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

合作研究：用于量化和预测佛罗里达州圣达菲河地表地下水混合和养分输送的高分辨率传感器网络。

• 批准号: 0854516
• 负责人: Reed Maxwell
• 金额: $ 7.96万
• 依托单位: Colorado School of Mines
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2012-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0854516&HistoricalAwards=false
• 关键词: Collaborative; Research; Resolution; Sensor; Networks

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.

摘要这一奖项是根据2009年的《美国复苏与再投资法》（公法111-5）资助的。智力优点：该项目提议检验以下假设：可以使用类似于水通量的时间分辨率测量的自然示踪剂（即每天或每天每天）来预测河流水文，地表水 - 地面水混合和生物地球化学之间的耦合。 该假设将通过高时间分辨率监测和选定溶质的测量测量进行检验。 这些结果将为水文，水理学以及化学动力学和通量的概念和数值模型提供信息和改善。 假设测试将解决三个特定的科学问题：1）表面/地下水混合的时间和纵向动力学是什么，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）的传递？ 2）将高分辨率流化学数据吸收到集成的平行流域模型（例如PARFLOW）中如何改善河流流量，地下水高度，表面/地下水混合和溶质传输的预测？ 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善流量和表面/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。 这条河跨越了受限制和无限制的喀斯特佛罗里达含水层的边界，导致了两个化学良好的源水端终端成员：地表径流和地下水。 这些最终成员以动态的比例混合，具体取决于河流排放。 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. 高分辨率采样技术以及水文和水文动力学的建模将通过与巴尔的摩水域测试床位地点的相互作用来测试。 采样将与现有的流量基础设施（例如USGS盖台站）共同列，并将结果与​​代理数据结合在一起，以辨别河水的来源，到达那里需要多长时间以及它带来了什么。 这些信息将提供给认知水管理机构和利益相关者群体，以更好地为管理和政策决策提供信息。 数据将在CUAHSI水文信息系统中组织，以进行数据存储和检索，与任务代理数据的串联，并提供基于Web的档案数据访问。 该项目将支持正在进行的研究生和本科研究。