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: 2043150
• 负责人: James McNamara
• 金额: $ 11.08万
• 依托单位: Boise 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=2043150&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.

喀斯特含水层在高度坚实的岩层（例如石灰石）下形成的地区形成，并作为世界人口大约四分之一人口的主要饮用水源。这些含水层的特征是地下水，孔，断裂和导管中的复杂地下水充电，存储和流动模式。在美国西部和全球的许多山区地区，拥有喀斯特含水层，大部分年度降水都在冬季下雪。在这些以雪为主导的喀斯特分水岭中，融雪融化的含水层在夏季稀缺且需求量很高的夏季维持水流。这些流域对降水和温度的年度变化和长期趋势敏感。这给可持续水资源管理带来了挑战，特别是当缺乏对山喀斯特分水岭对攀登变异性的反应的定量理解时。由于这些分水岭固有的地形和地质异质性而发生的复杂的充值和放电过程，存在此类知识差距。该项目将克服这些局限性，并为改善水资源管理的科学依据。资金将支持多个大学的研究生和本科研究。通过宣传和教育活动，该项目还将吸引当地利益相关者，公众和K-12学生。拟议的研究的总体目标是理解和预测雪为主导的雪山喀斯特喀斯特含水层的水文反应。这项为期三年的项目将将以空间分布的基于物理的融雪模型与代表高度复杂的喀斯特含水层系统的数据驱动的深度学习模型相结合。将在各种空间和时间尺度上收集现场观测和地球化学数据集（包括流和流中的离子和同位素），以识别充值和放电特征，同时还测试深度学习模型的预测能力和物理表现。具体而言，该项目将（1）量化时空地下水的排放和流量响应对融雪/降雨事件的响应，强度和持续时间变化，（2）确定年间气候变化和水域物理特性如何影响水文行为，（3）测试在不同的位置和不同位置的组合和数据模型的方法。结果将导致人们对积雪主导的喀斯特分水岭如何应对气候变异性的反应，并洞悉模型方法的鲁棒性，以预测或转移到其他地区。该奖项反映了NSF的法定任务，并通过使用基金会的知识优点和广泛的criperia来评估，以评估来获得珍贵的支持。