Research, part of a special feature on Convergent Science for Sustainable Regional Systems

INTRODUCTION

Billions of people and critical ecosystems rely on managed water resource systems (Jackson et al. 2001, Cosgrove and Loucks 2015). These systems involve the storage and transport of water through some combination of seasonal snowpacks, constructed reservoirs, groundwater aquifers, lakes, rivers, aqueducts, and pipes—facilitated by both human activities and hydrological processes. Water resource systems are designed to buffer human communities, and in some cases, ecosystems, from hydrologic extremes such as drought and flooding (Loucks and van Beek 2017). However, many contemporary water resource systems were designed for different conditions than we have today (Woodhouse et al. 2005, Rao et al. 2018, Chen et al. 2021, Siirila-Woodburn et al. 2021), and now climate change, shifting land use, and rising water demand are diminishing their buffering capacity (Viviroli et al. 2011). The need to adapt water resource management to new conditions is increasingly acute, yet the complexity of doing so remains a formidable challenge.

Water resource management is widely acknowledged to be a “wicked” problem because it is replete with uncertainties and risks and lacks one clear path or point of resolution (Rittel and Webber 1973, Liebman 1976). However, the nature of wicked problems—having multiple paths toward multiple forms of resolution—can be viewed as an opportunity if only the paths and points of resolution can be identified and agreed upon (Reed and Kasprzyk 2009). A major barrier to realizing this opportunity is effective integration and co-production of knowledge across and beyond academic disciplines (Reed and Kasprzyk 2009, Montanari et al. 2013). Given the complex social, ecological, and technological interactions that define water resource systems (Markolf et al. 2018, Gittins et al. 2021), reaching beyond conventional disciplinary training to find new, integrated approaches is vital.

Convergence research is an emerging approach that draws on the traditions of multi-, inter-, and transdisciplinary research (e.g., Nikitina 2006, Yong et al. 2014, Gilligan 2021) to promote new thinking and innovation through novel framework development and deep integration of knowledge, tools, and modes of thinking (Gropp 2016, Tornow et al. 2018, Peek et al. 2020, Angeler et. al 2020). According to Gropp (2016), a convergent approach “augments a more traditional transdisciplinary approach to research by framing challenging research questions at inception.” Its focus on “specific and compelling problems” and “pressing societal needs” (Tornow et al. 2018) makes convergence research well suited to the wicked problem of adapting water resource management to new conditions. Convergence research is also meant to augment other approaches with focus on “fostering the collaborations needed for successful inquiry” (Gropp 2016). In this way, convergence research builds on the traditions of team science and team building research that study approaches and efficacy of collaboration in research teams (Fiore 2008, Klein et al. 2009). Convergence research, however, is more acutely confronted with the cognitive and epistemic difficulties researchers experience in non-conventional research teams (Boon and van Baalen 2019) due to its emphasis on deep integration across disciplines.

Elsewhere in this Special Feature, we identify systems thinking as a core approach for convergence research (Morgan et al. 2025). Systems thinking is a body of theory and practical application that aims to infer how systems behave by gaining a deeper understanding of their underlying structure (Richmond 1994). In this definition, a “system” is defined as a collection of interconnected elements that generate behavioral patterns over time (Meadows 2008). Systems thinkers have developed a constellation of modeling approaches (Box 1), ranging from conceptual and qualitative to computational and quantitative, where “models” refer to any stylized representation of a system (Frigg and Hartmann 2006) and “modeling” refers to the formulation of models with the purpose of exploring systems’ underlying structures. System dynamics modeling is one such set of approaches that emphasizes the interactions of stocks (elements that can accumulate or be depleted) and flows (elements that make stocks increase or decrease) to describe system behavior (Richmond 1994). System dynamics modeling is intuitively useful for describing water resource systems because water stocks (e.g., reservoirs) and flows (e.g., rivers) are key elements. However, stocks and flows can also be used to describe less physical elements, such as the accumulation and loss of trust in water management authorities among members of a population (Richmond 1994). Even so, system dynamics modeling conventions can be limiting, so systems thinkers deploy a variety of other modeling approaches to meet different needs. Here, we use “system dynamics modeling” to refer to qualitative and quantitative models that emphasize stocks and flows, and “system modeling” to refer to all other modeling approaches used by systems thinkers. Systems thinking can advance the development of convergence research programs because it provides a breadth of methods that are accessible to a range of users and relevant to a range of knowledge types (Morgan et al. 2025), but which all facilitate the formation of explicit agreements within research teams about interconnections between elements of a system (Richmond 1994, Meadows 2008).

Despite its promise for convergence research in general, we suggest that current methods in systems thinking are incomplete for convergence research on water resource management specifically. First, it is difficult to understand the behavior of water resource systems without acknowledging that they are multi-scaled, with processes at fine spatial and temporal scales influencing processes at coarse scales and vice versa (Ostrom 2009, Sivapalan and Blöschl 2015, York et al. 2019). Systems thinking should ideally reflect this multi-layeredness to accurately model different behaviors observed at different scales and to preserve relationships between the nuances available at finer scales and the abstractions necessary at coarser scales (Famiglietti and Wood 1994, Madrid et al. 2013, Belmont and Foufoula-Georgiou 2017). Yet, most system modeling methodologies start with delineating one set scope and scale for practical purposes (Midgley and Rajagopalan 2021). Second, water resources research often stands out for the number and diversity of perspectives and priorities involved from both stakeholders and outside researchers (Sivapalan and Blöschl 2015, Flint et al. 2023a), which would ideally be well represented in a convergence research program (Morgan et al. 2025). Although collaborative modeling across and beyond academic disciplines has long been a core idea in systems thinking (Box 1; Vennix 1996, Lamb and Rhodes 2010, Hovmand et al. 2012, Langsdale et al. 2013), modeling teams are usually convened for a single project, causing models to represent a snapshot in time from the perspective of a particular group of participants (e.g., Tidwell et al. 2004, Dhirasasna and Sahin 2019, Luna-Reyes et al. 2019). Ongoing collaboration, when it occurs, is not formalized in the modeling process, and emerging ideas or questions from evolving community partnerships and research collaborations are typically treated as new projects rather than continuations. Although this research approach may lead to broad knowledge shifts over time, it hinders integration of diverse perspectives into addressing a specific problem, as a convergent approach demands. Moreover, we posit that a convergent approach will only be effective if it prioritizes creating a culture of belonging (inclusion) and removing barriers for participation in research teams (equity), as doing so will allow deeper integration of knowledge. Although systems thinking commonly asks who was included and excluded from a project (Midgley and Rajagopalan 2021), it only very recently has begun asking explicitly about how its collaborative approaches might address issues of equity and inclusion (e.g., Polaine et al. 2022, Isar 2023). Third, these limits on collaborative and multi-scale approaches together limit the adaptiveness of a systems thinking project to new ideas, new questions, and new collaborations that may change the design, scope, or scale of models. Adaptive approaches are necessary to address the rapidly changing conditions that water resource systems are experiencing (Pahl-Wostl 2007), but are currently lacking in system modeling.

