Confronting Management Challenges in Highly Uncertain Natural Resource Systems: a Robustness–Vulnerability Trade-off Approach

This paper presents a framework for the study of policy implementation in highly uncertain natural resource systems in which uncertainty cannot be characterized by probability distributions. We apply the framework to parametric uncertainty in the traditional Gordon–Schaefer model of a fishery to illustrate how performance can be sacrificed (traded-off) for reduced sensitivity and hence increased robustness, with respect to model parameter uncertainty. With sufficient data, our robustness–vulnerability analysis provides tools to discuss policy options. When less data are available, it can be used to inform the early stages of a learning process. Several key insights emerge from this analysis: (1) the classic optimal control policy can be very sensitive to parametric uncertainty, (2) even mild robustness properties are difficult to achieve for the simple Gordon–Schaefer model, and (3) achieving increased robustness with respect to some parameters (e.g., biological parameters) necessarily results in increased sensitivity (decreased robustness) with respect to other parameters (e.g., economic parameters). We thus illustrate fundamental robustness–vulnerability trade-offs and the limits to robust natural resource management. Finally, we use the framework to explore the effects of infrequent sampling and delays on policy performance.     

The spatial statistics formalism applied to mapping electromagnetic radiation in urban areas

Determining the electromagnetic radiation levels in urban areas is a complicated task. Various approaches have been taken, including numerical simulations using different models of propagation, sampling campaigns to measure field values with which to validate theoretical models, and the formalism of spatial statistics. In the work, we present here that this latter technique was used to construct maps of electric field and its associated uncertainty from experimental data. For this purpose, a field meter and a broadband probe sensitive in the 100-kHz–3-GHz frequency range were used to take 1,020 measurements around buildings and along the perimeter of the area. The distance between sampling points was 5 m. The results were stored in a geographic information system to facilitate data handling and analysis, in particular, the application of the formalism of spatial statistical to the analysis of the distribution of the field levels over the study area. The spatial structure was analyzed using the variographic technique, with the field levels at non-sampled points being interpolated by kriging. The results indicated that, in the urban area analyzed in the present work, the linear density of sampling points could be reduced to a distance which coincides with the length of the blocks of buildings without the statistical parameters varying significantly and with the field level maps being reproduced qualitatively and quantitatively.     

Correlated Parameters Uncertainty Propagation in a Rainfall-Runoff Model,Considering 2-Copula; Case Study: Karoon III River Basin

Hydrological models are widely used to investigate practical issues of water resources. Parametric uncertainty is considered as one of the most important sources of uncertainty in environmental researches. Generally, it is assumed that the parameters are independent mutually, but correlation within the parameter space is an important factor having the potential to cause uncertainty. The objective and innovation of this study was to address the effects of parameters correlation on a continuous hydrological model uncertainty. HEC-HMS with soil moisture accounting (SMA) infiltration method was used to model daily flows and simulate certainty bounds for Karoon III basin, southwest of IRAN, in two scenarios, independent and correlated parameters using 2-copula. The parameters were represented by probability distributions, and the effect on prediction error were evaluated using Latin hypercube sampling (LHS) on Monte Carlo simulation (MCS). Saturated hydraulic conductivity (K), Clark storage-coefficient (R), and time of concentration (tc) were chosen for investigation, based on observed sensitivity analysis of simulated peak over threshold (POT). One hundred runs were randomly generated from 100 parameter sets captured from LHS of parameters distributions in each sub-basin. Using generated parameter sets, 100 continuous hydrographs were simulated and values of certainty bounds calculated. Results showed that when 2-copula correlated R and tc, with 0.656 Kendall’s Tau and 0.818 Spearman’s Rho coefficients, were propagated, decreasing of outputs’ sharpness was more than when considering K and R (K-R), with 0.166 and 0.262; therefore, incorporation of correlations in the MCS is important, especially when the correlation coefficients exceed 0.65. The model was evaluated at the outlet of the basin using daily stream flow data. Model reliability was better for above-normal flows than normal and below-normal. Reliability increases of simulated flow when considering correlated R-tc was more than K-R because of the correlation values. Incorporation of copula for K-tc not only did not improve the model reliability but also decreased it. Results showed improvement of model reliability, by decreasing predicted error of hydrologic modeling, when dealing with correlated parameters in the system.     

