The incorporation of computational workflows is well documented in disciplines like astrophysics, medicine, and environmental sciences. Their ability to represent computational components and data interactions enables automated scientific workflows, leading to increased efficiency and reproducibility. Diverse approaches have been proposed to improve the experience of crafting scientific experiments into computational workflows where manual and semi-automatic methods are well documented. In previous work, we proposed a method for automating workflow composition. However, component-to-component integration remains a challenge as common metadata notations are not enough to automatically identify equivalent variables under implicit scientific assumptions. In this work, we present VaR-O, an ontology to semantically identify equivalent variables based on implicit design decisions taken by domain experts when building scientific components. We also implement a tie-breaking strategy for component selection during the automated workflow composition process.

This Chapter will help those designing interdisciplinary graduate-level environmental courses to incorporate evidence-based approaches using the EMBeRS Framework (see Chap. 8 , This Volume) exemplified by a graduate course developed over seven years at the University of Texas at El Paso (UTEP).

Study region: The Middle Rio Grande (MRG), defined by the portion of the basin from Elephant Butte Reservoir in New Mexico to the confluence with the Rio Conchos in Far West Texas, U.S.A. and Northern Chihuahua, Mexico.Study focus: The future of water for the MRG and many other arid and semi-arid regions of the world is challenged by a changing climate, agricultural intensification, growing urban pop-ulations, and a segmented governance system in a transboundary setting. The core question for such settings is: how can water be managed so that competing agricultural, urban, and envi-ronmental sectors can realize a sustainable future? We synthesize results from interdisciplinary research aimed at "water futures", considering possible, probable, and preferable outcomes from the known drivers of change in the MRG in a stakeholder participatory mode. We accomplished this by developing and evaluating scenarios using a suite of scientifically rigorous computer models, melded with the input from diverse stakeholders.New hydrological insights for the region: Under likely scenarios without significant interventions, relatively cheap and easy to access water will be depleted in about 40 years. Interventions to mitigate this outcome will be very costly. A new approach is called for based on "adaptive cooperation" among sectors and across jurisdictions along four important themes: information sharing, water conservation, greater development and use of alternative water sources, and new limits to water allocation/withdrawals coupled with more flexibility in uses.

This chapter will introduce the Employing Model-based Reasoning in Socio-Environmental Synthesis (EMBeRS) Framework for facilitating learning across disciplines and integrating those perspectives to address specific wicked problems.

The purpose of this chapter is to provide general concepts, principles and methods for planning and implementing evaluation processes for collaborative team projects. Evaluation is a process that involves collecting and analyzing information about a project or program's activities, characteristics, and outcomes. Its purpose is to make judgments about the outcomes, improve effectiveness, and inform decisions. The terms assessment and evaluation are seen as distinct in some contexts such that evaluation is described as summative, occurring at the end of a project, and designed to pass judgement on the results based on predetermined criteria or standards; and assessment is described as formative, occurring during the project as it is implemented, and designed to improve performance. However, in project and program evaluation practice, these two processes are often intertwined with a focus on continuous improvement and adaptation of a project as it unfolds, as well as collecting evidence of achievement of the goals at specific points in the project timeline and at the end of the project. This type of evaluation is called developmental evaluation and it’s an iterative, ongoing process that combines both formative and summative aspects to support ongoing project design modifications as the project unfolds [19]. There are many approaches to evaluation based on the specific context of the project to be evaluated and the purpose of the evaluation. These can include judging the performance of the project based upon expert or user defined criteria, evaluating the functional characteristics of the project including processes and outcomes, providing evidence to support decision making relevant to the project’s operation and results, and/or supporting the participation and role of stakeholders in the project (Fitzpatrick et al. in Program evaluation: alternative approaches and practical guidelines, Pearson, New York, 2011). To determine the best approach, start with key questions: Developmental evaluation is a type of evaluation often used to support collaborative initiatives, especially in complex and dynamic situations [21]. In this type of evaluation, periodic evaluations are performed to assess the impact or influence of the collaboration, while also continuously and iteratively assessing the strategies and activities designed to achieve the desired outcomes. Developmental evaluation is used to provide feedback on the project design as it is being implemented and is useful for innovative projects. Evaluation and research have different purposes but may overlap. Evaluation supports improvements, judgements, and actionable learning while research generates knowledge about how the world works and why. Research informs evaluation—the more information that exists about the goals of the collaboration and how the various strategies and activities will achieve the outcomes, the more the evaluation can draw on that knowledge. However, the primary purpose of evaluation is to determine the effectiveness of the project activities and strategies in achieving pre-defined outcomes. Evaluation outcomes may include research-related outcomes. Success is determined by results from validated instruments used in evaluation, the participants’ perspectives, other experts in the field, and/or peer-reviewed publications. If the design of the project itself is based on research questions, then evaluation can serve to test specific theoretical frameworks.

