RII Track-2 FEC: IGM--A Framework for Harnessing Big Hydrological Datasets for Integrated Groundwater Management

RII Track-2 FEC：IGM——利用大水文数据集进行地下水综合管理的框架

基本信息

批准号：
2019561
负责人：
Prabhakar Clement
金额：
$ 599.85万
依托单位：
University of Alabama Tuscaloosa
依托单位国家：
美国
项目类别：
Cooperative Agreement
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2019561&HistoricalAwards=false
关键词：
RII Track FEC IGM Framework

项目摘要

Groundwater depletion is a major water management problem that is of global concern. Locally, the Southeastern US has experienced increased water stress due to the mismanagement of its water resources, especially during drought periods. Rapid agricultural expansion and unplanned urbanization have further aggravated this problem. Given that water-related industries contribute to over 150 billion of US dollars in annual revenues, the long-term sustainability of freshwater resources is of paramount importance to this region. While mapping the availability of water in topsoil, reservoirs, and rivers continues to receive much attention, mapping of groundwater storage changes at a fine spatiotemporal resolution over large areas is currently lacking. This is important because groundwater contributes around 40 percent of freshwater usage in the conterminous US, and its contribution in some Southeastern states, e.g., Mississippi, is over two-thirds. Groundwater also indirectly sustains surface water resources, and hence its actual contribution to freshwater usage is even larger than reported. The goal of this project is to harness the big data to implement an integrated groundwater management (IGM) framework that will provide new scientific insights and make useful groundwater predictions at an unprecedented fine spatiotemporal resolution. The IGM framework integrates hydrological, geological, and satellite datasets with machine learning tools and high-resolution simulation models. The information generated will be made available to a wide group of stakeholders through a web-based platform to help develop engineering and policy solutions. The research tasks and workforce development efforts will be jointly accomplished by a team of interdisciplinary researchers at five universities: The University of Alabama, Louisiana State University, University of Mississippi, Tuskegee University, and Southern University.Prediction of groundwater storage changes at fine spatiotemporal scales is challenging due to lack of information about recharge fluxes, which are influenced by variations in natural land surface processes (e.g., precipitation and evapotranspiration) and anthropogenic interventions such as irrigation and pumping. The inability to map subsurface heterogeneities is another major limitation. In this study, we will harness big hydrologic datasets using science-based process models and machine learning tools to develop groundwater level and recharge maps at fine spatiotemporal scales. Novel contributions from this effort will include the development of new machine learning algorithms (such as convolutional and long-short term memory networks constrained by conservation principles), a new hydrogeological database derived from well log data, new machine learning tools for developing geological cross-sections from well log data, physically-realistic process models that use novel methods for estimating plant transpiration under climatic stress, and a new web platform for sharing groundwater level and recharge datasets. The integrated groundwater management framework will help answer several important science questions: 1) How well can we predict the groundwater levels and recharge at fine temporal resolution? 2) How different is the efficiency of data driven models compared to process-based models for obtaining groundwater recharge, and what are the advantages of a hybrid approach? and 3) What are the physical controls on groundwater drought-recovery processes?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.

地下水耗尽是全球关注的主要水管理问题。在当地，由于水资源管理不善，尤其是在干旱期间，美国东南部的水压力增加了。快速的农业扩张和计划外的城市化进一步加剧了这个问题。鉴于与水相关的行业每年收入超过1500亿美元，因此淡水资源的长期可持续性对该地区至关重要。在绘制表土中的水的供应量，储层和河流继续受到关注，但目前缺乏在较大的大面积上的良好时空分辨率上绘制地下水存储的变化。这很重要，因为地下水在美国的征收淡水使用量约为40％，并且在某些东南部州（例如密西西比州）的贡献超过三分之二。地下水还间接维持地表水资源，因此其对淡水使用情况的实际贡献甚至比报道的还要大。该项目的目的是利用大数据实施综合的地下水管理（IGM）框架，该框架将提供新的科学见解，并以前所未有的良好时空分辨率进行有用的地下水预测。 IGM框架将水文，地质和卫星数据集与机器学习工具和高分辨率仿真模型相结合。生成的信息将通过基于Web的平台提供给各种利益相关者，以帮助开发工程和政策解决方案。研究任务和劳动力发展工作将由五所大学的跨学科研究人员团队共同完成：阿拉巴马大学，路易斯安那州立大学，密西西比大学，托斯基吉大学，托斯基吉大学和南方大学。预定了在空间范围内的地下水量的变化，这是由于陆地上缺乏越来越多的范围，这是由于对弗洛克斯的自然范围而受到影响的挑战，这是由于缺乏越来越多的效果。（例如，降水和蒸散量）和人为干预措施，例如灌溉和抽水。无法映射地下异质性是另一个主要限制。在这项研究中，我们将使用基于科学的工艺模型和机器学习工具来利用大型水文数据集，以开发地下水水平并在良好的时空尺度上充电图。 Novel contributions from this effort will include the development of new machine learning algorithms (such as convolutional and long-short term memory networks constrained by conservation principles), a new hydrogeological database derived from well log data, new machine learning tools for developing geological cross-sections from well log data, physically-realistic process models that use novel methods for estimating plant transpiration under climatic stress, and a new web platform for sharing groundwater level and recharge数据集。综合地下水管理框架将有助于回答几个重要的科学问题：1）我们如何在良好的时间分辨率下如何预测地下水水平和充电？ 2）与获得地下水充电的基于过程的模型相比，数据驱动模型的效率有何不同？混合方法的优势是什么？ 3）地下水干旱恢复过程的物理控制是什么？该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来获得支持。

项目成果

期刊论文数量（23）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

PyTheis—A Python Tool for Analyzing Pump Test Data

PyTheis——用于分析泵测试数据的 Python 工具

DOI：
10.3390/w13162180
发表时间：
2021
期刊：
Water
影响因子：
3.4
作者：
Chang, Sun Woo;Memari, Sama S.;Clement, T. Prabhakar
通讯作者：
Clement, T. Prabhakar

Comparison of Data-Driven Groundwater Recharge Estimates with a Process-Based Model for a River Basin in the Southeastern USA

美国东南部河流流域数据驱动的地下水补给估算与基于过程的模型的比较

DOI：
10.1061/jhyeff.heeng-5882
发表时间：
2023
期刊：
Journal of Hydrologic Engineering
影响因子：
2.4
作者：
Gonzalez, Mauricio Osorio;Preetha, Pooja;Kumar, Mukesh;Clement, T. Prabhakar
通讯作者：
Clement, T. Prabhakar

Accounting for uncertainty in complex alluvial aquifer modeling by Bayesian multi-model approach

通过贝叶斯多模型方法解释复杂冲积含水层建模的不确定性