The complexity of explaining highly scientific information and juggling a plethora of social values is leading agencies and communities such as those in the Palouse Basin to explore the use of participatory modeling processes using system dynamics. Participatory system dynamics as a methodology creates a transparent nexus of science, policy options, social concerns and local knowledge that enhances discussion of issues surrounding the use of natural resources. The process of developing a systems model uses the tenets of scientific theory, hypothesis testing and clear statements of assumptions. A unique aspect of the Palouse basin project is the use of system dynamics to describe ground water dynamics in a sole source confined aquifer system. There are, as of yet, no standards for analyzing participatory modeling projects, therefore, we use case study analysis to describe the process, insights and qualitative measurements of success.

Water resource governance, much like the systems it endeavors to manage, must be resilient and adaptive. Effective, resilient and adaptive water resource governance requires continuing stakeholder engagement to address the complex nature of human and natural systems. Engagement is an adaptive and iterative process of education and empowerment, building relationships and trust, and facilitating collaboration. Collaborative modeling is a methodology that integrates diverse stakeholder perspectives, fosters discussions, and creates space for problem identification and consensus-based strategies and solutions to current water resource challenges. We define collaborative modeling broadly, such that it includes a wide range of systems thinking exercises, as well as dynamic models. By focusing on the relationships and interconnections in the system, collaborative modeling facilitates clarification of mental models and the communication of science. We will describe our work in two interstate basins and how it has evolved over time as these basins strive to develop collaborative governance, and find solutions for their water resource challenges.

Frequent droughts, seasonal precipitation, and growing agricultural water demand in the Yakima River Basin (YRB), located in Washington State, increase the challenges of optimizing water provision for agricultural producers. Increasing water storage through managed aquifer recharge (MAR) can potentially relief water stress from single and multi-year droughts. In this study, we developed an aggregated water resources management tool using a System Dynamics (SD) framework for the YRB and evaluated the MAR implementation strategy and the effectiveness of MAR in alleviating drought impacts on irrigation reliability. The SD model allocates available water resources to meet instream target flows, hydropower demands, and irrigation demand, based on system operation rules, irrigation scheduling, water rights, and MAR adoption. Our findings suggest that the adopted infiltration area for MAR is one of the main factors that determines the amount of water withdrawn and infiltrated to the groundwater system. The implementation time frame is also critical in accumulating MAR entitlements for single-year and multi-year droughts mitigation. In addition, adoption behaviors drive a positive feedback that MAR effectiveness on drought mitigation will encourage more MAR adoptions in the long run. MAR serves as a promising option for water storage management and a long-term strategy for MAR implementation can improve system resilience to unexpected droughts.

Groundwater resource management involves complex socio-hydrological systems characterized by dynamic feedback, uncertainty, and common misconceptions among decision-makers. While deterministic models like MODFLOW simulate physical hydrology effectively, they fall short in capturing the social, legal, and behavioral dynamics shaping groundwater use. System dynamics (SD) modeling offers a robust alternative by incorporating feedback loops, delays, and nonlinearities. Yet, model conceptualization remains one of the most challenging steps in SD practice. Experienced modelers often apply a “Lego-like” approach—assembling new models from pre-validated sub-models. However, this strategy depends on access to well-documented sub-model libraries, which are typically unavailable to newcomers. To address this barrier, we systematically review and classify socio-economic sub-models from existing groundwater SD literature, organizing them by system archetypes and generic structures. The resulting modular library offers a practical resource that supports newcomers in building structured, scalable models. This approach improves conceptual clarity, enhances model reusability, and facilitates faster development of SD models tailored to groundwater systems. The study concludes by identifying directions for future research, including expanding the sub-model library, clarifying criteria for base-model selection, improving integration methods, and applying these approaches through diverse case studies to further strengthen the robustness and utility of groundwater SD modeling.