Framework For Uncertainty Assessment Research Articles

Assessing uncertainty in building material emissions using scenario-aware Monte Carlo simulation

Global greenhouse gas emissions from the built environment remain high, driving innovative approaches to develop and adopt building materials that can mitigate some of those emissions. However, life-cycle assessment (LCA) practices still lack standardized quantitative uncertainty assessment frameworks, which are urgently needed to robustly assess mitigation efforts. Previous works emphasize the importance of accounting for the three types of uncertainties that may exist within any quantitative assessment: parameter, scenario, and model uncertainty. Herein, we develop a quantitative uncertainty assessment framework that distinguishes between different types of uncertainties and suggest how these uncertainties could be handled systematically through a scenario-aware Monte Carlo simulation (MCS). We demonstrate the framework’s decision-informing power through a case study of two multilevel ordinary Portland cement (OPC) manufacturing scenarios. The MCS utilizes a first-principles-based OPC life-cycle inventory, which mitigates some of the model uncertainty that may exist in other empirical-based cement models. Remaining uncertainties are handled by scenario specification or sampling from developed probability distribution functions. We also suggest a standardized method for fitting distributions to parameter data by enumerating through and implementing distributions based on the Kolmogorov–Smirnov test. The level of detail brought by the high-resolution parameter breakdown of the model allows for developing emission distributions for each process of OPC manufacturing. This approach highlights how specific parameters, along with scenario framing, can impact overall OPC emissions. Another key takeaway includes relating the uncertainty of each process to its contributions to total OPC emissions, which can guide LCA modelers in allocating data collection and refinement efforts to processes with the highest contribution to cumulative uncertainty. Ultimately, the aim of this work is to provide a standardized framework that can provide robust estimates of building material emissions and be readily integrated within any uncertainty assessment.

Cost-effectiveness uncertainty may bias the decision of coal power transitions in China

A transition away from coal power always maintains a high level of complexity as there are several overlapping considerations such as technical feasibility, economic costs, and environmental and health impacts. Here, we explore the cost-effectiveness uncertainty brought by policy implementation disturbances of different coal power phaseout and new-built strategies (i.e., the disruption of phaseout priority) in China based on a developed unit-level uncertainty assessment framework. We reveal the opportunity and risk of coal transition decisions by employing preference analysis. We find that, the uncertainty of a policy implementation might lead to potential delays in yielding the initial positive annual net benefits. For example, a delay of six years might occur when implementing the prior phaseout practice. A certain level of risk remains in the implementation of the phaseout policy, as not all strategies can guarantee the cumulative positive net benefits from 2018–2060. Since the unit-level heterogeneities shape diverse orientation of the phaseout, the decision-making preferences would remarkably alter the selection of a coal power transition strategy. More strikingly, the cost-effectiveness uncertainty might lead to missed opportunities in identifying an optimal strategy. Our results highlight the importance of minimizing the policy implementation disturbance, which helps mitigate the risk of negative benefits and strengthen the practicality of phaseout decisions.

Evaluation of the Impact of Multi-Source Uncertainties on Meteorological and Hydrological Ensemble Forecasting

Evaluating the impact of multi-source uncertainties in complex forecasting systems is essential to understanding and improving the systems Previous studies have paid little attention to the influence of multi-source uncertainties in complex meteorological and hydrological forecasting systems. In this study, we developed a general ensemble framework based on Bayesian model averaging (BMA) for evaluating the impact of multi-source uncertainties in complex forecast systems. Based on this framework, we used eight numerical weather prediction products from the International Grand Global Ensemble (TIGGE) dataset, four hydrological models with different structures, and 1000 sets of parameters to comprehensively account for the input, structure, and parameter uncertainties. The framework’s application to the Chitan Basin in China revealed that the numerical weather prediction input uncertainty in the forecasting system was more significant than the hydrological model uncertainty. The hydrological model structure uncertainty was more prominent than the parameter uncertainty. The accuracy of the numerical weather prediction dominates the accuracy of the forecast of high flows. In addition, the structures and parameters of the hydrological model and their interactions contributed to the main uncertainty of the low flow forecasts. The streamflow was more realistically represented when the three uncertainty sources were considered jointly. By accounting for the significant uncertainty sources in complex forecast systems, the BMA ensemble forecasting produces more realistic and reliable predictions and reduces the influences of other incomplete considerations. The developed multi-source uncertainty assessment framework improves our understanding of the complex meteorological and hydrological forecasting system. Therefore, the framework is promising for improving the accuracy and reliability of complex forecasting systems.