To address these limitations, we propose a new framework aimed at making systems thinking more broadly collaborative, adaptive, and multi-scalar for applications to convergence research on water resource systems. Here, we describe how we developed and applied the Collaborative, Adaptive, and Multi-Scale (CAMS) systems thinking framework to build a convergence research team around the tasks of characterizing a water resource system and hypothesizing water management dynamics there. The CAMS framework applies systems thinking as a method to engage and integrate the knowledge and interests of an evolving research team and to build models of a water resource system across spatial and temporal scales. We used the Santa Fe Watershed in New Mexico, USA as a case study system due to the confluence of opportunities and challenges there that reflect water management issues throughout the western USA (Box 2). We asked the following research questions:

1. Does the CAMS framework effectively facilitate the process of building a convergence research program on water resource management through its collaborative, adaptive, and multi-scale design?
2. What disciplinary and epistemological divides are bridged with the CAMS framework, and which are not?
3. Is the CAMS framework inclusive to diverse teams of researchers, and can it be used to foster inclusive and equitable collaboration with others in the future?

Our first question asked how and to what extent the collaborative, adaptive, and multi-scalar features of the framework contributed to the goals of convergence research in general and to addressing our identified limitations of systems thinking for water resources specifically. Our second question asked this in more depth by focusing on the frameworks’ contribution to addressing the cognitive and epistemic difficulties inherent in a convergent team science approach. Our third question about inclusive and equitable collaboration uniquely focused on whether the framework was able to create a culture of belonging and remove systemic barriers for participation, which we believe is central to fully realizing its collaborative and adaptive design.

Our overarching hypothesis was that the multi-scalar and adaptive design of the CAMS framework would provide more and deeper opportunities for collaboration and allow more insightful characterization of water resource systems than other systems thinking methods alone, thus making it an effective approach for convergence research on water resources. We further hypothesized that the use of systems thinking in a multi-scalar and adaptive framework would facilitate an inclusive environment for participants by bridging divides formed by disciplinary-specific and scale-dependent knowledge. Following the practice of collaborative inquiry (Bray et al. 2000, Walther et al. 2017), we drew on the collective experience of the coauthors to investigate these research questions. The coauthors include participants from all modeling stages of the framework application, represent a broad set of expertise and relationships to water resources research, and were therefore able to reflect on the research questions from a variety of perspectives. Below, we describe the framework design and the methodology of its first set of applications. We then discuss the research questions in relation to this first application of the framework as well as its potential for ongoing use.

A FRAMEWORK FOR COLLABORATIVE, ADAPTIVE, MULTI-SCALE SYSTEMS THINKING

The first feature of the framework we describe is its multi-scale design, which allows users to combine individual systems thinking methods and models into a common framework over time. Models are organized across three levels: (1) a coarse-scale level containing a single conceptual model that describes major themes of interest within a system and their interrelationships (Fig. 1a(i), in blue), (2) a mid-scale level containing a set of system dynamics models that address more broadly scoped research questions and represent intermediate spatial and temporal scales (Fig. 1a(ii), in green), and (3) a fine-scale level containing a set of system dynamics models that address narrowly scoped research questions and represent fine spatial and temporal scales (Fig. 1a(iii), in yellow). The framework is depicted as two intersecting triangles, one with a broad base at the top and the other with a broad base at the bottom, to emphasize that it is bidirectionally hierarchical. By bidirectionally hierarchical, we mean that it is hierarchical from the bottom to top in that the scope and scale of any one model increases in this direction, and it is hierarchical from the top to bottom in that the number of models needed to represent the system increases in this direction (Fig. 1).

The framework’s multi-scale design allows research teams to start model development at any level. We recommend a “middle-out” modeling approach wherein modeling begins at the mid scale, following previous analyses of multi-scale modeling approaches across the fields of physics, engineering, nanoscience, and biology (Batterman and Green 2021). A “middle-out” approach is both practically and epistemically preferable for several reasons. First, mid-scale modeling does not require the level of detailed information necessary to model at the finest scale, which may not be available at the start of a project and may not reflect the scope of initial research questions. Second, “bottom-up” approaches starting at the finest scale are likely to fail to capture the behavior of multi-scale systems because they inevitably miss factors that uniquely emerge at coarser scales (Batterman and Green 2021). Finally, “top-down” approaches are likely to overlook important elements because of the cognitive constraints of considering the complexity of a system at a broad scope and scale. In contrast, a “middle-out” approach allows a team to start with a level of complexity appropriate for a new collaboration, provides the raw material needed to model at other scopes and scales, and provides points of connection between models at other scopes and scales, as we detail below.

The framework’s adaptive design becomes apparent as modeling progresses beyond the initial level. Starting from one or more mid-scale models, a set of processes embedded in the framework guide the creation of coarse- and fine-scale models and encourage revision of models over time (Fig. 1b–c). We generalize these processes in Fig. 1 and provide specific examples of systems thinking and team science methods we used to accomplish these steps in the following section. Regardless of the exact methods used, these processes cause the creation or modification of models at any level to feedback to models on other levels by initiating a reevaluation and possibly a reformulation of models at those levels. These feedbacks make all models included in the framework adaptive to new information, collaborations, and questions and allow consideration of dynamics that interact across scales. Together, the three levels of modeling and embedded revision processes allow for flexible and interacting levels of different scope and scale while providing practical limits and tools to allow modeling at each level to proceed.

The collaborative design of the framework comes from the use of collaborative system modeling methods, but deepens the potential of these methods through adaptive and multi-scaled features and through a focus on creating an inclusive environment. Specifically, the embedded revision process provides explicit opportunities for the research team to add new members as additional questions and gaps in knowledge are identified, and the use of models at multiple scales encourages inclusion of team members whose expertise applies better to coarser or finer scales. These features build on the rich tradition of collaborative methods in system modeling (e.g., Vennix 1996, Tidwell et al. 2004, Lamb and Rhodes 2010, Hovmand et al. 2012, Langsdale et al. 2013) by removing barriers created by a potential team member’s scale-dependent knowledge or time-dependent availability to participate and contribute. Collaborative activities then employ techniques such as individual self-reflection, structured sharing activities, and discussion of participants’ relationship to the research to break down power structures and encourage a sense of belonging within research teams (Hovmand et al. 2012, Hattery et al. 2022).

