Residual and contact herbicide transport through field lysimeters via preferential flow

Usage of glyphosate [N-(phosphonomethyl)-glycine] and glufosinate [2-amino-4-(hydroxy-methylphosphinyl)butanoic acid] may reduce the environmental impact of agriculture because they are more strongly sorbed to soil and may be less toxic than many of the residual herbicides they replace. Preferential flow complicates the picture, because due to this process, even strongly sorbed chemicals can move quickly to ground water. Therefore, four monolith lysimeters (8.1 m(2) by 2.4 m deep) were used to investigate leaching of contact and residual herbicides under a worst-case scenario. Glufosinate, atrazine (6-chloro-N(2)-ethyl-N(4)-isopropyl-1,3,5-triazine-2,4-diamine), alachlor [2-chloro-N-(2,6-diethylphenyl)-N-(methoxymethyl) acetamide], and linuron (3-3,4-dichlorophenyl-1-methoxy-1-methylurea) were applied in 1999 before corn (Zea mays L.) planting and glyphosate, alachlor, and metribuzin [4-amino-6-(1,1-dimethylethyl)-3-(methylthio)-1,2,4-triazin-5(4H)-one] were applied in 2000 before soybean [Glycine max (L.) Merr.] planting. A high-intensity rainfall was applied shortly after herbicide application both years. Most alachlor, metribuzin, atrazine, and linuron losses occurred within 1.1 d of rainfall initiation and the peak concentration of the herbicides coincided (within 0.1 d of rainfall initiation in 2000). More of the applied metribuzin leached compared with alachlor during the first 1.1 d after rainfall initiation (2.2% vs. 0.035%, P < 0.05). In 1999, 10 of 24 discrete samples contained atrazine above the maximum contaminant level (atrazine maximum contaminant level [MCL] = 3 mug L(-1)) while only one discrete sample contained glufosinate (19 mug L(-1), estimated MCL = 150 mug L(-1)). The results indicate that because of preferential flow, the breakthrough time of herbicides was independent of their sorptive properties but the transport amount was dependent on the herbicide properties. Even with preferential flow, glyphosate and glufosinate were not transported to 2.4 m at concentrations approaching environmental concern.     

Sensitivity and uncertainty analysis of agro-ecological modeling for saffron plant cultivation using GIS spatial decision-making methods

The main objective of this research is to model the uncertainty associated with GIS-based multi-criteria decision analysis (MCDA) for crop suitability assessment. To achieve this goal, an integrated approach using GIS-MCDA in association with Monte Carlo simulation (MCS) and global sensitivity analysis (GSA) were applied for Saffron suitability mapping in East-Azerbaijan Province in Iran. The results of this study indicated that integration of MCDA with MCS and GSA could improve modeling precision by reducing data variance. Results indicated that applying the MCS method using the local training data leads to computing the spatial correlation between criteria weights and characteristics of the study area. Results of the GSA method also allow us to obtain the priority of criteria and identify the most important criteria and the variability of outputs under uncertainty conditions for model inputs. The findings showed that, commonly used primary zoning methods, without considering the interaction effects of variables, had significant errors and uncertainty in the output of MCDA-based suitability models, which should be minimized by the advanced complementarity of sensitivity and uncertainty analysis.     

Overland flow model for asphalt oil spills

Along the asphalt production line, the hot asphalt oil needs to undergo a cooling process that involves temporary storage in a tank. Safety is always a concern as to what if the tank is ruptured. Scenario studies of asphalt oil spills are required when designing the layout and open space for such a cooling process in the refinery yard. Applying the laminar flow theory to an asphalt oil spill, this paper presents an overland flow model to calculate the possible range of spread. The major parameters used in this model include fluid viscosity, ground slope, hydraulic conveyance, and asphalt oil volume. As a simplified model for a quasi-steady state flow condition, a hot asphalt oil spill for a typical case in a refinery yard can run approximately 400 to 600 feet before the asphalt mass cools down and becomes solid-like.     

