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、州和地方三级机构联合管理。这种分散的结构允许在水的储存和分配方面做出灵活的决定，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动，这加剧了管理未来水文变化的挑战。水的可用性、基础设施管理、监管和用水模式都以复杂的方式相互依赖。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，以便更好地理解分散管理的影响并相应地进行规划。该项目重点关注加州中央山谷，通过不断发展的基础设施和监管网络管理地表水和地下水，以支持农业、城市和环境。该团队的目标是提供研究结果，以推进水可持续性的关键挑战，同时也训练学生进行跨学科研究。该项目将促进对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描述基础设施运营商、监管机构和地方/区域水区之间的相互作用，从而实现对紧急行为和协调机会的广泛评估。具体来说，这项工作将为解决与规模、异质性和不确定性相关的社会环境系统中的知识差距做出三个主要的科学贡献。首先，它将推进数据驱动的方法，以表征异质性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，它将量化并将模拟系统级结果的不确定性归因于外源水文气候、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，以支持向面临分散背景下预测和管理方面类似挑战的水系统转移，在这种情况下，有关水分配和监管的多层次制度决策共同发展。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.