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

喀斯特含水层形成于石灰岩等高度可溶岩层下面的地区，是世界上约四分之一人口的主要饮用水源。这些含水层的特点是复杂的地下水补给、储存和在天坑、孔隙、裂缝和管道中的流动模式。在美国西部和世界各地拥有喀斯特含水层的许多山区，大部分年降水量在冬季以雪的形式出现。在这些积雪占主导地位的喀斯特流域，融雪补给含水层，在夏季降水稀少、需水量高的时候维持水流。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，特别是在对山区喀斯特流域对气候变率的响应缺乏定量了解的情况下。这些知识缺口的存在是由于这些流域固有的地形和地质非均质性导致了复杂的补给和排放过程。该项目将克服这些限制，为改进水资源管理提供坚实的科学基础。资金将支持多所大学的研究生和本科生的研究。通过外展和教育活动，该项目还将吸引当地利益相关者、公众和K-12学生。本研究的首要目标是了解和预测积雪主导的山地喀斯特含水层的水文响应。这个为期三年的项目将整合一个空间分布的、基于物理的融雪模型和一个数据驱动的深度学习模型，该模型代表了高度复杂的喀斯特含水层系统。将在不同的空间和时间尺度上收集现场观测和地球化学数据集（包括河流流量、河流和泉水中的离子和同位素），以确定补给和排放特征，同时也测试深度学习模型的预测能力和物理代表性。具体而言，该项目将(1)量化不同强度和持续时间的融雪/降雨事件对地下水排放和河流流量的时空响应；(2)确定年际气候变率和流域物理性质如何影响水文行为；(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。这些结果将有助于更好地理解积雪主导的山地喀斯特流域如何响应气候变率，并为预测模型方法的稳健性或对其他地区的可转移性提供见解。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.