THE FRAMEWORK IN ACTION

A workshop to jumpstart the research team at the mid-scale

We emphasize that the CAMS framework can be initiated through a variety of methods, as long as it uses systems thinking concepts and tools. Here, we describe how we initiated the process at the mid-scale (Fig. 1a(ii), in green) with a 5-d workshop series using techniques from the Group Model Building literature (Richardson and Andersen 1995, Vennix 1996, Hovmand et al. 2012, Rouwette 2016). The Group Model Building literature describes a family of group facilitation techniques that are linked to computer models and developed with a group in a meeting setting. Our workshop brought together 28 researchers from a wide range of disciplines and levels of expertise to learn about system dynamics modeling, hypothesize dynamics in the Santa Fe Watershed (Box 2), and model those dynamics.

The workshop is described in detail in Append. 1A and summarized in Fig. 3. Briefly, academic disciplines represented by participants included biology, economics, geoscience, law, education, engineering, geography, health sciences, journalism, communications, planning, hydrology, agronomy, social psychology, and policy, although this list does not reflect all spheres of knowledge that participants identified with. Participants were not funded. Activities on day 1 supported participants in taking ownership of the workshop by (a) discussing and revising themes related to water management and (b) learning about each other’s expertise and relationship to the Santa Fe Watershed. On day 2, participants brainstormed and defined elements influencing water management in the Santa Fe Watershed, where “elements” were defined as parts of the system that are in some way quantifiable and can change over time (Anderson and Johnson 1997). On day 3, participants broke out into four teams and co-produced research questions that reflected the interaction of elements they individually found most interesting. On day 4, teams classified elements as stocks and flows and began building system dynamics models around their research questions. On day 5, participants discussed and revised their models, articulated the stories these models told to the larger group, and discussed points of connection between the four models. Finally, on day 6, participants reviewed results and discussed continuation of the research team into next steps. Much of this discussion centered on challenges around defining scope and scale in system dynamics models to accurately represent their hypotheses and research questions. Participants concluded that important next steps should include (a) synthesizing the four mid-scale models and other elements discussed in the workshop into one conceptual model and (b) creating opportunities to refine parts of the four models into more detailed, finer-scale models. These discussions led directly to the development of the CAMS framework and the creation of models at coarser and finer scales.

Revising mid-scale system dynamics models into a coarse-scale conceptual model

Twelve participants of the workshop, including the two facilitators, continued to work together after the workshop with the objective of synthesizing the four mid-scale models and other elements discussed in the workshop into one coarse-scale conceptual model (Fig. 1b [Steps 1–2], in blue). Participants represented seven academic disciplines, including biology, geoscience, engineering, geography, policy, planning, and social psychology. We developed the conceptual model using (a) a series of group exercises to define coarse-scale elements and (b) a structural analysis to hypothesize and visualize the interrelationships of those elements.

First, several group exercises focused on synthesizing all 293 elements used or discussed in the creation of the four mid-scale system dynamics models into a list of 20 themes. Themes were identified by participants individually sorting the 293 elements into categories of their choosing via an asynchronous open card sort activity using online software (https://provenbyusers.com), followed by the team reconvening to reconcile differences in category choices, further refine the themes, and create a shared understanding of their meaning. Activities to create a shared understanding of their meaning resulted in the creation of a related framework (Fuzzy Social-Ecological-Technological Systems (Fuzzy SETS)) and is described elsewhere in this Special Issue (Lin et al. 2025). The 20 final themes (Append. 1B) then became elements to include in the coarse-scale conceptual model.

Second, we used structural analysis to define how the 20 elements may relate. In the context of systems thinking, structural analysis refers to a series of techniques that define the dependencies and influences of elements in a system based on surveys of participant knowledge (Duperrin and Godet 1973, Godet 1986, Arcade et al. 1999, Bashir et al. 2020). Structural analysis is often used to inform development of causal loop and stock-and-flow diagrams. Here, we introduce a novel application of the structural analysis technique Cross-impact Matrix Multiplication Analysis (MICMAC) to create a coarse-scale conceptual model that represents a series of hypotheses about drivers and dependencies in the Santa Fe Watershed from the perspective of our research team. The method and results are described in detail in Append. 1B. Briefly, participants received a survey for each element x asking “If (element x) changed, what could be its direct influence on (element y)?” for every possible element y. Participants answered on a scale of 0 (no influence) to 3 (high influence), and also indicated their confidence about the relationships on a scale of 0 (not confident at all) to 3 (very confident). This resulted in 20 surveys of 19 questions each, which participants completed based on their interest and expertise in each element. We used each element’s mean influence vs. dependence in the system to categorize elements that describe their role in the system (Arcade et al. 1999). MICMAC categorizes elements into four main types and eight subtypes (Table 1) based on their mean location on an influence vs. dependence biplot, and we additionally used mean confidence and variability of responses to aid interpretation into types (Fig. B7 in Append. 1B). To create the conceptual model, we first interpreted MICMAC element types into a conceptual model template that highlights exogenous vs. endogenous vs. “core” endogenous elements and their interrelationships (Fig. 4a). Finally, we mapped our structural analysis results onto this template (Fig. 4b).

The MICMAC method provides insights into system behavior by interpreting influences and dependencies in light of systems thinking theory. In the context of our research team, these insights provided emergent team-level ideas and hypotheses to explore further. For example, the analysis found that the Santa Fe Watershed system has many possible core endogenous elements (Fig. 4b), which indicates an unpredictable and unstable system (Fig. B7, B8 in Append. 1B; Arcade et al. 1999). Contributing to possible instability is the fact that we identified no possible determinant elements (Fig. 4), which are those that can act as easy-to-access intervention points in the system to influence its behavior (Arcade et al. 1999). The closest element to the determinant element range was “incorporation of traditional watershed knowledges [into watershed management]” (Fig. 4b), which was defined by the team as the extent to which the knowledge, histories, innovations, and practices of Indigenous and local communities are incorporated into shared public understanding about a watershed, as well as systems of formal and informal governance, policy, management, and other activities in a watershed (adapted from Secretariat of the Convention on Biological Diversity (SCBD) 2023). However, this element also had the lowest mean confidence in responses. Insights from this analysis are discussed in Append. 1B and below.

Revising mid-scale models into fine-scale models

Coarse- and mid-scale models inevitably obscure nuances that may be important to explore to address research questions and/or management challenges. Coarse- and mid-scale models can also be difficult to parameterize with data and empirical relationships because elements are sometimes too broadly defined to match to observational data sets. Fine-scale models, in contrast, allow detailed consideration of the dynamics implicit in coarse- and mid-scale models, and can be linked together to inform existing mid-scale models or define new ones.

There will likely be many options through which to develop greater detail to move from a mid-scale to fine-scale model. One potential pathway is to engage groups of researchers to select a specific element, or connection of elements, at the mid-scale, conduct a detailed literature review of that element, and construct a detailed system dynamics model (Fig. 1c (Steps 1–3)).