Assessment of sampling and analytical uncertainty of trace element contents in arable field soils

Assessment of trace element contents in soils is required in Germany (and other countries) before sewage sludge application on arable soils. The reliability of measured element contents is affected by measurement uncertainty, which consists of components due to (1) sampling, (2) laboratory repeatability (intra-lab) and (3) reproducibility (between-lab). A complete characterization of average trace element contents in field soils should encompass the uncertainty of all these components. The objectives of this study were to elucidate the magnitude and relative proportions of uncertainty components for the metals As, B, Cd, Co, Cr, Mo, Ni, Pb, Tl and Zn in three arable fields of different field-scale heterogeneity, based on a collaborative trial (CT) (standardized procedure) and two sampling proficiency tests (PT) (individual sampling procedure). To obtain reference values and estimates of field-scale heterogeneity, a detailed reference sampling was conducted. Components of uncertainty (sampling person, sampling repetition, laboratory) were estimated by variance component analysis, whereas reproducibility uncertainty was estimated using results from numerous laboratory proficiency tests. Sampling uncertainty in general increased with field-scale heterogeneity; however, total uncertainty was mostly dominated by (total) laboratory uncertainty. Reproducibility analytical uncertainty was on average by a factor of about 3 higher than repeatability uncertainty. Therefore, analysis within one single laboratory and, for heterogeneous fields, a reduction of sampling uncertainty (for instance by larger numbers of sample increments and/or a denser coverage of the field area) would be most effective to reduce total uncertainty. On the other hand, when only intra-laboratory analytical uncertainty was considered, total sampling uncertainty on average prevailed over analytical uncertainty by a factor of 2. Both sampling and laboratory repeatability uncertainty were highly variable depending not only on the analyte but also on the field and the sampling trial. Comparison of PT with CT sampling suggests that standardization of sampling protocols reduces sampling uncertainty, especially for fields of low heterogeneity.     

Optimized contaminated land investigation at minimum overall cost to achieve fitness-for-purpose

A methodology for optimized contaminated land investigation (OCLI) is described that balances the uncertainty of measurements against the cost of taking the measurements and the financial losses that may arise from misclassification of the land. Uncertainty from the sources of both field sampling and chemical analysis is estimated using existing techniques, based on the taking of duplicated samples. The actual costs of sampling and analysis and the expected costs that could arise from either 'false positive' or 'false negative' classification of areas of land were estimated. A loss function was constructed that calculates the expectation of financial loss that will arise for a given uncertainty of measurement. The function shows a clear minimum value of cost at an optimal value of uncertainty. Application of this OCLI technique to two case studies demonstrated this minimum value. Below the optimum value of uncertainty, the costs increased due to higher measurement costs. Above the optimum, the costs increased due to increasing risk of factors such as unnecessary remediation or potential litigation over undetected contamination. Many areas for further development of OCLI are identified, but the technique is demonstrated as a useful new approach to judging fitness-for-purpose of such measurements.     

Monitoring nutrient transport in large rivers

The problem of estimating nutrient transport in large rivers and the uncertainty of such load estimates was studied both empirically and theoretically. In the empirical part of the study, time series of data from the Rhine, Meuse, Vistula and Oder Rivers were examined. The results of this data analysis justify the use of linear interpolation to estimate concentrations prevailing between sampling occasions. A special study of the spatial variation of concentrations within different cross-sections of the Vistula river showed that such variation can contribute substantially to the uncertainty of load estimates. In general, however, sampling at one point in the cross-section did not result in biased load estimates. In the theoretical part of the study, simple ARMA (autoregressive-moving average) models were used to derive generally applicable formulas for the expected mean square error of load estimates based on serially dependent concentration data. These formulas were then used to estimate the uncertainty of calculated nutrient loads in the Rhine and the Vistula, respectively.     

Selection of appropriate sampling stations in a lake through mapping

Much valuable information is obtained from water quality measurements and monitoring of lakes around the world. A powerful tool is the use of mapping techniques, as it offers potential use in water quality research. Both remote sensing techniques and traditional water quality monitoring are required to collect data at sampling stations. This study suggests another approach to determine the most appropriate distribution of sampling stations in water reservoirs that will be mapped for water quality parameters. Tests were conducted for the proposed approach for Secchi disc depth (SDD), chlorophyll-a, turbidity and suspended solids parameters in Lake Beysehir, Turkey. Results of analysis are available for a total of 30 sampling stations in August 2006. Ten sampling stations were used to model Lake Beysehir while the others were used for validation of the model. Sampling stations that offered the best representation of the lake for each parameter were determined. Then, the best representative sampling stations for all parameters in the study were determined. Moreover, in order to confirm the accuracy of these re-determined sampling stations, modelling was performed on the results of the analysis of June 2006, and it was found that the values obtained from the re-determined sampling stations were acceptable.     