Many societal-relevant challenges, including environmental ones, require comprehensive approaches that integrate decoupled data, models, and perspectives. Integrating data and models is critical for these approaches but can also become cumbersome. Computational workflows are widely used to integrate heterogeneous data and computational processes within or across domains. However, creating computational workflows may require computational and domain expertise not necessarily possessed by potential users. This paper presents our efforts to enable automated multivariable workflow composition implemented as a workflow composer in the Sustainable Water for Integrated Modeling (SWIM) platform. We describe the uninformed search algorithm used in the workflow composer and an initial evaluation with a case study that requires integrating two water (balance) models that cover the Middle Rio Grande in the U.S. Southwest region. Preliminary results show that the evaluation of integrating models should not only consider the technical and scientific perspective but also how users understand and use the results of these complex systems. Efforts toward automating the model-to-model integration can significantly support scientific endeavors and decision-making by enabling various stakeholders to use scientific models.

Water sustainability in cities has become a priority concern due to growing city populations and climate change. This is particularly important for cities that face severe water challenges, such as the twin border cities of Ciudad Juarez, Chihuahua in Mexico, and El Paso, Texas, USA. While the municipal utilities and government make immediate decisions about water sourcing, pricing, and use, both are public agencies, subject to democratic participation and decision-making. An integrated platform solution may be convenient for stakeholders that interact with multiple aspects of a complex and dynamic system, such as those involved in water sustainability. The Sustainable Water through Integrated Modeling (SWIM) platform provides comprehensible regional water models publicly on the Web that would otherwise only be accessible to domain experts. SWIM leverages future scenario analysis for citizen engagement. This paper presents the motivation, architecture, user interface, and capabilities of SWIM and how it can interoperate with Smart City ICT platforms to enable dynamic systems modeling for decision-making in a Smart City sustainable environment.

The interpretation and use of scientific models are relevant to public discourse and decision-making about future water scenarios.Tools to use these scientific models aim to facilitate understanding water systems; however, the information in these tools can be so vast, complex, and prone to uncertainties that users may find using some of these tools a cumbersome task.Users can be presented with numerous outputs, visualizations, and vocabulary that are irrelevant or do not align with their perspective (i.e., user role) in the water system under study.To address these issues, the SWIM platform extends the capabilities of traditional Web-based graphical interfaces to foster the use of scientific models, in particular, water sustainability models.With the integration of a recommender system and a dynamic interface, SWIM provides users with a list of prioritized outputs (i.e., modeling results) based on their perspective, to reduce the overload of data presented to users.Users are also provided with a range of output visualization graphs and narrative elements (i.e., contextual natural language descriptions) to support the interpretation of model results.The SWIM interface design provides a seamless high-level workflow for models developed in different modeling software tools and languages.The SWIM platform is designed to foster interoperability by providing open APIs and knowledge bases to access data, metadata, and models (i.e., Model-As-A-Service).This presentation highlights the key elements of SWIM that enable the interpretation of scientific model outputs from different perspectives and the interoperability features of this platform.

Modelling complex socio-environmental problems requires integration of knowledge across disparate fields of expertise. A key challenge is understanding how social learning across disciplines occurs in scientific research teams, in order that integrated knowledge is co-created. This article introduces a new framework for training researchers to integrate their knowledge across disciplines, based on current understanding of how inter- and transdisciplinary learning in research teams occurs. The framework was generated from a synthesis of learning, cognitive, and social science theories, and combines facilitated, structured negotiation processes with co-creation of boundary objects. It was used in two, 9 to 10-day intensive training workshops for doctoral students. This article describes the framework, workshop design, analysis of data collected during the workshops related to knowledge integration processes, what has been learned from the results, and the impact on participants. All participants indicated the experience was transformative, provided knowledge and skills unavailable elsewhere, filled gaps in their graduate education programs, and improving confidence in their capacity for inter- and transdisciplinary research. Pre- and post-workshop surveys confirm that the framework changed participants’ knowledge, behaviors, and competencies for engaging across disciplines. Many students have reported they have used the framework in a variety of other research and education settings, indicating they are able to transfer their new competencies to other contexts. Findings contribute to understanding of how to more effectively train researchers to integrate knowledge across disciplines for complex societal problem solving.

