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Geophysical Research Letters

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Precipitation -- Remote sensing, Streamflow -- Forecasting, Hydrologic models, Uncertainty -- Mathematical models


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 errors through our hydrologic model. Some uncertainty scenarios are set up to incorporate and investigate the impact of the individual uncertainty sources from precipitation, parameters and also combined error sources on the hydrologic response. Also probabilistic measures are used to quantify the quality of ensemble prediction.


This is the publisher's final pdf. Originally published in Geophysical Research Letters ( and is copyrighted by American Geophysical Union (

* At the time of publication Hamid Moradkhani was affiliated with the University of California, Irvine



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