Articles | Volume 21, issue 9
Hydrol. Earth Syst. Sci., 21, 4927–4958, 2017
https://doi.org/10.5194/hess-21-4927-2017
Hydrol. Earth Syst. Sci., 21, 4927–4958, 2017
https://doi.org/10.5194/hess-21-4927-2017
Research article
29 Sep 2017
Research article | 29 Sep 2017

State and parameter estimation of two land surface models using the ensemble Kalman filter and the particle filter

Hongjuan Zhang et al.

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Cited articles

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Short summary
Applications of data assimilation (DA) arise in many fields of geosciences, perhaps most importantly in weather forecasting and hydrology. We want to investigate the roles of data assimilation methods and land surface models (LSMs) in joint estimation of states and parameters in the assimilation experiments. We find that all DA methods can improve prediction of states, and that differences between DA methods were limited but that the differences between LSMs were much larger.