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Seasonal streamflow prediction in Northern California basins using climate indices and Principal Component Regression


It has become more important in recent years to have a general estimate on how much water storage there will be during the spring season to support urban, industrial, and agricultural usage and forecasting drought. In this thesis, ground-based data and climate indices were applied to Principal Component Regression to build a prediction model to estimate seasonal streamflow in spring with winter temperature, precipitation and other climate indices. With data gathered from PRISM, NOAA, CEDC, and USGS and then testing all possible variables and combinations, it was found that using precipitation and temperature with climate indices such as SOI and PNA can provide useful information to forcaste streamflow in spring season.

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