Collaborative Research: Quantifying Watershed Dynamics in Snow-Dominated Mountainous Karst Watersheds Using Hybrid Physically Based and Deep Learning Models

合作研究：使用基于物理和深度学习的混合模型量化以雪为主的山地喀斯特流域的流域动态

• 批准号: 2043363
• 负责人: Bethany Neilson
• 金额: $ 38.23万
• 依托单位: Utah 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: 2021-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2043363&HistoricalAwards=false
• 关键词: Collaborative; Research; Quantifying; Watershed; Dynamics

Karst aquifers form in regions underlain by highly soluble rock formations, such as limestone, and serve as the primary drinking water source for about a quarter of the world’s population. These aquifers are characterized by complex groundwater recharge, storage, and flow patterns in sinkholes, pores, fractures, and conduits. In many mountainous areas of the western U.S. and worldwide that host karst aquifers, most of the annual precipitation falls in the winter as snow. In these snow-dominated karst watersheds, snowmelt recharges aquifers that sustain streamflow in summer when precipitation is scarce and water demand is high. These watersheds are sensitive to year-to-year variations and long-term trends in precipitation and temperature. This creates challenges for sustainable water resource management particularly when a quantitative understanding of mountainous karst watershed response to climate variability is lacking. Such knowledge gaps exist due to complex recharge and discharge processes that occur because of topographical and geological heterogeneities inherent in these watersheds. This project will overcome these limitations and provide a sound scientific basis for improved water resources management. Funding will support both graduate and undergraduate research at multiple universities. Through outreach and educational activities, the project will also engage local stakeholders, the general public, and K-12 students.The overarching goal of the proposed research is to understand and predict hydrologic responses of snow-dominated mountainous karst aquifers. The three-year project will integrate a spatially distributed, physically based snowmelt model with a data-driven, deep learning model that represents the highly complex karst aquifer system. Field observational and geochemical data sets (including streamflow, and ions and isotopes in stream and spring water) will be collected at various spatial and time scales to identify recharge and discharge characteristics, while also testing the predictive capability and physical representativeness of the deep learning model. Specifically, the project will (1) quantify the spatiotemporal groundwater discharge and streamflow response to snowmelt/rainfall events with varying intensity and duration, (2) determine how interannual climate variability and watershed physical properties influence hydrologic behavior, and (3) test the combined physically based and data-driven modeling approach in different locations and climate conditions. The outcomes will lead to improved understanding of how snow-dominated mountainous karst watersheds respond to climate variability and provide insight into the robustness of the modeling approach for forecasting or transferability to other regions.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.

岩溶含水层形成于石灰岩等高度溶解的岩层之下，是世界上约四分之一人口的主要饮用水水源。这些含水层以复杂的地下水补给、储存和在天坑、孔隙、裂缝和管道中的流动模式为特征。在美国西部和世界各地拥有岩溶含水层的许多山区，每年的降雨量大多以雪的形式落在冬天。在这些以雪为主的喀斯特流域，积雪融化补充了含水层，在夏季降水稀少、用水需求高的时候，含水层维持着溪流流动。这些流域对降水和温度的年际变化和长期趋势很敏感。这给可持续水资源管理带来了挑战，特别是在缺乏对山区喀斯特流域对气候变化的反应的量化了解的情况下。由于这些流域固有的地形和地质不均质性，复杂的补给和排放过程导致了这种知识差距的存在。该项目将克服这些限制，为改进水资源管理提供可靠的科学基础。资金将支持多所大学的研究生和本科生研究。通过外展和教育活动，该项目还将吸引当地利益攸关方、普通公众和K-12学生参与。拟议研究的首要目标是了解和预测积雪控制的山区喀斯特含水层的水文响应。这个为期三年的项目将把空间分布的、基于物理的融雪模型与代表高度复杂的岩溶含水层系统的数据驱动的深度学习模型结合起来。将在不同的空间和时间尺度收集实地观测和地球化学数据集(包括径流、溪流和泉水中的离子和同位素)，以确定补给和排放特征，同时还测试深度学习模型的预测能力和物理代表性。具体地说，该项目将(1)量化不同强度和持续时间的融雪/降雨事件的时空地下水流量和径流响应，(2)确定年际气候变异性和流域物理特性如何影响水文行为，(3)在不同地点和气候条件下测试基于物理和数据驱动的组合建模方法。这些成果将有助于更好地理解雪山喀斯特流域如何响应气候变化，并为预测或转移到其他地区的建模方法的稳健性提供洞察。这一奖项反映了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

Detailed streamflow data for understanding hydrologic responses in the Logan River Observatory

用于了解洛根河天文台水文响应的详细水流数据

• DOI: 10.1002/hyp.14268
• 发表时间: 2021
• 期刊: Hydrological Processes
• 影响因子: 3.2
• 作者: Neilson, Bethany T.;Tennant, Hyrum;Strong, Patrick A.;Horsburgh, Jeffery S.
• 通讯作者: Horsburgh, Jeffery S.

Evaluation of hydrograph separation techniques with uncertain end‐member composition

端元成分不确定的过程过程分离技术评估

• DOI: 10.1002/hyp.14693
• 发表时间: 2022
• 期刊: Hydrological Processes
• 影响因子: 3.2
• 作者: Lukens, Eileen;Neilson, Bethany T.;Williams, Kenneth H.;Brahney, Janice
• 通讯作者: Brahney, Janice

Hybrid Physically Based and Deep Learning Modeling of a Snow Dominated, Mountainous, Karst Watershed

• DOI: 10.1029/2021wr030993
• 发表时间: 2022-03
• 期刊: Water Resources Research
• 影响因子: 5.4
• 作者: Tianfang Xu;Q. Longyang;C. Tyson;R. Zeng;B. Neilson

Effects of Meteorological Forcing Uncertainty on High-Resolution Snow Modeling and Streamflow Prediction in a Mountainous Karst Watershed

• DOI: 10.1016/j.jhydrol.2023.129304
• 发表时间: 2023-02
• 期刊: Journal of Hydrology
• 影响因子: 6.4
• 作者: C. Tyson;Q. Longyang;B. Neilson;R. Zeng;Tianfang Xu