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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/hess-2019-324
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2019-324
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 26 Aug 2019

Submitted as: research article | 26 Aug 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Hydrology and Earth System Sciences (HESS).

Do surface lateral flows matter for data assimilation of soil moisture observations into hyperresolution land models?

Yohei Sawada1,2 Yohei Sawada
  • 1Institute of Engineering Innovation, the University of Tokyo, Tokyo, Japan
  • 2Meteorological Research Institute, Japan Meteorological Agency, Tsukuba, Japan

Abstract. It is expected that hyperresolution land modeling substantially innovates the simulation of terrestrial water, energy, and carbon cycles. The major advantage of hyperresolution land models against conventional one-dimensional land surface models is that hyperresolution land models can explicitly simulatelateral water flows. Despite many efforts on data assimilation of hydrological observations into those hyperresolution land models, how and when surface water flows driven by local topography matter for data assimilation of soil moisture observations has not been fully clarified. Here I perform two minimalist synthetic experiments where soil moisture observations are assimilated into an integrated surface-groundwater land model by an ensemble Kalman filter. A horizontal background error covariance provided by overland flows is important to adjust the unobserved state and parameter variables. However, the non-Gaussianity of the background error provided by the nonlinearity of a topography-driven surface flow harms the performance of data assimilation. It is difficult to efficiently constrain model states at the edge of the area where the topography-driven surface flow reaches by linear-Gaussian filters, which brings the new challenge in land data assimilation for hyperresolution land models. This study highlights the importance of surface lateral flows in hydrological data assimilation.

Yohei Sawada
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Yohei Sawada
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Short summary
Hydrologic data assimmilation is the area in which methods to integrate hydrological models and observations are investigated. Recently, hydrological or land models are increasing their complexity with very high spatial resolution. However, it is unclear that the current data assimilation method can directly be applied to those hyperresolution models so that I investigated the applicability and limitation of the existing method by minimalistic numerical experiments.
Hydrologic data assimmilation is the area in which methods to integrate hydrological models and...
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