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: 1835794
• 负责人: Laura Condon
• 金额: $ 69.96万
• 依托单位: University of Arizona
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1835794&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.

该奖项支持设计和实施软件框架，以模拟各种规模的水的运动。了解当地和全国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。但是，由于进入技术障碍，建模进步并不容易转化为更广泛的科学家和专业人士社区。该软件平台从计算机模型中汲取灵感，并采用了能够分析大数据的超级计算机，以在美国大陆上提供空前的水流动模拟。将水文学家和计算机科学家结合起来，该项目背后的团队设想了一个广泛的用户社区，这些用户将有多种与软件框架互动的方法。对于有兴趣产生自己的方案感兴趣的水文科学家，该框架将有助于与水文模型的直接互动，并能够即时生成模拟。相反，该框架还将为一组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用于其他分析的模型数据。溪流和地下水是水文学领域的巨大挑战。数十年的模型开发以及求解器技术和软件工程方面的进步已经使整个美国水文周期的大规模高分辨率模拟了，但仍然存在实质性的技术和沟通挑战。在该奖项的支持下，一个计算机科学家和水文学家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，以转换水文科学及其他地区的模拟和数据驱动的发现。该项目正在推进这些国家规模水文模型背后的科学，加速了它们的功能并为用户互动构建了新的界面。该框架将计算和领域科学（水文学）社区融合在一起，以更快地从工具（模型，大数据，高性能计算）移动到发现。它促进了衰老的国家规模模拟，这是水文社区和更广泛的水位依赖性系统（例如生物系统，能源和粮食生产）的更广泛社区的前所未有的资源。这些模拟将使社区能够解决有关从分水岭到国家规模的动态的科学问题。此外，该框架旨在促进多种互动模式，并吸引水文社区之外的广泛用户。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无需计算机科学或水文学背景的内置工具可视化和绘制。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，因此该框架将为计算增强的水文发现提供门户。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包装视频和教育模块。该奖项由NSF高级网络基础设施办公室颁发，由地球科学界内的跨裁判活动计划共同支持NSF地球科学局。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响评论标准评估，被视为值得支持的。

项目成果

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

Water Table Depth Estimates over the Contiguous United States Using a Random Forest Model

• DOI: 10.1111/gwat.13362
• 发表时间: 2023-10
• 期刊: Groundwater
• 影响因子: 2.6
• 作者: Yueling Ma;E. Leonarduzzi;Amy Defnet;Peter Melchior;L. Condon;Reed M. Maxwell

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.

Exploring the Signal Filtering Properties of Idealized Watersheds Using Spectral Analysis

• DOI: 10.1016/j.advwatres.2023.104441
• 发表时间: 2023-04
• 期刊: Advances in Water Resources
• 影响因子: 4.7
• 作者: Abram Farley;L. Condon

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