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Models of socio-environmental or social-ecological systems (SES) commonly address problems requiring interdisciplinary scientific expertise and input from a heterogeneous group of stakeholders. In SES modelling multiple interactions occur on different scales among various phenomena. These scale phenomena include the technical, such as system variables, process detail, inputs and outputs, which most often require spatial, temporal, thematic and organisational choices. From a good practice and project efficiency perspective, the problem scoping and conceptual model formulation phase of modelling is the one to address well from the outset. During this phase, intense and substantive discussions should arise regarding appropriate scales at which to represent the different phenomena. Although the details of these discussions influence the path of model development, they are seldom documented and as a result often forgotten. We draw upon personal experience with existing protocols and communications in recent literature to propose preliminary guidelines for documenting these early discussions about the scale(s) of the studied phenomena. Our guidelines aim to aid modelling group members in building and capturing the richness of their rationale for scoping and scale decisions. The resulting transcripts are intended to promote transparency of modelling decisions and provide essential support for the justification of the final model for its intended use. They also facilitate adaptive modifications of the pathway of model development via retracing decisions and iterative reflection upon alternative scale options.

In this synthesis, we assess present research and anticipate future development needs in modeling water quality in watersheds. We first discuss areas of potential improvement in the representation of freshwater systems pertaining to water quality, including representation of environmental interfaces, in‐stream water quality and process interactions, soil health and land management, and (peri‐)urban areas. In addition, we provide insights into the contemporary challenges in the practices of watershed water quality modeling, including quality control of monitoring data, model parameterization and calibration, uncertainty management, scale mismatches and provisioning of modeling tools. Finally, we make three recommendations to provide a path forward for improving watershed water quality modeling science, infrastructure and practices. These include building stronger collaborations between experimentalists and modelers, bridging gaps between modelers and stakeholders, and cultivating and applying procedural knowledge to better govern and support water quality modeling processes within organizations.

System-of-systems approaches for integrated assessments have become prevalent in recent years. Such approaches integrate a variety of models from different disciplines and modeling paradigms to represent a socio-environmental (or social-ecological) system aiming to holistically inform policy and decision-making processes. Central to the system-of-systems approaches is the representation of systems in a multi-tier framework with nested scales. Current modeling paradigms, however, have disciplinary-specific lineage, leading to inconsistencies in the conceptualization and integration of socio-environmental systems. In this paper, a multidisciplinary team of researchers, from engineering, natural and social sciences, have come together to detail socio-technical practices and challenges that arise in the consideration of scale throughout the socio-environmental modeling process. We identify key paths forward, focused on explicit consideration of scale and uncertainty, strengthening interdisciplinary communication, and improvement of the documentation process. We call for a grand vision (and commensurate funding) for holistic system-of-systems research that engages researchers, stakeholders, and policy makers in a multi-tiered process for co-creation of knowledge and solutions to major socio-environmental problems.

Similar to other modelling methodologies, the potential of system dynamics to contribute to system understanding and decision making depends upon the practices applied by the modeller. However lessons about many of these practices are often unreported. This paper contributes to the methodology of system dynamics modelling of socio-ecological systems by 1) examining issues modellers face during the modelling process, and 2) providing guidance on how to effectively design and implement system dynamics modelling. This is achieved through an investigation of five case studies, drawing on lessons from these experiences. This is complemented by a literature review of system dynamics applied within the context of integrated modelling and environmental DSS. The case studies cover a variety of environmental issues and system dynamics modelling methods and tools. Although we used system dynamics as the common lens from which lessons are drawn, many of these insights transcend to other integrated modelling approaches.

The complex and multidisciplinary nature of environmental problems requires that they are dealt with in an integrated manner. Modeling and software have become key instruments used to promote sustainability and improve environmental decision processes, especially through systematic integration of various knowledge and data and their ability to foster learning and help make predictions. This book presents the current state-of-the-art in environmental modeling and software and identifies the future challenges in the field.* State-of-the-art in environmental modeling and software theory and practice for integrated assessment and management serves as a starting point for researchers * Identifies the areas of research and practice required for advancing the requisite knowledge base and tools, and their wider usage* Best practices of environmental modeling enables the reader to select appropriate software and gives the reader tools to integrate natural system dynamics with human dimensions

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.

