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.     

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.     

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.     

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。     

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.     

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.     

Project appraisal and planning for overdeveloped countries: II. Shadow pricing under uncertainty

In the uncertainty that surrounds the future availability of nonrenewable natural resources and the efficacy of technological advance, the economic costing of resources should be undertaken probabilistically. While optimistic assumptions entail little change from the costing procedures used in conventional cost-benefit analysis, even moderately pessimistic assumptions lead to a much increased cost for nonrenewable resources. These lead in turn to a reappraisal of the value of investment and of the cost ascribed to other factors of production. Even when optimistic assumptions are deemed the more plausible, a utility-maximizing evaluation may still give more emphasis to pessimism.     

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.     

Review of airborne emissions from agricultural fumigants: design and uncertainty considerations for the use of the integrated horizontal flux method

Ground-level area sources, such as those associated with the use of agricultural fumigants, waste disposal sites, wastewater lagoons, and other applications, present a challenge in terms of characterizing atmospheric flux as a function of time. Studies are costly in terms of field activities and laboratory analysis. The optimization of field study design, therefore, is essential to conduct cost-effective research. The collection of on-field profile data for airborne concentration, wind speed, and wind direction can be used in conjunction with the integrated horizontal flux (IHF) method to empirically compute complex source terms as a function of time. This paper focuses on complicating factors and field study design issues for the use of the IHF method. Insights and examples are drawn from five field research studies. The methods and results of characterizing the uncertainty and method precision in the emission fitting for the IHF method also are presented.     

The data game: Measurement,uncertainty, and the challenge of quality

“Measurement, Uncertainty, and the Challenge of Quality,” by Carl Frankel, argues that corporations expect too much of science and measurement, in parallel with a culture that is similarly disposed. While science and measurement are essential to environmental progress, they are also limited in important and often overlooked ways. For instance, risk assessment provides only grossly approximate answers, and subjective quality—a central component of sustainable development—is rarely measured at all. New strategies for measuring the qualitative aspects of experience are urgently required.     

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.