Investigating Contaminant Transport in Large Watersheds with New Methods for Automatic Calibration, Sensitivity and Uncertainty Analysis Including Application to Design of Sensor

利用自动校准、灵敏度和不确定性分析（包括应用于传感器设计）的新方法研究大流域的污染物迁移

• 批准号: 0711491
• 负责人: Christine Shoemaker
• 金额: $ 41.59万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-04-15 至 2012-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0711491&HistoricalAwards=false
• 关键词: Investigating; Contaminant; Transport; Large; Watersheds

Project Abstract for Resubmitted Proposal 0711491 (PI Shoemaker)In order to make effective use of watershed field data, it is necessary to have a watershed model. It is also essential to have a computationally feasible method for calibrating a model and assessing the uncertainty of model predictions. The focus of this project is on computationally feasible methods for spatially distributed models of large watersheds, including nutrient transport as well as flow. In this proposal new and recently developed methods by the PI will be applied to the Cannonsville Watershed (1,200 km2), which is a source of New York City?s water supply.The objectives of the proposed research include:1. Automatic Calibration and Uncertainty Analysis: We aim to provide a transformative general methodology to do automatic calibration, multivariate sensitivity analysis, and uncertainty analysis for large watershed models. Earlier methods require thousands of simulations, which could take over a year of computation for a large model. The focus for this proposal is to apply the new methods for automatic calibration and uncertainty analysis for the first time to watersheds. 2. Augmentation of Sensor/monitoring Networks: We will also develop a procedure to determine the best locations to add new sensors or monitoring stations to integrate with an existing data collection network. The analysis uses a watershed model to evaluate the value of new data obtained by the new sensors and compares to the values of alternative schemes for data collection that differ in terms of constituents measured and the location or times of measurement. The analysis incorporates the tradeoff between resources and accuracy.3. Broader Impact: We will aim to have a broad impact by a) generating methods and software that can be used internationally with many watersheds and models, b) provide better predictive tools for the Cannonsville which has a huge environmental impact and an effect on millions of people, c) continue the PI?s practice of recruiting and training underrepresented PhD students, and work with Cornell ADVANCE program to help women faculty, and d) use REUs and augment course materials.4. Intellectual Merit: The intellectual merit is associated with the importance of the methods for watershed analysis, and the originality of the methods being developed.

为了有效利用流域野外数据，有必要建立流域模型。同样重要的是要有一个计算上可行的方法来校准模型和评估模型预测的不确定性。本项目的重点是研究大流域空间分布模型的计算可行方法，包括养分运输和流量。在该提案中，PI最近开发的新方法将应用于卡农斯维尔流域（1,200平方公里），这是纽约市的一个来源？供水系统。拟进行的研究目标包括：1。自动校准和不确定性分析：我们的目标是提供一种变革性的通用方法来进行大型流域模型的自动校准，多变量敏感性分析和不确定性分析。早期的方法需要数千次模拟，对于一个大型模型来说，这可能需要一年多的计算时间。本文的重点是首次将新的自动校准和不确定度分析方法应用于流域。2. 增强传感器/监测网络：我们还将制定一套程序，以确定增加新传感器或监测站的最佳位置，以便与现有的数据收集网络相结合。该分析使用分水岭模型来评估新传感器获得的新数据的价值，并与在测量成分和测量地点或时间方面不同的其他数据收集方案的价值进行比较。分析包含了资源和准确性之间的权衡。更广泛的影响：我们的目标是产生广泛的影响：a)产生可以在许多流域和模型中使用的国际方法和软件，b)为具有巨大环境影响和影响数百万人的Cannonsville提供更好的预测工具，c)继续PI？3)招募和培训代表性不足的博士生，并与康奈尔大学ADVANCE项目合作，帮助女性教师，以及d)使用reu和增加课程材料。智力价值：智力价值与分水岭分析方法的重要性和正在开发的方法的独创性有关。