Integrated assessment models, decision support systems (DSS) and Geographic Information Systems (GIS) are examples of a growing number of computer-based tools designed to provide decision and information support to people engaged in formulating and implementing environmental policy and management. It is recognised that environmental policy and management users are often not as receptive to using such tools as desired but that little research has been done to uncover and understand the reasons. There is a diverse range of environmental decision and information support tools (DISTs) with uses including organisational and participatory decision support, and scientific research. The different uses and users of DISTs each present particular needs and challenges to the tool developers. The lack of appreciation of the needs of end-users by developers has contributed to the lack of success of many DISTs. Therefore it is important to engage users and other stakeholders in the tool development process to help bridge the gap between design and use. Good practice recommendations for developers to involve users include being clear about the purpose of the tool, working collaboratively with other developers and stakeholders, and building social and scientific credibility.

Models have become indispensable in environmental assessment, planning and management. However as models have increasingly been developed and disseminated, the risk of their misuse or misunderstanding of their capabilities has increased. Whether a model is used for simulation, prediction, decision making or communication of scientific analyses, it is important that its development and application conform to protocols or standards that help to maximise the scientific soundness, utility and defensibility of models and their outputs. The complexity and uncertainty inherent in environmental assessment make the pursuit of good modelling practice especially important, in spite of limited time and resources. This paper is an attempt to identify the key components of best modelling practice and our collective progress in its achievement, taking into account previous relevant reviews undertaken by several authors and agencies. The details are always likely to be the subject of lively debate, but the general components of ‘good modelling practice’ are probably not controversial. They are clear purpose, adequate reporting, and serious evaluation. Although these are common strands in the various definitions of good modelling practice the emphasis varies between different types of model application. For this reason it is important that good practice should not become overly prescriptive.We report a preliminary analysis which suggests that progress towards improving modelling practice is slow. This is despite very widespread agreement on what constitutes good practice. Why is this so? In the research community at least, the drivers for model development and evaluation are funding and publication. If modelling practice needs to be improved, and we think it does, sponsors and journals need to take a lead in creating an environment where developing a model requires that the work be undertaken under some system of good modelling practice. The suggestion has been made of a ‘good pract

The Water Framework Directive (WFD) imposes a new approach to water resource management inthe EU states. Uncertainty surrounding its implementation, however, could badly affect the achievement ofthe objectives set by the Directive. Although not directly linked to a set of techniques to deal with it, the WFDand accompanying guideline documents identify uncertainty as a factor likely to play a significant role inassessing the risk of failing to achieve the objectives and setting up the required programmes of measures. In this paper, by addressing the initial description of a river basin we analyse uncertainty in socioeconomicdescriptors such as demographic and water-use data. Socioeconomic data, models and evaluation techniquessupporting the economic analysis of water uses are crucial parts of a Decision Support System (DSS) aimed at facilitating the WFD implementation.

The spatial spread of pollen is modeled by transport equations from atmospheric physics. These equations are coupled with a population dynamic and genetic model. The resulting model consists of a set of coupled partial differential equations which is solved by finite element methods. As example the spatial spread of a resistant trait is simulated.

MULINO is an EU RTD project, funded under the FP5-EESD programme. It aims at providing aDecision Support System (DSS) targeted at solving decision problems in the management of water resources.Through the integration of socio-economic and environmental modelling techniques with geographicinformation system (GIS) capabilities and multi-criteria decision aids, the MULINO-DSS aspires to be anoperational tool which meets the needs of European water management authorities and facilitates theimplementation of the EU Water Framework Directive. The application-driven approach to developing theMULINO-DSS combines the scientific background of the consortium members with local knowledge anddecision support needs, expressed by five user groups. The diversity of cultural, socio-economic andenvironmental characteristics of the case studies requires that the tool is capable of a common approach todifferent decision cases but also flexible enough to adapt to the specific objectives and constraints of a givendecision problem. The DPSIR framework (Driving forces – Pressure – State – Impact – response) has beenchosen as the overall conceptual framework of the DSS. A demonstration of the first MULINO-DSSprototype is presented through an application example in the Vela catchment that belongs to the VeniceLagoon Watershed (north-east Italy). The decision act refers to the choice among alternative actions (publicworks) for the improvement of Vela’s water quality.