Special Issue Paper
Comparison of approaches for snowpack estimation in New York City watersheds
Version of Record online: 29 MAY 2013
Copyright © 2013 John Wiley & Sons, Ltd.
Special Issue: Eastern Snow Conference
Volume 27, Issue 21, pages 3050–3060, 15 October 2013
How to Cite
Schneiderman, E. M., Matonse, A. H., Zion, M. S., Lounsbury, D. G., Mukundan, R., Pradhanang, S. M. and Pierson, D. C. (2013), Comparison of approaches for snowpack estimation in New York City watersheds. Hydrol. Process., 27: 3050–3060. doi: 10.1002/hyp.9868
- Issue online: 11 SEP 2013
- Version of Record online: 29 MAY 2013
- Accepted manuscript online: 27 APR 2013 01:21AM EST
- Manuscript Accepted: 22 APR 2013
- Manuscript Received: 27 AUG 2012
- snow modelling;
- Catskill Mountains
Snow is a substantial component of historical annual precipitation in New York City (NYC) water supply watersheds in the Catskill Mountains, and the pattern of snow accumulation and snowmelt has important implications for the management of the reservoirs and watersheds that are part of the NYC water supply. NYC currently estimates reservoir basin-scale snowpack throughout the snow season by extrapolation from biweekly snow survey data. These estimates are complemented by the NOAA Snow Data Assimilation System (SNODAS) product. Snowpack models are used in short-term projections to support reservoir operations and long-term simulations to evaluate the potential effects of climate change, land use change, and watershed management on the water supply. We tested three snowpack estimation approaches compared with snow survey data: the lumped-parameter temperature index approach from the Generalized Watershed Loading Function (GWLF) watershed model, a spatially distributed temperature index (SDTI) model, and the spatially distributed NOAA SNODAS product. Of the spatially distributed approaches, SNODAS estimated the spatial variability of snow water equivalent (SWE) among snow survey sites within a basin better than the SDTI model. All three snowpack estimation approaches, including the lumped-parameter GWLF model, performed well in estimating basin-wide SWE for most of the basins studied. Copyright © 2013 John Wiley & Sons, Ltd.