Collaborative Research: High Resolution Sensor Networks for Quantifying and Predicting Surface-Groundwater Mixing and Nutrient Delivery in the Santa Fe River, Florida.

摘要本奖项由2009年美国复苏与再投资法案（公法111-5）资助。智力优势：本项目提出测试这样一种假设，即以时间分辨率测量的与水通量（即每日或次每日）相似的天然示踪剂可用于预测河流水文、地表水-地下水混合和生物地球化学之间的耦合。该假设将通过对选定溶质的高时间分辨率监测和测量进行检验。这些结果将为水文、水文地质、化学动力学和通量的概念和数值模型提供信息和改进。假设检验将解决三个具体的科学问题：1)地表/地下水混合的时间和纵向动态是什么，这些动态如何影响生态相关溶质（硝酸盐、磷酸盐、H+、溶解有机碳）的输送？2)如何将高分辨率河流化学数据同化到集成的、平行的流域模型（如PARFLOW）中，以改进对河流流量、地下水高程、地表水/地下水混合和溶质运移的预测？3)如何将高分辨率化学测量和任务机构数据整合到贝叶斯网络模型中，以提高对河流流量和地表水/地下水混合比的实时预测？这项工作将集中在北佛罗里达的圣达菲河。这条河穿过佛罗里达蓄水层的封闭和非封闭岩溶的边界，产生两种化学特征良好的水源末端成员：地表径流和地下水。这些末端构件根据河流流量以动态比例混合。末端成员应该能够使用原位连续传感器进行区分，主要用于特定电导率，以及高分辨率自动采样，以允许对颜色，pH，硝酸盐，磷酸盐和主要离子浓度进行补充测量。更广泛的影响：这项工作将通过促进分布式水文传感能力的发展而影响科学界，这是WATERS试验台站点的基础。高分辨率采样技术和水文和水文地质动力学建模将通过与巴尔的摩水域试验台的相互作用在各个地点进行测试。采样将与现有的流量基础设施（如美国地质勘探局测量站）一起进行，结果将与机构数据相结合，以确定河水来自何处，到达那里需要多长时间，以及它携带了什么。这些信息将提供给有认识的水管理机构和利益相关者团体，以便更好地为管理和决策提供信息。这些数据将在国家水文信息系统水文信息系统中进行组织，以便储存和检索数据，与特派团机构的数据相连接，并提供基于网络的档案数据访问。该项目将支持正在进行的研究生和本科生的研究。

ABSTRACTThis award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). Intellectual Merit: This project proposes to test the hypothesis that natural tracers measured at temporal resolution similar to water fluxes (i.e., daily or sub-daily) can be used to predict coupling between riverine hydrology, surface water-groundwater mixing, and biogeochemistry. The hypothesis will be tested with high temporal resolution monitoring and measurements of selected solutes. These results will inform and improve conceptual and numerical models of hydrologic, hydrogeologic, and chemical dynamics and fluxes. Hypothesis testing will address three specific science questions: 1) What are the temporal and longitudinal dynamics of surface/groundwater mixing, and how do these affect the delivery of ecologically relevant solutes (nitrate, phosphate, H+, dissolved organic carbon)? 2) How does assimilation of high resolution stream chemistry data into integrated, parallel watershed models (e.g., PARFLOW) improve predictions of stream flow, groundwater elevation, surface/groundwater mixing and solute transport? 3) How does the incorporation of high resolution chemical measurements and mission agency data into Bayesian network models improve real-time predictions of stream flow and surface/groundwater mixing ratios? The work will be focus on the Santa Fe River in North Florida. This river crosses the boundary of the confined and unconfined karstic Floridan Aquifer, resulting in two chemically well-characterized source water end-members: surface runoff and groundwater. These end members mix in dynamic proportions depending on river discharge. The end members should be able to be discriminated using in situ continuous sensors, primarily for specific conductivity, and high resolution auto-sampling to allow complimentary measurements of color, pH, nitrate, phosphate, and major ion concentrations.Broader Impacts: This work will impact the scientific community by contributing to the development of distributed hydrologic sensing capabilities, which is the basis of the WATERS test-bed sites. High resolution sampling techniques and modeling of hydrologic and hydrogeologic dynamics will be tested across sites by interactions with the Baltimore WATERS test-bed site. The sampling will be co-located with existing flow infrastructure (e.g., USGS gaging stations) and results will be combined with agency data to discern where river water comes from, how long it took to get there, and what it carries. This information will be provided to cognizant water management agencies and stakeholder groups to better inform management and policy decisions. The data will be organized in the CUAHSI hydrologic information system for data storage and retrieval, concatenation with mission agency data, and to provide web-based access to archival data. The project will support on-going graduate and undergraduate research.