The development and application of a multilevel decision analysis model for the remediation of contaminated groundwater under uncertainty

A study was initiated which combined elements of stochastic hydrology, risk assessment, simulation modeling, cost analysis and decision making to define the optimum remediation choice(s) for a Superfund site in the southern United States. The effort focused upon the premise that groundwater remediation is inherently complex due to uncertainties in the geological matrix as well as in contaminant concentrations at points of compliance and/or exposure. The technical analyst should supply the decision maker with estimates of these uncertainties as well as the cost penalties required to reduce them to manageable levels. Monte Carlo transport modeling was employed to define the probability of contaminant excursions from the site, while geostatistical simulation identified a joint plume configuration and its attendant probability. Bayesian modeling was used to define the worth of additional data. These individual components were combined within a Decision Model to identify optimum remediation configurations for a given levels of risk tolerance which could be supplied by the decision maker or affected community. Sensitivity analyses were conducted to define ranges over which the decision would not be affected by variation in the respective decision parameter.     

Efficacy of Bacteroides measurements for reducing the statistical uncertainty associated with hydrologic flow and fecal loads in a mixed use watershed

This paper presents an analysis of the occurrence and uncertainty of source-specific Bacteroides and Escherichia coli in a stream in a mixed land-use watershed with human, cattle, and wildlife fecal inputs located in a karstic geologic region during baseflow conditions. The objectives of the study were to evaluate the occurrence, hydrologic significance, and source of fecal mass in the stream using assays for total Bacteroides (AllBac) and bovine-specific Bacteroides (BoBac), and then to compare these measurements with E. coli densities and loads. Samples were collected during baseflow conditions over several months at seven different main channel sites in the Stock Creek watershed, a 49.3 km2 basin located in Knoxville, TN (USA). We determined instantaneous loads for total fecal loads, bovine fecal loads, and E. coli from measured flow rates and the representative Bacteroides fecal masses and/or E. coli densities. The study indicated a strong correlation between total fecal load (kg d(-1)), bovine fecal load (kg d(-1)), E. coli load rate (CFU d(-1)), 7-d antecedent precipitation, and turbidity. The various datasets were used to establish parameter correlations and spatial dependencies throughout the watershed. The data analysis demonstrated two prevalent patterns throughout the watershed: (i) a runoff-dominated transport and occurrence; and (ii) potential groundwater-dominated transport and occurrence.     

Risk explicit interval linear programming model for long-term planning of vehicle recycling in the EU legislative context under uncertainty

With the number of vehicles expected to increase to 1.85 billion by 2030 and the scrap generated from end-of-life vehicles (ELVs) expected to be 3.71 billion tonnes, there is a strong motivation to properly process the flow of these materials. The EU Directive on end-of-life vehicles (EU ELV Directive) aims to increase recovery and recycling rates of ELVs in order to reduce waste and improve environmental performances. Long-term optimization planning of vehicle recycling is increasingly important. However, there is a lack of research of uncertainties in the vehicle recycling system, none of the previous studies analyzed the linkage and trade-offs between decision risk and system performances, and no previous research was reported on interval-based programming for vehicle recycling planning problem. In order to meet the imposed eco-efficiency quotas, maximize system profit and minimize decision risk, and at the same time fill the identified research gaps, a risk explicit interval linear programming model for optimal long-term planning in the EU vehicle recycling factories was developed. It can create optimal plans for procuring vehicle hulks, sorting of generated material fractions, allocation of sorted waste flows and allocation of sorted metals for desired value of the system aspiration level. A numerical study demonstrated the potentials and applicability of the proposed model. Vehicle recycling factories aim at reaching the highest possible level of quantity and quality of sorted metal flows. The future eco-efficiency quotas will not endanger their business. The success of the final phase of implementation of the EU ELV Directive is not jeopardized, because even the future eco-efficiency quotas were reached in all created test problems. Quantity of land-filled wastes will be radically reduced after January 1, 2015. The model results and trade-offs would be valuable for supporting the EU vehicle recycling factories in creating optimal long-term production strategies and reducing the risk for uncertain situations.     

