and help to identify areas where model‐specification uncertainty is high. As such, we further stress that this uncertainty should be strongly considered when making forest management plans, notably by adopting adaptive management strategies so as to reduce future risks.     

Risk management decisions for pesticides and threatened and endangered species: The role of uncertainty analysis

Despite data gaps and information shortfalls, government agencies in the United States are expected to produce timely and defensible decisions to regulate pesticide use under the Federal Insecticide, Fungicide, and Rodenticide Act and in compliance with the Endangered Species Act. The decision to register a pesticide is predicated on a conclusion that no unreasonable effects will accrue to the environment, including threatened and endangered species. We recognize that the definition of acceptable risk is a policy judgment stemming from legislative language and judicial interpretation. However, a common risk assessment approach with similar technical underpinnings and a high degree of transparency used by all the agencies would be cost effective and more likely to achieve consensus among interested parties. Quantitative probabilistic risk assessment (PRA) methods can be used to develop risk estimates and to describe the level of confidence in these estimates. PRA methods can also differentiate among the contributions of natural stochasticity, measurement variability, and lack of knowledge. Because this approach enhances transparency and increases understanding of the implications of limited data sets and associated assumptions, we encourage the appropriate agencies to implement PRA methods as a means of reaching common ground when assessing risks of pesticides to listed species.     

Selected applications of reduced uncertainty factors in noncancer risk assessment

Guidelines have been developed within the Environmental Protection Agency (EPA) for applying uncertainty factors to noncancer risk assessments. In many recent cases, reduced values for uncertainty factors of less than 10 have been used. The specific circumstances that justify reduced uncertainty are: partial definition of the sensitive subpopulation among humans, partial database limitations, use of a minimal LOAEL, risk assessment for an essential nutrient, and risk assessments based upon studies in nonhuman primates. Details of the rationale for each of these circumstances are provided.