Bayesian Framework Helps History Matching of Permian EOR Field With Data Uncertainty

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 200807, “Closing the Loop on a History Match for a Permian EOR Field With Relative Permeability Data Uncertainty,” by Usman Aslam, Emerson, and Jorge Burgos, SPE, and Craig Williams, Occidental Petroleum, et al. The paper has not been peer reviewed. _ Reservoir production forecasts are inherently uncertain because of the lack of quality data available to build predictive reservoir models. Traditionally, a best estimate for relative permeability data is assumed during the history-matching process despite significant uncertainty. Performing sensitivities around the best-estimate relative permeability case will cover only part of the uncertainty space. In the complete paper, the authors present an application of a Bayesian framework for uncertainty assessment and efficient history matching of a Permian carbon-dioxide (CO2) enhanced oil recovery field for reliable production forecasting. Field Details Regional and Structural Geology. The Central Basin Platform (CBP) is a positive tectonic feature that separates the Delaware and Midland sub-basins of the Permian. The study field is located on the northeastern end of the CBP, with the Permian (Guadalupian) -aged reservoir composed of San Andres Formation dolostone. The total thickness of the unit is approximately 1,500 ft, with the main reservoir within the middle 600 ft. Structural Framework. The area of interest features 270 wells that have a total depth in the San Andres formation or deeper. Of these, 234 have digital open- or cased-hole logs that were used to correlate formation tops. After reviewing all well logs, it became clear that, historically, the zones were primarily identifiable from porosity picks, which are more subjective than markers identified with gamma ray methods. A new sequence stratigraphic framework was developed based on core descriptions and outcrop analogs. This correlation framework was then extrapolated to well logs. After a grid of north/south and west/east cross sections was correlated across the field and a series of loop ties was made for quality-control purposes, structure and isopach maps were created for each zone. Reservoir Description. At the time of discovery, natural gas was trapped at the structural high point of the study field. Above the gas/oil interface is the gas cap. Below the gas was an oil accumulation, which extended to the producing oil/water contact (POWC). The POWC was defined by early drilling as the maximum depth where water-free oil was produced. The base of the transition zone is the top of the residual oil zone (ROZ); this reservoir interval extends to the free-water level and is an interval believed to have been waterflooded naturally. The ROZ is the target for CO2 injection. Grid Construction and Property Modeling. A full-field detailed 3D reservoir characterization was modeled at 50-ft × 50-ft grid increments and an approximate 2-ft average layer thickness. The reservoir model consists of 114,222,528 cells. A total of 235 wells in the unit area was correlated and used to constrain the structural framework of the model. Quantitative porosity log suites were available in 233 of the wells in the model area, which were used to create the full-field porosity property in the 3D model. The logs were normalized to the core data and upscaled.

