In order to use environmental models effectively for management and decision-making, it is vital to establish an appropriate level of confidence in their performance. This paper reviews techniques available across various fields for characterising the performance of environmental models with focus on numerical, graphical and qualitative methods. General classes of direct value comparison, coupling real and modelled values, preserving data patterns, indirect metrics based on parameter values, and data transformations are discussed. In practice environmental modelling requires the use and implementation of workflows that combine several methods, tailored to the model purpose and dependent upon the data and information available. A five-step procedure for performance evaluation of models is suggested, with the key elements including: (i) (re)assessment of the model's aim, scale and scope; (ii) characterisation of the data for calibration and testing; (iii) visual and other analysis to detect under- or non-modelled behaviour and to gain an overview of overall performance; (iv) selection of basic performance criteria; and (v) consideration of more advanced methods to handle problems such as systematic divergence between modelled and observed values.

Position paper: Characterising performance of environmental models

Models are increasingly being relied upon to inform and support natural resource management. They are incorporating an ever broader range of disciplines and now often confront people without strong quantitative or model-building backgrounds. These trends imply a need for wider awareness of what constitutes good model-development practice, including reporting of models to users and sceptical review of models by users. To this end the paper outlines ten basic steps of good, disciplined model practice. The aim is to develop purposeful, credible models from data and prior knowledge, in consort with end-users, with every stage open to critical review and revision. Best practice entails identifying clearly the clients and objectives of the modelling exercise; documenting the nature (quantity, quality, limitations) of the data used to construct and test the model; providing a strong rationale for the choice of model family and features (encompassing review of alternative approaches); justifying the techniques used to calibrate the model; serious analysis, testing and discussion of model performance; and making a resultant statement of model assumptions, utility, accuracy, limitations, and scope for improvement. In natural resource management applications, these steps will be a learning process, even a partnership, between model developers, clients and other interested parties.

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Position Paper11Position papers aim to synthesise some key aspect of the knowledge platform for environmental modelling and software issues. The review process is twofold - a normal external review process followed by extensive review by EMS Board members. See the Editorial in this issue.: Ten iterative steps in development and evaluation of environmental models

https://digitalcommons.usu.edu/cgi/viewcontent.cgi?article=1018&context=cee_facpub

Hydro-economic models: concepts, design, applications, and future prospects.

http://www.seamless-ip.org/DVD_Consortium_SEAMLESS/6_Dissemination/SEAMLESS-overview-AGSY.pdf

Integrated assessment of agricultural systems: a component - based framework for the European Union (Seamless)

Integrated environmental modeling (IEM) is inspired by modern environmental problems, decisions, and policies and enabled by transdisciplinary science and computer capabilities that allow the environment to be considered in a holistic way. The problems are characterized by the extent of the environmental system involved, dynamic and interdependent nature of stressors and their impacts, diversity of stakeholders, and integration of social, economic, and environmental considerations. IEM provides a science-based structure to develop and organize relevant knowledge and information and apply it to explain, explore, and predict the behavior of environmental systems in response to human and natural sources of stress. During the past several years a number of workshops were held that brought IEM practitioners together to share experiences and discuss future needs and directions. In this paper we organize and present the results of these discussions. IEM is presented as a landscape containing four interdependent elements: applications, science, technology, and community. The elements are described from the perspective of their role in the landscape, current practices, and challenges that must be addressed. Workshop participants envision a global scale IEM community that leverages modern technologies to streamline the movement of science-based knowledge from its sources in research, through its organization into databases and models, to its integration and application for problem solving purposes. Achieving this vision will require that the global community of IEM stakeholders transcend social, and organizational boundaries and pursue greater levels of collaboration. Among the highest priorities for community action are the development of standards for publishing IEM data and models in forms suitable for automated discovery, access, and integration; education of the next generation of environmental stakeholders, with a focus on transdisciplinary research, development, and decision making; and providing a web-based platform for community interactions (e.g., continuous virtual workshops).

/pdf/integrated-environmental-modeling-a-vision-and-roadmap-for-1nvakawpfk.pdf

Integrated environmental modeling: A vision and roadmap for the future

Environmental processes have been modelled for decades. However. the need for integrated assessment and modeling (IAM) has,town as the extent and severity of environmental problems in the 21st Century worsens. The scale of IAM is not restricted to the global level as in climate change models, but includes local and regional models of environmental problems. This paper discusses various definitions of IAM and identifies five different types of integration that Lire needed for the effective solution of environmental problems. The future is then depicted in the form of two brief scenarios: one optimistic and one pessimistic. The current state of IAM is then briefly reviewed. The issues of complexity and validation in IAM are recognised as more complex than in traditional disciplinary approaches. Communication is identified as a central issue both internally among team members and externally with decision-makers. stakeholders and other scientists. Finally it is concluded that the process of integrated assessment and modelling is considered as important as the product for any particular project. By learning to work together and recognise the contribution of all team members and participants, it is believed that we will have a strong scientific and social basis to address the environmental problems of the 21st Century.

Progress in integrated assessment and modelling

Publisher Summary This chapter suggests that teams of researchers and partners should work together to build integrated tools to analyze and represent the known processes underway, to understand the environmental problems of the 21 st century. Environmental problems are caused by complex interactions among physical and human systems that demand analytical skills beyond those of a single discipline. Integrated assessment and modeling (IAM) is applied at the global level and at the local/regional level. The central role of human decisions and actions as drivers of environmental change is recognized in new IAM approaches. Various definitions of IAM and five different types of integration that are needed for the effective solution of environmental problems are also discussed. The fixture is then depicted in the form of two brief scenarios: one optimistic and the other pessimistic. The evolution of IAM and its current state are reviewed and examples of recent case studies are identified. Integrated assessment is introduced as a means to respond to the need for better indicators of sustainability. The issues of complexity and validation are recognized as complex. Communication is identified as a central issue, both internally among team members and externally with decision makers, stakeholders, and other scientists. Links with other research groups are recognized and points of shared interest are identified. The process of integrated assessment and modeling is considered as important as the product for any particular project.

P. Tarikhi

Papers

Progress in integrated assessment and modelling

The Potential for Integrated Assessment and Modeling to Solve Environmental Problems: Vision, Capacity, and Direction