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: 2049688
• 负责人: Diogo Bolster
• 金额: $ 24.78万
• 依托单位: University of Notre Dame
• 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 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2049688&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.

对分水岭功能的深刻理解对于负责任地管理水资源是必要的。然而，充分描述或理解流域内发生的所有复杂过程往往是不可行的，而且代价高昂。许多令人担忧的地点根本没有足够的长期数据来预测溶质将如何通过流域输送，也缺乏做出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，以便即使在数据稀缺的地区也能更好地预测溶质运移。该项目利用现有的数据和高分辨率模型，在已经非常详细描述的地点建造，以评估类似流域的水流和运输过程是如何联系在一起的。这些信息将导致简单的统计模型，这些模型可以基于不太详细的特征来捕捉真实流域的复杂性。预计这些模型将允许翻译来自网站的知识，这些网站已经进行了大量投资，以改进相对数据匮乏的网站的模型。该项目还在创造新的教育工具，培训本科生和研究生，并接触实用的流域管理人员，以更好地了解他们对溶质运移模型在现实世界中的应用的需求。本研究主要采用多区域相关速度模型(MD-CVM)来描述流域内表层和地下的耦合流动和输运。基于拉格朗日粒子的流管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在聚集在一起时相互作用，将分水岭几何图形简化为一棵树，而不牺牲工艺级的真实感。流管方法还将加强速度关联，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的关联，但以前的模型缺乏这一特征。使用速度相关性的优势是，它们在概念上很简单，但可以产生稳健的模型，在不同的地点显示出前景。由此产生的动态耦合而又现实的流域表示将扩大可用于理解和优化管理真实分水岭的工具。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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

Markovian Models for Microplastic Transport in Open‐Channel Flows

明渠流中微塑料传输的马尔可夫模型

• DOI: 10.1029/2021wr031746
• 发表时间: 2022
• 期刊: Water Resources Research
• 影响因子: 5.4
• 作者: Xing, Liming;Bolster, Diogo;Liu, Haifei;Sherman, Thomas;Richter, David H.;Rocha‐Brownell, Kyle;Ru, Zhiming
• 通讯作者: Ru, Zhiming

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