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

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

Mineral reserves under price uncertainty

National reporting organizations and regulatory bodies for the minerals and mining sector are requiring publicly reported Ore-Reserve estimates to take account of uncertainties. Whilst methodologies that account for physical uncertainty appear relatively well developed, methodologies which can take account of economic uncertainty appear much less so. To counter this shortfall, we present an efficient and general methodology which can quantify the effect of price uncertainty within reserve estimates, providing both the expected reserve size and the associated distribution (box whisker plot). This statistical information can be used by interested parties to understand precisely where the reserve risks lie, which we highlight in a worked example.     

Propagation of uncertainties in soil and pesticide properties to pesticide leaching

In the new Dutch decision tree for the evaluation of pesticide leaching to groundwater, spatially distributed soil data are used by the GeoPEARL model to calculate the 90th percentile of the spatial cumulative distribution function of the leaching concentration in the area of potential usage (SP90). Until now it was not known to what extent uncertainties in soil and pesticide properties propagate to spatially aggregated parameters like the SP90. A study was performed to quantify the uncertainties in soil and pesticide properties and to analyze their contribution to the uncertainty in SP90. First, uncertainties in the soil and pesticide properties were quantified. Next, a regular grid sample of points covering the whole of the agricultural area in the Netherlands was randomly selected. At the grid nodes, realizations from the probability distributions of the uncertain inputs were generated and used as input to a Monte Carlo uncertainty propagation analysis. The analysis showed that the uncertainty concerning the SP90 is 10 times smaller than the uncertainty about the leaching concentration at individual point locations. The parameters that contribute most to the uncertainty about the SP90 are, however, the same as the parameters that contribute most to uncertainty about the leaching concentration at individual point locations (e.g., the transformation half-life in soil and the coefficient of sorption on organic matter). Taking uncertainties in soil and pesticide properties into account further leads to a systematic increase of the predicted SP90. The important implication for pesticide regulation is that the leaching concentration is systematically underestimated when these uncertainties are ignored.     

Handling uncertainty through adaptiveness in planning approaches: comparing adaptive delta management and the water diplomacy framework

Planners and water managers seek to be adaptive to handle uncertainty through the use of planning approaches. In this paper, we study what type of adaptiveness is proposed and how this may be operationalized in planning approaches to adequately handle different uncertainties. We took a comparative case study approach to study two planning approaches: the water diplomacy framework (WDF) and adaptive delta management (ADM). We found that the approaches differ in their conceptualization of uncertainty and show that different types of adaptiveness are used in the approaches. While WDF builds on collaborative adaptive management as a set of ongoing adjustments and continuous learning to handle uncertainty, ADM deliberately attempts to anticipate future adaptations through a set of tools which allows for seizing opportunities and avoiding lock-in and lock-out mechanisms. We conclude that neither of the approaches is fully able to account for different uncertainties. Both approaches may benefit from specific insights in what uncertainty and adaptiveness entail for the development of water management plans.     

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

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.     

Sensitivity and Uncertainty of Ground‐Water Discharge Estimates for Semiarid Shrublands1

Abstract: We proposed a step‐by‐step approach to quantify the sensitivity of ground‐water discharge by evapotranspiration (ET) to three categories of independent input variables. To illustrate the approach, we adopt a basic ground‐water discharge estimation model, in which the volume of ground water lost to ET was computed as the product of the ground‐water discharge rate and the associated area. The ground‐water discharge rate was assumed to equal the ET rate minus local precipitation. The objective of this study is to outline a step‐by‐step procedure to quantify the contributions from individual independent variable uncertainties to the uncertainty of total ground‐water discharge estimates; the independent variables include ET rates of individual ET units, areas associated with the ET units, and precipitation in each subbasin. The specific goal is to guide future characterization efforts by better targeting data collection for those variables most responsible for uncertainty in ground‐water discharge estimates. The influential independent variables to be included in the sensitivity analysis are first selected based on the physical characteristics and model structure. Both regression coefficients and standardized regression coefficients for the selected independent variables are calculated using the results from sampling‐based Monte Carlo simulations. Results illustrate that, while as many as 630 independent variables potentially contribute to the calculation of the total annual ground‐water discharge for the case study area, a selection of seven independent variables could be used to develop an accurate regression model, accounting for more than 96% of the total variance in ground‐water discharge. Results indicate that the variability of ET rate for moderately dense desert shrubland contributes to about 75% of the variance in the total ground‐water discharge estimates. These results point to a need to better quantify ET rates for moderately dense shrubland to reduce overall uncertainty in estimates of ground‐water discharge. While the approach proposed here uses a basic ground‐water discharge model taken from an earlier study, the procedure of quantifying uncertainty and sensitivity can be generalized to handle other types of environmental models involving large numbers of independent variables.     

