Hans Jørgen Henriksen

Selecting among five common modelling approaches for integrated environmental assessment and management

The design and implementation of effective environmental policies need to be informed by a holistic understanding of the system processes (biophysical, social and economic), their complex interactions, and how they respond to various changes. Models, integrating different system processes into a unified framework, are seen as useful tools to help analyse alternatives with stakeholders, assess their outcomes, and communicate results in a transparent way. This paper reviews five common approaches or model types that have the capacity to integrate knowledge by developing models that can accommodate multiple issues, values, scales and uncertainty considerations, as well as facilitate stakeholder engagement. The approaches considered are: systems dynamics, Bayesian networks, coupled component models, agent-based models and knowledge-based models (also referred to as expert systems). We start by discussing several considerations in model development, such as the purpose of model building, the availability of qualitative versus quantitative data for model specification, the level of spatio-temporal detail required, and treatment of uncertainty. These considerations and a review of applications are then used to develop a framework that aims to assist modellers and model users in the choice of an appropriate modelling approach for their integrated assessment applications and that enables more effective learning in interdisciplinary settings. We review five common integrated modelling approaches.Model choice considers purpose, data type, scale and uncertainty treatment.We present a guiding framework for selecting the most appropriate approach.

Methodology for construction, calibration and validation of a national hydrological model for Denmark

An integrated groundwater/surface water hydrological model with a 1 km2 grid has been constructed for Denmark covering 43,000 km2. The model is composed of a relatively simple root zone component for estimating the net precipitation, a comprehensive three-dimensional groundwater component for estimating recharge to and hydraulic heads in different geological layers, and a river component for streamflow routing and calculating stream–aquifer interaction. The model was constructed on the basis of the MIKE SHE code and by utilising comprehensive national databases on geology, soil, topography, river systems, climate and hydrology. The present paper describes the modelling process for the 7330 km2 island of Sjaelland with emphasis on the problems experienced in combining the classical paradigms of groundwater modelling, such as inverse modelling of steady-state conditions, and catchment modelling, focussing on dynamic conditions and discharge simulation. Three model versions with different assumptions on input data and parameter values were required until the performance of the final, according to pre-defined accuracy criteria, model was evaluated as being satisfactory. The paper highlights the methodological issues related to establishment of performance criteria, parameterisation and assessment of parameter values from field data, calibration and validation test schemes. Most of the parameter values were assessed directly from field data, while about 10 ‘free’ parameters were subject to calibration using a combination of inverse steady-state groundwater modelling and manual trial-and-error dynamic groundwater/surface water modelling. Emphasising the importance of tests against independent data, the validation schemes included combinations of split-sample tests (another period) and proxy-basin tests (another area).

Quality assurance in model based water management - review of existing practice and outline of new approaches

Quality assurance (QA) is defined as protocols and guidelines to support the proper application of models. In the water management context we classify QA guidelines according to how much focus is put on the dialogue between the modeller and the water manager as: (Type 1) Internal technical guidelines developed and used internally by the modellers organisation; (Type 2) Public technical guidelines developed in a public consensus building process; and (Type 3) Public interactive guidelines developed as public guidelines to promote and regulate the interaction between the modeller and the water manager throughout the modelling process. State-of-the-art QA practices vary considerably between different modelling domains and countries. It is suggested that these differences can be explained by the scientific maturity of the underlying discipline and differences in modelling markets in terms of volume of jobs outsourced and level of competition. The structure and key aspects of new generic guidelines and a set of electronically based supporting tools that are under development within the HarmoniQuA project are presented. Model credibility can be enhanced by a proper modeller-manager dialogue, rigorous validation tests against independent data, uncertainty assessments, and peer reviews of a model at various stages throughout its development.

More is not always better: coping with ambiguity in natural resources management

Coping with ambiguities in natural resources management has become unavoidable. Ambiguity is a distinct type of uncertainty that results from the simultaneous presence of multiple valid, and sometimes conflicting, ways of framing a problem. As such, it reflects discrepancies in meanings and interpretations. Under the presence of ambiguity it is not clear what problem is to be solved, who should be involved in the decision processes or what is an appropriate course of action. Despite the extensive literature about methodologies and tools to deal with uncertainty, not much has been said about how to handle ambiguities. In this paper, we discuss the notions of framing and ambiguity, and we identify five broad strategies to handle it: rational problem solving, persuasion, dialogical learning, negotiation and opposition. We compare these approaches in terms of their assumptions, mechanisms and outcomes and illustrate each approach with a number of concrete methods.

An evolutionary Bayesian belief network methodology for optimum management of groundwater contamination

An integrated methodology, based on Bayesian belief network (BBN) and evolutionary multi-objective optimization (EMO), is proposed for combining available evidence to help water managers evaluate implications, including costs and benefits of alternative actions, and suggest best decision pathways under uncertainty. A Bayesian belief network is a probabilistic graphical model that represents a set of variables and their probabilistic relationships, which also captures historical information about these dependencies. In complex applications where the task of defining the network could be difficult, the proposed methodology can be used in validation of the network structure and the parameters of the probabilistic relationship. Furthermore, in decision problems where it is difficult to choose appropriate combinations of interventions, the states of key variables under the full range of management options cannot be analyzed using a Bayesian belief network alone as a decision support tool. The proposed optimization method is used to deal with complexity in learning about actions and probabilities and also to perform inference. The optimization algorithm generates the state variable values which are fed into the Bayesian belief network. It is possible then to calculate the probabilities for all nodes in the network (belief propagation). Once the probabilities of all the linked nodes have been updated, the objective function values are returned to the optimization tool and the process is repeated. The proposed integrated methodology can help in dealing with uncertainties in decision making pertaining to human behavior. It also eliminates the shortcoming of Bayesian belief networks in introducing boundary constraints on probability of state values of the variables. The effectiveness of the proposed methodology is examined in optimum management of groundwater contamination risks for a well field capture zone outside Copenhagen city.

