This paper reviews available models for estimating surface erosion and sediment delivery to streams from unsealed roads. It summarises current progress and identifies directions for ongoing research and model development. The paper provides a framework for assessing road erosion and sediment delivery models and it includes an overview of road erosion and sediment delivery processes and how they are commonly represented in models. Seven road models are reviewed in terms of their representations of erosion and sediment delivery processes, assumptions, application and limitations. While simple models are thought to be more useful and easily applied for land management purposes, more complex models provide a basis for building and consolidating scientific knowledge. This article reveals some of the limitations and needs of existing road erosion models. These include limitations of their ancestor hillslope erosion models, the imbalance between representation of erosion processes versus sediment delivery, a lack of representation of subsurface flow interception and the lack of model testing and uncertainty analysis. One of the most fundamental limitations to developing improved models of road erosion and delivery is access to data of an appropriate standard.

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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.

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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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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.

This chapter begins by briefly discussing the three major classes of groundwater dependent ecosystems (GDEs), namely: (I) GDEs that reside within groundwater (e.g. karsts; stygofauna); (II) GDEs requiring the surface expression of groundwater (e.g. springs; wetlands); and (III) GDEs dependent upon sub-surface availability of groundwater within the rooting depth of vegetation (e.g. woodlands; riparian forests). We then discuss a range of techniques available for identifying the location of GDEs in a landscape, with a primary focus of class III GDEs and a secondary focus of class II GDEs. These techniques include inferential methodologies, using hydrological, geochemical and geomorphological indicators, biotic assemblages, historical documentation, and remote sensing methodologies. Techniques available to quantify groundwater use by GDEs are briefly described, including application of simple modelling tools, remote sensing methods and complex modelling applications. This chapter also outlines the contemporary threats to the persistence of GDEs across the world. This involves a description of the "natural" hydrological attributes relevant to GDEs and the