WATER QUALITY MODEL OUTPUT UNCERTAINTY AS AFFECTED BY SPATIAL RESOLUTION OF INPUT DATA1

ABSTRACT: Resolution of the input GIS data used to parameterize distributed‐parameter hydrologic/water quality models may affect uncertainty in model outputs and impact the subsequent application of model results in watershed management. In this study we evaluated the impact of varying spatial resolutions of DEM, land use, and soil data (30 × 30 m, 100 × 100 m, 150 × 150 m, 200 × 200 m, 300 × 300 m, 500 × 500 m, and 1,000 × 1,000 m) on the uncertainty of SWAT predicted flow, sediment, NO₃‐N, and TP transport. Inputs included measured hydrologic, meteorological, and watershed characteristics as well as water quality data from the Moores Creek watershed in Washington County, Arkansas. The SWAT model output was most affected by input DEM data resolution. A coarser DEM data resolution resulted in decreased representation of watershed area and slope and increased slope length. Distribution of pasture, forest, and urban areas within the watershed was significantly affected at coarser resolution of land use and resulted in significant uncertainty in predicted sediment, NO₃‐N, and TP output. Soils data resolution had no significant effect on flow and NO₃‐N predictions; however, sediment was overpredicted by 26 percent, and TP was underpredicted by 26 percent at 1,000 m resolution. This may be due to change in relative distribution of various hydrologic soils groups (HSGs) in the watershed. Minimum resolution for input GIS data to achieve less than 10 percent model output error depended upon the output variable of interest. For flow, sediment, NO₃‐N, and TP predictions, minimum DEM data resolution should range from 30 to 300 m, whereas minimum land use and soils data resolution should range from 300 to 500 m.     

Growth of the Decision Tree: Advances in Bottom‐Up Climate Change Risk Management

There has recently been a return in climate change risk management practice to bottom‐up, robustness‐based planning paradigms introduced 40 years ago. The World Bank's decision tree framework (DTF) for “confronting climate uncertainty” is one incarnation of those paradigms. In order to better represent the state of the art in climate change risk assessment and evaluation techniques, this paper proposes: (1) an update to the DTF, replacing its “climate change stress test” with a multidimensional stress test; and (2) the addition of a Bayesian network framework that represents joint probabilistic behavior of uncertain parameters as sensitivity factors to aid in the weighting of scenarios of concern (the combination of conditions under which a water system fails to meet its performance targets). Using the updated DTF, water system planners and project managers would be better able to understand the relative magnitudes of the varied risks they face, and target investments in adaptation measures to best reduce their vulnerabilities to change. Next steps for the DTF include enhancements in: modeling of extreme event risks; coupling of human‐hydrologic systems; integration of surface water and groundwater systems; the generation of tradeoffs between economic, social, and ecological factors; incorporation of water quality considerations; and interactive data visualization.     

基于不确定性的水资源配置双层模型及其实证研究

针对水资源管理系统的不确定性和复杂性,引入区间参数表达系统中的不确定信息,建立了反映水资源管理者和使用者之间层次关系的区间双层规划模型,并以北京市丰台区水资源管理系统为例进行实证研究.同时,基于交互式算法和模糊满意度算法,用Lingo软件编程求解,确定了丰台区水资源的优化配置方案.规划结果表明:丰台区水资源管理系统的供需水量基本达到平衡,75%的供水量来源于本地地表水和南水北调水源;规划期内的系统经济效益预期达到2.22×10~9~5.16×10~9元,COD排放量将达到1.47×10~4~1.89×10~4t.区间结果提高了优化方案的灵活性,系统满意度体现了上层决策者与下层决策者的交互.其次,通过设计4种COD最大允许排放情景,说明了不同的COD排放约束将对规划结果产生重要影响.最后,通过3种模型的对比分析可以为丰台区水资源配置提供不同发展情景下的优化方案.     

石墨炉原子吸收分光光度法测定土壤中铍的不确定度评定研究

根据石墨炉原子吸收分光光度法(GFAAS)测定土壤中铍的过程,建立相应的数学模型并对模型中各个参数进行了不确定度来源分析.依据测量不确定度的评定理论,对样品称量、定容体积、标准溶液的配制、曲线拟合、仪器测量重复性、干物质含量等影响不确定度的分量进行计算,给出了合成标准不确定度和扩展不确定度,结果表明,测定结果的不确定性主要来源于标准拟合引入的不确定度,其次为仪器重复测定引入的不确定度,该评定方法为石墨炉原子吸收分光光度法测定土壤中重金属元素的不确定度评定提供参考依据.     

A Hydraulic MultiModel Ensemble Framework for Visualizing Flood Inundation Uncertainty

While deterministic forecasts provide a single realization of potential inundation, the inherent uncertainty associated with forecasts also needs to be conveyed for improved decision support. The objective of this study was to develop an ensemble framework for the quantification and visualization of uncertainty associated with flood inundation forecast maps. An 11‐member ensemble streamflow forecast at lead times from 0 to 48 hr was used to force two hydraulic models to produce a multimodel ensemble. The hydraulic models used are (1) the International River Interface Cooperative along with Flow and Sediment Transport with Morphological Evolution of Channels solver and (2) the two‐dimensional Hydrologic Engineering Center‐River Analysis System. Uncertainty was quantified and augmented onto flood inundation maps by calculating statistical spread among the ensemble members. For visualization, a series of probability flood maps conveying the uncertainty in forecasted water extent, water depth, and flow velocity was disseminated through a web‐based decision support tool. The results from this study offer a framework for quantifying and visualizing model uncertainty in forecasted flood inundation maps.     