We implemented this approach within groups of graduate students as part of a course in environmental systems modeling (see Append. 1C for details). Graduate students came from a variety of disciplines, including engineering, geoscience, geography, anthropology, and planning. Students learned system dynamics modeling principles and implemented fine-scale models in GoldSim software (GoldSim, Seattle, Washington, USA). An example fine-scale system dynamics model that was created through this pathway is shown in Fig. 5. We stress that this is just one potential pathway for fine-scale model creation and revision. A similar process could be conducted with groups of researchers and/or community members.

The ongoing revision process

Initial models at each level of the CAMS framework provide the raw materials for ongoing revision. Revision of mid-scale models can occur by connecting fine-scale models through common elements, incorporating new elements that were identified in fine-scale modeling activities into existing mid-scale models, or both. The process of connecting fine-scale models can also highlight additional elements needed to make connections. When all new elements identified through these activities are incorporated into existing or new mid-scale models, we can identify new feedbacks and system behaviors relevant to intermediately scoped research questions (Fig. 1c (Step 4)).

New mid-scale models created from fine-scale models can initiate the revision of coarse-scale models (Fig. 1b (Steps 1–2)). Existing coarse-scale models can also initiate the revision of mid-scale models (Fig. 1b (Steps 3–5)). In the former, revision of mid-scale models provides new elements to consider for inclusion in the coarse-scale model if the team determines that they are not already represented. This can be done by repeating or amending the MICMAC method, or by ad hoc additions of new elements to the model through team discussion. In the latter, the team can discuss the coarse-scale model and identify new questions and modeling needs prompted by its emergent dynamics. For example, we identified the incorporation of traditional watershed knowledges into watershed management as a likely important but uncertain element in our initial coarse-scale model. This prompted a discussion about how our coarse-scale modeling team lacked members with deep knowledge on this topic, and it was decided that the next formulation of a modeling team must prioritize inclusion of community members and researchers with understanding of traditional watershed knowledges and their role in watershed management.

Ongoing modeling and revision will be supported by the storage of detailed model descriptions, metadata, GoldSim player files, and reproducible code in a public repository that is currently under development (tnatlas.erams.com). These materials are also available by contacting the coauthors.

DISCUSSION

The CAMS framework represents a novel perspective in water resource management by integrating collaborative, adaptive, and multi-scale approaches into a flexible system modeling platform designed for long-term, iterative use. The framework’s ability to engage and integrate knowledge across diverse and evolving research teams and model water resource systems across spatial and temporal scales is an important advancement in modeling complex and multi-scale systems. This approach is particularly relevant given the dynamic nature of water resource systems facing unprecedented social, ecological, and hydrological change (Carpenter et al. 2011, Viviroli et al. 2011). The Santa Fe Watershed, with its vulnerability to drought, wildfires, and over-extraction of groundwater, served as an excellent case study for initiating the CAMS framework. The framework’s application in the Santa Fe Watershed demonstrated its utility in understanding complex interactions within a water resource system and identifying potential intervention points.

The novelty of this approach also opens considerations for future work. Below, we discuss the framework’s strengths and limitations in response to our original research questions, synthesized from reflections of the coauthors.

Strengths: the CAMS framework effectively facilitated cross-discipline convergence research on water resources through its collaborative, adaptive, and multi-scale design

The CAMS framework has the potential to adeptly address the “wicked” nature of water resource management—characterized by uncertainties, risks, and lack of clear resolution paths—by guiding the use of systems thinking methods in convergence research. One of the most challenging aspects of convergence research and characterizing the multi-faceted nature of wicked problems is the handling and integration of knowledge from many sources about many topics (DeFries and Nagendra 2017, Peek et al. 2020, Morgan et al. 2025). The CAMS framework introduced methodological innovations to address this challenge, including structuring models across three levels (coarse-, mid-, and fine-scale) and employing a “middle-out” approach for model development. This approach balanced the need for detailed information from bottom-up approaches and the cognitive constraints of top-down approaches, allowing for a more nuanced understanding of the system at the early stages of an ongoing research program.

The CAMS framework demonstrated the utility of a multi-scale, multi-model approach in integrating complex information by providing insights that would have been unlikely to emerge from independent modeling projects. One such insight was the role of trust in the Santa Fe Watershed. Trust between various parties was included in three of the four mid-scale models created in the system dynamics modeling workshop, reflecting the important role that workshop participants believed it to play in water resource management (Append. 1A). This outcome echoes other research that has identified human relationships as key to successful river management (e.g., Flint et al. 2023b). The role of trust in mid-scale models was then synthesized into the coarse-scale model as the concept of social capital (Append. 1B). Notably, the coarse-scale modeling team identified social capital as influential on water resource management but also highly dependent on other factors in the system. This dynamic suggests that it would be a challenging intervention point for changing water management because modifying it could require whole-system change (Arcade et al. 1999). Thus, although trust or social capital is potentially impactful on water management, our results suggest that more research is needed on how to effectively influence highly embedded factors in a deeply interconnected system. Only by considering multiple models across scales did this future research direction become clear.

The framework’s collaborative and adaptive design allowed the modeling team to evolve, and this provided important opportunities for convergence. Specifically, the intellectually diverse nature of the modeling teams at every stage of the project tested the framework’s ability to deeply integrate varied knowledge. As we expected, the use of multiple system modeling methods harnessed intellectual diversity by mixing quantitative and qualitative approaches. Less expectedly but equally important, we found that working across multiple system modeling methods had the advantage of developing participant’s metacognitive skills. Developing metacognitive skills—the process of thinking about one’s own thinking and learning—is essential to conducting convergence research because it helps participants understand how other disciplines construct knowledge and thus bridges epistemological divides (Boon and van Baalen 2019). Specifically, the CAMS approach introduced systems thinking to each modeling team by emphasizing the concept of systems as composed of elements and relationships, and prompted discussion of boundary judgements and team members’ perspectives on the modeling process throughout. This approach provided a common ground that was understandable from varied disciplinary perspectives and which is shared across systems thinking approaches (Cabrera 2006, Midgley and Rajagopalan 2021), thereby allowing participants to work together using multiple systems thinking tools. Modeling with this foundation developed participant’s metacognitive skills by helping participants become aware of how they were setting system boundaries, seeing how elements combined into systems, and appreciating the implications of including and not including different perspectives (Cabrera 2006). The framework thus made use of systems thinking’s tradition of methodological pluralism (Box 1) but went beyond it to provide a new convergence-minded approach in “third wave” systems thinking (Midgley and Rajagopalan 2021) by emphasizing metacognitive skills.

Limitations: bridging disciplinary divides is an important first step for convergence research on water resources, but not the last

A central characteristic of contemporary systems thinking is evaluating how the scope and boundaries of a project are set (Box 1), generally referred to as “boundary critique” (Midgley and Rajagopalan 2021). The present study addressed this topic in a novel way by asking if the CAMS framework was inclusive to diverse teams of researchers. In addition, our question about equity acknowledges that future participants—particularly those outside academia—may face considerable systemic barriers that need to be addressed to allow effective and ethical participation in a research team (Hattery et al. 2022).