Assessing the uncertainty of deep learning soil spectral models using Monte Carlo dropout

The acquisition of soil information using infrared spectroscopy is now widely practised in soil sciences. In conjunction with machine learning models, spectral data are used to predict soil properties in an effort to complement or replace soil laboratory analyses. However, the assessment of uncertainty of spectral models is still not a common practice. Here, we evaluate two methods to assess the uncertainty of a deep learning soil spectral model for soil organic carbon prediction. We focused on how both methods evaluate in and out-of-domain uncertainty which signals if the model “knows” when it is making predictions on new data that differs from the data used during training, where it is expected to generate wider prediction intervals. The methods corresponded to a) bootstrapping, a general framework for uncertainty assessment commonly used in soil modelling and b) Monte Carlo dropout, specifically used to assess the uncertainty of deep learning models. Using the LUCAS Vis–NIR spectra database for predicting soil organic carbon, we demonstrate that both models correctly assessed the uncertainty for in-domain data, showing similar results. However, when predicting on out-of-domain data, Monte Carlo dropout was capable of generating much wider prediction intervals compared to bootstrapping, correctly signalling that the new data is different from the data used during training. This uncertainty should accompany the prediction and be propagated when the prediction is being used in downstream applications.

Are Global Environmental Uncertainties Inevitable? Measuring Desertification for the SDGs

Continuing uncertainty about the present magnitudes of global environmental change phenomena limits scientific understanding of human impacts on Planet Earth, and the quality of scientific advice to policy makers on how to tackle these phenomena. Yet why global environmental uncertainties are so great, why they persist, how their magnitudes differ from one phenomenon to another, and whether they can be reduced is poorly understood. To address these questions, a new tool, the Uncertainty Assessment Framework (UAF), is proposed that builds on previous research by dividing sources of environmental uncertainty into categories linked to features inherent in phenomena, and insufficient capacity to conceptualize and measure phenomena. Applying the UAF shows that, based on its scale, complexity, areal variability and turnover time, desertification is one of the most inherently uncertain global environmental change phenomena. Present uncertainty about desertification is also very high and persistent: the Uncertainty Score of a time series of five estimates of the global extent of desertification shows limited change and has a mean of 6.8, on a scale from 0 to 8, based on the presence of four conceptualization uncertainties (terminological difficulties, underspecification, understructuralization and using proxies) and four measurement uncertainties (random errors, systemic errors, scalar deficiencies and using subjective judgment). This suggests that realization of the Land Degradation Neutrality (LDN) Target 15.3 of the UN Sustainable Development Goal (SDG) 15 (“Life on Land”) will be difficult to monitor in dry areas. None of the estimates in the time series has an Uncertainty Score of 2 when, according to the UAF, evaluation by statistical methods alone would be appropriate. This supports claims that statistical methods have limitations for evaluating very uncertain phenomena. Global environmental uncertainties could be reduced by devising better rules for constructing global environmental information which integrate conceptualization and measurement. A set of seven rules derived from the UAF is applied here to show how to measure desertification, demonstrating that uncertainty about it is not inevitable. Recent review articles have advocated using ‘big data’ to fill national data gaps in monitoring LDN and other SDG 15 targets, but an evaluation of a sample of three exemplar studies using the UAF still gives a mean Uncertainty Score of 4.7, so this approach will not be straightforward.

Shyft v4.8: a framework for uncertainty assessment and distributed hydrologic modeling for operational hydrology

Abstract. This paper presents Shyft, a novel hydrologic modeling software for streamflow forecasting targeted for use in hydropower production environments and research. The software enables rapid development and implementation in operational settings and the capability to perform distributed hydrologic modeling with multiple model and forcing configurations. Multiple models may be built up through the creation of hydrologic algorithms from a library of well-known routines or through the creation of new routines, each defined for processes such as evapotranspiration, snow accumulation and melt, and soil water response. Key to the design of Shyft is an application programming interface (API) that provides access to all components of the framework (including the individual hydrologic routines) via Python, while maintaining high computational performance as the algorithms are implemented in modern C++. The API allows for rapid exploration of different model configurations and selection of an optimal forecast model. Several different methods may be aggregated and composed, allowing direct intercomparison of models and algorithms. In order to provide enterprise-level software, strong focus is given to computational efficiency, code quality, documentation, and test coverage. Shyft is released open-source under the GNU Lesser General Public License v3.0 and available at https://gitlab.com/shyft-os (last access: 22 November 2020), facilitating effective cooperation between core developers, industry, and research institutions.

