TITLE:
Identification of Influential Sea Surface Temperature Locations and Predicting Streamflow for Six Months Using Bayesian Machine Learning Regression
AUTHORS:
N. K. Shrestha, G. Urroz
KEYWORDS:
Streamflow, Prediction, SWE, Temperature, RVM
JOURNAL NAME:
Journal of Water Resource and Protection,
Vol.7 No.3,
February
16,
2015
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.