Stakeholder driven update and improvement of a national water resources model

It is generally acknowledged that water management must be based on an integrated approach, considering the entire freshwater cycle. This has in particularly been endorsed in Europe by the European Water Framework Directive (WFD) imposing integrated management considering all waters. Although not prescribed by the WFD, integrated hydrological modelling may be necessary to support the management according to the directive as also suggested by several research projects initiated by the EU commission. To ensure a coherent and consistent management across various institutions and authorities, having different responsibilities and operating at various scales, a common tool integrating all relevant knowledge and data is imperative. By the end of 2003, a numerical national water resources model was constructed for Denmark, which has been applied in several national assessments. At the regional level there has, however, been some reluctance to use the model, primarily because the model did not contain the most recent data and understanding obtained from detailed local studies. The model has therefore been subject to a comprehensive update focussing on utilising the system understanding from the local studies. This process was largely stakeholder driven by involvement of predominantly the technical staff at the regional water authorities. Local knowledge is continuously improved urging the model update to be an on-going process. Based on experience from the update of the Danish national water resources model, three levels of model updating have been identified: 1) Basic data update - keeping the model up-to-date with respect to input data, 2) improving the model description by including new or more detailed data, and 3) reconstructing the model concept. The three levels vary with respect to technical tasks, challenges and stakeholder involvement. Two utility programs developed to optimise the updating process and support the uptake of data and knowledge from local users are furthermore presented. Finally, some of the challenges in operating a national model with multiple users belonging to different institutions with varying demands are discussed.

Comparative reflections on the use of modelling tools in conflictive water management settings: The Mancha Occidental aquifer, Spain

Participatory methods provide an increasingly accepted path to integrated assessment. This paper reflects on the role of two participatory modelling initiatives implemented in a highly conflictive setting: the Mancha Occidental aquifer, Spain. The methodologies are described within the context of the case study, examining their potential relevance to integrated assessment from a conceptual standpoint. The strengths and weaknesses of each approach are analysed in absolute and relative terms, attending to the different stages of the modelling process. The focus then shifts to explore the implications of this work within the context of participatory integrated assessment and scenario analysis. This serves the purpose of establishing the reasons why the tools have been useful in the eyes of stakeholders, and how the case-specific findings of this project may be relevant to other settings.

Use of Bayesian belief networks for dealing with ambiguity in integrated groundwater management.

In integrated groundwater management, different knowledge frames and uncertainties need to be communicated and handled explicitly. This is necessary in order to select efficient adaptive groundwater management strategies. In this connection, Bayesian belief networks allow for integration of knowledge, for engaging stakeholders and for dealing with multiple knowledge frame uncertainties. This is illustrated for the case of the Upper Guadiana Basin, Spain, where Bayesian belief networks with stakeholder involvement were used for dealing with the ambiguities related to sustainable groundwater exploitation.

An evolutionary Bayesian belief network methodology for participatory decision making under uncertainty: An application to groundwater management

An integrated participatory approach based on Bayesian belief network (BBN) and evolutionary multiobjective optimization is proposed as an efficient decision-making tool in complex management problems. The proposed methodology incorporates all the available evidence and conflicting objectives to evaluate implications of alternative actions in the decision-making process and suggests best decision pathways under uncertainty. A BBN provides a framework within which the contributions of stakeholders can be taken into account. It allows a range of different factors and their probabilistic relationship to be considered simultaneously. It takes into account uncertainty by assigning probability to those variables whose states are not certain. The integration of BBN with evolutionary multiobjective optimization allows the analysis of tradeoff between different objectives and incorporation and acknowledgement of a broader set of decision goals into the search and decision-making process. The proposed methodology can be used as a decision support tool to model decision-making processes for complex problems. It deals with uncertainties in decision making pertaining to human behavior and checks for consistency of the developed BBN structure and the parameters of the probabilistic relationship by uncovering discrepancies in the decision analysis process (e.g., bias in completeness or redundancy of the model based on a utility function). It generates a set of efficient management options (appropriate combinations of interventions) that balances conflicting objectives. The effectiveness of the proposed methodology is discussed through application to a real case study. It is shown that it successfully identifies any inconsistencies in the developed BBN models and generates large numbers of management options that achieve an optimal tradeoff between different objectives.

Bayesian Networks as a Participatory Modelling Tool for Ground Water Protection

Participatory approaches help to better control and accelerate the integration, to make the decision-making process more transparent and comparable across transboundary river basins and scales, and to increase confidence in an integrated model-based planning process. Integration and participation can be significantly enhanced by using decision support systems (DSS) to assist the planning process, as they provide tools and platforms for collecting data from many sources, integrating models of different nature (physical, socioeconomical, and decisional), evaluating the effects of different planning alternatives, and in some cases, negotiating them. Models are an essential component of DSSs because they provide the system representation based on which the planning process is carried on. This chapter describes the use of Bayesian Networks (BNs)—a relatively recent modeling technique that is encountering wide diffusion in the environmental modeling community—in groundwater protection problems. The advantages of the BNs are that they are graphical, focus dialogue, integrate different types of data, and are interactive, trans- and interdisciplinary, and can quantify difficult cases. The graphical nature of BNs facilitates formal discussion of the structure of the proposed model. Also, the ability of a BN to describe the uncertain relationships among variables is ideal to describe the relationship between events that may not be well understood and intrinsically uncertain.