Abstract
In this study, the authors ask the question: Can a more superior precipitation product be developed by merging individual products according to their a priori hydrologic predictability? The performance of three widely used high-resolution satellite precipitation products [Tropical Rainfall Measuring Mission (TRMM) real-time precipitation product 3B42 (3B42-RT), the NOAA/Climate Prediction Center morphing technique (CMORPH), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS)] was evaluated in terms streamflow predictability for the entire Mississippi River basin using the Variable Infiltration Capacity (VIC) macroscale hydrologic model. A merging concept that was not based on a single universal merging formula for the whole basin but rather used a “localized” (grid box by grid box) approach for merging precipitation products was then explored. In this merging technique, the a priori (historical) hydrologic predictive skill of each product for each grid box was first identified. Prior to streamflow routing, the corresponding accuracy of the spatially distributed simulations of soil moisture and runoff were used as proxy for weights in merging the precipitation products. It was found that the merged product derived on the basis of runoff predictability outperformed its counterpart merged product derived on the basis of soil moisture simulation. Results indicate that such a grid box by grid box merging concept that leverages a priori information on predictability of individual products has the potential to yield a more superior product for streamflow prediction than what the individual products can deliver for hydrologic prediction.
