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.     

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.     

基于GIS的农业非点源污染负荷时空分异研究——以宁波市章溪河流域为例

应用基于CIS AreView的AnnAGNPS模型,进行了宁波市章溪河流域的非点源污染负荷的时空分异研究。结合航片解译,利用ArcGIS9．0、TOPAZ模块等建立了研究区非点源污染空间数据库;结合现场资料、相关文献收集,并利用模型参数的CIS提取技术,建立了研究区非点源污染属性数据库。结果表明：在空间分布上,各子流域吸附态氮空间分异较大,溶解态磷的空间分异最小,不同土地利用类型中林地和果园相比农田非点源污染负荷小,不同土壤类型中,水稻土比黄壤和红壤的污染负荷大;在时间分布上,非点源污染负荷主要集中在汛期,超过全年的55％,枯水期低于全年的4％。     

关于不确定度评定中两个问题的讨论

通过对环境监测仪器分析不确定度评定中,标准不确定度和相对标准不确定度的单位来源以及关于相对标准不确定度两种计算方法的讨论,得出标准不确定度是有量纲的、相对标准不确定度是无量纲的;最大允许误差较易获得,用这种方法计算不确定度有很好的可靠性。相对标准偏差计算不确定度则结果更好。     

水质的生化需氧量(BOD5)稀释与接种法测定结果不确定度评定

在实际工作中，运用测量过程的合并样本标准差来评定A类不确定度比较客观。本文根据JJF1059—1999《测量不确定度评定与表示》，通过实例，阐述了用稀释接种法测定水中生化需氧量不确定度的评定方法。     

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.     

SENSITWITY ANALYSIS OF SIMULATED CONTAMINATED SEDIMENT TRANSPORT1

ABSTRACT: A simulation analysis of contaminated sediment transport involves model selection, data collection, model calibration and verification, and evaluation of uncertainty in the results. Sensitivity analyses provide information to address these issues at several stages of the investigation. A sensitivity analysis of simulated contaminated sediment transport is used to identify the most sensitive output variables and the parameters most responsible for the output variable sensitivity. The output variables included are streamflow and the flux of sediment and Cs¹³⁷. The sensitivities of these variables are measured at the field and intermediate scales, for flood and normal flow conditions, using the HSPF computer model. A sensitivity index was used to summarize and compare the results of a large number of output variables and parameters. An extensive database was developed to calibrate the model and conduct the sensitivity analysis on a 6.2 mi² catchment in eastern Tennessee. The fluxes of sediment and Cs¹³⁷ were more sensitive than streamflow to changes in parameters for both flood and normal flow conditions. The relative significance of specific parameters on output variable sensitivity varied according to the type of flow condition and the location in the catchment. An implications section illustrates how sensitivity analysis results can help with model selection, planning data collection, calibration, and uncertainty analysis.     

Parameter Uncertainty Reduction for SWAT Using Grace,Streamflow, and Groundwater Table Data for Lower Missouri River Basin1

Abstract: This study incorporates the newly available Gravity Recovery and Climate Experiment (GRACE) water storage data and water table data from well logs to reduce parameter uncertainty in Soil and Water Assessment Tool (SWAT) calibration using a SUFI2 (sequential uncertainty fitting) framework for the Lower Missouri River Basin. Model evaluations are performed in multiple stages using a multiobjective function consisting of multisite streamflow and GRACE water storage data as well as a groundwater component. Results show that (1) a model calibrated with both streamflow and GRACE data simultaneously can maintain the water balance for the whole basin, but may improperly partition surface flow and base flow. Additional inclusion of the groundwater constraint can significantly improve the model performance in groundwater hydrological processes. In our case, the estimation of specific yield of shallow aquifers has been increased to 10^?2 from previous much underestimated level (<10^?3). (2) The daily streamflow data are needed to confine the parameters related to water flow in channels such as the Manning’s coefficient, which are less sensitive to the monthly simulations. (3) Parameters are nonuniformly sensitive for different goal variables, and thus, proper specification of a prior distribution of parameters may be the key factor for global optimization algorithms to obtain stable and realistic model performance.     

SWMHMS - SMALL WATERSHED MONTHLY HYDROLOGIC MODELING SYSTEM1

ABSTRACT: SWMHMS is a conceptual computer modeling program developed to simulate monthly runoff from a small nonurban watershed. The input needed to run model simulations include daily precipitation, monthly data for evapotranspiration determination (average temperature, crop consumptive coefficients, and percent daylight hours), and six watershed parameter values. Evapotranspiration was calculated with the Blaney-Criddle equation while surface runoff was determined using the Soil Conservation Service curve number procedure. For watershed parameter evaluation, SWMHMS provides options for both optimization and sensitivity analysis. Observed runoff data are required along with the model input previously mentioned in order to conduct parameter optimization. SWMEIMS was tested with data from six watersheds located in different regions of the United States. Model accuracy was generally found to be very good except on watersheds having substantial snowfall accumulation. In having only six watershed parameters, SWMHMS is less complex to use than many other computer programs that calculate monthly runoff. Consequently, SWMHMS may find its greatest application as an educational tool for students learning principles of hydrologic modeling, such as parameter evaluation procedures and the impacts of input data uncertainty on model results.     

