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.

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。智力优点：该项目提出测试的假设，自然示踪剂测量的时间分辨率类似于水通量（即，逐日或亚逐日）可用于预测河流水文、地表水-地下水混合和地球化学之间的耦合。 将通过对选定溶质进行高时间分辨率监测和测量来检验该假设。 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，以及这些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)如何将高分辨率的河流化学数据同化为集成的并行流域模型（例如，PARFLOW）改进水流、地下水位、地表水/地下水混合和溶质运移的预测？ 3)将高分辨率化学测量和使命机构数据纳入贝叶斯网络模型如何改进对水流和地表水/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。 这条河穿过封闭和非封闭的岩溶Floridan含水层的边界，导致两个化学性质良好的源水终端成员：地表径流和地下水。 这些端部构件根据河流流量以动态比例混合。 终端成员应该能够区分使用原位连续传感器，主要是特定的电导率，和高分辨率自动采样，以允许免费测量的颜色，pH值，硝酸盐，磷酸盐，和主要离子concentration.Broader影响：这项工作将影响科学界的分布式水文传感能力的发展，这是沃茨的试验台网站的基础。 将通过与巴尔的摩沃茨试验台现场的相互作用，在各现场测试高分辨率采样技术和水文及水文地质动力学建模。 采样将与现有的流动基础设施（例如，美国地质勘探局测量站）和结果将结合机构的数据，以辨别河水来自哪里，需要多长时间才能到达那里，以及它携带什么。 这些信息将提供给认识到这一点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定提供信息。 这些数据将在CUAHSI水文信息系统中进行整理，以便进行数据存储和检索，与使命机构的数据连接，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研究。