The interdisciplinary research (IR) that is necessary for the creation of innovative solutions for the many complex environmental challenges facing society requires collaboration and the sharing and integration of knowledge from different disciplines in teams. Higher education programs should deploy effective pedagogical approaches to train students in interdisciplinary, team research collaboration. This paper discusses the design of three learning modules that supported the development of collaboration and teamwork skills among doctoral students during an IR workshop held in 2017 at the University of Texas at El Paso. The module activities were scaffolded to provide multiple opportunities for students to develop knowledge about the impacts that individual dispositional characteristics and differences in epistemological philosophies can have on teamwork processes. The activities and the workshop overall created opportunities for the students to apply this knowledge in a variety of authentic, collaborative contexts. An inquiry approach to pedagogical practice was used to address two key questions: (1) Did the learning modules increase knowledge of the impact of sharing dispositional features of team members on the practice of IR? (2) How confident were workshop participants in their ability to adapt to dispositional and epistemological diversity during future IR team activities? Results from a post-workshop questionnaire data, group reflections, and retrospective pre- and post-assessment showed (1) participants learned and practiced essential collaborative skills in authentic contexts; (2) the modules were valued and helped participants recognize the important role that personal dispositional characteristics have on the development of effective IR teams; (3) participants’ confidence in adapting to differences among team members increased; and (4) participants recognized that effective collaboration is an emergent property of a team that benefits from the overall intentionality of using a defined process and communication strategy.

Given the rapid emergence of data science techniques in the sustainability sciences and the societal importance of many of these applications, there is an urgent need to prepare future scientists to be knowledgeable in both their chosen science domain and in data science. This article provides an overview of required competencies, educational programs and courses that are beginning to emerge, the challenges these pioneering programs face, and lessons learned by participating instructors, in the broader context of sustainability science competencies. In addition to data science competencies, competencies collaborating across disciplines are essential to enable sustainability scientists to work with data scientists. Programs and courses that target both sets of competencies—data science and interdisciplinary collaboration—will improve our workforce capacity to apply innovative new approaches to yield solutions to complex sustainability problems. Yet developing these competencies is difficult and most instructors are choosing instructional approaches through intuition or trial and error. Research is needed to develop effective pedagogies for these specific competencies.

The wide variety in descriptions, implementations, and accessibility of scientific models poses a huge challenge for model interoperability. Model interoperability is key in the automation of tasks including model integration, seamless access to distributed models, data reuse and repurpose. Current approaches for model interoperability include the creation of generic standards and vocabularies to describe models, their inputs and outputs. These domain-agnostic standards often do not provide the fine-grained level required to describe a specific domain or task, and extending such standards requires a considerable amount of effort and time that is deviated from the purpose of producing scientific breakthrough and results. This paper presents a semi-structured, knowledge-based framework implemented with a service-driven architecture: The Sustainable Water through Integrated Modelling Framework (SWIM). SWIM is part of an ongoing effort to expose water sustainability models on the Web with the goal of enabling stakeholder engagement and participatory modelling. SWIM is a science-driven platform, leveraged by the technology advances on service-oriented architectures (SOA), schemaless database managers (NoSQL) and widely used Web-based frontend frameworks. The SWIM semi-structured knowledge model is flexible enough to adapt on-the-go as the underlying water sustainability models grow in complexity. SWIM fosters the sharing and reuse of data and models generated in the system by providing the descriptions of models, inputs, and outputs of each run using relevant metadata mapped to widely-used standards with JSON-LD, a JSON extension for linked data.

The creation of scientific models to understand water availability under different scenarios is an important step towards pursuing a sustainable water future. A wide variety of scientific models have been created for understanding the different elements driving water availability in urban, agricultural and ecological settings. The Sustainable Water through Integrated Modeling Framework (SWIM) enables a wide range of stakeholders to run water-sustainability model scenarios through participatory modeling. Although SWIM is a science-driven platform, it was created with input from diverse stakeholders with the goal of improving how water models can be used and shared. SWIM aims to foster a better understanding on the impact that decisions about water usage can have. This paper describes our efforts towards translating the science behind the models generated in SWIM into English and Spanish explanations, also known as narratives. We anticipate that narratives will better communicate the meaning of specific water-economics scenarios under different perspectives, including urban, agriculture and environmental. Thus, assisting stakeholders in decision making.