Analysing the sensitivity behaviour of two hydrology models

Recently, the New Morris Method has been presented as an effective sensitivity analysis tool for mathematical models. The New Morris Method estimates the sensitivity of an output parameter to a given set of input parameters (first-order effects) and the extent these parameters interact with each other (second-order effects). This method requires the specification of two parameters (runs and resolution) that control the sampling of the output parameter to determine its sensitivity to various inputs. The criteria for these parameters have been set on the analysis of a well-behaved analytical function (see Cropp and Braddock, Reliab. Eng. Syst. Saf. 78:77–83, 2002), which may not be applicable to other physical models that describe complex processes. This paper will investigate the appropriateness of the criteria from (Cropp and Braddock, 2002) and hence the effectiveness of the New Morris Method to determine the sensitivity behaviour of two hydrologic models: the Soil Erosion and Deposition System and Griffith University Representation of Urban Hydrology. In the first case, this paper will separately analyse the sensitivity of an output parameter on a set of input parameters (first- and second-order effects) for each model and discuss the physical meaning of these sensitivities. This will be followed by an investigation into the sampling criteria by exploring the convergence of the sensitivity behaviour for each model as the sampling of the parameter space is increased. By comparing these trends to the convergence behaviour from Cropp and Braddock (2002), we will determine how well the New Morris Method estimates the sensitivity for each model and whether the sampling criteria are appropriate for these models. It will be shown that the New Morris Method can provide additional insight into the functioning of these models, and that, under a different metric, the sensitivity behaviour of these models does converge confirming the sampling criteria set by Cropp and Braddock.     

Water and waste load allocation in rivers with emphasis on agricultural return flows: application of fractional factorial analysis

In this paper, a new methodology is developed to handle parameter and input uncertainties in water and waste load allocation (WWLA) in rivers by using factorial interval optimization and the Soil, Water, Atmosphere, and Plant (SWAP) simulation model. A fractional factorial analysis is utilized to provide detailed effects of uncertain parameters and their interaction on the optimization model outputs. The number of required optimizations in a fractional factorial analysis can be much less than a complete sensitivity analysis. The most important uncertain inputs and parameters can be also selected using a fractional factorial analysis. The uncertainty of the selected inputs and parameters should be incorporated real time water and waste load allocation. The proposed methodology utilizes the SWAP simulation model to estimate the quantity and quality of each agricultural return flow based on the allocated water quantity and quality. In order to control the pollution loads of agricultural dischargers, it is assumed that a part of their return flows can be diverted to evaporation ponds. Results of applying the methodology to the Dez River system in the southwestern part of Iran show its effectiveness and applicability for simultaneous water and waste load allocation in rivers. It is shown that in our case study, the number of required optimizations in the fractional factorial analysis can be reduced from 64 to 16. Analysis of the interactive effects of uncertainties indicates that in a low flow condition, the upstream water quality would have a significant effect on the total benefit of the system.     

Soil subsampling in environmental sciences: the role of granulometry

Uncertainty associated to analytical results is an issue of major interest for the whole analytical community. A large effort has been made to improve analytical techniques and procedures aimed to achieve a well characterized uncertainty associated with analysis. However, it is becoming increasingly recognised that uncertainty deriving from sampling and subsampling can even dominate the global uncertainty budget. A study on subsampling activities on different soil typologies has been performed by granulometry determinations. The differences between sieving methodologies based on both wet and dry mode have been studied. Subsampling is approached by replicated measurements providing a quantitative assessment of the distribution heterogeneity, a suitable method validation scheme and an empirical determination of uncertainty.     

Modelling of spatial contaminant probabilities of occurrence of chlorinated hydrocarbons in an urban aquifer

In this study, a 3D urban groundwater model is presented which serves for calculation of multispecies contaminant transport in the subsurface on the regional scale. The total model consists of two submodels, the groundwater flow and reactive transport model, and is validated against field data. The model equations are solved applying finite element method. A sensitivity analysis is carried out to perform parameter identification of flow, transport and reaction processes. Coming from the latter, stochastic variation of flow, transport, and reaction input parameters and Monte Carlo simulation are used in calculating probabilities of pollutant occurrence in the domain. These probabilities could be part of determining future spots of contamination and their measure of damages. Application and validation is exemplarily shown for a contaminated site in Braunschweig (Germany), where a vast plume of chlorinated ethenes pollutes the groundwater. With respect to field application, the methods used for modelling reveal feasible and helpful tools to assess natural attenuation (MNA) and the risk that might be reduced by remediation actions.     