Setting Priorities for Research on Pollution Reduction Functions of Agricultural Buffers

The success of buffer installation initiatives and programs to reduce nonpoint source pollution of streams on agricultural lands will depend the ability of local planners to locate and design buffers for specific circumstances with substantial and predictable results. Current predictive capabilities are inadequate, and major sources of uncertainty remain. An assessment of these uncertainties cautions that there is greater risk of overestimating buffer impact than underestimating it. Priorities for future research are proposed that will lead more quickly to major advances in predictive capabilities. Highest priority is given for work on the surface runoff filtration function, which is almost universally important to the amount of pollution reduction expected from buffer installation and for which there remain major sources of uncertainty for predicting level of impact. Foremost uncertainties surround the extent and consequences of runoff flow concentration and pollutant accumulation. Other buffer functions, including filtration of groundwater nitrate and stabilization of channel erosion sources of sediments, may be important in some regions. However, uncertainty surrounds our ability to identify and quantify the extent of site conditions where buffer installation can substantially reduce stream pollution in these ways. Deficiencies in predictive models reflect gaps in experimental information as well as technology to account for spatial heterogeneity of pollutant sources, pathways, and buffer capabilities across watersheds. Since completion of a comprehensive watershed-scale buffer model is probably far off, immediate needs call for simpler techniques to gage the probable impacts of buffer installation at local scales.     

Evaluating the impact of uncertainty and variability on the value chain optimization of a forest biomass power plant using Monte Carlo Simulation

Mathematical programming models have been used to optimize the design and management of forest bioenergy supply chains. A deterministic mathematical model is beneficial for making optimum decisions; however, its applicability to real-world problems may be limited because it does not capture all the complexities, including uncertainties in the parameters, in the supply chain. In this paper, a combination of Monte Carlo Simulation and optimization model is used to evaluate the impact of uncertainty in biomass quality, availability and cost, and electricity prices on the supply chain of a forest biomass power plant. The optimization model is a deterministic mixed integer non-linear model with monthly time steps over a 1-year planning horizon. Variability in biomass quality, i.e. moisture content (MC) and higher heating value (HHV), based on the historical data of a real case study is studied in detail and fitted probability distributions are used in the model, while for electricity prices different scenarios are considered. The results show that the impact of variability in the MC on profit is higher than that of uncertainty in HHV. It is observed that the annual profit ranges between $13.3 million and $17.9 million in the presence of all possible uncertainties while its average is $15.5 million. Uncertainty in biomass availability and cost and electricity price results in the risks of having annual profit of less than $14 million and low monthly storage levels.     

Adaptive management for a turbulent future

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.     

Analysis of uncertainty consideration in environmental assessment: an empirical study of Canadian EA practice

Identifying and communicating uncertainty is core to effective environmental assessment (EA). This study evaluates the extent to which uncertainties are considered and addressed in Canadian EA practice. We reviewed the environmental protection plans, follow-up programs, and panel reports (where applicable) of 12 EAs between 1995 and 2012. The types of uncertainties and levels of disclosure varied greatly. When uncertainties were acknowledged, practitioners adopted five different approaches to address them. However, uncertainties were never discussed or addressed in depth. We found a lack of suitable terminology and consistency in how uncertainties are disclosed, reflecting the need for explicit guidance, and we present recommendations for improvement. Canadian Environmental Impact Statements are not as transparent with respect to uncertainties as they should be, and uncertainties in EA need to be better considered and communicated.     

Info-gap theory and robust design of surveillance for invasive species: The case study of Barrow Island

Surveillance for invasive non-indigenous species (NIS) is an integral part of a quarantine system. Estimating the efficiency of a surveillance strategy relies on many uncertain parameters estimated by experts, such as the efficiency of its components in face of the specific NIS, the ability of the NIS to inhabit different environments, and so on. Due to the importance of detecting an invasive NIS within a critical period of time, it is crucial that these uncertainties be accounted for in the design of the surveillance system. We formulate a detection model that takes into account, in addition to structured sampling for incursive NIS, incidental detection by untrained workers. We use info-gap theory for satisficing (not minimizing) the probability of detection, while at the same time maximizing the robustness to uncertainty. We demonstrate the trade-off between robustness to uncertainty, and an increase in the required probability of detection. An empirical example based on the detection of Pheidole megacephala on Barrow Island demonstrates the use of info-gap analysis to select a surveillance strategy.     

