Engineering Commons^™

Investigating The Impact Of Remotely Sensed Precipitation And Hydrologic Model Uncertainties On The Ensemble Streamflow Forecasting, Hamid Moradkhani, K. Hsu, Y. Hong, S. Sorooshian

Civil and Environmental Engineering Faculty Publications and Presentations

In the past few years sequential data assimilation (SDA) methods have emerged as the best possible method at hand to properly treat all sources of error in hydrological modeling. However, very few studies have actually implemented SDA methods using realistic input error models for precipitation. In this study we use particle filtering as a SDA method to propagate input errors through a conceptual hydrologic model and quantify the state, parameter and streamflow uncertainties. Recent progress in satellite-based precipitation observation techniques offers an attractive option for considering spatiotemporal variation of precipitation. Therefore, we use the PERSIANN-CCS precipitation product to propagate input …