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Participatory water resources management requires modeling techniques that are accurate and flexible, yet stakeholder-friendly. While different modeling frameworks offer advantages and disadvantages, System Dynamics (SD) models have seen sustained use as a stakeholder-friendly approach for water resources modelling. In contrast, physically-based models are more appropriate to model the hydrological components of coupled human-water systems. Proposed as a way to combine the relative strengths of both modelling paradigms, model coupling allows researchers to build participatory SD models with stakeholders, while delegating the hydrological components of the overall model to an external hydrological model. Recently developed to facilitate model coupling, the Tinamït Python package presents an extensible outward-facing Application Programming Interface (API). It allows for the development of extensions (wrappers) that expand compatibility with different physically-based models. However, no watershed hydrological model has yet been connected to this API. In the present study, a socket and JavaScript Object Notation-based communication protocol was developed with the goal of facilitating the coupling of models written in languages such as FORTRAN. This novel protocol served to develop a Tinamïtcompatible wrapper for the hydrological model SWAT+, allowing it to be coupled to human-water SD models. The novel coupling protocol was then applied to a case study of Tanzania's Usa Basin. This approach provides the modeler with the benefits of both physically-based and SD models, thereby allowing the detection of potential far-reaching effects of policy decisions, within a system that remains flexible and easily adaptable to other watersheds. [ABSTRACT FROM AUTHOR]

Unconventional sources of data that enhance our understanding of internal interactions between socio-economic and hydrological processes are central to modeling human–water systems. Participatory modeling (PM) departs from conventional modeling tools by informing and conceptualizing human–water systems through stakeholder engagement. However, the implementation of many PM processes remains biased, particularly in regions where marginalized communities are present. Many PM processes are not cognizant of differentiation and diversity within a society and tend to treat communities as homogeneous units with similar capabilities, needs, and interests. This undifferentiation leads to the exclusion of key actors, many of whom are associated with marginalized communities. In this study, a participatory model-building framework (PMBF), aiming to ensure the inclusiveness of marginalized stakeholders – who (1) have low literacy, (2) are comparatively powerless, and/or (3) are associated with a marginalized language – in participatory modeling, is proposed. The adopted approach employs interdisciplinary storylines to inform and conceptualize human–water systems. The suggested method is underpinned by the multi-level perspective (MLP) framework, which was developed by Geels et al. (2002) to conceptualize socio-technical transitions and modified in this study to accommodate the development of interdisciplinary storylines. A case study was conducted in Atitlán Basin, Guatemala, to understand the relationships that govern the lake's cultural eutrophication problem. This research integrated key stakeholders from the Indigenous Mayan community, associated with diverse literacy ranges, and emerging from three different marginalized linguistic backgrounds (Kaqchikel, Tz'utujil, and K'iche'), in the PM activity. The proposed approach facilitated the participation of marginalized stakeholders. Moreover, it (1) helped develop an understanding of mechanisms governing the eutrophication of the lake, (2) initiated a dialogue between Indigenous Peoples and non-Indigenous stakeholders, and (3) extracted potential solutions targeting the system's leverage points. The participatory model-building activity generated three submodules: (1) agriculture, (2) tourism, and (3) environmental awareness. Each submodule contained socioculturally specific mechanisms associated with nutrient discharge to Lake Atitlán. The delineation of such nuanced relationships helps develop well-targeted policies and best management practices (BMPs). Additionally, the suggested process helped decrease the impact of power imbalances in water resources management and empowered community-based decision-making. [ABSTRACT FROM AUTHOR]

In classic resilience thinking, there is an implicit focus on controlling functional variation to maintain system stability. Modern approaches to resilience thinking deal with complex, adaptive system dynamics and true uncertainty; these contemporary frameworks involve the process of learning to live with change and make use of the consequences of transformation and development. In a socio-environmental context, the identification of metrics by which resilience can be effectively and reliably measured is fundamental to understanding the unique vulnerabilities that characterize coupled human and natural systems. We developed an innovative procedure for stakeholder-friendly quantification of socio-environmental resilience metrics. These metrics were calculated and analyzed through the application of discrete disturbance simulations, which were produced using a dynamically coupled, biophysical-socioeconomic modeling framework. Following the development of a unique shock-response assessment regime, five metrics (time to baseline-level recovery, rate of return to baseline, degree of return to baseline, overall post-disturbance perturbation, and corrective impact of disturbance) describing distinct aspects of systemic resilience were quantified for three agroecosystem variables (farm income, watertable depth, and crop revenue) over a period of 30 years (1989-2019) in the Rechna Doab basin of northeastern Pakistan. Using this procedure, we determined that farm income is the least resilient variable of the three tested. Farm income was easily diverted from the "normal" functional paradigm for the Rechna Doab socio-environmental system, regardless of shock type, intensity, or duration combination. Crop revenue was the least stable variable (i.e., outputs fluctuated significantly between very high and very low values). Water-table depth was consistently the most robust and resistant to change, even under physical shock conditions. The procedure developed here should improve the ease with which stakeholders are able to conduct quantitative resilience analyses. [ABSTRACT FROM AUTHOR]