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

EAR-Climate：利用下一代可微的全球水文模型更好地了解气候变化下的全球低流量动态

• 批准号: 2221880
• 负责人: Chaopeng Shen
• 金额: $ 42万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2221880&HistoricalAwards=false
• 关键词: EAR; Climate; Towards; Better; Understanding

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.

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。评估气候变化对水资源的影响需要全球水文模型，但其准确性往往有限。模型方程有时是任意的，我们不能充分利用现有数据的价值。特别是，当河流干涸时，传统模型难以描述低流量时期，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、对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，也不一定遵守我们知道是正确的物理定律，比如质量守恒定律。这项工作将寻求建立一种新的水文建模类型，目前被称为水文学中的可微分建模，或者简单地说，可微分水文学。该项目开发的下一代全球水文模型不仅可以提高我们估计未来低流量的能力，而且还将在水文学领域建立一条新的途径，将机器学习和过程的最佳方面结合起来。新的途径将提供灵活的提出新的科学问题，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才模型设计的限制，在这种设计中，可解释性被模型的通用性所取代。为了实现项目目标，人工智能的层次将被剥离，以利用其核心技术之一，即可微分编程，来构建可学习的基于过程的模型。下一代水文模型将基于全球水文数据发展，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。该项目将描述可归因于结构缺陷的错误，提高数据稀疏区域预测的可靠性，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分享。研究成果将通过教师，融入到研究生、研讨会和研讨会参加者、高中生的教育活动中。该项目由水文科学计划以及地球科学理事会和先进网络基础设施办公室之间的合作资助，以支持地球科学领域的人工智能/机器学习和开放科学活动。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。

项目成果

Differentiable modelling to unify machine learning and physical models for geosciences

• DOI: 10.1038/s43017-023-00450-9
• 发表时间: 2023-07
• 期刊: Nature Reviews Earth & Environment
• 影响因子: 42.1
• 作者: Chaopeng Shen;A. Appling;P. Gentine;Toshiyuki Bandai;H. Gupta;A. Tartakovsky;M. Baity-Jesi;F. Fenicia;Daniel Kifer;Li Li-Li;Xiaofeng Liu;Wei Ren;Y. Zheng;C. Harman;M. Clark;M. Farthing;D. Feng;Praveen Kumar;Doaa Aboelyazeed;F. Rahmani;Yalan Song;H. Beck;Tadd Bindas;D. Dwivedi;K. Fang;Marvin Höge;Christopher Rackauckas;B. Mohanty;Tirthankar Roy;Chonggang Xu;K. Lawson

Differentiable, Learnable, Regionalized Process‐Based Models With Multiphysical Outputs can Approach State‐Of‐The‐Art Hydrologic Prediction Accuracy

• DOI: 10.1029/2022wr032404
• 发表时间: 2022-03
• 期刊: Water Resources Research
• 影响因子: 5.4
• 作者: D. Feng;Jiangtao Liu;K. Lawson;Chaopeng Shen

Evaluating a global soil moisture dataset from a multitask model (GSM3 v1.0) with potential applications for crop threats

通过多任务模型 (GSM3 v1.0) 评估全球土壤湿度数据集以及作物威胁的潜在应用

• DOI: 10.5194/gmd-16-1553-2023
• 发表时间: 2023
• 期刊: Geoscientific Model Development
• 影响因子: 5.1
• 作者: Liu, Jiangtao;Hughes, David;Rahmani, Farshid;Lawson, Kathryn;Shen, Chaopeng
• 通讯作者: Shen, Chaopeng

The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment

• DOI: 10.5194/hess-27-2357-2023
• 发表时间: 2023-06
• 期刊: Hydrology and Earth System Sciences
• 影响因子: 6.3
• 作者: D. Feng;H. Beck;K. Lawson;Chaopeng Shen