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Long-Lead Seasonal Prediction of Streamflow over the Upper Colorado River Basin: The Role of the Pacific Sea Surface Temperature and Beyond

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  • 1 a Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, California
  • | 2 b Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China
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Abstract

We have developed two statistical models for extended seasonal predictions of the upper Colorado River basin (UCRB) natural streamflow during April–July: a stepwise linear regression (reduced to a simple regression with one predictor) and a neural network model. Monthly, basin-averaged soil moisture, snow water equivalent (SWE), precipitation, and the Pacific sea surface temperature (SST) are selected as potential predictors. Pacific SST predictors (PSPs) are derived from a dipole pattern over the Pacific (30°S–65°N) that is correlated with the lagging streamflow. For both models, the correlation between the hindcasted and observed streamflow exceeds 0.60 for lead times less than 4 months using soil moisture, SWE, and precipitation as predictors. This correlation is higher than that of an autoregression model (correlation ~ 0.50). Since these land surface and atmospheric variables have no statistically significant correlations with the streamflow, PSPs are then incorporated into the models. The two models have a correlation of ~0.50 using PSPs alone for lead times from 6 to 9 months, and such skills are probably associated with stronger correlation between SST and streamflow in recent decades. The similar prediction skills between the two models suggest a largely linear system between SST and streamflow. Four predictors together can further improve short-lead prediction skills (correlation ~ 0.80). Therefore, our results confirm the advantage of the Pacific SST information in predicting the UCRB streamflow with a long lead time and can provide useful climate information for water supply planning and decisions.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Siyu Zhao, siyu_zhao@atmos.ucla.edu

Abstract

We have developed two statistical models for extended seasonal predictions of the upper Colorado River basin (UCRB) natural streamflow during April–July: a stepwise linear regression (reduced to a simple regression with one predictor) and a neural network model. Monthly, basin-averaged soil moisture, snow water equivalent (SWE), precipitation, and the Pacific sea surface temperature (SST) are selected as potential predictors. Pacific SST predictors (PSPs) are derived from a dipole pattern over the Pacific (30°S–65°N) that is correlated with the lagging streamflow. For both models, the correlation between the hindcasted and observed streamflow exceeds 0.60 for lead times less than 4 months using soil moisture, SWE, and precipitation as predictors. This correlation is higher than that of an autoregression model (correlation ~ 0.50). Since these land surface and atmospheric variables have no statistically significant correlations with the streamflow, PSPs are then incorporated into the models. The two models have a correlation of ~0.50 using PSPs alone for lead times from 6 to 9 months, and such skills are probably associated with stronger correlation between SST and streamflow in recent decades. The similar prediction skills between the two models suggest a largely linear system between SST and streamflow. Four predictors together can further improve short-lead prediction skills (correlation ~ 0.80). Therefore, our results confirm the advantage of the Pacific SST information in predicting the UCRB streamflow with a long lead time and can provide useful climate information for water supply planning and decisions.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Siyu Zhao, siyu_zhao@atmos.ucla.edu

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