Collaborative Research: Framework: Software: NSCI : Computational and data innovation implementing a national community hydrologic modeling framework for scientific discovery

合作研究：框架：软件：NSCI：计算和数据创新实施国家社区水文建模框架以促进科学发现

• 批准号: 2054506
• 负责人: Reed Maxwell
• 金额: $ 59.35万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2054506&HistoricalAwards=false
• 关键词: Collaborative; Research; Framework; Software; NSCI

This award supports the design and implementation of a software framework to simulate the movement of water at various scales. Understanding the movement and availability of water locally and across the country is of paramount importance to economic productivity and human health of our nation. Hydrologic scientists, are actively tackling these challenges using increasingly complex computational methods. However, modeling advances have not been easily translated to the broader community of scientists and professionals due to technical barriers to entry. This software platform draws from computer models and employs supercomputers capable of analyzing big data to provide unprecedented simulations of water movement over the continental US. Combining hydrologists and computer scientists the team behind the project envision a broad community of users who will have multiple ways to interact with the software framework. For the hydrologic scientist who is interested in generating their own scenarios the framework will facilitate direct interaction with the hydrologic models and the ability to generate simulations on the fly. Conversely, the framework will also provide a set of static output and a range of tools for a broader set of users who would like to evaluate hydrologic projections locally or extract model data for use in other analyses.Continental scale simulation of water flow through rivers, streams and groundwater is an identified grand challenge in hydrology. Decades of model development, combined with advances in solver technology and software engineering have enabled large-scale, high-resolution simulations of the hydrologic cycle over the US, yet substantial technical and communication challenges remain. With support from this award, an interdisciplinary team of computer scientists and hydrologists is developing a framework to leverage advances in computer science transforming simulation and data-driven discovery in the Hydrologic Sciences and beyond. This project is advancing the science behind these national scale hydrologic models, accelerating their capabilities and building novel interfaces for user interaction. The framework brings computational and domain science (hydrology) communities together to move more quickly from tools (models, big data, high-performance computing) to discoveries. It facilitates decadal, national scale simulations, which are an unprecedented resource for both the hydrologic community and the much broader community of people working in water dependent systems (e.g., biological system, energy and food production). These simulations will enable the community to address scientific questions about water availability and dynamics from the watershed to the national scale. Additionally, this framework is designed to facilitate multiple modes of interaction and engage a broad spectrum of users outside the hydrologic community. We will provide easy-to-access pre-processed datasets that can be visualized and plotted using built-in tools that will require no computer science or hydrology background. Recognizing that most hydrology training does not generally include High Performance Computing and data analytics or software engineering, this framework will provide a gateway for computationally enhanced hydrologic discovery. Additionally, for educators we will develop packaged videos and educational modules on different hydrologic systems geared towards K-12 classrooms.This award by the NSF Office of Advanced Cyberinfrastructure is jointly supported by the Cross-Cutting Activities Program of the Division of Earth Sciences within the NSF Directorate for 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.