The coauthors found the CAMS framework inclusive of the disciplinary and epistemological diversity as applied here, which was broad in an academic sense but not in other ways. This was facilitated by the design of the framework as collaborative across disciplines and scales and adaptive to evolving team membership. For example, coauthors reported that knowing that models could be iterated on over time allowed space for team members to participate at different stages according to their changing capacities. The fact that the facilitators were knowledgeable of and experienced with inclusive practices in academic settings also facilitated the effective use of self-reflection, structured sharing activities, and discussion of participants’ relationship to the research across modeling activities to break down power structures within the research team and make the process more collaborative and inclusive (Hattery et al. 2022). One outcome of prioritizing inclusion was the development of the related Fuzzy SETS framework, which allowed different views about social, ecological, and technological system elements to coexist without violating the principles of others (Lin et al. 2025).

Inclusion across academic settings was also sought in the initial recruitment to the mid-scale workshop. The workshop was broadly advertised through email listservs subscribed to by researchers interested in water resources at the University of New Mexico and through the Intermountain West Transformation Network, a collaborative research network described elsewhere in this Special Feature (see Morgan et al. 2025). However, we note that initial recruitment and participation strongly influenced team membership and modeling elements explored thereafter, and therefore functioned as a bottleneck to intellectual diversity included in the first iteration of the framework. To test the bridging of additional disciplinary and epistemological divides, the team’s composition could have been evaluated and revised at an earlier date.

Adding novel disciplinary and epistemological perspectives was further encouraged by the adaptive design of the CAMS framework through its embedded revision process. For example, evaluating uncertainty in the coarse-scale structural analysis was novel to this study and effectively highlighted important gaps in expertise in the research team around the role of traditional knowledges in watershed management. In addition, discussing existing models provides a point of entry that may be appropriate and accessible for new collaborators and prompts formulation of new coarse- and fine-scale models (Fig. 1b–c). As an example, one of our initial mid-scale models suggested that trust between community members and local news media organizations is a key factor influencing the effective uptake of information from public water education campaigns (Append. 1A). This hypothesis could be explored by discussing it with community members and including them in the process of developing a fine-scale model using techniques from the Group Model Building literature (Richardson and Andersen 1995, Vennix 1996, Rouwette 2016). Such hypotheses can also be explored by seeking new collaborators with data or data-gathering methods appropriate for parameterizing models (Fig. 1c (Step 1)).

To allow for even broader collaboration, community members and other researchers can be engaged in future work in the formulation of new questions, elements, or models at any scale without any engagement with prior versions. These elements can replace existing elements in the framework entirely, be integrated into existing models through the revision process, or be used to reflect on the influence of the participants’ disciplinary and epistemological perspectives. Together, these revision approaches provide a continuum of possible collaboration—from simple outreach to true co-creation of research—so that the CAMS framework can be applied appropriately to various contexts and stages of community partnership (Carson et al. 2022). However, ongoing use will push the framework’s capacity to remain inclusive and integrative of diverse perspectives beyond what could be evaluated in the present study.

As adaptive revision processes occur, additional and larger epistemological divides will challenge the framework’s adaptability. For example, more accessible approaches are needed to effectively integrate complex social dynamics like trust in quantitative systems dynamics models (Luna-Reyes and Anderson 2003, Gunda et al. 2018). Our fine-scale modeling teams found this challenging, and as a result, their system dynamics models tended to favor readily quantifiable elements such as reservoir levels. Further still, ontological divides—different ways of perceiving reality—may become apparent. For example, the coarse-scale modeling team identified traditional knowledges as a potential input to the system. This revealed that the team was unable to understand such knowledge as intrinsic to the living whole of a water resource system, as some Indigenous perspectives might describe it (Chief et al. 2016, Muller et al. 2019). Bridging such divides may be essential to conducting truly innovative convergence research (Morgan et al. 2025). To address this challenge and realize it as an opportunity, the CAMS framework must incorporate emerging “fourth wave” approaches in systems thinking that move beyond rational analysis to embrace experiential, presentational, and practical ways of knowing (Midgley and Rajagopalan 2021).

With more epistemological, ontological, and methodological diversity, creating an inclusive environment within research teams will become more challenging and more important. Convergence-minded research techniques, such as systems thinking, are tools to create inclusive research teams because they remove some technical barriers between participants to facilitate collaboration. However, we assert that inclusivity is not only a prerequisite for convergence research but is its very foundation. Effective inclusivity extends beyond simply amplifying marginalized voices; it shapes our approach to convergence research, ensuring that pursuit of novel insights is rooted in ethical practices that transcend transactional or extractive models of research. Thus, it is crucial for facilitators to know the audience and be trained in inclusive practices for that audience, or, better yet, share the identities and perspectives of marginalized members of the research team (Hattery et al. 2022). Still, those representing larger epistemological and ontological divides might want to keep their knowledge separate from modeling activities even if “fourth wave” systems thinking ideas and inclusivity are achieved (Muller et al. 2019). As such, more research is needed on how the CAMS framework can be used alongside diverse ways of knowing as a complementary tool rather than a means to integrate it with other knowledge.

Continuing use of the CAMS framework should further consider how inclusion and equity influence team evolution and how this influences models. In the present application, some members of the mid-scale modeling team opted out over time, particularly in the transition from an intensive workshop to more protracted methods of coarse- and fine-scale modeling. We do not know if this reflected a lack of a sense of belonging (an inclusivity issue), a lack of bandwidth (an equity issue), or simply a lack of interest. We do know that it impacted the coarse-scale model, as demonstrated by uncertainty about traditional knowledge’s role in watershed management (Fig. B5, B7 in Append. 1B). Future work should evaluate in more depth how the makeup and consistency of evolving research teams impact model development, especially as more non-academic participants become involved. Such work could explicitly compare what is prioritized by different research teams and use the results to reflect on how participant identities and relationships to the research impact results. Future work should also provide financial incentives when possible to reduce team turnover and address equity barriers for early career researchers and non-academics. Despite these uncertainties, we note that the CAMS framework adeptly allowed modeling to progress as the team evolved, which is essential to promoting its long-term use and its responsiveness to new information and collaborative opportunities.

CONCLUSION

Convergence research, driven by deep integration and co-production of knowledge within diverse teams, is needed to better anticipate and plan for a challenging future for water resource management. We developed and applied a new framework to build a convergence research team around the task of characterizing the Santa Fe Watershed as a complex system and hypothesizing water management dynamics there. The CAMS framework successfully engaged and integrated the knowledge and interests of an intellectually diverse research team and modeled water resource management in the Santa Fe Watershed across spatial and temporal scales. The insights gained from this case study, such as the potential importance of trust and traditional knowledges in watershed management, can inform more effective and sustainable water management strategies.

