Climate Variability and Prediction in the Little Colorado River Basin Matt Switanek 1 1 Department of Hydrology and Water Resources University of Arizona.

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Presentation transcript:

Climate Variability and Prediction in the Little Colorado River Basin Matt Switanek 1 1 Department of Hydrology and Water Resources University of Arizona

Predictive Capacity Area of Study Little Colorado River Basin –69,400 km 2 –Average yearly discharge 1.98*10 8 m 3 /year (161,000 acre*ft/year)

Data Temperature and Precipitation –1/8 degree interpolated data set for the contiguous US –505 gridcells in the Little Colorado Sea Surface Temperature (SST) –International Comprehensive Ocean Atmosphere Data Set –2 degree resolution at a monthly timestep

Data Preparation Used daily basin (spatial) averages of temperature and precipitation to obtain –Monthly average temperatures –Monthly sums of precipitation Monthly SSTs were spatially averaged over 20° longitude by 10° latitude windows –Initially smooth the data and help fill in spatial and temporal gaps

Principal Components Analysis Performed on SSTs for each month independently, considering the domain in the Pacific of 125:2:289 E, -44:2:56 N Obtained 12 sets of Principal Component time series.

Statistical Significance

January SST Anomalies: Spatial Map

July SST Anomalies: Spatial Map

Conditioning First, correlate January SST’s PC’s 1 & 2 average with seasonal precipitation (JFM, FMA …) to get an idea of the strength of correlation between the two time series Next, observe the distribution of precipitation when PC’s 1 & 2 average exceeds a threshold value (.6) Perform a difference of means test to observe how confident we are that the distribution of precipitation data that meets the condition and the distribution that does not are from different populations

All (std dev) All (mm) Conditioned (std dev) Conditioned (mm) 25% % %

SSTs Correlated with Precipitation

Moving Window Correlations vs. Principal Components