Statistical Simulation to Estimate Uncertain Behavioral Parameters of Hybrid Energy-Economy Models

In energy-economy modeling, new hybrid models attempt to combine the technological explicitness of bottom-up models with the macroeconomic feedbacks and statistically estimated behavioral parameters of top-down models. However, statistical estimation of behavioral parameters (portraying firm and household technology choices) with such models is challenged by the number of uncertain variables and the lack of historical data on technologies in terms of capital costs, operating costs, and market shares. Multiple combinations of parameter values might equally explain past technology choices. This paper reports on the application of a Bayesian statistical simulation approach for estimating the most likely values for these key behavioral parameters in order to best explain past technology choices and then simulate policies to influence future technology choices. The method included (1) data collection of key technology market shares, capital costs, and operating costs over the past; (2) backcasting a hybrid energy-economy model over a historical time period; and (3) the application of Markov chain Monte Carlo statistical simulation using the Metropolis–Hastings algorithm as a tool for estimating distributions for key parameters in the model. The results provide a means of indicating the uncertainty bounds around key behavioral parameters when generating forecasts of the effect of certain policies. However, the results also indicate that this approach may have limited applicability, given that future available technologies may differ substantially from past technologies and that it is difficult to separate the effects of parameter uncertainty from model structure uncertainty.     

Feasibility of relating interferon production by wild voles to types of chemical contamination of their environment

In the fall of 1980, a limited field sampling and laboratory analysis profect was undertaken to explore the feasibility of relating immunological responses of field mice (voles) living wild in an area of environmental concern to the level of chemical contamination of that area. The voles were collected in the vicinity of Love Canal by biological sampling teams already there to collect voles for other purposes.The project helped identify those areas of practical uncertainty that must be clarified before the rate of interferon production in voles can be considered as a possible indicator of chemical contamination. Two lines of research are proposed: developing optimumin vitro interferon bioassays systems for vole leukocytes; and characterizing the interferon production responses of voles following controlled exposures to selected carcinogens and other chemicals.     

The Benefits of Cooperation Under Uncertainty: the Case of Climate Change

This article presents an analysis of the behaviour of countries defining their climate policies in an uncertain context. The analysis is made using the S-CWS model, a stochastic version of an integrated assessment growth model. The model includes a stochastic definition of the climate sensitivity parameter. We show that the impact of uncertainty on policy design critically depends on the shape of the damage function. We also examine the benefits of cooperation in the context of uncertainty: We highlight the existence of an additional benefit of cooperation, namely risk reduction.     

Response Surface Method Coupled with First-Order Reliability Method Based Methodology for Groundwater Flow and Contaminant Transport Model for the Uranium Tailings Pond Site

Groundwater flow and contaminant transport modelling is carried out to predict the concentrations of radionuclides in groundwater beneath the tailings pond. This is used as a tool for radiological impact assessment studies. The geo-hydrological parameters involved in the modelling exhibit inherent uncertainty associated with their values. This uncertainty may get propagated to the model output, i.e. the dose to the public. The propagation of the uncertainty in the input parameters to the output is modelled using suitable methods of probabilistic analysis. Response surface method coupled with first-order reliability method is used to develop a methodology for estimating the probability that the dose rate value through drinking water pathway at a location around the tailings pond exceeds the WHO guidelines for drinking water, termed as the probability of exceedance of acceptable levels. This method also gives the estimate of sensitivity of the probability of exceedance to the different input parameters. It is observed that the probability of failure decreases as the distance from tailings pond centre is increased and beyond a distance of around 0.5 km from tailings pond centre, the probability reduces to zero. The work also brings out the importance of quantifying the uncertainty in case of actual field problems where there is wide variation in the values of the various parameters within the domain under study.     

Characterization of the Leachate in an Urban Landfill by Physicochemical Analysis and Solid Phase Microextraction-GC/MS

The aim of this study is to evaluate extensively the characterization and identification of major pollutant parameters by paying attention to the organic chemical pollution for unregulated dumping site leachate in Eskişehir/Turkey. The study that is first and only one research has been very important data related with before new sanitary landfill site in Eskişehir city. For this purpose, in this study leachate samples were collected in-situ at monthly interval for a period of 8 months. Firstly, thirty three physicochemical parameters were monitored. Secondly, SPME technique was used for identification of organic pollutants. Meteorological data were also recorded for the same sampling period to correlate meteorological data and physicochemical parameters. Mean values are used in the correlation analysis. Correlation is shown only for the relationship between air temperature and NO₃ ⁻. No correlation has been found between rain and leachate quality parameters since the amount of rain was very low during the sampling period. However, analysis results were generally decreased in winter season when each parameter and each sampling point are examined separately. According to correlation between every parameter, especially solid content and dissolved oxygen concentration of leachate is affecting to other parameters. Also, sodium and potassium are changing proportionally with same parameters (suspended solids, fixed solids, dissolved oxygen) and high correlation between chloride and heavy metal concentration is showing. The results were statistically evaluated by use of SPSS 10.0 program. Second part of the study, the leachate was extracted by Solid Phase Microextraction (SPME) technique and then analyzed. Of the methodologies tested in this study, the best one selected was based on 100 μ m polydimethylsiloxane coated fiber (PDMS), headspace with heating (Δ HS) sampling mode and an extraction time of 15 min. at a temperature of 50 ^o C. Thirty three organic compounds in leachate were identified by GC/MS.     

