Dynamic Adaptive Pathways for Groundwater Sustainability: Exploring Human-Water Interactions under Uncertain Conditions

Abstract:

In regions experiencing aquifer depletion, planning for groundwater sustainability requires both accurate accounting of current groundwater budgets, and an assessment of future conditions—including human decisions. Hydrologic variability, climate change effects on water flows, changing water infrastructure and operations, and inherent uncertainties in modeling, challenge the transition to groundwater sustainability.

To implement sensible policies that account for these uncertainties, we propose to develop water rules based on dynamic adaptive pathways. For that, we use California’s Central Valley—one of the most productive agricultural regions in the world but with significant problems of groundwater depletion—as a case study. The Sustainable Groundwater Management Act of 2014 (SGMA) requires to bring groundwater basins into balance by 2040. To achieve sustainability more than half million acres of farmland might need to be permanently retired of production. Local agencies are especially sensitive to developing plans that minimize the potential economic losses and risks of the transition to sustainable groundwater use. On the other side, groundwater depletion cause many impacts: puts at risk thousands of drinking water wells, results in significant economic impacts on infrastructure given the associated subsidence, and increases energy use and greenhouse gas emissions, among others.

To improve decision-making under uncertainty, we analyze trade-offs between groundwater operation rules, agricultural production, drinking water wells at risk, energy use, and sustainable groundwater levels using a dynamic adaptive pathways method. Dynamic adaptive pathways help prioritize short-term mitigation actions, define hedging actions to reduce the uncertain adverse effects of the plan, and implement actions to take advantage of likely available opportunities and reduce the likelihood of failure. We show also how an iterative process that reduces the uncertainties over time reduces the social and economic risks significantly.