Table of Contents
Journal of Climatology
Volume 2014 (2014), Article ID 284137, 10 pages
http://dx.doi.org/10.1155/2014/284137
Research Article

An Efficient Prediction Model for Water Discharge in Schoharie Creek, NY

1School of Computing, Engineering and Mathematics, University of Brighton, Brighton BN2 4GJ, UK
2Division of Math, Science and Technology, Nova Southeastern University, 3301 College Avenue, Fort Lauderdale-Davie, FL 33314, USA
3School of Environment and Technology, University of Brighton, Brighton BN2 4GJ, UK
4Department of Geological Sciences, University of Florida, 241 Williamson Hall, P.O. Box 112120, Gainesville, FL 32611, USA
5Department of Biometry and Statistics, State University of New York at Albany, One University Place, Rensselaer, NY 12144, USA

Received 27 June 2013; Revised 9 December 2013; Accepted 16 December 2013; Published 12 February 2014

Academic Editor: Maite deCastro

Copyright © 2014 Katerina G. Tsakiri et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Flooding normally occurs during periods of excessive precipitation or thawing in the winter period (ice jam). Flooding is typically accompanied by an increase in river discharge. This paper presents a statistical model for the prediction and explanation of the water discharge time series using an example from the Schoharie Creek, New York (one of the principal tributaries of the Mohawk River). It is developed with a view to wider application in similar water basins. In this study a statistical methodology for the decomposition of the time series is used. The Kolmogorov-Zurbenko filter is used for the decomposition of the hydrological and climatic time series into the seasonal and the long and the short term component. We analyze the time series of the water discharge by using a summer and a winter model. The explanation of the water discharge has been improved up to 81%. The results show that as water discharge increases in the long term then the water table replenishes, and in the seasonal term it depletes. In the short term, the groundwater drops during the winter period, and it rises during the summer period. This methodology can be applied for the prediction of the water discharge at multiple sites.