The use of guide to the expression of uncertainty in measurement for uncertainty management in National Greenhouse Gas Inventories

Research basis for annual greenhouse gases (GHG) emissions assessment is national and branch statistics data. Quality and confidence of greenhouse gases inventory through assessment methodologies, preparation procedures and processing of data is confirm. Request at National Greenhouse Gases Inventory which contain assessment and analyses uncertainty elements on Intergovernmental Panel on Climate Change-GPG 2000 and IPCC 2006 are determine. Main approaches for uncertainty assessment in environmental and metrological guides are considered. The algorithm of expressing uncertainty and scheme for estimating uncertainty according to GPG 2000 and IPCC 2006 are proposed. The use approaches GUM 1993 for uncertainty assessment for greenhouse gases inventory are proposed.     

Predicting streamflow response to fire-induced landcover change: implications of parameter uncertainty in the MIKE SHE model

Fire is a primary agent of landcover transformation in California semi-arid shrubland watersheds, however few studies have examined the impacts of fire and post-fire succession on streamflow dynamics in these basins. While it may seem intuitive that larger fires will have a greater impact on streamflow response than smaller fires in these watersheds, the nature of these relationships has not been determined. The effects of fire size on seasonal and annual streamflow responses were investigated for a medium-sized basin in central California using a modified version of the MIKE SHE model which had been previously calibrated and tested for this watershed using the Generalized Likelihood Uncertainty Estimation methodology. Model simulations were made for two contrasting periods, wet and dry, in order to assess whether fire size effects varied with weather regime. Results indicated that seasonal and annual streamflow response increased nearly linearly with fire size in a given year under both regimes. Annual flow response was generally higher in wetter years for both weather regimes, however a clear trend was confounded by the effect of stand age. These results expand our understanding of the effects of fire size on hydrologic response in chaparral watersheds, but it is important to note that the majority of model predictions were largely indistinguishable from the predictive uncertainty associated with the calibrated model - a key finding that highlights the importance of analyzing hydrologic predictions for altered landcover conditions in the context of model uncertainty. Future work is needed to examine how alternative decisions (e.g., different likelihood measures) may influence GLUE-based MIKE SHE streamflow predictions following different size fires, and how the effect of fire size on streamflow varies with other factors such as fire location.     

Spatially explicit sensitivity and uncertainty analysis for multicriteria-based vulnerability assessment

This research analyses the application of spatially explicit sensitivity and uncertainty analysis for GIS (Geographic Information System) multicriteria decision analysis (MCDA) within a multi-dimensional vulnerability assessment regarding flooding in the Salzach river catchment in Austria. The research methodology is based on a spatially explicit sensitivity and uncertainty analysis of GIS-CDA for an assessment of the social, economic, and environmental dimensions of vulnerability. The main objective of this research is to demonstrate how a unified approach of uncertainty and sensitivity analysis can be applied to minimise the associated uncertainty within each dimension of the vulnerability assessment. The methodology proposed for achieving this objective is composed of four main steps. The first step is computing criteria weights using the analytic hierarchy process (AHP). In the second step, Monte Carlo simulation is applied to calculate the uncertainties associated with AHP weights. In the third step, the global sensitivity analysis (GSA) is employed in the form of a model-independent method of output variance decomposition, in which the variability of the different vulnerability assessments is apportioned to every criterion weight, generating one first-order (S) and one total effect (ST) sensitivity index map per criterion weight. Finally, in the fourth step, an ordered weighted averaging method is applied to model the final vulnerability maps. The results of this research demonstrate the robustness of spatially explicit GSA for minimising the uncertainty associated with GIS-MCDA models. Based on these results, we conclude that applying the variance-based GSA enables assessment of the importance of each input factor for the results of the GIS-MCDA method, both spatially and statistically, thus allowing us to introduce and recommend GIS-based GSA as a useful methodology for minimising the uncertainty of GIS-MCDA.     