原子吸收法测定铜检出限的测量不确定度评估

对原子吸收法测定铜检出限的测量不确定度进行分析探讨,建立了不确定度的评估方法。影响铜检出限测量不确定度的主要因素包括标准溶液不确定度;拟合曲线不确定度;检测仪器不确定度;吸光值量化误差不确定度等,提供了上述各因素的计算方法及过程。     

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.     

METHODOLOGIES FOR CALIBRATION AND PREDICTIVE ANALYSIS OF A WATERSHED MODEL1

ABSTRACT: The use of a fitted parameter watershed model to address water quantity and quality management issues requires that it be calibrated under a wide range of hydrologic conditions. However, rarely does model calibration result in a unique parameter set. Parameter nonuniqueness can lead to predictive nonuniqueness. The extent of model predictive uncertainty should be investigated if management decisions are to be based on model projections. Using models built for four neighboring watersheds in the Neuse River Basin of North Carolina, the application of the automated parameter optimization software PEST in conjunction with the Hydrologic Simulation Program Fortran (HSPF) is demonstrated. Parameter nonuniqueness is illustrated, and a method is presented for calculating many different sets of parameters, all of which acceptably calibrate a watershed model. A regularization methodology is discussed in which models for similar watersheds can be calibrated simultaneously. Using this method, parameter differences between watershed models can be minimized while maintaining fit between model outputs and field observations. In recognition of the fact that parameter nonuniqueness and predictive uncertainty are inherent to the modeling process, PEST's nonlinear predictive analysis functionality is then used to explore the extent of model predictive uncertainty.     

MODELING THE BIG BLACK RIVER: A COMPARISON OF WATER QUALITY MODELS1

ABSTRACT: The Mississippi Department of Environmental Quality uses the Steady Riverine Environmental Assessment Model (STREAM) to establish effluent limitations. While the U.S. Environmental Protection Agency has approved of its use, questions arise regarding the model's simplicity. The objective of this research was to compare STREAM with the more commonly utilized Enhanced Stream Water Quality Model (QUAL2E). The comparison involved a statistical evaluation procedure based on sensitivity analyses, input probability distribution functions, and Monte Carlo simulation with site‐specific data from a 46‐mile (74‐km) reach of the Big Black River in central Mississippi. Site specific probability distribution functions were derived from measured rates of reaeration, sediment oxygen demand, photosynthesis, and respiration. Both STREAM and QUAL2E reasonably predicted daily average dissolved oxygen (DO) based on a comparison of output probability distributions with observed DO. Observed DO was consistently within 90 percent confidence intervals of model predictions. The STREAM approach generally overpredicted while QUAL2E generally matched observed DO. Using the more commonly assumed lognormal distribution as opposed to a Weibull distribution for two of the sensitive input parameters resulted in minimal differences in the statistical evaluations. The QUAL2E approach had distinct advantages over STREAM in simulating the growth cycle of algae.     

BAYESIAN MODELS OF FORECASTED TIME SERIES1

Bayesian Processor of Forecasts (BPF) combines a prior distribution, which describes the natural uncertainty about the realization of a hydrologic process, with a likelihood function, which describes the uncertainty in categorical forecasts of that process, and outputs a posterior distribution of the process, conditional upon the forecasts. The posterior distribution provides a means of incorporating uncertain forecasts into optimal decision models. We present fundamentals of building BPF for time series. They include a general formulation, stochastic independence assumptions and their interpretation, computationally tractable models for forecasts of an independent process and a first-order Markov process, and parametric representations for normal-linear processes. An example is shown of an application to the annual time series of seasonal snowmelt runoff volume forecasts.     

工业总排放水中Cr~(6+)含量测量不确定度的评定

采用分光光度法对工业总排放水中Cr6+进行不确定度评定,充分考虑测量重复性、标准溶液的配制、标准曲线的制备等因素对测量的影响,测得Cr6+的合成标准不确定度为0.023mg/L。     

Assessing representativeness of places for conservation reservation and heritage listing

Problems arising from application of the representative criterion for conservation and natural heritage evaluation are discussed. An ecological basis to this criterion is suggested that focuses on those key environmental factors dominating biotic response. A methodology is proposed that utilizes computer-based methods of establishing and interrogating spatial data bases (geographic information systems), environmental modeling, and numeric analysis. An example is presented illustrating some of the advantages and limitations of classification and dimension reduction techniques in both defining bioenvironments and displaying their spatial distribution. The advantages of this method for representativeness evaluation are that it maximizes the utility of available data, is explicit and repeatable, and enables large areas to be analyzed at relatively fine scales.     

