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.

对流域功能的深刻理解对于负责任的水资源管理是必要的。然而，充分描述或了解流域内发生的所有复杂过程往往是不可行的，而且代价高昂。许多令人担忧的地区根本没有足够的长期数据来预测溶质如何通过流域运输，也缺乏进行此类预测所需的时间和金钱。该项目的目的是建立一个框架，以便更好地预测溶质的迁移，即使在数据匮乏的地区也是如此。该项目利用现有数据和高分辨率模型，在已经非常详细地描述了特征的地点建立，以评估类似流域的流量和运输过程是如何相互关联的。这些信息将导致简单的统计模型，可以在不太详细的特征基础上捕捉真实流域的复杂性。预计这些模型将允许从大量投资的网站转换知识，以改进相对缺乏数据的网站的模型。该项目还创建了新的教育工具，培训本科生和研究生，并与应用流域管理人员接触，以更好地了解他们对溶质运输模型在现实世界中的应用的需求。本研究采用的方法主要是利用最新的多域相关速度模型（md - cvm）来表示流域地下和地表的耦合流动和输送。拉格朗日粒子沿流的数值方法是该方法的核心，它耦合了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相互作用，当它们聚集在一起时，将分水岭的几何形状简化为一棵树，而不会牺牲过程级的真实感。流管方法还将加强速度相关性，这是以前的模型在流域尺度上所缺乏的一个新特征，尽管有证据表明存在持续的相关性。使用速度关联的优点是，它们在概念上很简单，但产生的健壮模型在不同的站点上显示出希望。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最佳管理实际流域的工具。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

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.