Argument-based assessment of predictive uncertainty of data-driven environmental models

Increasing volumes of data allow environmental scientists to use machine learning to construct data-driven models of phenomena. These models can provide decision-relevant predictions, but confident decision-making requires that the involved uncertainties are understood. We argue that existing frameworks for characterizing uncertainties are not appropriate for data-driven models because of their focus on distinct locations of uncertainty. We propose a framework for uncertainty assessment that uses argument analysis to assess the justification of the assumption that the model is fit for the predictive purpose at hand. Its flexibility makes the framework applicable to data-driven models. The framework is illustrated using a case study from environmental science. We show that data-driven models can be subject to substantial second-order uncertainty, i.e., uncertainty in the assessment of the predictive uncertainty, because they are often applied to ill-understood problems. We close by discussing the implications of the predictive uncertainties of data-driven models for decision-making.

Simulation-based approach to apply uncertainty evaluation framework, for PSS economic models

PSS offerings are characterized by the presence of uncertainties due to the lack of information, in the design stage of the offer, about future events that the decision makers will face. These uncertainties must be anticipated to validate the profitability of PSS projects. In this paper, an approach to assess uncertainty is presented, then applied to a study case. This approach is an integration of the classical uncertainty management framework together with the quantitative uncertainty assessment framework. In the first part of this article, a literature review on uncertainty identification and assessment in the PSS context is presented. Then, an uncertainty assessment approach is proposed, with the methods and tools to implement it. Finally, the authors describe the results of the application to an industrial case study.

Event-based uncertainty assessment of sediment modeling in a data-scarce catchment

Uncertainties in watershed modeling need to be addressed to increase predictive accuracy and enhance model interpretation. Nevertheless, event-based characteristics incorporating management concerns were rarely considered in the sediment modeling within an uncertainty assessment framework, which might lead to biased decision makings in watershed management. In this study, the event-based likelihood measure was developed to improve the understanding and prediction of the sediment dynamics in a data-scarce catchment in the Three Gorge Reservoir Region (TGRR), China. The uncertainty assessment is based on the Generalized Likelihood Uncertainty Estimation (GLUE) approach applied to the Hydrological Simulation Program - Fortran (HSPF) model. The impact of the Nash-Sutcliffe efficiency (NSE) as the traditionally used likelihood measure was also investigated as a comparison. The results showed that the event-based likelihood measure had advantages in judging critical parameters. Sensitive parameters in the soil erosion and channel transport and their impacts on different phrases of sedimentograph were recognized. The event-based likelihood measure was much more discriminating by efficiently eliminating unsatisfactory parameter sets. Better validity in efficiently reducing the parameter uncertainty and a higher identifiability was achieved by the newly developed likelihood measure. The event-based likelihood measure resulted in more progressive reductions in predictive uncertainty in terms of uncertainty bounds and evaluation criteria. The proposed uncertainty assessment framework yields improved predictions and an increased understanding of sediment response behaviors in a data-limited environment.

A probabilistic appraisal of rainfall-runoff modeling approaches within SWAT in mixed land use watersheds

