Towards best practice implementation and application of models for analysis of water resources management scenarios

Water resources management models are widely used to evaluate planning or operational scenarios to support water resource management decision-making. However, the approaches to modelling used in the past have led to problems, such as modellers having difficulty establishing the credibility of their model with stakeholders, and stakeholders having difficulty understanding and trusting model results. A best practice approach to the implementation and application of water resources management models based on a quality assurance procedure is an appropriate means of overcoming these difficulties, and there are a number of guidelines and papers available promoting this approach. However, guidance in these on the use of models to analyse water resource planning scenarios is limited or not provided. This paper therefore provides guidance on the implementation and application of water resources management models with an emphasis on scenario analysis. This guidance is principally intended for practising modellers, and also for peer reviewers and stakeholders such as managers, decision makers, and community-based groups. Adoption strategies and recommendations for future directions are also discussed.     

Incentive- and compulsion-based cooperative approach in water resources quality management

Simulating control systems for water resources quality is considered. A hierarchical approach to organization of these systems and hierarchical control methods are taken as the basis. Moving from non-cooperative to cooperative relations is proved to be reasonable. A new way to distribute the payoff in the cooperative game is proposed for the problem of water resources quality control. Typical examples are given to illustrate various optimality principles in the cooperative game.     

Detection and resolution of conflicting change operations in version management of process models

Version management of process models requires that different versions of process models are integrated by applying change operations. Conflict detection between individually applied change operations and conflict resolution support are integral parts of version management. For conflict detection it is utterly important to compute a precise set of conflicts, since the minimization of the number of detected conflicts also reduces the overhead for merging different process model versions. As not every syntactic conflict leads to a conflict when taking into account model semantics, a computation of conflicts solely on the syntax leads to an unnecessary high number of conflicts. Moreover, even the set of precisely computed conflicts can be extensive and their resolution means a significant workload for a user. As a consequence, adequate support is required that guides a user through the resolution process and suggests possible resolution strategies for individual conflicts. In this paper, we introduce the notion of syntactic and semantic conflicts for change operations of process models. We provide a method how to efficiently compute conflicts precisely, using a term formalization of process models and consider the subsequent resolution of the detected conflicts based on different strategies. Using this approach, we can significantly reduce the number of overall conflicts and reduce the amount of work for the user when resolving conflicts.     

Avoidance control mechanics for food-chain models subject to uncertainties

We consider a general food-chain system subject to uncertain disturbances. A Lyapunov design methodology is used to establish control growth policies of a qualitative nature aimed at restricting the fluctuations of size of the chain elements (populations, consumers, resources), and therefore to facilitate transition from growth (decay) to manageable population levels.     

Heterogeneous transformation of uncertainties of propositions amonginexact reasoning models

Cooperation between different expert systems within a heterogeneous distributed expert system is essentially based on the transformations between the reasoning models built into the structures of the involved systems. The general criteria for a transformation between heterogeneous inexact reasoning models have been formulated. Using an algebraic structure of semigroup (with individual unit elements) of inexact reasoning models, the criteria are natural and easily satisfied by homomorphic mapping as an implementation. The criteria presented are too weak to ensure the existence of a unique mapping between the models concerned, but they are strong enough to guarantee reasonable transformations. These general considerations are illustrated using the example of three inexact reasoning models used by EMYCIN, PROSPECTOR, and MYCIN, where the homomorphic transformations among them are constructed and discussed     

Quantile-based optimization under uncertainties using adaptive Kriging surrogate models

