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Christopher J. Martinez, Ph.D
Variation in large-scale climate phenomena such as the El Nino-Southern Oscillation (ENSO) can provide valuable predictive information for regional climate and hydrologic conditions in many parts of the world.
Christopher J. Martinez, Ph.D
279 Frazier Rogers Hall
PO Box 110570, University of Florida
Gainesville, FL 32611
279 Frazier Rogers Hall
PO Box 110570, University of Florida
Gainesville, FL 32611
Phone: (352) 392-1864 x279
Fax: (352) 392-4092
Email: chrisjm@ufl.edu
Fax: (352) 392-4092
Email: chrisjm@ufl.edu
Incorporating Large-scale Climate Information in Seasonal Streamflow
Forecasts
Specific objectives of this work include:
• Identify significant relationships between large-scale climate datasets including Sea Surface Temperatures (SSTs), 500mb Geopotential Heights, and Sea Level Pressures with streamflow, precipitation, and urban demand.
• Evaluate the use of indices of large-scale climate datasets to improve probabilistic streamflow forecasts.
• Assess the improvement in forecasting future streamflow extractions using streamflow forecasts based on climate information.
Results have shown that tropical SSTs in the Nino3 and Nino3.4 regions can be used to forecast winter streamflow up to 6-9 months in advance. These SST indices, as well as time series derived from Singular Value Decomposition (SVD) analysis, have been incorporated into probability of exceedance streamflow forecast models.
This work has been published in the Journal of Hydrology (link)
• Identify significant relationships between large-scale climate datasets including Sea Surface Temperatures (SSTs), 500mb Geopotential Heights, and Sea Level Pressures with streamflow, precipitation, and urban demand.
• Evaluate the use of indices of large-scale climate datasets to improve probabilistic streamflow forecasts.
• Assess the improvement in forecasting future streamflow extractions using streamflow forecasts based on climate information.
Results have shown that tropical SSTs in the Nino3 and Nino3.4 regions can be used to forecast winter streamflow up to 6-9 months in advance. These SST indices, as well as time series derived from Singular Value Decomposition (SVD) analysis, have been incorporated into probability of exceedance streamflow forecast models.
This work has been published in the Journal of Hydrology (link)