A probabilistic approach is presented to assess the performance validity of the empirical Curve Number (CN) and physically-based Green and Ampt (G&A) rainfall-runoff methods in the SWAT model. Specifically, the effects of modeling uncertainties on characterization of the hydrologic budgets and streamflow regimes at various spatial scales and upstream land use conditions are investigated. A Bayesian total uncertainty assessment framework, which explicitly accounts for uncertainties from model parameters, inputs, structure, and measurement data, was employed to explore uncertainties in streamflow simulation using SWAT with different rainfall-runoff methods in a mixed-land use watershed. While the models were trained for streamflow estimation only at the watershed outlet, the performances of the models were compared at different stream locations within the watershed. At the watershed outlet, the CN method had a slightly better, but not significant, performance in terms of streamflow error statistics. Similar results were obtained for the predominantly forested and agricultural tributaries. However, in tributaries with higher percentage of developed land, G&A outperformed the CN method in simulating streamflow based on various performance metrics. In general, the 95% prediction intervals from the models with G&A method covered a higher percentage of observed streamflow especially during the high flow events. However, they were approximately 20–45% wider than the corresponding 95% prediction intervals from the CN methods. Using 95% prediction interval for estimated flow duration curves, results indicated that the models with CN methods underestimated high flow events especially in tributaries with highly developed land use. However, the CN methods generated higher water yields to streams than the G&A method. The results of this study have important implications for the selection and application of appropriate rainfall-runoff methods within complex distributed hydrologic models particularly when simulating hydrologic responses in mixed-land use watersheds. In the present study, while CN and G&A methods in the SWAT model performed similarly at the outlet of a mixed-land use watershed, G&A captured the internal processes more realistically. The subsequent effects on the representation of internal hydrological processes and budgets are discussed.

Uncertainty Assessment of Synthetic Design Hydrographs for Gauged and Ungauged Catchments

AbstractDesign 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.

An interval Taylor-based method for transient stability assessment of power systems with uncertainties

Transient stability assessment needs to be further studied when confronted with uncertainties brought in by the integration of renewable energy and measurement errors. This paper outlines a novel method based on interval Taylor expansion incorporated optimal solution technique to solve the uncertainty model for transient stability assessment. Uncertainty assessment framework for power systems transient stability is described as ordinary differential equations with interval variables, which can be equivalently transformed into three sets of ordinary differential equations with only deterministic parameters using the interval Taylor method with second-order expansion. In order to further reduce the overestimation, a quadratic programming model is constructed to estimate upper and lower boundaries of state variables. Application of the proposed method is implemented in SIMB system and IEEE-30 system and the results are demonstrated in details. Comparisons with affine arithmetic (AA)-based method and Monte Carlo method verify the effectiveness and better performance of the proposed method.

Uncertainty assessment of flood inundation modelling with a 1D/2D random field

Abstract An uncertainty assessment framework based on Karhunen–Loevè expansion (KLE) and probabilistic collocation method (PCM) was introduced to deal with flood inundation modelling under uncertainty. The Manning's roughness for channel and floodplain were treated as 1D and 2D, respectively, and decomposed by KLE. The maximum flow depths were decomposed by the 2nd-order PCM. Through a flood modelling case with steady inflow hydrographs based on five designed testing scenarios, the applicability of KLE-PCM was demonstrated. The study results showed that the Manning's roughness assumed as a 1D/2D random field could efficiently alleviate the burden of random dimensionality within the analysis framework, and the introduced method could significantly reduce repetitive runs of the physical model as required in the traditional Monte Carlo simulation (MCS). The study sheds some light on reducing the computational burden associated with flood modelling under uncertainty which is useful for the related damage quantification and risk management.

Pilot study on uncertainty analysis in EFSA Reasoned Opinions on the modification of pesticide maximum residue levels.