Process design for the environment: A multi-objective framework under uncertainty

Designing chemical processes for the environment requires consideration of several indexes of environmental impact including ozone depletion, global warming potentials, human and aquatic toxicity, photochemical oxidation, and acid rain potentials. Current methodologies, such as the generalized waste reduction algorithm (WAR), provide a first step towards evaluating these impacts. However, to address the issues of accuracy and the relative weights of these impact indexes, one must consider the problem of uncertainties. Environmental impacts must also be weighted and balanced against other concerns, such as their cost and long-term sustainability. These multiple, often conflicting, goals pose a challenging and complex optimization problem, requiring multi-objective optimization under uncertainty. This paper will address the problem of quantifying and analyzing the various objectives involved in process design for the environment. Towards this goal, we proposed a novel multi-objective optimization framework under uncertainty. This framework is based on new and efficient algorithms for multi-objective optimization and for uncertainty analysis. This approach finds a set of potentially optimal designs where trade-offs can be explicitly identified, unlike cost-benefit analysis, which deals with multiple objectives by identifying a single fundamental objective and then converting all the other objectives into this single currency. A benchmark process for hydrodealkylation (HDA) of toluene to produce benzene modeled in the ASPEN simulator is used to illustrate the usefulness of the approach in finding environmentally friendly and cost-effective designs under uncertainty.     

Sensitivity of Stream flow and Water Table Depth to Potential Climatic Variability in a Coastal Forested Watershed1

Dai, Zhaohua, Carl C. Trettin, Changsheng Li, Devendra M. Amatya, Ge Sun, and Harbin Li, 2010. Sensitivity of Streamflow and Water Table Depth to Potential Climatic Variability in a Coastal Forested Watershed. Journal of the American Water Resources Association (JAWRA) 1–13. DOI: 10.1111/j.1752-1688.2010.00474.x Abstract: A physically based distributed hydrological model, MIKE SHE, was used to evaluate the effects of altered temperature and precipitation regimes on the streamflow and water table in a forested watershed on the southeastern Atlantic coastal plain. The model calibration and validation against both streamflow and water table depth showed that the MIKE SHE was applicable for predicting the streamflow and water table dynamics for this watershed with an acceptable model efficiency (E > 0.5 for daily streamflow and >0.75 for monthly streamflow). The simulation results from changing temperature and precipitation scenarios indicate that climate change influences both streamflow and water table in the forested watershed. Compared to current climate conditions, the annual average streamflow increased or decreased by 2.4% with one percentage increase or decrease in precipitation; a quadratic polynomial relationship between changes in water table depth (cm) and precipitation (%) was found. The annual average water table depth and annual average streamflow linearly decreased with an increase in temperature within the range of temperature change scenarios (0-6°C). The simulation results from the potential climate change scenarios indicate that future climate change will substantially impact the hydrological regime of upland and wetland forests on the coastal plain with corresponding implications to altered ecosystem functions that are dependent on water.     

Modification of uncertainty analysis in adapted material flow analysis: Case study of nitrogen flows in the Day-Nhue River Basin,Vietnam

Nitrogen flows impacted by human activities in the Day-Nhue River Basin in northern Vietnam have been modeled using adapted material flow analysis (MFA). This study introduces a modified uncertainty analysis procedure and its importance in MFA. We generated a probability distribution using a Monte Carlo simulation, calculated the nitrogen budget for each process and then evaluated the plausibility under three different criterion sets. The third criterion, with one standard deviation of the budget value as the confidence interval and 68% as the confidence level, could be applied to effectively identify hidden uncertainties in the MFA system. Sensitivity analysis was conducted for revising parameters, followed by the reassessment of the model structure by revising equations or flow regime, if necessary. The number of processes that passed the plausibility test increased from five to nine after reassessment of model uncertainty with a greater model quality. The application of the uncertainty analysis approach to this case study revealed that the reassessment of equations in the aquaculture process largely changed the results for nitrogen flows to environments. The significant differences were identified as increased nitrogen load to the atmosphere and to soil/groundwater (17% and 41%, respectively), and a 58% decrease in nitrogen load to surface water. Thus, modified uncertainty analysis was considered to be an important screening system for ensuring quality of MFA modeling.     

