Climate-Informed Adaptive Management and
Planning to Meet Urban Water Supply and Flood Mitigation Goals in the
Columbia University/IRI: Gavin Gong, Upmanu Lall, Casey Brown, Peter Kolesar
Collaborators: Delaware River Basin Commission, New York City Dept. of Environmental Protection, New York State Dept. of Environmental Conservation, Delaware River Foundation, The Nature Conservancy, Trout Unlimited, Hydrologics, Inc.
Funder: NOAA Climate Program Office, FY2007, SARP Element
The goal of this project is to incorporate climate and
weather information into water resources management for the Delaware River
Basin (DRB), in the context of the existing problems identified by DRB stakeholders. One problem is the lack of flexible operating
rules that equitably meet the competing demands on the basin, which include
federally mandated upstream diversions and instream
flow rates for
We will work directly with collaborating DRB stakeholders to improve decision processes that will address these problems. To facilitate stakeholder engagement and implementation of results, our approach will be to build upon the management system that is already in place. By refining modeling and monitoring tools that are already used and trusted, the incorporation of climate and weather information will be more readily understood and therefore easier to accept. This is especially important given the probabilistic nature of climate forecasts and associated risks that must be communicated (e.g., if a climate forecast turns out to be inaccurate).
We propose to first develop a robust, probabilistic hydroclimatic forecasting and assessment capability for the DRB, then use it to design an effective, flexible and implementable adaptive Decision Support Tool for the DRB. These objectives will be achieved via a phased approach, in order to reconcile the short-term problem resolution needs within the DRB and the research application focus of the NOAA SARP element, with the research advancement needs that often arise when addressing complex, multi-user water resource management problems. The first phase will explicitly apply existing state-of-the-art climate-based forecasting techniques to the DRB, and refine the existing OASIS simulation system to explicitly incorporate this new information. The second phase consists of incremental improvements to the DST, through novel enhancements to hydroclimatic forecasting techniques and/or the DST, e.g. using Bayesian models and networks.
By providing an innovative yet familiar adaptive management system that can address competing demands by leveraging climate information, our results will benefit all DRB stakeholders, and by extension the societal interests represented by them. Our collaborating DRB stakeholders are amenable to this approach, and have expressed their written support for the proposed work.