Legal requirements for risk and vulnerability assessments in Norwegian land-use planning

Land-use planning has been promoted as an important tool for adaptation to climate change. However, its success depends on effective communication between researchers and decision-makers. A “risk and vulnerability assessment” (RVA) is one method to facilitate the interaction between the groups. RVAs have been performed in Norwegian land-use planning for about 20 years, and been mandatory for new development the last few years. The first part of the article reviews the history and current legal regulation of RVAs in Norway. Continuing, the main part discusses the distribution of authority and responsibility for different parts of the process and result. The key finding is that significant decisions are deferred to municipal discretion, for example, the necessity (if doubtful) and scope of the assessments. Other topics (such as the burden of costs and eligible performers) are largely unregulated, prompting some arguments about the need for further legislative effort.     

非点源污染河流水环境容量的不确定性分析

基于河流一维水环境容量计算模型和实测水文水质参数的统计分析,应用MonteCarlo模拟方法,分析模型各输入参数的灵敏度以及水环境容量值的概率分布,建立了非点源污染河流水环境容量的分期不确定性分析方法.本方法表达了由于获取的河流系统信息不确定性和非点源污染发生的随机性引起的水环境容量计算结果不确定性,给出了不同水文期在不同可信度下的河流水环境容量,为实现非点源污染的总量控制提供了可靠的基础.应用本方法,对长乐江的总氮水环境容量进行了不确定性分析.结果表明,根据水质控制目标,枯水期、平水期、丰水期中90%可信度的总氮水环境容量分别为487.9、949.8、1392.8kg·d-1,其中稀释容量是各水文期水环境容量的主要组成部分.据此,各水文期流域内的总氮现状入河量需削减1258.3～3591.2kg·d-1,丰水期是削减量最大的时期.不确定性分析方法计算得到的水环境容量是基于非点源污染河流水文水质状况的实际变化,这相对于按某一设计流量来确定水环境容量的常规方法更为科学、合理,拓展了水环境容量的研究思路和方法.     

Resolving structural uncertainty in natural resources management using POMDP approaches

In recent years there has been a growing focus on the uncertainties of natural resources management, and the importance of accounting for uncertainty in assessing management effectiveness. This paper focuses on uncertainty in resource management in terms of discrete-state Markov decision processes (MDP) under structural uncertainty and partial observability. It describes the treatment of structural uncertainty with approaches developed for partially observable resource systems. In particular, I show how value iteration for partially observable MDPs (POMDP) can be extended to structurally uncertain MDPs. A key difference between these process classes is that structurally uncertain MDPs require the tracking of system state as well as a probability structure for the structure uncertainty, whereas with POMDPs require only a probability structure for the observation uncertainty. The added complexity of the optimization problem under structural uncertainty is compensated by reduced dimensionality in the search for optimal strategy. A solution algorithm for structurally uncertain processes is outlined for a simple example in conservation biology. By building on the conceptual framework developed for POMDPs, natural resource analysts and decision makers who confront structural uncertainties in natural resources can take advantage of the rapid growth in POMDP methods and approaches, and thereby produce better conservation strategies over a larger class of resource problems.     

Uncertainty characterization approaches for risk assessment of DBPs in drinking water: A review

The management of risk from disinfection by-products (DBPs) in drinking water has become a critical issue over the last three decades. The areas of concern for risk management studies include (i) human health risk from DBPs, (ii) disinfection performance, (iii) technical feasibility (maintenance, management and operation) of treatment and disinfection approaches, and (iv) cost. Human health risk assessment is typically considered to be the most important phase of the risk-based decision-making or risk management studies. The factors associated with health risk assessment and other attributes are generally prone to considerable uncertainty. Probabilistic and non-probabilistic approaches have both been employed to characterize uncertainties associated with risk assessment. The probabilistic approaches include sampling-based methods (typically Monte Carlo simulation and stratified sampling) and asymptotic (approximate) reliability analysis (first- and second-order reliability methods). Non-probabilistic approaches include interval analysis, fuzzy set theory and possibility theory. However, it is generally accepted that no single method is suitable for the entire spectrum of problems encountered in uncertainty analyses for risk assessment. Each method has its own set of advantages and limitations. In this paper, the feasibility and limitations of different uncertainty analysis approaches are outlined for risk management studies of drinking water supply systems. The findings assist in the selection of suitable approaches for uncertainty analysis in risk management studies associated with DBPs and human health risk.     

Ranking Canadian universities: a quantitative approach for sustainability assessment using uD-SiM

This paper introduces a model that enables a comparison between universities based on sustainability indicators related to environmental, economic, social and educational aspects. The proposed model is based on a driving force-pressure-state-exposure-effect-action (DPSEEA) framework and is called uncertainty-based DPSEEA-Sustainability index Model (uD-SiM). The uD-SiM applies the concept of causality and develops sustainability index (SI), which is an outcome of nonlinear relationships of sustainability indicators in different stages of DPSEEA. In this paper, this fuzzy-based multi-criteria decision-making model is used to evaluate the sustainability of five Canadian universities, namely the University of British Columbia, the University of Toronto, the University of Alberta, the McGill University and the Memorial University. The final ranking results are compared with the Green report card ranking for 2010 through SI. The application of various actions and strategies that can be applied to different stages of the framework to improve sustainability in higher education institutions is also discussed.