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

在美国许多缺水地区，地表水和地下水资源由联邦、州和地方三级机构管理。这种分散的结构允许关于水储存和分配的灵活决定，但也给长期协调带来了挑战。规划者和用水者必须预见到系统中其他人的行动，这加剧了对未来水文学中的可变性进行管理的挑战。水的可获得性、基础设施管理、监管和用水模式都以复杂的方式相互依存。必须在水资源规划的工程模型中捕捉到这些跨尺度的相互作用，以便更好地了解分散管理的影响并进行相应的规划。该项目的重点是加利福尼亚州的中央山谷，那里的地表水和地下水通过不断发展的基础设施和监管网络进行管理，以支持农业、城市和环境。该小组的目标是提供研究结果，以推进水资源可持续性的关键挑战，同时培训学生进行跨学科研究。该项目将促进对分散的水资源系统的反馈以及由此产生的系统级动态的定量理解。这将通过数据驱动的方法来实现，以描述基础设施运营商、监管机构和当地/区域水区之间的互动，从而能够对紧急行为和协调机会进行广泛评估。具体地说，这项工作将做出三项主要的科学贡献，解决社会环境系统中与规模、异质性和不确定性有关的知识差距。首先，它将推进数据驱动的方法，以描述不同类型的水用户和机构的多尺度系统中决策关系的结构。其次，它将开发一个数据驱动的框架，量化动态水资源管理决策的驱动因素和后果，以支持双向反馈预测。第三，它将量化模拟系统级结果中的不确定性，并将其归因于外源水气候、社会动态及其相互作用的不确定性。这项工作将最终产生开放的数据集和模型，以支持向水系统的转移，在分散的背景下，在预测和管理方面面临类似的挑战，其中关于水分配和监管的多层次机构决策共同演变。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.