Beyond its innovations toward adaptively characterizing complex, multi-scale systems, applying the CAMS framework highlighted the essential role of inclusivity in convergence research on water resources. Water resource systems are replete with multifaceted stakeholder perspectives and management dynamics that require diverse research teams to characterize. Inclusivity in convergence research goes beyond mere representation; it creates a culture of belonging to ensure that participants from different perspectives feel valued and heard. Inclusive environments encourage open communication, collaboration, and the co-creation of knowledge, facilitating a deeper integration of ideas. Ultimately, inclusion in convergence research not only enhances the quality and depth of insights but also contributes to the ethical and equitable advancement of knowledge. This lays the foundation for more effective solutions to the complex issue of water resource management. For the CAMS framework to be used to identify strategic interventions in water management, inclusivity must be a core consideration.

LITERATURE CITED

Anderson, V., and L. Johnson. 1997. Systems thinking basics. Pegasus Communications, Cambridge, Massachusetts, USA.

Angeler, D. G., C. R. Allen, and A. Carnaval. 2020. Convergence science in the Anthropocene: navigating the known and unknown. People and Nature 2(1):96-102. https://doi.org/10.1002/pan3.10069

Arcade, J., M. Godet, M. Meunier, and F. Roubelat. 1999. Structural analysis with the MICMAC method and actor’s strategy with MACTOR method. Futures Research Methodology, American Council for the United Nations University: The Millennium Project. Washington, D.C., USA.

Bashir, H., U. Ojiako, and C. Mota. 2020. Modeling and analyzing factors affecting project delays using an integrated social network-fuzzy MICMAC approach. Engineering Management Journal 32(1):26-36. https://doi.org/10.1080/10429247.2019.1656595

Batterman, R. W., and S. Green. 2021. Steel and bone: mesoscale modeling and middle-out strategies in physics and biology. Synthese 199(1-2):1159-1184. https://doi.org/10.1007/s11229-020-02769-y

Belmont, P., and E. Foufoula-Georgiou. 2017. Solving water quality problems in agricultural landscapes: new approaches for these nonlinear, multiprocess, multiscale systems. Water Resources Research 53(4):2585-2590. https://doi.org/10.1002/2017WR020839

Boon, M., and S. van Baalen. 2019. Epistemology for interdisciplinary research-shifting philosophical paradigms of science. European Journal for Philosophy of Science 9: 16. https://doi.org/10.1007/s13194-018-0242-4

Bray, J. N., J. Lee, L. L. Smith, and L. Yorks. 2000. Collaborative inquiry in practice: action, reflection, and making meaning. Sage Publications, Thousand Oaks, California, USA.

Cabrera D. 2006. Systems thinking. Dissertation, Cornell University, Ithaca, New York, USA.

Carpenter, S. R., E. H. Stanley, and M. J. Vander Zanden. 2011. State of the world’s freshwater ecosystems: physical, chemical, and biological changes. Annual Review of Environment and Resources 36:75-99. https://doi.org/10.1146/annurev-environ-021810-094524

Carson, M. A., D. M. Doberneck, Z. Hart, H. Kelsey, J. Y. Pierce, D. E. Porter, M. L. Richlen, L. Schandera, and H. A. Triezenberg. 2022. A strategic framework for community engagement in oceans and human health. Community Science 1(1): e2022CSJ000001. https://doi.org/10.1029/2022CSJ000001

Chen, D., P. Zhang, K. Seftigen, T. Ou, M. Giese, and R. Barthel. 2021. Hydroclimate changes over Sweden in the twentieth and twenty-first centuries: a millennium perspective. Geografiska Annaler: Series A, Physical Geography 103(2):103-131. https://doi.org/10.1080/04353676.2020.1841410

Chief, K., A. Meadow, and K. Whyte. 2016. Engaging southwestern tribes in sustainable water resources topics and management. Water 8(8): 350. https://doi.org/10.3390/w8080350

Claridge, T. 2014. What is social capital? Institute for Social Capital, Indooroopilly, Queensland, Australia. https://www.socialcapitalresearch.com/what-is-social-capital/

Claridge, T. 2020. Trust and trustworthiness. Institute for Social Capital, Indooroopilly, Queensland, Australia. https://www.socialcapitalresearch.com/trust-and-trustworthiness/

Cosgrove, W. J., and D. P. Loucks. 2015. Water management: current and future challenges and research directions. Water Resources Research 51(6):4823-4839. https://doi.org/10.1002/2014WR016869

DeFries, R., and H. Nagendra. 2017. Ecosystem management as a wicked problem. Science 356(6335):265-270. https://doi.org/10.1126/science.aal1950

Dhirasasna, N., and O. Sahin 2019. A multi-methodology approach to creating a causal loop diagram. Systems, 7(3): 42. https://doi.org/10.3390/systems7030042

Donohue, S., K. G. Zachek, A. J. Webster, T. L. Schroeder, and A. Mulchandani. 2023. Engaging early-stage undergraduate students in research through a science communication fellowship. Paper presented at the American Society for Engineering Education (ASEE) Annual Conference and Exposition, 25–28 June 2023, Baltimore, Maryland, USA.

Duperrin, J. C., and M. Godet. 1973. Méthode de hiérarchisation des éléments d’un système: essai de prospective du système de l’énergie nucléaire dans son contexte sociétal. Dissertation, Centre national de l'entrepreneuriat (CNE); Commissariat à l’énergie atomique et aux énergies alternatives (CEA), Grenoble, France.

Famiglietti, J. S., and E. F. Wood. 1994. Multiscale modeling of spatially variable water and energy balance processes. Water Resources Research 30(11):3061-3078. https://doi.org/10.1029/94WR01498

Fiore, S. M. 2008. Interdisciplinarity as teamwork: how the science of teams can inform. Small Group Research 39(3):251-277. https://doi.org/10.1177/1046496408317797

Flint, C. G., L. A. Henderson, and M. Vorenburg Hensley. 2023a. Fostering interactional resilience in social-ecological riverine landscapes: a case study from the Santa Fe River watershed in New Mexico, US. Pages 341-362 in M. Thoms and I. Fuller, editors. Resilience and riverine landscapes. Elsevier, Amsterdam, The Netherlands. https://doi.org/10.1016/B978-0-323-91716-2.00007-8

Flint, C. G., B. M. Holdaway, and C. S. Rogers. 2023b. Human–river relationships depend on human–human relationships: river and watershed organizations in three western US states. River Research and Applications 40(9):1687-1697. https://doi.org/10.1002/rra.4136