Improvement of the BCR three step sequential extraction procedure prior to the certification of new sediment and soil reference materials

The Standards, Measurements and Testing Programme (formerly BCR) of the European Commission proposed a three-step sequential extraction procedure for sediment analysis, following extensive expert consultations and two interlaboratory studies. This scheme was recently used to certify the extractable trace element contents of a sediment reference material (CRM 601). Although this procedure offers a means to ensure the comparability of data in this field, some difficulties concerning the interlaboratory reproducibility still remain, and a new project is currently being conducted to determine the causes of poor reproducibility in the extraction scheme. The final objective of the project is the certification of new sediment and soil reference materials for their extractable contents of Cd, Cr, Cu, Ni, Pb and Zn. This paper presents the results of a small-scale interlaboratory study, which aimed to test a revised version of the extraction schemes by comparing the original and the modified protocols using the CRM 601 sample. This work offers an improvement to the BCR sequential extraction procedure through intercomparison exercises. This improved procedure will allow the obtaining of CRMs to validate analytical data in the analysis of soils and sediments, and it will also facilitate comparability of data in the European Union.     

A dynamic statistical experiment for atmospheric interactions

Interactions among atmospheric parameters exist at different scales. The pristine approach for observational or model data analysis involves changing the input parameters one at a time (OAT) and studying the effect on the system. Limitations of this approach for atmospheric applications are discussed. A fractional factorial (FF) based study is evolved and a methodology is outlined involving dynamic graphical analysis. Observational data from the FIFE and HAPEX‐MOBILHY experiments are utilized with a vegetation and soil moisture scheme dynamically coupled in a planetary boundary layer model to demonstrate the robustness of this approach. Both low‐resolution and high‐resolution designs are considered. Various aspects of the vegetation‐atmosphere interactions are delineated. Results obtained from the interaction‐based FF approach differ considerably from the earlier OAT‐type studies.     

Monitoring Studies of Augusta Bay Marine Waters

The monitoring of water quality today provides a great quantity of data consisting of the values of the parameters measured as a tunction of bme or as spatial function.In the marine environment, and especially in the suspended material, increasing importance is being given to the presence of particular pollution indices. With the increase in the number of sampling points, the amount of data increases and examining the results and their consequent interpretation becomes more difficult. To overcome such difficulties, numerous chemometric techniques have been introduced in environmental chemistry, such as Principal Component Analysis (PCA).The use of the PCA in this work has been applied to the analysis of twenty three different sampling points in three seasonal sampling cruises in the same year. This led to recognition of the influence and the localisation of wastewaters in the Augusta bay after measuring the water pollution parameters.The PCA made evident the difference between some sampling sites whose data were initially thought to be similar where the presence of hot industrial water discharge or urban wastewater determines the permanent water quality.Furthermore, it has allowed a choice of more significant parameters for monitoring programs and more representative sampling site locations.     

Improving Efficiency Of Uncertainty Analysis In Complex Integrated Assessment Models: The Case Of The Rains Emission Module

Abstact Ever since the Regional Acidification Information and Simulation Model (RAINS) has been constructed, the treatment of uncertainty has remained an issue of major interest. In a recent review of the model performed for the Clean Air for Europe (CAFE) programme of the European Commission, a more systematic and structured uncertainty analysis has been recommended. This paper aims at contributing to the scientific debate how this can be achieved. Because of its complex structure on the one hand and limited research resources (time, computational capacities) on the other hand a full-blown uncertainty analysis in RAINS is hardly feasible. Therefore, all types of uncertainty require more efficient ways for uncertainty analysis. With respect to parameter uncertainty, we propose to focus research efforts for uncertainty analysis on key parameters. Among different approaches to select key parameters that have been discussed in the literature screening methods seem to be particularly appropriate for complex, deterministic Integrated Assessment models such as RAINS. Surprisingly, in Integrated Assessment modelling for air pollution problems of screening design have not been taken up so far. As a case study we consider the emission module of RAINS. We show that its structure allows for a straightforward and effective screening procedure