DISES: Decentralized management of integrated water resources: Understanding cross-scale decision feedbacks to support coordinated sustainability

University Of California-Davis, Davis CA

Investigators

Abstract

In many water-scarce regions of the U.S., surface and groundwater resources are managed by a combination of institutions at the federal, state, and local scale. This decentralized structure allows flexible decisions regarding water storage and allocation, but also creates challenges for long-term coordination. Planners and water users must anticipate the actions of others in the system, which intensifies the challenge of managing for variability in future hydrology. Water availability, infrastructure management, regulation, and patterns of water consumption all depend on each other in complex ways. These cross-scale interactions must be captured in engineering models of water resources planning to better understand the impacts of decentralized management and plan accordingly. This project focuses on California’s Central Valley, where surface and groundwater are managed through an evolving network of infrastructure and regulation to support agriculture, cities, and the environment. The team aims to provide findings to advance the critical challenge of water sustainability, while also training students in interdisciplinary research. This project will advance quantitative understanding of the feedbacks governing decentralized water resources systems, and the system-level dynamics that result. This will be achieved through data-driven approaches to describe interactions among infrastructure operators, regulators, and local/regional water districts, enabling a broad evaluation of emergent behavior and opportunities for coordination. Specifically, this work will make three primary scientific contributions addressing knowledge gaps in socio-environmental systems related to scale, heterogeneity, and uncertainty. First, it will advance data-driven approaches to characterize the structure of decision-making relationships in a multi-scale system of heterogeneous water users and institutions. Second, it will develop a data-driven framework to quantify the drivers and consequences of dynamic water management decisions to support prediction of bi-directional feedbacks. Third, it will quantify and attribute the uncertainty in simulated system-level outcomes to uncertainty in exogenous hydroclimate, social dynamics, and their interactions. This work will culminate in the production of open datasets and models to support transfer to water systems facing similar challenges in prediction and management arising from the decentralized context, in which multi-level institutional decisions regarding water allocation and regulation co-evolve. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.