Frigg, R. and S. Hartmann. 2006. Models in Science. In E. N. Zalta and U. Nodelman, co-principal editors. The Stanford Encyclopedia of Philosophy, Stanford University, Stanford, California, USA. https://plato.stanford.edu/entries/models-science/

Gilligan, J. M. 2021. Expertise across disciplines: establishing common ground in interdisciplinary disaster research teams. Risk Analysis 41(7):1171-1177. https://doi.org/10.1111/risa.13407

Gittins, J. R., J. R. Hemingway, and J.-C. Dajka. 2021. How a water-resources crisis highlights social-ecological disconnects. Water Research 194: 116937. https://doi.org/10.1016/j.watres.2021.116937

Godet, M. 1986. Introduction to la prospective: seven key ideas and one scenario method. Futures 18(2):134-157. https://doi.org/10.1016/0016-3287(86)90094-7

Gropp, R. E. 2016. NSF: time for big ideas. BioScience 66(11):920. https://doi.org/10.1093/biosci/biw125

Gunda, T., B. L. Turner, and V. C. Tidwell. 2018. The influential role of sociocultural feedbacks on community-managed irrigation system behaviors during times of water stress. Water Resources Research 54(4):2697-2714. https://doi.org/10.1002/2017WR021223

Hattery, A. J., E. Smith, S. Magnuson. A. Monterrosa, K. Kafonek, C. Shaw, R. D. Mhonde, and L. C. Kanewske. 2022. Diversity, equity, and inclusion in research teams: the good, the bad, and the ugly. Race and Justice 12(3):505-530. https://doi.org/10.1177/21533687221087373

Hovmand, P. S., D. F. Andersen, E. Rouwette, G. P. Richardson, K. Rux, and A. Calhoun. 2012. Group model-building ‘scripts’ as a collaborative planning tool. Systems Research and Behavioral Science 29(2):179-193. https://doi.org/10.1002/sres.2105

Isar, R. 2023. Exploring inclusive practices in schools: a systems thinking perspective. Journal of PGR Pedagogic Practice 3:11-17. https://doi.org/10.31273/jppp.vol3.2023.1474

Jackson, R. B., S. R. Carpenter, C. N. Dahm, D. M. McKnight, R. J. Naiman, S. L. Postel, and S. W. Running. 2001. Water in a changing world. Ecological Applications 11(4):1027-1045. https://doi.org/10.1890/1051-0761(2001)011[1027:WIACW]2.0.CO;2

Klein, C., D. Diaz Granados, E. Salas, H. Le, C. S. Burke, R. Lyons, and G. F. Goodwin. 2009. Does team building work? Small Group Research 40(2):181-222. https://doi.org/10.1177/1046496408328821

Lamb, C. T., and D. H. Rhodes. 2010. Collaborative systems thinking: uncovering the rules. IEEE Aerospace and Electronic Systems Magazine 25(11):4-10. https://doi.org/10.1109/MAES.2010.5638799

Langsdale, S., A. Beall, E. Bourget, E. Hagen, S. Kudlas, R. Palmer, D. Tate, and W. Werick. 2013. Collaborative modeling for decision support in water resources: principles and best practices. Journal of American Water Resources Association 49:629-638. https://doi.org/10.1111/jawr.12065

Liebman, J. C. 1976. Some simple-minded observations on the role of optimization in public systems decision-making. Interfaces 6(4):102-108. https://doi.org/10.1287/inte.6.4.102

Lin, Y. C., A. J. Webster, C. Scruggs, R. Bixby, D. Cadol, L. Crossey, P. de Lancer Julnes, K. Huang, A. Johnson, M. Morgan, A. Mulchandani, A. B. Stone, and M. C. Stone. 2025. Fuzzy SETS: acknowledging multiple membership of elements within social-ecological-technological systems (SETS) theory. Ecology and Society 30(1):22. https://doi.org/10.5751/ES-15764-300122

Lopez, M., E. Margolis, A. Tillery, S. Bassett, and A. Hook, 2024. Pre-fire assessment of post-fire debris-flow hazards in the Santa Fe Municipal Watershed. International Journal of Wildland Fire 33(9): WF23065. https://doi.org/10.1071/WF23065

Loucks, D. P., and E. van Beek. 2017. Water resources planning and management: an overview. Pages 1-149 in D. P. Loucks and E. van Beek. Water resource systems planning and management: an introduction to methods, models, and applications. Springer Nature, London, UK. https://doi.org/10.1007/978-3-319-44234-1_1

Luna-Reyes, L. F. and D. L. Andersen. 2003. Collecting and analyzing qualitative data for system dynamics: methods and models. System Dynamics Review: The Journal of the System Dynamics Society 19(4):271-296. https://doi.org/10.1002/sdr.280

Luna-Reyes, L. F., L. J. Black, W. Ran, D. L. Andersen, H. Jarman, G. P. Richardson, and D. F. Andersen. 2019. Modeling and simulation as boundary objects to facilitate interdisciplinary research. Systems Research and Behavioral Science 36(4):494-513. https://doi.org/10.1002/sres.2564

Madrid, C., V. Cabello, and M. Giampietro. 2013. Water-use sustainability in socioecological systems: a multiscale integrated approach. BioScience 63(1):14-24. https://doi.org/10.1525/bio.2013.63.1.6

Manning, A. H. 2011. Mountain-block recharge, present and past, in the eastern Española Basin, New Mexico, USA. Hydrogeology Journal 2(19):379-397. https://doi.org/10.1007/s10040-010-0696-8

Markolf, S. A., M. V. Chester, D. A. Eisenberg, D. M. Iwaniec, C. I. Davidson, R. Zimmerman, T. R. Miller, B. L. Ruddell, and H. Chang. 2018. Interdependent infrastructure as linked social, ecological, and technological systems (SETSs) to address lock-in and enhance resilience. Earth’s Future 6(12):1638-1659. https://doi.org/10.1029/2018EF000926

Meadows, D. H. 2008. Thinking in systems: a primer. Chelsea Green Publishing, White River Junction, Vermont, USA, and London, UK.

