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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/hess-2017-731
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
08 Jan 2018
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Hydrology and Earth System Sciences (HESS).
Assimilation of river discharge in a land surface model to improve estimates of the continental water cycles
Fuxing Wang1, Jan Polcher1, Philippe Peylin2, and Vladislav Bastrikov2 1Laboratoire de Météorologie Dyna mique, IPSL, CNRS, Ecole Polyte chnique, 91128, Palaiseau, France
2Laboratoire des sciences du climat et de l'environnement, IPSL, CEA, Orme des Merisiers, 91191, Gif sur Yvette, France
Abstract. The river discharge plays an important role in earth’s water cycles, but it is difficult to estimate due to un-gauged rivers, human activities, and measurement errors. One approach is based on the observed flux and a simple annual water balance model (ignoring human processes) for ungauged rivers, but it only provides annual mean values which is insufficient for oceanic modellings. Another way is by forcing a land surface model (LSM) with atmospheric conditions. It provides daily values but with uncertainties associated to models.

We use data assimilation techniques by merging the modelled river discharges by ORCHIDEE (without human processes currently) LSM and the observations from Global Runoff Data Center (GRDC) to obtain optimized discharges over the entire basin. The model systematic errors and human impacts (e.g., dam operation, irrigation, etc.) are taken into account by an optimization parameter x (with annual variation), which is applied to correct model intermediate variables runoff and drainage over each sub-watershed. The method is illustrated over Iberian Peninsula with 27 GRDC stations over the period 1979–1989. ORCHDIEE represents a realistic discharge over north of Iberian Peninsula with small model systematic errors, while the model overestimates discharges by 30 %–150 % over south and northeast region where the blue water footprint is large. The bias (absolute value) has been significantly reduced to less than 30 % after assimilation, and the assimilation result is not sensitive to assimilation strategies. This method also corrects the discharge bias for the basins without observations assimilated by extrapolating the correction from adjacent basins. The correction increases the inter-annual variability of river discharge because of the fluctuation of water usage. The E (P-E) of GLEAM (Global Land Evaporation Amsterdam Model, v3.1a) is lower (higher) than the bias corrected value, which could be due to the different P forcing and probably the missing processes in the GLEAM model.


Citation: Wang, F., Polcher, J., Peylin, P., and Bastrikov, V.: Assimilation of river discharge in a land surface model to improve estimates of the continental water cycles, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-731, in review, 2018.
Fuxing Wang et al.
Fuxing Wang et al.
Fuxing Wang et al.

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Short summary
This work improves river discharge estimation by takes the advantages of observation and model simulation. The new estimation takes into account both gauged and un-gauged rivers, and it compensates model systematic errors and missing processes (e.g., human water usage). This improved estimation is important not only for water resources management and ecosystem health over continent, but also for ocean dynamics and salinity.
This work improves river discharge estimation by takes the advantages of observation and model...
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