Collaborative Research: Correlated velocity models as dynamic upscaling and model translation tools for watershed-scale hydrobiogeochemical cycling

合作研究：相关速度模型作为流域规模水生地球化学循环的动态升级和模型转换工具

• 批准号: 2049687
• 负责人: Nicholas Engdahl
• 金额: $ 30.02万
• 依托单位: Washington State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2049687&HistoricalAwards=false
• 关键词: Collaborative; Research; Correlated; velocity; models

A strong understanding of watershed function is necessary for responsible stewardship of water resources. However, fully characterizing or understanding all the complex processes that occur within watersheds is often not feasible and prohibitively costly. Many locations of concern simply do not have enough long-term data to predict how solutes will be transported through watersheds, and lack the time and money required to make such predictions. The purpose of this project is to develop a framework whereby better predictions of solute transport can be made, even in data scarce regions. The project leverages existing data and high-resolution models, constructed at sites that have already been characterized in great detail, to assess how flow and transport processes in similar watersheds are related. This information will lead to simple statistical models that can capture the complexity of real watersheds based on less detailed characterizations. The models are expected to allow the translation of knowledge from sites where great investments have been made to improve models of relatively data-poor sites. The project is also creating new educational tools, training undergraduate and graduate students, and reaching out to applied watershed managers to better understand their needs for real-world applications of solute transport models. The approach used in this research focuses on using recent multi-domain correlated velocity models (MD-CVMs) to represent coupled subsurface and surface flow and transport in watersheds. Lagrangian particle-based numerical methods along streamtubes are the core of this approach, which couples interactions between particles to accurately represent crucial mixing and reaction processes. The water and solutes from each streamtube interact as they come together, simplifying the watershed geometry into a tree without sacrificing process-level realism. The streamtube approach will also enforce velocity correlations, which is a novel feature at watershed scales that is lacking in previous models despite evidence that persistent correlations exist. The advantage of using velocity correlations is that they are conceptually simple but yield robust models that show promise across different sites. The resulting dynamically coupled, yet realistic, representations of watersheds will expand the tools available for understanding and optimally managing real watersheds.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.

对于负责任的水资源管理，对流域功能有深刻的了解。但是，完全表征或理解流域内发生的所有复杂过程通常是不可行的，而且昂贵的。许多关注的位置根本没有足够的长期数据来预测如何通过分水岭运输溶质，并且缺乏做出此类预测所需的时间和金钱。该项目的目的是开发一个框架，即使在数据稀缺区域，也可以更好地预测溶质传输的预测。该项目利用现有的数据和高分辨率模型，该模型在已经详细表征的站点上构建，以评估类似流域中的流量和运输过程如何相关。这些信息将导致简单的统计模型，这些模型可以基于较不详细的特征来捕获真实流域的复杂性。预计这些模型将允许从已进行大量投资以改善相对数据贫困站点的模型的网站上翻译知识。该项目还正在创建新的教育工具，培训本科生和研究生，并与应用分水岭的经理联系，以更好地了解他们对溶质运输模型现实应用应用的需求。 这项研究中使用的方法着重于使用最近的多域相关速度模型（MD-CVM）来表示耦合的地下和表面流和流域中的传输。基于拉格朗日粒子的数值方法是这种方法的核心，该方法将粒子之间的相互作用伴随着准确代表关键的混合和反应过程。每个流管的水和溶液在聚在一起时相互作用，将流域的几何形状简化为树，而无需牺牲过程级别的现实主义。 StreamTube方法还将强制执行速度相关性，尽管有证据表明存在持续的相关性，但在以前模型中缺少流域量表的新功能。使用速度相关性的优点在于它们在概念上是简单的，但产生了强大的模型，这些模型在不同站点上都表现出了希望。流域的动态耦合但现实的表示形式将扩展可用于理解和最佳管理实际分水岭的工具。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评估标准通过评估来获得支持的。

项目成果

Parallelized domain decomposition for multi-dimensional Lagrangian random walk mass-transfer particle tracking schemes

多维拉格朗日随机游走传质粒子跟踪方案的并行域分解

• DOI: 10.5194/gmd-16-833-2023
• 发表时间: 2023
• 期刊: Geoscientific Model Development
• 影响因子: 5.1
• 作者: Schauer, Lucas;Schmidt, Michael J.;Engdahl, Nicholas B.;Pankavich, Stephen D.;Benson, David A.;Bolster, Diogo
• 通讯作者: Bolster, Diogo

Next Generation Computers Warrant Next Generation Groundwater Models

下一代计算机保证下一代地下水模型

• DOI: 10.1111/gwat.13325
• 发表时间: 2023
• 期刊: Groundwater
• 影响因子: 2.6
• 作者: Engdahl, Nicholas B.