Long-Range Hydrologic Forecasting in El Niño Southern Oscillation-Affected Coastal Watersheds: Comparison of Climate Model and Weather Generator Approach
Publication: Journal of Hydrologic Engineering
Volume 20, Issue 12
Abstract
Streamflow forecasting is essential for the proper management of water resources, especially when severe droughts cause water resource scarcity. Streamflow forecasting using physically based or conceptual hydrologic models is a common approach. However, these models rely on the predicted climate data, which are at times unrealistic and depart significantly from actual observed data, resulting in an unreliable forecast. Because the sea surface temperature (SST) in the Niño 3.4 region has a potential teleconnection with streamflow in the El Niño Southern Oscillation (ENSO)-affected regions, the streamflow forecasting ability of a model can be enhanced by using SST in data-driven models. In fact, conceptual models cannot incorporate SST data as input. Therefore, in this study, an adaptive neuro-fuzzy inference system (ANFIS) was used to infuse SST data (from the equatorial Pacific) with predicted precipitation and temperature for streamflow forecasting with one-to-three months’ lead time. For the forecasted climate data, two methods were used: (1) ENSO-conditioned weather sequences, and (2) climate data from the Climate Forecast System version 2 (CFSv2) model. The forecasted streamflow, after systematic error correction, was postvalidated with observed streamflow from 1982 to 1988. The streamflow forecasting at one-month lead time was found to be better than that of the three-month lead time. The root-mean square error and percentage bias for one-month lead time forecast using CFSv2 were and 7%, whereas these statics using ENSO-conditioned weather-sequence data were and 10.5%, respectively. This research concludes that the climate model approach is a better choice for moderately sized watersheds for streamflow forecast with a one-month lead time. Conversely, the weather-generator approach is more suitable for streamflow forecasting with a three-month lead time, especially for low-flow conditions.
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Published In
Journal of Hydrologic Engineering
Volume 20 • Issue 12 • December 2015
Copyright
© 2015 American Society of Civil Engineers.
History
Received: Mar 25, 2014
Accepted: Feb 3, 2015
Published online: May 18, 2015
Discussion open until: Oct 18, 2015
Published in print: Dec 1, 2015
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