Midgley, G., and Rajagopalan, R. 2021. Critical systems thinking, systemic intervention, and beyond. Pages 1-51 in G. S. Metcalf, K. Kijima, and H. Deguchi, editors. Handbook of systems sciences. Springer Nature, London, UK. https://doi.org/10.1007/978-981-13-0370-8_7-1

Montanari, A., G. Young, H. H. G. Savenije, D. Hughes, T. Wagener, L. L. Ren, D. Koutsoyiannis, C. Cudennec, E. Toth, S. Grimaldi, G. Blöschl, K. Beven, H. Gupta, M. Hipsey, B. Schaefli, B. Arheimer, E. Boegh, S. J. Schymanski, G. Di Baldassarre, B. Yu, P. Hubert, Y. Huang, A. Schumann, D. A. Post, V. Srinivasan, C. Harman, S. Thompson, M. Rogger, A. Viglione, H. McMillan, G. Characklis, Z. Pang, and V. Belyaev. 2013. “Panta Rhei—everything flows”: change in hydrology and society—the IAHS scientific decade 2013–2022. Hydrological Sciences Journal 58(6):1256-1275. https://doi.org/10.1080/02626667.2013.809088

Morgan, M., Y. C. Lin, M. Walsh-Dilley, A. J. Webster, A. B. Stone, K. Chief, R. Rushforth, S. Hall, H. Love, P. Townsend, J. Boll, K. Freeman, N. Estrada, R. Morrison, and M. C. Stone. 2025. Convergence, transdisciplinarity and the science of team science: an interepistemic approach. Ecology and Society 30(1):3. https://doi.org/10.5751/ES-15492-300103

Muller, S., S. Hemming, and D. Rigney. 2019. Indigenous sovereignties: relational ontologies and environmental management. Geographical Research 57(4):399-410. https://doi.org/10.1111/1745-5871.12362

Nikitina, S. 2006. Three strategies for interdisciplinary teaching: contextualizing, conceptualizing, and problem-centring. Journal of Curriculum Studies 38(3):251-271. https://doi.org/10.1080/00220270500422632

Ostrom, E. 2009. A general framework for analyzing sustainability of social-ecological systems. Science 325(5939):419-422. https://doi.org/10.1126/science.1172133

Pahl-Wostl, C. 2007. Transitions towards adaptive management of water facing climate and global change. Water Resources Management 21:49-62. https://doi.org/10.1007/978-1-4020-5591-1_4

Peek, L., J. Tobin, R. M. Adams, H. Wu, and M. C. Mathews. 2020. A framework for convergence research in the hazards and disaster field: the natural hazards engineering research infrastructure CONVERGE facility. Frontiers in Built Environment 6: 110. https://doi.org/10.3389/fbuil.2020.00110

Polaine, X. K., R. Dawson, C. L. Walsh, J. Amezaga, M. Peña-Varón, C. Lee, and S. Rao. 2022. Systems thinking for water security. Civil Engineering and Environmental Systems 39(3):205-223. https://doi.org/10.1080/10286608.2022.2108806

Proven by Users. 2022. Online card sorting. https://provenbyusers.com

Rao, M. P., E. R. Cook, B. I. Cook, J. G. Palmer, M. Uriarte, N. Devineni, U. Lall, R. D. D’Arrigo, C. A. Woodhouse, M. Ahmed, and M. U. Zafar. 2018. Six centuries of Upper Indus Basin streamflow variability and its climatic drivers. Water Resources Research 54(8):5687-5701. https://doi.org/10.1029/2018WR023080

Reed, P. M., and J. Kasprzyk. 2009. Water resources management: the myth, the wicked, and the future. Journal of Water Resources Planning and Management 135(6):411-413. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000047

Richardson, G. P., and D. F. Andersen. 1995. Teamwork in group model building. System Dynamics Review 11(2):113-137. https://doi.org/10.1002/sdr.4260110203

Richmond, B. 1994. System dynamics/systems thinking: Let’s just get on with it. System Dynamics Review 10(2-3):135-157. https://doi.org/10.1002/sdr.4260100204

Rittel, H. W., and M. M. Webber. 1973. Dilemmas in a general theory of planning. Policy Sciences 4(2):155-169. https://doi.org/10.1007/BF01405730

Rouwette, E. A. 2016. The impact of group model building on behavior. Pages 213-241 in M. Kunc, J. Malpass, and L. White. Behavioral operational research: theory, methodology and practice. Springer Nature, London, UK. https://doi.org/10.1057/978-1-137-53551-1_11

Secretariat of the Convention on Biological Diversity (SCBD). 2023. Traditional knowledge. In Glossary, UNESCO Institute for Statistics, Montreal, Quebec, Canada. http://uis.unesco.org/en/glossary-term/traditional-knowledge

Siirila-Woodburn, E. R., A. M. Rhoades, B. J. Hatchett, L. S. Huning, J. Szinai, C. Tague, P. S. Nico, D. R. Feldman, A. D. Jones, W. D. Collins, and L. Kaatz. 2021. A low-to-no snow future and its impacts on water resources in the western United States. Nature Reviews Earth and Environment 2(11):800-819. https://doi.org/10.1038/s43017-021-00219-y

Sivapalan, M., and G. Blöschl. 2015. Time scale interactions and the coevolution of humans and water. Water Resources Research 51(9):6988-7022. https://doi.org/10.1002/2015WR017896

Tidwell, V. C., H. D. Passell, S. H. Conrad, and R. P. Thomas. 2004. System dynamics modeling for community-based water planning: application to the middle Rio Grande. Aquatic Sciences 66:357-372. https://doi.org/10.1007/s00027-004-0722-9

Tornow, J. S., J. Kurose, W. J. .Lewis, D. M. Tilbury, W. E. Easterling, C. S. Iacono, R. L. Keiser, A. Kinney, and F. Cook. 2018. Dear Colleague Letter: Growing Convergence Research. National Science Foundation, Washington, D.C., USA. https://www.nsf.gov/pubs/2018/nsf18058/nsf18058.jsp

Vennix, J. A. 1996. Group model building. System Dynamics 2:123-132.

Viviroli, D., D. R. Archer, W. Buytaert, H. J. Fowler, G. B. Greenwood, A. F. Hamlet, Y. Huang, G. Koboltschnig, M. I. Litaor, J. I. López-Moreno, and S. Lorentz. 2011. Climate change and mountain water resources: overview and recommendations for research, management and policy. Hydrology and Earth System Sciences 15(2):471-504. https://doi.org/10.5194/hess-15-471-2011

Walther, J., N. W. Sochacka, L. C. Benson, A. E. Bumbaco, N. Kellam, A. L. Pawley, and C. M. Phillips. 2017. Qualitative research quality: a collaborative inquiry across multiple methodological perspectives. Journal of Engineering Education 106(3):398-430. https://doi.org/10.1002/jee.20170

Woodhouse, C. A., K. E. Kunkel, D. R. Easterling, and E. R. Cook. 2005. The twentieth-century pluvial in the western United States. Geophysical Research Letters 32(7: L07701). https://doi.org/10.1029/2005GL022413

Yong, K., S. J. Sauer, and E. A. Mannix. 2014. Conflict and creativity in interdisciplinary teams. Small Group Research 45(3):266-289. https://doi.org/10.1177/1046496414530789

York, A. M., A. Sullivan, and J. C. Bausch. 2019. Cross-scale interactions of socio-hydrological subsystems: examining the frontier of common pool resource governance in Arizona. Environmental Research Letters 14(12): 125019. https://doi.org/10.1088/1748-9326/ab51be