EAR-Climate: Towards Better Understanding of Global Low Flow Dynamics Under Climate Change With Next-Generation, Differentiable Global Hydrologic Models

淡水资源对世界各地许多相互竞争的人类和生态系统需求至关重要。需要全球水文模型来评估气候变化对水资源的影响，但其准确性往往有限。模型方程有时是任意的，我们没有充分利用现有数据的价值。特别是，传统的模型在描述河流干涸的低流量时期时有困难，有时预测的趋势与观测记录相反。这些错误可能导致气候缓解战略不足，干旱准备不足或救灾资源分配不当。机器学习模型往往是准确的，但它们对人类来说仍然具有挑战性，不适合提出精确的问题，也不一定尊重我们所知道的物理定律，如质量守恒。这项工作将寻求建立一个新的流派水文模拟目前称为微分水文模拟或简单地说，微分水文。从该项目开发的下一代全球水文模型不仅将提高我们估计未来低流量的能力，而且这项工作还将建立一个结合机器学习和过程的最佳方面的水文学新途径。新的途径将提供灵活的方式来提出新的科学问题，并从大数据中学习答案。因此，水文学家将不再受到人工智能中通用模型设计的限制，其中可解释性被交易为模型通用性。为了实现项目目标，人工智能的各个层将被剥离，以利用其核心技术之一，即可区分编程，来构建可学习的基于过程的模型。下一代水文模型将根据全球水文数据发展，减少结构性缺陷，建立全球地下水参数化方案，并解决规模问题。该项目将描述结构缺陷造成的误差，提高数据稀疏地区预测的可靠性，并提高模型的物理意义。结果将分发给气候变化影响评估界。 低流量预测也将通过与在非洲有足迹的非营利组织的持续合作来分享。这项研究工作将纳入研究生、研讨会和讲习班参加者以及高中生通过教师开展的教育活动中。以及地球科学理事会和高级网络基础设施办公室之间的合作，以支持人工智能/地球科学中的ML和开放科学活动。该奖项反映了NSF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进行评估，被认为值得支持审查标准。

Freshwater resources are critically important to many competing human and ecosystem needs around the world. Global hydrologic models are needed to assess climate change impacts on water resources, but their accuracy is often limited. Model equations are sometimes arbitrary and we do not fully leverage the value of available data. In particular, traditional models have trouble describing low flow periods when rivers run dry, and sometimes predict trends that are opposite to observational records. These errors could lead to inadequate climate mitigation strategies, under-preparation for drought, or misallocation of disaster relief resources. Machine learning models tend to be accurate, but they remain challenging for humans to decipher, are not well suited to ask precise questions, and do not necessarily respect physical laws we know to be true such as the conservation of mass. This work will seek to build a new genre of hydrologic modeling currently termed differentiable modeling in hydrology¸ or, simply, differentiable hydrology. Not only will the next-generation global hydrologic models developed from this project improve our ability to estimate future low flows, but this work will also establish a new avenue in hydrology that combines the best aspects of machine learning and processes. The new avenue will provide the flexibly to ask new scientific questions and learn the answers from big data. As a result, hydrologists will no longer be limited by the generalist model design in artificial intelligence where interpretability is traded for model genericity. To achieve the project goals, the layers of artificial intelligence will be peeled off to harness one of its core technologies, namely, differentiable programming, to build learnable process-based models. Next-generation hydrologic models will evolve based on global hydrologic data, reduce structural deficiencies, build global parameterization schemes for groundwater, and address scale issues. The project will characterize errors attributable to structural deficiencies, improve reliability of predictions in data-sparse regions, and improve model physical significance. Outcomes will be disseminated to the climate change impact assessment community. Low flow predictions will also be shared through continuing collaboration with non-profit organizations with footprints in Africa. The research effort will be incorporated into educational activities for graduate students, symposium and workshop attendants, and high-schoolers via their teachers.This project is funded by the Hydrologic Sciences program, as well as a collaboration between the Directorate for Geosciences and Office of Advanced Cyberinfrastructure to support AI/ML and open science activities in the geosciences.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.