USGS Grant Number:
Year Established: 2010 Start Date: 2010-09-01 End Date: 2012-08-31
Total Federal Funds: $122,136 Total Non-Federal Funds: $122,140
Principal Investigators: Paul Ferre
Abstract: The major challenges to accurate hydrologic prediction are: 1) capturing the inherent complexity of hydrogeologic systems in models; and 2) acquiring sufficient informative data to characterize the critical hydrologic processes. Commonly, these modeling and measurement bottlenecks are seen as two interacting, yet separate, aspects of hydrologic science. We propose a novel approach that combines cutting edge tools in hydrologic modeling with a new approach to monitoring network design that addresses both of these fundamental limitations jointly. Specifically, we propose to test whether multi-model analysis combined with a discriminatory approach to data collection leads to the selection of more informative hydrogeologic measurement and monitoring networks. Based on our findings, we will develop a stand-alone tool that can interface with readily available hydrologeologic software to implement Multi-Model Analysis with Discriminatory Data Collection (MMA-DDC). Our research will contribute to and leverage two ongoing USGS water resources investigations: the Upper San Pedro River, in Arizona, and the Matanuska-Susitna Valley, in Alaska. The San Pedro has been characterized and modeled extensively over the past 30 years to address competing environmental and human demands on the water resources. In contrast, the Mat-Su, which is Alaska’s most important agricultural region and is experiencing rapid population increases, has limited historic hydrogeologic data. A more comprehensive investigation of the water resources in the Mat-Su began within the past five years. We will use these two study sites to develop and test our proposed approach (San Pedro) and to demonstrate how it can be applied at the early stages of a water resources investigation (Mat-Su). Our investigation will show that Multi-Model Analysis with Discriminatory Data Collection (MMA-DDC) is a highly transferable approach to water resources investigations that will improve management for combined environmental, municipal, and agricultural use.