Two-stage planning for sustainable water-quality management under uncertainty

In water-quality management problems, uncertainties may exist in a number of impact factors and pollution-related processes (e.g., the volume and strength of industrial wastewater and their variations can be presented as random events through identifying a statistical distribution for each source); moreover, nonlinear relationships may exist among many system components (e.g., cost parameters may be functions of wastewater-discharge levels). In this study, an inexact two-stage stochastic quadratic programming (ITQP) method is developed for water-quality management under uncertainty. It is a hybrid of inexact quadratic programming (IQP) and two-stage stochastic programming (TSP) methods. The developed ITQP can handle not only uncertainties expressed as probability distributions and interval values but also nonlinearities in the objective function. It can be used for analyzing various scenarios that are associated with different levels of economic penalties or opportunity losses caused by improper policies. The ITQP is applied to a case of water-quality management to deal with uncertainties presented in terms of probabilities and intervals and to reflect dynamic interactions between pollutant loading and water quality. Interactive and derivative algorithms are employed for solving the ITQP model. The solutions are presented as combinations of deterministic, interval and distributional information, and can thus facilitate communications for different forms of uncertainties. They are helpful for managers in not only making decisions regarding wastewater discharge but also gaining insight into the tradeoff between the system benefit and the environmental requirement.     

Uncertainty Models for Estimates of Physical Characteristics of River Segments Over Large Areas

Spatially comprehensive estimates of the physical characteristics of river segments over large areas are required in many large‐scale analyses of river systems and for the management of multiple basins. Remote sensing and modeling are often used to estimate river characteristics over large areas, but the uncertainties associated with these estimates and their dependence on the physical characteristics of the segments and their catchments are seldom quantified. Using test data with varying degrees of independence, we derived analytical models of the uncertainty associated with estimates of upstream catchment area (CA), segment slope, and mean annual discharge for all river segments of a digital representation of the hydrographic network of France. Although there were strong relationships between our test data and estimates at the scale of France, there were also large relative local uncertainties, which varied with the physical characteristics of the segments and their catchments. Discharge and CA were relatively uncertain where discharge was low and catchments were small. Discharge uncertainty also increased in catchments with large rainfall events and low minimum temperature. The uncertainty of segment slope was strongly related to segment length. Our uncertainty models were consistent across large regions of France, suggesting some degree of generality. Their analytical formulation should facilitate their use in large‐scale ecological studies and simulation models.     

An uncertainty based multi-criteria ranking system for open pit mining cut-off grade strategy selection

Cut-off grade strategy (COGS) is a concept that directly influences the financial, technical, economical, and environmental issues in relation to the exploitation of a mineral resource. Despite the simple definition of cut-off grade, the COGS problem is one of the complex and complicated problems in the mine planning process. From the optimization point of view, the COGS with an objective of maximizing the present value of future cash flows is a non-linear and a non-convex problem that even in its deterministic form can be solved using approximate optimization methods. This optimization problem will also be more complex and complicated under uncertainty conditions. This paper proposes an uncertainty based multi-criteria ranking system to investigate the problem of COGS selection considering metal price and geological uncertainties. The proposed system aims at selection of the best COGS among technically feasible alternative COGSs under uncertainty circumstances. Our developed system is based on integrating metal price and geological uncertainties as well as operating flexibility to close the mine early. We incorporate this operating flexibility into the proposed system using a Monte Carlo based real options (RO) valuation model. For this purpose, in addition to the expected value, other risk criteria are considered to rank the alternatives. These risk criteria include abilities of strategies in producing extra profits, minimizing losses, and achieving the predefined goals of the production. In this study, the technically possible COGSs are generated using the Lane comprehensive algorithm. To demonstrate the effectiveness of the proposed system, we utilize data of an Iranian gold mine. Results show that the proposed system outperforms conventional methods in the sense that it shows significantly lower average mis-ranking than the other methods and also selects a strategy with a higher value. The sensitivity analysis of the proposed system relative to the gold price shows that the system is highly dependent on the parameters of the stochastic process used to model the evolution of the metal price. Therefore, special consideration should be given in estimating stochastic process parameters.     

