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.

对流域功能的深入了解对于负责任的水资源管理是必要的。然而，充分描述或理解流域内发生的所有复杂过程通常是不可行的，而且成本高昂。许多令人关注的地点根本没有足够的长期数据来预测溶质如何通过流域运输，并且缺乏进行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，即使在数据稀缺的地区也可以更好地预测溶质运输。该项目利用现有数据和高分辨率模型（在已详细描述的地点构建）来评估类似流域中的流量和运输过程如何相互关联。这些信息将产生简单的统计模型，可以根据不太详细的特征来捕获真实流域的复杂性。这些模型预计将能够转换来自已投入大量资金以改进数据相对匮乏的站点的模型的知识。该项目还创建新的教育工具，培训本科生和研究生，并联系应用流域管理者，以更好地了解他们对溶质输运模型的实际应用的需求。 本研究中使用的方法侧重于使用最新的多域相关速度模型（MD-CVM）来表示流域中耦合的地下和地表流动和传输。沿着流管的基于拉格朗日粒子的数值方法是该方法的核心，它耦合粒子之间的相互作用以准确地表示关键的混合和反应过程。来自每个流管的水和溶质在聚集在一起时相互作用，将分水岭几何形状简化为树，而不牺牲过程级的真实性。流管方法还将加强速度相关性，这是分水岭尺度上的一个新特征，尽管有证据表明存在持久相关性，但以前的模型缺乏这一特征。使用速度相关性的优点在于，它们在概念上很简单，但产生的稳健模型在不同站点上都显示出前景。由此产生的动态耦合但现实的流域表示将扩展可用于理解和优化管理真实流域的工具。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

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.