该奖项支持软件框架的设计和实施，以模拟不同尺度的水的运动。了解当地和全国范围内水的流动和可用性对于我们国家的经济生产力和人类健康至关重要。水文科学家正在使用日益复杂的计算方法积极应对这些挑战。然而，由于技术进入壁垒，建模的进步尚未轻易转化为更广泛的科学家和专业人士群体。该软件平台借鉴计算机模型，并采用能够分析大数据的超级计算机，对美国大陆上空的水运动进行前所未有的模拟。该项目背后的团队将水文学家和计算机科学家结合起来，设想了一个广泛的用户社区，他们将有多种方式与软件框架交互。对于有兴趣生成自己的场景的水文科学家来说，该框架将促进与水文模型的直接交互以及动态生成模拟的能力。相反，该框架还将为更广泛的用户提供一组静态输出和一系列工具，这些用户希望在本地评估水文预测或提取模型数据以用于其他分析。流经河流的水流的大陆尺度模拟，溪流和地下水是水文学中公认的巨大挑战。数十年的模型开发，加上求解器技术和软件工程的进步，已经能够对美国的水文循环进行大规模、高分辨率的模拟，但仍然存在巨大的技术和通信挑战。在该奖项的支持下，由计算机科学家和水文学家组成的跨学科团队正在开发一个框架，以利用计算机科学的进步来改变水文科学及其他领域的模拟和数据驱动的发现。该项目正在推进这些国家级水文模型背后的科学，加速其能力并构建新颖的用户交互界面。该框架将计算和领域科学（水文学）社区聚集在一起，以便更快地从工具（模型、大数据、高性能计算）转向发现。它促进了十年、国家规模的模拟，这对于水文界和更广泛的依赖水系统（例如生物系统、能源和粮食生产）工作的人们来说都是前所未有的资源。这些模拟将使社区能够解决有关从流域到国家范围内的水资源可用性和动态的科学问题。此外，该框架旨在促进多种交互模式并吸引水文界之外的广泛用户。我们将提供易于访问的预处理数据集，可以使用内置工具进行可视化和绘图，无需计算机科学或水文学背景。认识到大多数水文培训通常不包括高性能计算和数据分析或软件工程，该框架将为计算增强的水文发现提供一个网关。此外，对于教育工作者，我们将为 K-12 课堂开发有关不同水文系统的打包视频和教育模块。该奖项由 NSF 高级网络基础设施办公室颁发，并得到了地球科学部跨领域活动计划的共同支持。 NSF 地球科学理事会。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力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

A high-resolution, 3D groundwater-surface water simulation of the contiguous US: Advances in the integrated ParFlow CONUS 2.0 modeling platform

美国本土高分辨率 3D 地下水-地表水模拟：集成 ParFlow CONUS 2.0 建模平台的进展

• DOI: 10.1016/j.jhydrol.2023.130294
• 发表时间: 2023-11
• 期刊: Journal of Hydrology
• 影响因子: 6.4
• 作者: Yang, Chen;Tijerina;Tran, Hoang V.;Condon, Laura E.;Maxwell, Reed M.
• 通讯作者: Maxwell, Reed M.

Continental Scale Hydrostratigraphy: Basin‐Scale Testing of Alternative Data‐Driven Approaches

大陆尺度水文地层学：盆地——替代数据的尺度测试——驱动方法

• DOI: 10.1111/gwat.13357
• 发表时间: 2023-10
• 期刊: Groundwater
• 影响因子: 2.6
• 作者: Tijerina‐Kreuzer, Danielle;Swilley, Jackson S.;Tran, Hoang V.;Zhang, Jun;West, Benjamin;Yang, Chen;Condon, Laura E.;Maxwell, Reed M.
• 通讯作者: Maxwell, Reed M.

Sandtank-ML: An Educational Tool at the Interface of Hydrology and Machine Learning

Sandtank-ML：水文学和机器学习接口的教育工具

• DOI: 10.3390/w13233328
• 发表时间: 2021-12
• 期刊: Water
• 影响因子: 3.4
• 作者: Gallagher, Lisa K.;Williams, Jill M.;Lazzeri, Drew;Chennault, Calla;Jourdain, Sebastien;O’Leary, Patrick;Condon, Laura E.;Maxwell, Reed M.
• 通讯作者: Maxwell, Reed M.

ParFlow Sand Tank: A tool for groundwater exploration

ParFlow 砂罐：地下水勘探工具

• DOI: 10.21105/jose.00179
• 发表时间: 2023-03
• 期刊: Journal of Open Source Education
• 作者: Condon, Laura;Farley, Abram;Jourdain, Sebastien;O’leary, Patrick;Avery, Patrick;Gallagher, Lisa;Chennault, Calla;Maxwell, Reed
• 通讯作者: Maxwell, Reed

The ParFlow Sandtank: An interactive educational tool making invisible groundwater visible

ParFlow Sandtank：一种交互式教育工具，让不可见的地下水变得可见

• DOI: 10.3389/frwa.2022.909918
• 发表时间: 2022-08
• 期刊: Frontiers in Water
• 影响因子: 2.9
• 作者: Gallagher, Lisa K.;Farley, Abram J.;Chennault, Calla;Cerasoli, Sara;Jourdain, Sébastien;O'Leary, Patrick;Condon, Laura E.;Maxwell, Reed M.
• 通讯作者: Maxwell, Reed M.