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

Cornell University, Ithaca NY

Investigators

Abstract

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.