Collaborative Research: Using Surface Information for Quantitative Modeling of the Subsurface

协作研究：利用地表信息进行地下定量建模

• 批准号: 1719670
• 负责人: Paola Passalacqua
• 金额: $ 27.72万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1719670&HistoricalAwards=false
• 关键词: Collaborative; Research; Using; Surface; Information

Surface connections, such as those among channels in river networks, are important for understanding the development and evolution of landscapes, such as densely populated coastal river deltas. Connections in the subsurface are critical in understanding groundwater flow and solute transport. Preferential flowpaths, in fact, can quickly deliver contaminants to water supply wells, a particularly important problem in densely populated coastal areas. Establishing a quantitative link between surface and subsurface patterns will greatly advance our capability to predict the movement of contaminants in groundwater, thus improving access to clean water and limiting pollution and health risks. We propose to investigate quantitatively how the dynamics of surface networks create subsurface networks, and thus determine how surface information can be used to predict properties of the subsurface. This will enable us to better predict sustainability and manage water resources in densely populated deltas such as the Ganges-Brahmaputra Delta, where high concentrations of arsenic are widespread in the groundwater of the upper delta, and salinity problems are pervasive in the lower delta. The models and data analysis tools developed as part of this project will be released as open source and we will collaborate with Bangladeshi institutions to disseminate our findings. Our driving hypothesis is that the 3D subsurface structure can be predicted by combining information on (i) the modern surface network snapshot, (ii) the surface network kinematics (i.e., its temporal evolution), and (iii) accommodation and net sedimentation. We further hypothesize that (iv) the nature of the surface-to-subsurface translation exerts a major influence on structural connectivity and solute transport through the resulting aquifer system. Our goal is to develop new methods to translate surface channel networks to obtain quantitative models of subsurface architecture and flow and transport processes. We will perform this analysis with a combination of experimental, numerical modeling, and observational approaches and we will verify our findings by collecting lithologic and geochemical data in the Ganges-Brahmaputra Delta. Our findings will provide critical information about the predictability of subsurface structure given the surface channel network and its kinematics, and will allow quantification of the factors influencing this predictability. The proposed work will also further the development of quantitative metrics of connectivity of surface networks and subsurface 3D structures and flowpaths, and improve our ability to model the subsurface structure of large deltaic systems where spatial heterogeneity and large spatial extent prevent full characterization via field observations. By extending this structural understanding to dynamic solute transport behavior, the proposed research will enhance the predictability of contaminant migration in highly heterogeneous systems.

地表连接，如河流网络中的河道之间的连接，对于理解景观的发展和演变，如人口密集的沿海河流三角洲，非常重要。地下水的连通性对于理解地下水流和溶质运移至关重要。事实上，优先流路可以快速地将污染物输送到供水威尔斯井，这在人口密集的沿海地区是一个特别重要的问题。在地表和地下模式之间建立定量联系，将大大提高我们预测地下水中污染物移动的能力，从而改善清洁水的获取，限制污染和健康风险。我们建议定量研究如何动态的表面网络创建地下网络，从而确定如何表面的信息可以用来预测地下的属性。这将使我们能够更好地预测可持续性和管理人口密集的三角洲，如恒河-雅鲁藏布江三角洲，其中高浓度的砷在上三角洲的地下水中广泛存在，盐度问题在下三角洲普遍存在。作为该项目的一部分开发的模型和数据分析工具将作为开源发布，我们将与孟加拉国机构合作传播我们的研究结果。我们的驱动假设是3D地下结构可以通过组合以下信息来预测：（i）现代表面网络快照，（ii）表面网络运动学（即，其时间演变），和（iii）住宿和净沉降。我们进一步假设，（四）表面到地下翻译的性质产生了重大影响，通过所产生的含水层系统的结构连通性和溶质运移。我们的目标是开发新的方法来翻译表面通道网络，以获得定量模型的地下结构和流动和运输过程。我们将结合实验，数值模拟和观测方法进行分析，并通过收集恒河-雅鲁藏布江三角洲的岩性和地球化学数据来验证我们的研究结果。我们的研究结果将提供关键信息的可预测性的地下结构给定的表面通道网络及其运动学，并将允许量化的因素影响这种可预测性。拟议的工作还将进一步发展的定量指标的连接性的表面网络和地下三维结构和流路，并提高我们的能力，模拟地下结构的大型三角洲系统的空间异质性和大的空间范围，防止通过现场观察充分表征。通过将这种结构理解扩展到动态溶质运移行为，拟议的研究将提高高度非均质系统中污染物迁移的可预测性。

项目成果

Global rates and patterns of channel migration in river deltas

河流三角洲河道迁移的全球速率和模式

• DOI: 10.1073/pnas.2103178118
• 发表时间: 2021
• 期刊: Proceedings of the National Academy of Sciences
• 作者: Jarriel, Teresa;Swartz, John;Passalacqua, Paola
• 通讯作者: Passalacqua, Paola

Reconstructing subsurface sandbody connectivity from temporal evolution of surface networks

从地表网络的时间演化重建地下砂体的连通性

• DOI: 10.1111/bre.12668
• 发表时间: 2022
• 期刊: Basin Research
• 影响因子: 3.2
• 作者: Steel, Elisabeth;Paola, Chris;Chadwick, Austin J.;Hariharan, Jayaram;Passalacqua, Paola;Xu, Zhongyuan;Michael, Holly A.;Brommecker, Hannah;Hajek, Elizabeth A.
• 通讯作者: Hajek, Elizabeth A.

RivGraph: Automatic extraction and analysis of river and delta channel network topology

• DOI: 10.21105/joss.02952
• 发表时间: 2021
• 期刊: J. Open Source Softw.
• 作者: J. Schwenk;J. Hariharan

Channel Migration in Experimental River Networks Mapped by Particle Image Velocimetry

• DOI: 10.1029/2021jf006300
• 发表时间: 2021-11
• 期刊: Journal of Geophysical Research: Earth Surface
• 作者: A. Chadwick;E. Steel;R. Williams‐Schaetzel;P. Passalacqua;C. Paola

Effects of Geologic Setting on Contaminant Transport in Deltaic Aquifers

• DOI: 10.1029/2022wr031943
• 发表时间: 2022-08
• 期刊: Water Resources Research
• 影响因子: 5.4
• 作者: Zhongyuan Xu;J. Hariharan;P. Passalacqua;E. Steel;A. Chadwick;C. Paola;A. Paldor;H. Michael