Tools for Environmental Observatory Design and Implementation: Sensor Networks, Dynamic Bayesian Nutrient Flux Modeling, and Cyberinfrastructure Advancement

这个项目是为评估需要解决的问题而资助的几个“试验床”项目之一，这些问题需要为研究流域尺度的水文、物理、化学和生物过程之间的相互作用而进行具有成本效益的环境观测站的设计。该项目旨在开发更好和更简单的方法来量化城市地区暴雨径流中的污染物负荷。监测指标将使用贝叶斯建模技术，建立易于测量的替代物与主要水质变量之间的关系，并将评估优化采样时间和频率的方法，以将污染物负荷的不确定性降至最低。这个话题在全国范围内引起了相当大的兴趣，因为准确测量城市雨水等来源的负荷的内在成本，这些来源受到浓度和流量的高频变化的影响。该项目将涉及本科生和研究生，并包括一系列广泛的外展活动，面向研究大盐湖流域水污染问题的研究人员、学生和从业人员。该项目的成果可在全国暴雨管理领域具有重要的实际应用价值。

0610075StevensThis project is one of several "test-bed" projects funded to evaluate issues that need to be resolved for cost-effective design of environmental observatories for research on interactions among hydrological, physical, chemical, and biological processes at the watershed scale. This project seeks to develop better and simpler methods to quantify pollutant loadings in storm-water runoff from urban areas. The PIs will use Bayesian modeling techniques to develop relationships between easily measured surrogates and key water quality variables and will evaluate ways to optimize the timing and frequency of sampling to minimize pollutant loading uncertainties. This topic is of considerable interest nationwide because of the inherent costs of accurately measuring loadings from sources like urban storm water, which are subject to high frequency variations in both concentrations and flows. The project will involve both under-graduate and graduate students and includes a broad set of outreach activities to researchers, students and practitioners working water pollution problems in the Great Salt Lake Basin. Results of the project could have important practical applications nationally in the area of storm-water management.