| description 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 | |
