References

HESS

Hydrology and Earth System Sciences

HESS

Hydrol. Earth Syst. Sci.

1607-7938

Copernicus Publications

Göttingen, Germany

10.5194/hess-15-3101-2011

Integrating coarse-scale uncertain soil moisture data into a fine-scale hydrological modelling scenario

Vernieuwe

¹ De Baets

¹ Minet

² Pauwels

V. R. N.

³ Lambot

² Vanclooster

² Verhoest

N. E. C.

Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Coupure links 653, 9000 Gent, Belgium

Earth and Life Institute-Environmental Sciences, Université Catholique de Louvain, Louvain-La-Neuve, Belgium

Laboratory of Hydrology and Water Management, Ghent University, Coupure links 653, 9000 Gent, Belgium

11 10 2011

15 10 3101 3114

2011

This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/

This article is available from https://hess.copernicus.org/articles/15/3101/2011/hess-15-3101-2011.html

The full text article is available as a PDF file from https://hess.copernicus.org/articles/15/3101/2011/hess-15-3101-2011.pdf

In a hydrological modelling scenario, often the modeller is confronted with external data, such as remotely-sensed soil moisture observations, that become available to update the model output. However, the scale triplet (spacing, extent and support) of these data is often inconsistent with that of the model. Furthermore, the external data can be cursed with epistemic uncertainty. Hence, a method is needed that not only integrates the external data into the model, but that also takes into account the difference in scale and the uncertainty of the observations. In this paper, a synthetic hydrological modelling scenario is set up in which a high-resolution distributed hydrological model is run over an agricultural field. At regular time steps, coarse-scale field-averaged soil moisture data, described by means of possibility distributions (epistemic uncertainty), are retrieved by synthetic aperture radar and assimilated into the model. A method is presented that allows to integrate the coarse-scale possibility distribution of soil moisture content data with the fine-scale model-based soil moisture data. The method is subdivided in two steps. The first step, the disaggregation step, employs a scaling relationship between field-averaged soil moisture content data and its corresponding standard deviation. In the second step, the soil moisture content values are updated using two alternative methods.

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