Long Range Streamflow Forecasting Based on Hydrologic and Climatic Data

The research reported herein centers on long range streamflow predictability that originate in the State of Colorado. Specifically, we want to improve forecasting seasonal and yearly streamflows based on atmospheric-oceanic forcing factors, such as geopotential height and hydrologic factors such as snow water equivalent. We applied Principal Component Analysis (PCA) for forecasting at individual sites. The study included comparison of forecasts using various combinations of predictors, i.e. hydrologic, atmospheric, and oceanic variables. The study brought into relevance the significant benefits of using atmospheric and oceanic predictors, in addition to hydrological predictors, for long range streamflow forecasting.