Applicability of the EFSA Scientific Committee revised draft Guidance on uncertainty in EFSA scientific assessment is tested in the context of an EFSA Reasoned Opinion on the modification of pesticide maximum residue levels (MRLs). EFSA purchased services for the preparation of a non‐regulatory Evaluation Report with example non‐standard uncertainties related to a fictitious application for the modification of MRLs. The Evaluation Report was assessed by EFSA in the format of a Reasoned Opinion and case‐specific examples of non‐standard uncertainty in the acute and chronic dietary risk assessments were analysed. Methods were selected from the general framework outlined in the Scientific Committee draft Guidance in order to apply a relatively simple strategy that could be considered for use in a regulatory context. The individual non‐standard uncertainties were assessed by sensitivity analysis with iterative back‐calculation of the parameter values that would lead to exceedance of the toxicological reference value (exceedance limit calculation), and quantified by subjective probability estimation. Non‐standard uncertainties affecting the chronic risk assessment were quantified by subjective upper bound probability percentile estimation and the combined estimated non‐standard uncertainty calculated by probability bounds analysis. Probability bounds analysis provides a relatively simple approach for calculating the probability related to a combination of uncertainties. The draft Guidance was found to provide a comprehensive range of methods for uncertainty analysis. However, process‐specific guidelines and practical procedures may need to be developed in order to implement the uncertainty assessment framework in routine pesticide risk assessments. The uncertainty assessment is intended to provide additional information on how certain the conclusions of the risk assessment are and thereby support the risk‐based decision‐making process by enabling risk managers to take account of uncertainty. The outcome of the pilot study will inform the EFSA Scientific Committee Working Group on how to further tailor the draft Guidance on uncertainty for the needs of the EFSA panels and units.

Quantifying Uncertainties in Extreme Flood Predictions under Climate Change for a Medium-Sized Basin in Northeastern China

Abstract This study develops a new variance-based uncertainty assessment framework to investigate the individual and combined impacts of various uncertainty sources on future extreme floods. The Long Ashton Research Station Weather Generator (LARS-WG) approach is used to downscale multiple general circulation models (GCMs), and the dynamically dimensioned search approximation of uncertainty approach is used to quantify hydrological model uncertainty. Extreme floods in a region in northeastern China are studied for two future periods: near term (2046–65) and far term (2080–99). Six GCMs and three emission scenarios (A1B, A2, and B1) are used. Results obtained from this case study show that the 500-yr flood magnitude could increase by 4.5% in 2046–65 and by 6.4% in 2080–99 in terms of median values; in worst-case scenarios, it could increase by 63.0% and 111.8% in 2046–65 and 2080–99, respectively. It is found that the combined effect of GCMs, emission scenarios, and hydrological models has a larger influence on the discharge uncertainties than the individual impacts from emission scenarios and hydrological models. Further, results show GCMs are the dominant contributor to extreme flood uncertainty in both 2046–65 and 2080–99 periods. This study demonstrates that the developed framework can be used to effectively investigate changes in the occurrence of extreme floods in the future and to quantify individual and combined contributions of various uncertainty sources to extreme flood uncertainty, which can guide future efforts to reduce uncertainty.

A GLUE‐based uncertainty assessment framework for tritium‐inferred transit time estimations under baseflow conditions

AbstractThe last decade has seen major technical and scientific improvements in the study of water transfer time through catchments. Nevertheless, it has been argued that most of these developments used conservative tracers that may disregard the oldest component of water transfer, which often has transit times greater than 5 years. Indeed, although the analytical reproducibility of tracers limits the detection of the older flow components associated with the most dampened seasonal fluctuations, this is very rarely taken into account in modelling applications. Tritium is the only environmental tracer at hand to investigate transfer times in the 5‐ to 50‐year range in surface waters, as dissolved gases are not suitable due to the degassing process. Water dating with tritium has often been difficult because of the complex history of its atmospheric concentration, but its current stabilization together with recent analytical improvements open promising perspectives. In this context, the innovative contribution of this study lies in the development of a generalized likelihood uncertainty estimation‐based approach for analysing the uncertainties associated with the modelling of transit time due to both parameter identification and tracer analytical precision issues. A coupled resampling procedure allows assessment of the statistical significance of the transfer time differences found in diverse waters. This approach was developed for tritium and the exponential‐piston model but can be implemented for virtually any tracer and model. Stream baseflow, spring and shallow aquifer waters from the Vallcebre research catchments, analysed for tritium in different years with different analytical precisions, were investigated by using this approach and taking into account other sources of uncertainty. The results showed three groups of waters of different mean transit times, with all the stream baseflow and spring waters older than the 5‐year threshold needing tritium. Low sensitivity of the results to the model structure was also demonstrated. Dual solutions were found for the waters sampled in 2013, but these results may be disambiguated when additional analyses will be made in a few years. Copyright © 2016 John Wiley & Sons, Ltd.