对水敏感性的机理与影响因素的研究

通过对咸淡水界面水敏感性机理与影响因素的研究,表明在非反应性微粒的释放迁移、反应性微粒的膨胀和絮凝作用与胶体吸附架桥作用下多孔介质中微粒物质的释放、膨胀、迁移、重沉积等引起的孔喉阻塞是导致水敏感性的机理所在;临界盐浓度、临界盐浓度变化率、临界离子强度、一临界流速和pH值是水敏感性的主要影响因素;指出水敏感性的研究对防止海水入侵具有重要的理论意义和实用价值。     

Dealing with uncertainty in collaborative planning: developing adaptive strategies for the IJsselmeer

Adaptive strategies to deal with uncertainty in water management are often collaboratively developed. So far, however, little attention has been paid to the influence of collaboration on handling uncertainty through adaptive planning. In this paper, we study how collaboration has influenced the handling of uncertainty through adaptive planning for water management strategies for the IJsselmeer area in the Netherlands. We show how a fixation on certainty, different perspectives among actors and unclear responsibilities between arenas affect the handling of uncertainty, and found that it is adversely affected by collaboration. The use of adaptive planning challenged current water uses and system functions, creating resistance from actors. We conclude that developing a shared problem perception, creating a common understanding of uncertainties and ensuring a clear demarcation between the water system, its societal functions and water usage, are necessary to make adaptive planning successful in handling uncertainty.     

Material flow analysis of paper in Korea. Part I. Data calculation model from the flow relationships between paper products

The flows of paper are analyzed throughout the papermaking processes, with the year 2007 and Korea defined as the system boundaries. In practice, the statistical data on the production, import and export of paper or pulp can be collected with relative ease from the government and industrial associations. However, the input data regarding the volumes of pulp and wastepaper used in different paper products, such as newsprint, printing papers, sanitary and household papers, specialty papers, and corrugating board base, are difficult to obtain because such information is generally kept confidential in the course of corporate operations.The production processes of paper products in Korea are modeled using information on raw materials, their compositions and production yields of products in order to identify and quantify the amounts of pulp and wastepaper used in each paper product. The material flows of paper are then analyzed based on the calculation model derived from the correlation of input and output flows between the individual processes throughout the entire paper lifecycle. Accuracy analysis using both mean absolute error (MAE) and mean absolute percent error (MAPE) is conducted to verify the amounts of pulp and wastepaper calculated from the proposed model against the volumes of domestically consumed pulp and wastepaper provided in the national statistics. Although the calculated values for the past (i.e., the 1980s and 1990s) differ to some degree from the statistical values, the data for the 2000s have a relatively higher level of accuracy, with the MAPE of the total pulp and recycling volume at 5.39% and 5.30%, respectively, thus validating the adequacy of the proposed modeling method. The proposed calculation model can be effectively used in the material flow analysis (MFA) of paper to reduce the burden of data collection and obtain relatively accurate results.     

A Sensitivity and Error Analysis Framework for Lake Eutrophication Modeling1

ABSTRACT: A framework for sensitivity and error analysis in mathematical modeling is described and demonstrated. The Lake Eutrophication Analysis Procedure (LEAP) consists of a series of linked models which predict lake water quality conditions as a function of watershed land use, hydrolgic variables, and morphometric variables. Specification of input variables as distributions (means and standard errors) and use of first-order error analysis techniques permits estimation of output variable means, standard errors, and confidence ranges. Predicted distributions compare favorably with those estimated using Monte-Carlo simulation. The framework is demonstrated by applying it to data from Lake Morey, Vermont. While possible biases exist in the models calibrated for this application, prediction variances, attributed chiefly to model error, are comparable to the observed year-to-year variance in water quality, as measured by spring phosphorus concentration, hypolimnetic oxygen depletion rate, summer chlorophyll-a, and summer transparency in this lake. Use of the framework provides insight into important controlling factors and relationships and identifies the major sources of uncertainty in a given model application.     