Uncertainties are inherent to real-world systems. Taking them into account is crucial in industrial design problems and this might be achieved through reliability-based design optimization (RBDO) techniques. In this paper, we propose a quantile-based approach to solve RBDO problems. We first transform the safety constraints usually formulated as admissible probabilities of failure into constraints on quantiles of the performance criteria. In this formulation, the quantile level controls the degree of conservatism of the design. Starting with the premise that industrial applications often involve high-fidelity and time-consuming computational models, the proposed approach makes use of Kriging surrogate models (a.k.a. Gaussian process modeling). Thanks to the Kriging variance (a measure of the local accuracy of the surrogate), we derive a procedure with two stages of enrichment of the design of computer experiments (DoE) used to construct the surrogate model. The first stage globally reduces the Kriging epistemic uncertainty and adds points in the vicinity of the limit-state surfaces describing the system performance to be attained. The second stage locally checks, and if necessary, improves the accuracy of the quantiles estimated along the optimization iterations. Applications to three analytical examples and to the optimal design of a car body subsystem (minimal mass under mechanical safety constraints) show the accuracy and the remarkable efficiency brought by the proposed procedure.     

Integrated water resources management of overexploited hydrogeological systems using Object-Oriented Bayesian Networks

Object-Oriented Bayesian Networks (OOBNs) have been used increasingly over the past few decades in fields as diverse as medicine, transport and aeronautics. In this paper, OOBNs are applied to the domain of integrated water management and used as a Decision Support System (DSS). This pioneering study, set in the Altiplano region of Murcia in Southern Spain, describes a method for the integrated analysis of a complex water system supplied by groundwater from four aquifers. This method is based on the development of a multivariable integrated technique based on Bayes' theorem. After identifying all relevant factors related to water management in the area these were then translated to variables within a Bayesian Network (BN) and the relationships between them investigated. Each network represented one of the four aquifer units. These individual BNs were then linked to form an OOBN which was used to represent the complex real-world situation. In this way a DSS to simulate the entire water system was constructed using a group of conventional Bns, linked to produce an OOBN. The main stakeholders of the region contributed to network design and construction throughout the entire process. The paper shows how this type of DSS can be used to evaluate the impacts of a range of management strategies that are available to local planners.     

A two-stage programming approach for water resources management under randomness and fuzziness

In this study, a fuzzy stochastic two-stage programming (FSTP) approach is developed for water resources management under uncertainty. The concept of fuzzy random variable expressed as parameters’ uncertainties with both stochastic and fuzzy characteristics was used in the method. FSTP has advantages in uncertainty reflection and policy analysis. FSTP integrates the fuzzy robust programming, chance-constrained programming and two-stage stochastic programming (TSP) within a general optimization framework. FSTP can incorporate pre-regulated water resources management policies directly into its optimization process. Thus, various policy scenarios with different economic penalties (when the promised amounts are not delivered) can be analyzed. FSTP is applied to a water resources management system with three users. The results indicate that reasonable solutions were generated, thus a number of decision alternatives can be generated under different levels of stream flows, α-cut levels and different levels of constraint-violation probability. The developed FSTP was also compared with TSP to exhibit its advantages in dealing with multiple forms of uncertainties.     

Identification and classification of uncertainties in the application of environmental models

In the support of environmental management, models are frequently used. The outcomes of these models however, rarely show a perfect resemblance to the real-world system behavior. This is due to uncertainties, introduced during the process of abstracting information about the system to include it in the model. To provide decision makers with realistic information about these model outcomes, uncertainty analysis is indispensable. Because of the multiplicity of frameworks available for uncertainty analysis, the outcomes of such analyses are rarely comparable. In this paper a method for structured identification and classification of uncertainties in the application of environmental models is presented. We adapted an existing uncertainty framework to enhance the objectivity in the uncertainty identification process. Two case studies demonstrate how it can help to obtain an overview of unique uncertainties encountered in a model. The presented method improves the comparability of uncertainty analyses in different model studies and leads to a coherent overview of uncertainties affecting model outcomes.     

