research

Investigating the impact of remotely sensed precipitation and hydrologic model uncertainties on the ensemble streamflow forecasting

Abstract

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 measure are used to quantify the quality of ensemble prediction. Copyright 2006 by the American Geophysical Union

Similar works

Full text

eScholarship - University of CaliforniaProvided a free PDF (195.62 KB)

/13030/qt4zt323p9oai:escholarship.org/ark:/13030/qt4zt323p9
Last time updated on March 11, 2020View original full text link

This paper was published in eScholarship - University of California.

Having an issue?

Is data on this page outdated, violates copyrights or anything else? Report the problem now and we will take corresponding actions after reviewing your request.