On inclusion of water resource management in Earth system models – Part 2: Representation of water supply and allocation and opportunities for improved modeling

Abstract. Human water use has significantly increased during the recent past. Water withdrawals from surface and groundwater sources have altered terrestrial discharge and storage, with large variability in time and space. These withdrawals are driven by sectoral demands for water, but are commonly subject to supply constraints, which determine water allocation. Water supply and allocation, therefore, should be considered together with water demand and appropriately included in Earth system models to address various large-scale effects with or without considering possible climate interactions. In a companion paper, we review the modeling of demand in large-scale models. Here, we review the algorithms developed to represent the elements of water supply and allocation in land surface and global hydrologic models. We note that some potentially important online implications, such as the effects of large reservoirs on land–atmospheric feedbacks, have not yet been fully investigated. Regarding offline implications, we find that there are important elements, such as groundwater availability and withdrawals, and the representation of large reservoirs, which should be improved. We identify major sources of uncertainty in current simulations due to limitations in data support, water allocation algorithms, host large-scale models as well as propagation of various biases across the integrated modeling system. Considering these findings with those highlighted in our companion paper, we note that advancements in computation and coupling techniques as well as improvements in natural and anthropogenic process representation and parameterization in host large-scale models, in conjunction with remote sensing and data assimilation can facilitate inclusion of water resource management at larger scales. Nonetheless, various modeling options should be carefully considered, diagnosed and intercompared. We propose a modular framework to develop integrated models based on multiple hypotheses for data support, water resource management algorithms and host models in a unified uncertainty assessment framework. A key to this development is the availability of regional-scale data for model development, diagnosis and validation. We argue that the time is right for a global initiative, based on regional case studies, to move this agenda forward.

An uncertainty assessment framework for forest planning adaptation to climate change

Uncertainty in forest planning is a prevailing problem affecting decision-making processes, especially those relating to climate change adaptation. Limited knowledge about uncertainty has prompted this empirical investigation of forest planners' understanding of uncertainty related to its recognition, its management and risk perception. We used a comprehensive uncertainty framework to address and test these uncertainties, with data from an online survey, to identify the views of 33 forest planners through Britain. Responses were analysed using non-parametric tests. The results showed that planners have significantly different views on uncertainty among economic, social and climatic categories. Uncertainty in the climatic category was more acutely perceived than in the economic and social categories. Planners preferred to practice active uncertainty management, as the results suggest they feel more able to manage uncertainty in forest models and their outcomes. Forest planners also indicated diverse perceptions of salient risks of change over the next 30years. The results show they may take action only to pests, drought and wind risks posing a threat to forests even though they perceived these risks potentially to be highly regulated and controlled by forestry policies. The findings provide a better understanding of uncertainty as a source of inertia to climate change adaptation in forestry, identify new research objectives and support the development of forestry policies for climate change adaptation.

Modern statistical and philosophical framework for uncertainty assessment in biometric performance testing

The question of estimating uncertainty in measurement is fundamental to all scientific fields. In the field of automated human recognition, lack of repeatability and reproducibility of measurements has been noted since at least the 1970s. This study discusses current approaches to estimation of measurement uncertainty within the broader context of scientific philosophy and measurement science. The authors discuss the Duhem–Quine thesis on testing holism and international standards on estimating and reporting uncertainty in laboratory measurements, then apply these concepts to the estimation of uncertainty in technology, scenario and operational testing in biometrics. The authors advocate for moving beyond the calculation of ‘coverage’ intervals as defined in the ISO/IEC ‘guidelines for the expression of uncertainty in measurement’ to full application of the concepts of uncertainty assessment.