Econometric analysis of global climate change

This paper reports on research that applies econometric time series methods to the analysis of global climate change. The aim of this research was to test hypotheses concerning the causes of the historically observed rise in global temperatures. Longer term applications include quantification of the contribution of different forcing variables to historic warming and use of the model as a module in integrated assessment. Research to date has comprised three stages. In the first stage we used the concept of Granger causality and differences between the temperature record in the northern and southern hemispheres to investigate the causes of temperature increase. In the second stage we tested various global change time series for the presence of stochastic trends. We found that most series contain a stochastic trend with the greenhouse gas series containing I(2) stochastic trends. In the third stage we developed a structural time series to investigate some of the hypotheses suggested by the earlier stages and further tested for the presence of an I(2) trend in hemispheric temperature series. We found that the two temperature series share a common I(2) stochastic trend that may have its source in radiative forcing due to greenhouse gases. There is a second non-stationary component that appears only in the northern hemisphere and appears to be related to radiative forcing due to anthropogenic sulphur emissions.     

Modeling uncertainties in sodium spatial dispersion using a computational intelligence-based kriging method

Classical and geostatistical methods have been used to create continuous surfaces from sampled data. A common geostatistical method is kriging, which provides an accurate estimation based on the existing spatial structure of sample points. However, kriging is sensitive to errors in the input data, the dispersion of the sample points, and the fit of the model to the variogram. The purpose of this research is to develop a new method to address the uncertainties resulting from the input data and choice of model in the kriging method. In our approach, the existing uncertainties in the input data are modeled by fuzzy computations, and the variogram variables are optimized by a genetic algorithm. To test this new hybrid method, sodium contamination values in the Zanjan aquifer were used. The results show a general improvement in accuracy compared with the ordinary kriging method. Consideration of all equations and values in fuzzy computations highlights the complexity of the computation. Herein, the integration problems experienced by other researchers when trying to use fuzzy kriging are resolved.     

Application of a GIS-based simulation tool to illustrate implications of uncertainties for water management in the Amudarya river delta

River basin management decisions have to be made under uncertainty. Relevant uncertainties especially in external driving forces can often not be sufficiently reduced. Rather than expecting to eliminate them, new management strategies should thus aim at taking them into account. Simulation tools can support a process of reasoning about the implications of uncertainties for the outcome of management policies in a specific river basin management context. Model supported scenario analysis of alternative strategies with authorities, managers and other stakeholders can assist in the development of new strategies. The tools provide factual knowledge on the outcome of policy options proposed as scenarios by the participants to the debate. The GIS-based simulation tool TUGAI has been developed to support assessment of the ecological effects of alternative water management strategies in the degraded Amudarya river delta. It combines a multi-objective water allocation model with simple models of landscape dynamics and a fuzzy based evaluation of habitat suitability for riverine Tugai forests. In this paper an example application of the tool for scenario analysis to illustrate the implications of uncertainty in future water supply to the delta area is demonstrated. Scenario analysis provides an assessment of the range and magnitude of the impact of those uncertainties on the ecological situation in the delta. The potential and limitations of applying simple simulation tools in participative settings for analysis and discussion of the potential impacts of uncertainties and development of cooping strategies are discussed.     

Interval fuzzy modeling applied to Wiener models with uncertainties.

This correspondence addresses the problem of interval fuzzy model identification and its use in the case of the robust Wiener model. The method combines a fuzzy identification methodology with some ideas from linear programming theory. On a finite set of measured data, an optimality criterion which minimizes the maximum estimation error between the data and the proposed fuzzy model output is used. The min-max optimization problem can then be seen as a linear programming problem that is solved to estimate the parameters of the fuzzy model in each fuzzy domain. This results in lower and upper fuzzy models that define the confidence interval of the observed data. The model is called the interval fuzzy model and is used to approximate the static nonlinearity in the case of the Wiener model with uncertainties. The resulting model has the potential to be used in the areas of robust control and fault detection.     

