Environmental modelling is transitioning from the traditional paradigm that focuses on the model and its quantitative performance to a more holistic paradigm that recognises successful model-based outcomes are closely tied to undertaking modelling as a social process, not just as a technical procedure. This paper redefines evaluation as a multi-dimensional and multi-perspective concept, and proposes a more complete framework for identifying and measuring the effectiveness of modelling that serves the new paradigm. Under this framework, evaluation considers a broader set of success criteria, and emphasises the importance of contextual factors in determining the relevance and outcome of the criteria. These evaluation criteria are grouped into eight categories: project efficiency, model accessibility, credibility, saliency, legitimacy, satisfaction, application, and impact. Evaluation should be part of an iterative and adaptive process that attempts to improve model-based outcomes and foster pathways to better futures.

The effectiveness of Integrated Water Resource Management (IWRM) modeling hinges on the quality of practices employed through the process, starting from early problem definition all the way through to using the model in a way that serves its intended purpose. The adoption and implementation of effective modeling practices need to be guided by a practical understanding of the variety of decisions that modelers make, and the information considered in making these choices. There is still limited documented knowledge on the modeling workflow, and the role of contextual factors in determining this workflow and which practices to employ. This paper attempts to contribute to this knowledge gap by providing systematic guidance of the modeling practices through the phases (Planning, Development, Application, and Perpetuation) and steps that comprise the modeling process, positing questions that should be addressed. Practice-focused guidance helps explain the detailed process of conducting IWRM modeling, including the role of contextual factors in shaping practices. We draw on findings from literature and the authors' collective experience to articulate what and how contextual factors play out in employing those practices. In order to accelerate our learning about how to improve IWRM modeling, the paper concludes with five key areas for future practice-related research: knowledge sharing, overcoming data limitations, informed stakeholder involvement, social equity and management of uncertainty.

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There are substantial challenges facing humanity in the water and related sectors and purposeful integration of the disciplines, connected sectors and interest groups is now perceived as essential to address them. This article describes and uses bibliometric analysis techniques to provide quantitative insights into the general landscape of Integrated Water Resource Assessment and Modelling (IWAM) research over the last 45 years. Keywords, terms in titles, abstracts and the full texts are used to distinguish the 13,239 IWAM articles in journals and other non-grey literature. We identify the major journals publishing IWAM research, influential authors through citation counts, as well as the distribution and strength of source countries. Fruitfully, we find that the growth in numbers of such publications has continued to accelerate, and attention to both the biophysical and socioeconomic aspects has also been growing. On the other hand, our analysis strongly indicates that the former continue to dominate, partly by embracing integration with other biophysical sectors related to waterenvironment, groundwater, ecology, climate change and agriculture. In the social sciences the integration is occurring predominantly through economics, with the others, including law, policy and stakeholder participation, much diminished in comparison. We find there has been increasing attention to management and decision support systems, but a much weaker focus 2 on uncertainty, a pervasive concern whose criticalities must be identified and managed for improving decision making. It would seem that interdisciplinary science still has a long way to go before crucial integration with the non-economic social sciences and uncertainty considerations are achieved more routinely.

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