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

该奖项是根据2009年《美国复苏和再投资法案》(公法111-5)资助的。智力价值：该项目建议测试这样一种假设，即以与水通量类似的时间分辨率测量的自然示踪剂(即每日或次日)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。这一假设将通过对选定的溶质进行高时间分辨率的监测和测量来验证。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并加以改进。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，以及这些如何影响与生态相关的溶质(硝酸盐、磷酸盐、H+、溶解有机碳)的输送？2)将高分辨率河流化学数据同化到综合的、并行的流域模型(例如，PARFLOW)如何改进对径流、地下水高程、地表/地下水混合和溶质运移的预测？3)将高分辨率化学测量和任务机构数据纳入贝叶斯网络模型如何改进对径流和地表/地下水混合比的实时预测？这项工作将集中在北佛罗里达州的圣达菲河上。这条河跨越了封闭和非封闭岩溶弗洛里丹含水层的边界，导致了两个具有良好化学特征的水源端部：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。应该能够使用现场连续传感器(主要是电导率)和高分辨率自动采样来区分最终成员，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。广泛影响：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，这是水域试验台地点的基础。高分辨率采样技术和水文和水文地质动力学模型将通过与巴尔的摩水域试验台现场的相互作用在不同地点进行测试。采样将与现有的流量基础设施(例如，USGS计量站)放在同一地点，结果将与机构数据结合起来，以识别河水从哪里来，需要多长时间才能到达，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攸关方团体，以便更好地为管理和政策决策提供信息。这些数据将被编入CUAHSI水文信息系统，用于数据存储和检索，与特派团机构数据连接，并提供对档案数据的网上访问。该项目将支持正在进行的研究生和本科生研究。

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.