嵌入式智能移动水资源监测管理平台设计

针对水资源监测系统监测站搭建调试工作艰难,线路管理维护不方便的问题,设计了一款基于嵌入式的采用CDMA方式进行无线采集和通信,以Wince下的数据库进行信息管理工作的可移动水资源监测系统。给出了系统工作原理及各模块软硬件设计的方法,并对基于Wince的智能设备下数据库的使用作出了说明。实验证明,该系统具有可靠性高、管理方便直观的特点。     

Fuzzy-stochastic-based violation analysis method for planning water resources management systems with uncertain information

In this study, a fuzzy-stochastic-based violation analysis (FSVA) approach is developed for the planning of water resources management systems with uncertain information, based on a multistage fuzzy-stochastic integer programming (FSIP) model. In FSVA, a number of violation variables for the objective and constraints are allowed, such that in-depth analyses of tradeoffs among economic objective, satisfaction degree, and constraint-violation risk can be facilitated. Besides, the developed method can deal with uncertainties expressed as probability distributions and fuzzy sets; it can also reflect the dynamics in terms of decisions for water-allocation and surplus-flow diversion, through transactions at discrete points of a complete scenario set over a multistage context. The developed FSVA method is applied to a case study of water resources management within a multi-stream, multi-reservoir and multi-period context. The results indicate that the satisfaction degrees and system benefits would be different under varied violation levels; moreover, different violation levels can also lead to changed water-allocation and surplus-flow diversion plans. Violation analyses are also conducted to demonstrate that violating different constraints have different effects on system benefit and satisfaction degree.     

飞机机电综合管理框架下的水系统建模与仿真

为了提高现行服役客机机载公共设备的综合化管理水平,搭建了以ARINCA29总线互连的分布式机电综合管理数字化仿真平台,建立了该仿真平台的飞机水系统受控模型.模型主要仿真水系统中水体的调度和分配,进行了水系统模型的功能和外特性仿真.仿真结果表明,在机电综合管理数字化仿真平台上,能够有效地实现实际水系统的所有功能特性,验证了机电综合管理数字化仿真平台的有效性,为飞机机电综合管理的最终实现提供了技术基础.     

RZWQM2 simulated management practices to mitigate climate change impacts on nitrogen losses and corn production

Gaseous and aqueous nitrogen (N) losses from corn (maize) production systems are concerns under projected climate change. In the present study, the Root Zone Water Quality Model (RZWQM2) was used to test the ability of agricultural management practices (N application rate, corn cultivar, planting date, tillage and controlled drainage) to mitigate future climate change effects on N losses and corn yield in a subsurface drained field in Iowa, USA. Under a future downscaled climate scenario, the simulated non-constant N₂O emission factor (EF), yield-scaled global warming potential and N loss through drainage increased with increasing fertilization above an optimal N rate of 120 kg N ha⁻¹. This rate represents the optimal tradeoff point between environmental issues and economic returns. While yields of the cultivar IB1068 DEKALB declined with climate change, yields of cultivar, IB 0090 GL 482 in the future climate were greater than historical yields of IB 1068 DEKALB.     

Modeling multibody systems with uncertainties. Part II: Numerical applications

This study applies generalized polynomial chaos theory to model complex nonlinear multibody dynamic systems operating in the presence of parametric and external uncertainty. Theoretical and computational aspects of this methodology are discussed in the companion paper “Modeling Multibody Dynamic Systems With Uncertainties. Part I: Theoretical and Computational Aspects”.In this paper we illustrate the methodology on selected test cases. The combined effects of parametric and forcing uncertainties are studied for a quarter car model. The uncertainty distributions in the system response in both time and frequency domains are validated against Monte-Carlo simulations. Results indicate that polynomial chaos is more efficient than Monte Carlo and more accurate than statistical linearization. The results of the direct collocation approach are similar to the ones obtained with the Galerkin approach. A stochastic terrain model is constructed using a truncated Karhunen-Loeve expansion. The application of polynomial chaos to differential-algebraic systems is illustrated using the constrained pendulum problem. Limitations of the polynomial chaos approach are studied on two different test problems, one with multiple attractor points, and the second with a chaotic evolution and a nonlinear attractor set.The overall conclusion is that, despite its limitations, generalized polynomial chaos is a powerful approach for the simulation of multibody dynamic systems with uncertainties.