Uncertainty assessment of the model RICEWQ in northern Italy

Model predictions are often seriously affected by uncertainties arising from many sources. Ignoring the uncertainty associated with model predictions may result in misleading interpretations when the model is used by a decision-maker for risk assessment. In this paper, an analysis of uncertainty was performed to estimate the uncertainty of model predictions and to screen out crucial variables using a Monte Carlo stochastic approach and a number of statistical methods, including ANOVA and stepwise multiple regression. The model studied was RICEWQ (Version 1.6.1), which was used to forecast pesticide fate in paddy fields. The results demonstrated that the paddy runoff concentration predicted by RICEWQ was in agreement with field measurements and the model can be applied to simulate pesticide fate at field scale. Model uncertainty was acceptable, runoff predictions conformed to a log-normal distribution with a short right tail, and predictions were reliable at field scale due to the narrow spread of uncertainty distribution. The main contribution of input variables to model uncertainty resulted from spatial (sediment-water partition coefficient and mixing depth to allow direct partitioning to bed) and management (time and rate of application) parameters, and weather conditions. Therefore, these crucial parameters should be carefully parameterized or precisely determined in each site-specific paddy field before the application of the model, since small errors of these parameters may induce large uncertainty of model outputs.     

Probe into the method of regional ecological risk assessment-a case study of wetland in the Yellow River Delta in China

Ecological risk assessment (ERA) is a new field of study for evaluating the risks associated with a possible eco-environmental hazard under uncertainty. Regional ERA is more complex than general ERA, as it requires that risk receptors, risk sources, risk exposure, uncertainty and especially spatial heterogeneity all be taken into account. In this paper, a five-step process of regional ERA is developed and tested through a wetland case study in the Yellow River Delta in China. First, indices and formulas are established for measuring degrees of ecological risk and damage to ecosystems. Using a combination of remote sensing data, historical records and survey data, and with the assistance of GIS techniques, the indices and formulas are then applied to the wetland in the study area. On the basis of the assessment results, we propose a number of countermeasures for the various risk zones in the Yellow River Delta.     

ICP-AES法测定土壤中铬含量的不确定度评定

根据等离子体原子发射光谱(ICP-AES)法测定土壤中铬的过程,建立相应的数学模型并对模型中各个参数进行了不确定度来源分析。结果表明,测定结果的不确定性主要来源于标准曲线的浓度与光谱强度拟合直线方程求铬含量的不确定度、标准溶液和配置标准工作溶液时的不确定度,其次为重复性不确定度;定容体积和样品称量过程的不确定度对测量结果的不确定度贡献相对较小。     

TMDL中MOS的定量估算方法及其应用

为了分析TMDL水污染控制管理模式中安全临界值MOS的影响因素,采用FOEA法对MOS中模拟计算的不确定性进行定量估算,通过不同水质达标率条件下MOS的设定,探讨水环境管理中不确定性因素对MOS的影响;将TMDL应用于珠江三角洲佛山水道的水环境管理中,运用动态水环境数学模型、考虑潮周期达标率的环境容量优化模型及遗传算法对TMDL进行求解.研究结果表明,所采用的FOEA法能较为准确地反映模型的不确定性对MOS的影响,而且从水质达标率的角度出发能合理地考察环境管理中的不确定性因素对MOS的影响,为定量化探讨MOS的设定给出了可行的求解思路及方法.     

Ensemble Streamflow Forecast: A GLUE‐Based Neural Network Approach1

Abstract: While training a Neural Network to model a rainfall‐runoff process, generally two aspects are considered: its capability to be able to describe the complex nature of the processes being modeled and the ability to generalize so that novel samples could be mapped correctly. The general conclusion is that, the smallest size network capable of representing the sample distribution is the best choice, as far as generalization is concerned. Oftentimes input variables are selected a priori in what is called an explanatory data analysis stage and are not part of the actual network training and testing procedures. When they are, the final model will have only a “fixed” type of inputs, lag‐space, and/or network structure. If one of these constituents was to change, one would obtain another equally “optimal” Neural Network. Following Beven and others' generalized likelihood uncertainty estimate approach, a methodology is introduced here that accounts for uncertainties in network structures, types of inputs, and their lag‐space relationships by looking at a population of Neural Networks rather than target in getting a single “optimal” network. It is shown that there is a wide array of networks that provide “similar” results, as seen by a likelihood measure, for different types of inputs, lag‐space, and network size combinations. These equally optimal networks expose the range of uncertainty in streamflow predictions and their expected value results in a better performance than any of the single network predictions.