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

重新提交的项目摘要0711491(Pi Shoemaker)为了有效利用流域实地数据，有必要建立流域模型。同样重要的是要有一种在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。该项目的重点是为大流域的空间分布模型提供计算上可行的方法，包括养分传输和流量。在这项建议中，PI最近开发的新方法将应用于坎农斯维尔流域(1200平方公里)，这是纽约市S供水的源头。拟议研究的目标包括：1.自动校准和不确定度分析：我们的目标是提供一种变革性的通用方法，用于对大型流域模型进行自动校准、多变量敏感性分析和不确定度分析。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。这项提议的重点是首次将自动校准和不确定度分析的新方法应用于流域。2.加强传感器/监测网络：我们还将制定一项程序，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，并将其与其他数据收集方案的值进行比较，这些方案在测量的成分和测量的位置或时间方面有所不同。该分析考虑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)生成可与许多流域和模型一起在国际上使用的方法和软件，b)为具有巨大环境影响和对数百万人产生影响的坎农斯维尔提供更好的预测工具，c)继续派？S招收和培训未被充分代表的博士生的做法，并与康奈尔大学高级计划合作帮助女性教员，以及d)使用REUS和扩充课程材料。智力价值：智力价值与流域分析方法的重要性以及正在开发的方法的独创性有关。

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.