Identification of Influential Sea Surface Temperature Locations and Predicting Streamflow for Six Months Using Bayesian Machine Learning Regression

Abstract

Sea surface temperature (SST) has significant influence in the hydrological cycle and affects the discharge in the stream. SST is an atmospheric circulation indicator which provides the predictive information about the hydrologic variability in the region around the world. Use of right location of SST for a given location of stream gage can capture the effect of oceanic-atmospheric interaction, improving the predictive ability of the model. This study aims on identifying the best locations of SST at the selected stream gage in the state of Utah that spatially covers the state from south to north, and use them for next six-month streamflow volume predictions. The data-driven model derived from the statistical learning theory was used in this study. Using an appropriate location of SST together with local climatic conditions and state of basin, an accurate and reliable streamflow was predicted for next six months. Influence of Pacific Ocean SST was observed to be stronger than that of Atlantic Ocean SST in the state of Utah. The SST of North Pacific developed the best model in most of the selected stream gages. Each model was ensured to be robust by the bootstrap analysis. The long-term streamflow prediction is important for water resource planning and management in the river basin scale and is a key step for successful water resource management in arid regions.