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，地表水资源和地下水资源由联邦、州和地方各级机构共同管理。这种分散的结构允许在水储存和分配方面做出灵活的决策，但也给长期协调带来了挑战。规划者和用水者必须预测系统中其他人的行为，这加剧了管理未来水文学变异性的挑战。水资源供应、基础设施管理、监管和用水模式都以复杂的方式相互依赖。这些跨尺度的相互作用必须在水资源规划的工程模型中得到体现，以便更好地理解分散管理的影响并进行相应的规划。该项目重点关注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。该团队旨在提供研究结果，以推进水可持续性的关键挑战，同时培训学生进行跨学科研究。该项目将促进对管理分散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。具体来说，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、异质性和不确定性相关的知识差距。首先，它将推进数据驱动的方法来描述异构水用户和机构的多尺度系统中的决策关系结构。其次，它将开发一个数据驱动的框架来量化动态水管理决策的驱动因素和后果，以支持双向反馈的预测。第三，它将量化模拟系统级结果的不确定性，并将其归因于外生水文气候、社会动态及其相互作用的不确定性。这项工作最终将产生开放数据集和模型，以支持向水系统的转移，该系统面临着来自分散背景的预测和管理方面的类似挑战，其中有关水分配和监管的多级机构决策共同发展。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点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.