Joint operation rules considering the uncertainty of energy available for multiple cascaded hydropower system

Multi-reservoir operation rules have been widely used in practice operation. The operation rules are often derived from historical or simulated run-off information through implicit stochastic optimization or parameterization-simulation-optimization. The output decisions of operation rules are usually obtained without considering inflow forecasting or using perfect runoff forecast information, which is hardly implemented in practical applications. This paper proposes robust joint operation rules for multiple cascaded reservoirs considering the uncertainty of energy available. The rule parameters are optimized using multi-step genetic algorithm for minimum-power maximization. A case study for a three-cascaded-reservoirs system shows that, compared with deterministic joint operation rules, the accuracy of energy available estimation of joint operation rules is increased by 2.3%, considering inflow uncertainty. The simulated minimum-power decision of joint operation rules considering the uncertainty of energy available is 40.4% higher than that of determinate joint operation rules. Results indicate that there is a possibility of obtaining greater and more effective power decisions through the joint operation rules considering the uncertainty of available energy.     

Effects of soil data resolution on SWAT model stream flow and water quality predictions

The prediction accuracy of agricultural nonpoint source pollution models such as Soil and Water Assessment Tool (SWAT) depends on how well model input spatial parameters describe the characteristics of the watershed. The objective of this study was to assess the effects of different soil data resolutions on stream flow, sediment and nutrient predictions when used as input for SWAT. SWAT model predictions were compared for the two US Department of Agriculture soil databases with different resolution, namely the State Soil Geographic database (STATSGO) and the Soil Survey Geographic database (SSURGO). Same number of sub-basins was used in the watershed delineation. However, the number of HRUs generated when STATSGO and SSURGO soil data were used is 261 and 1301, respectively. SSURGO, with the highest spatial resolution, has 51 unique soil types in the watershed distributed in 1301 HRUs, while STATSGO has only three distributed in 261 HRUS. As a result of low resolution STATSGO assigns a single classification to areas that may have different soil types if SSURGO were used. SSURGO included Hydrologic Response Units (HRUs) with soil types that were generalized to one soil group in STATSGO. The difference in the number and size of HRUs also has an effect on sediment yield parameters (slope and slope length). Thus, as a result of the discrepancies in soil type and size of HRUs stream flow predicted was higher when SSURGO was used compared to STATSGO. SSURGO predicted less stream loading than STATSGO in terms of sediment and sediment-attached nutrients components, and vice versa for dissolved nutrients. When compared to mean daily measured flow, STATSGO performed better relative to SSURGO before calibration. SSURGO provided better results after calibration as evaluated by R(2) value (0.74 compared to 0.61 for STATSGO) and the Nash-Sutcliffe coefficient of Efficiency (NSE) values (0.70 and 0.61 for SSURGO and STATSGO, respectively) although both are in the same satisfactory range. Modelers need to weigh the benefits before selecting the type of data resolution they are going to use depending on the watershed size and level of accuracy required because more effort is required to prepare and calibrate the model when a fine resolution soil data is used.     

Sustainability and the post-modern city: some guidelines for urban planning and transport practice in an age of uncertainty

Summary Sustainability is one of the key concepts that is associated with post-modernism. The old world with its modernist assumptions was based on increasing consumption of fossil fuels and other resources, reducing the natural and the diverse to simple, American-style mass production, with a strong state-based, large-scale approach to providing infrastructure. That old way no longer works but no way forward is obviously apparent. This paper looks at how the uncertainties produced by post-modernism can be put to advantage in providing a more sustainable postmodern city. It is suggested that the key principles are recognizing values, maximizing diversity and crossing boundaries. These are developed into some guidelines for urban planning and transport practice.This paper was delivered at the Global Forum '94 Academic Conference, Manchester, UK.