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现，以模拟不同尺度的水运动。了解当地和全国各地的水的流动和可用性对我国的经济生产力和人类健康至关重要。水文学科学家正在使用越来越复杂的计算方法积极应对这些挑战。然而，由于进入的技术障碍，建模的进展并不容易转化为更广泛的科学家和专业人士。该软件平台利用计算机模型，并利用能够分析大数据的超级计算机，对美国大陆的水运动进行前所未有的模拟。项目背后的团队结合了水文学家和计算机科学家，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进行交互。对于对生成自己的情景感兴趣的水文科学家来说，该框架将促进与水文模型的直接交互，并能够在飞行中生成模拟。相反，该框架还将为希望在当地评价水文预测或提取模型数据以供其他分析使用的更广泛的用户提供一套静态输出和一系列工具。大陆尺度模拟河流、溪流和地下水的水流是水文学领域公认的一个重大挑战。几十年的模型开发，结合求解器技术和软件工程的进步，已经实现了对美国水文循环的大规模、高分辨率模拟，但仍然存在重大的技术和通信挑战。在该奖项的支持下，一个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，在水文科学及其他领域转变模拟和数据驱动的发现。这个项目正在推进这些国家尺度水文模型背后的科学，加速它们的能力，并为用户交互建立新的界面。该框架将计算和领域科学（水文学）社区结合在一起，更快地从工具（模型、大数据、高性能计算）转向发现。它促进了年代际、国家尺度的模拟，这对水文界和更广泛的从事水依赖系统（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尺度的水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，并吸引水文界以外的广泛用户。我们将提供易于访问的预处理数据集，这些数据集可以使用内置工具进行可视化和绘图，不需要计算机科学或水文背景。认识到大多数水文学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水文学发现提供一个门户。此外，对于教育工作者，我们将开发针对K-12教室的不同水文系统的打包视频和教育模块。该奖项由美国国家科学基金会高级网络基础设施办公室颁发，由美国国家科学基金会地球科学理事会地球科学部的跨领域活动计划联合支持。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

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

• DOI: 10.3389/frwa.2022.909918
• 发表时间: 2022-08
• 作者: L. Gallagher;Abram Farley;Calla Chennault;Sara Cerasoli;S. Jourdain;P. O’leary;L. Condon;R. Maxwell

Continental Hydrologic Intercomparison Project, Phase 1: A Large‐Scale Hydrologic Model Comparison Over the Continental United States

• DOI: 10.1029/2020wr028931
• 发表时间: 2021-06
• 期刊: Water Resources Research
• 影响因子: 5.4
• 作者: D. Tijerina;L. Condon;Katelyn FitzGerald;A. Dugger;M. O'Neill;K. Sampson;D. Gochis;R. Maxwell

The Budyko shape parameter as a descriptive index for streamflow loss

• DOI: 10.3389/frwa.2023.1258367
• 发表时间: 2023-09
• 期刊: Frontiers in Water
• 影响因子: 2.9
• 作者: H. Tran;Chen Yang;L. Condon;Reed M Maxwell

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
• 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.

Continental Scale Hydrostratigraphy: Comparing Geologically Informed Data Products to Analytical Solutions

大陆尺度水文地层学：地质信息数据产品与分析解决方案的比较

• DOI: 10.1111/gwat.13354
• 发表时间: 2023
• 期刊: Groundwater
• 影响因子: 2.6
• 作者: Swilley, Jackson S.;Tijerina‐Kreuzer, Danielle;Tran, Hoang V.;Zhang, Jun;Yang, Chen;Condon, Laura E.;Maxwell, Reed M.
• 通讯作者: Maxwell, Reed M.