Impact of data quality and model complexity on prediction of pesticide leaching

Accurate input data for leaching models are expensive and difficult to obtain which may lead to the use of "general" non-site-specific input data. This study investigated the effect of using different quality data on model outputs. Three models of varying complexity, GLEAMS, LEACHM, and HYDRUS-2D, were used to simulate pesticide leaching at a field trial near Hamilton, New Zealand, on an allophanic silt loam using input data of varying quality. Each model was run for four different pesticides (hexazinone, procymidone, picloram and triclopyr); three different sets of pesticide sorption and degradation parameters (i.e., site optimized, laboratory derived, and sourced from the USDA Pesticide Properties Database); and three different sets of soil physical data of varying quality (i.e., site specific, regional database, and particle size distribution data). We found that the selection of site-optimized pesticide sorption (Koc) and degradation parameters (half-life), compared to the use of more general database derived values, had significantly more impact than the quality of the soil input data used, but interestingly also more impact than the choice of the models. Models run with pesticide sorption and degradation parameters derived from observed solute concentrations data provided simulation outputs with goodness-of-fit values closest to optimum, followed by laboratory-derived parameters, with the USDA parameters providing the least accurate simulations. In general, when using pesticide sorption and degradation parameters optimized from site solute concentrations, the more complex models (LEACHM and HYDRUS-2D) were more accurate. However, when using USDA database derived parameters, all models performed about equally.     

A comparison in water quality: The UK and the CIS

Summary A comparison is made of major issues relating to drinking water and river water quality in the UK and the CIS (USSR). Historical and legislative aspects are briefly reviewed. In both countries there is an imbalance between the location of fresh water sources and the distribution of the population. In each country standards are used to define water quality. These standards tend to be more exacting in the CIS. In the UK derogations are used to produce a more relaxed standard. Failure to comply with drinking water standards is common in the CIS (on average 20 percent of samples). In the UK, data are recorded on a different basis, but it appears that deviations from nitrate and pesticide standards are common. In both countries' water supplies, pollution appears to be extensive with few overall signs of significant improvement. This is attributable partly to lack of effective enforcement and weak penalties for transgressors. There is a high level of public concern regarding water quality and health in both the CIS and the UK. As a consequence there are increasing signs in each country of a national determination to implement the legislation more effectively. Needs for further actions are identified.Dr Olga Bridges was born and educated in Russia. Since coming to the UK she has held posts on Soviet Studies in various universities. Dr Bridges' early research was in attitudes to languages and nationality, but more recently it has centred on attitudes to environmental issues. To further her knowledge of scientific aspects of environmental problems, Dr Bridges is currently taking the Advanced Diploma in Environmental Practice at Farnborough College of Technology, UK.     

自然通风热老化试验箱换气量不确定度评定

自然通风热老化试验箱(以下简称老化试验箱)主要用于电线电缆的热老化性能测试试验。而需要对箱体性能进行评估的多为老化试验箱的生产厂、电线电缆生产厂的产品检验实验室和开展相关项目的检测机构等公司和部门。温度参数和换气量参数是老化试验箱计量的主要项目,其测量结果的准确程度将直接影响对箱体性能的评估,分析并计算出了换气量测量结果扩展不确定度,对换气量参数的测量结果的可信赖程度具有参考意义。     

A Stochastic Modeling Approach to Address Hydrogeologic Uncertainties in Modeling Wellhead Protection Boundaries in Karst Aquifers1

Abstract: Development of any numerical ground‐water model is dependent on hydrogeologic data describing the subsurface. These data are obtained from geologic core analyses, stratigraphic analyses, aquifer performance tests, and geophysical studies. But typically in remote areas, these types of data are very sparse and site‐specific in terms of the aerial extent of the resource to be modeled. Uncertainties exist as to how well the available data from a few locations defines a heterogeneous surficial aquifer such as the Biscayne Aquifer in Miami‐Dade County, Florida. This is particularly the case when an exceptionally conductive horizontal flow zone is detected at one site due to specialized testing that was not historically conducted at the other at sites that provided data for the model. Not adequately accounting for the potential effect of the high flow zone in the aquifer within a ground‐water numerical model, even though the zone may be of very limited thickness, might underpredict the well field protection capture boundaries. Applied Stochastic ground‐water modeling in determining well field protection zones is steadily becoming important in addressing the uncertainty of the hydrogeologic subsurface parameters, specifically in karstic heterogeneous aquifers. This is particularly important in addressing the uncertainty of a 60‐day travel time capture zone in the Northwest Well Field, Miami‐Dade County, where a predominantly high flow zone controls much of the flow in the production wells. A stochastic ground‐water modeling application along with combination of pilot points and regularization technique is presented to further consolidate the uncertainty of the subsurface.