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We present a variational data assimilation approach based on a Moving Horizon Estimation (MHE) applied to the HBV hydrological model. This framework enables the modification of the model inputs precipitation and temperature as well as the model states soil moisture, upper zone storage and lower zone storage. It considers data products for snow cover, snow water equivalent and soil moisture and observed streamflow. The performance of the framework is evaluated for three test sites: i) the data–dense catchment of the upper Main River (2419 km2), Germany, for which the HBV model already produces excellent results, ii) a comparable upstream catchment of the Nahe River (1468 km2), Germany, and iii) a data-sparse environment in the upper basin of Karasu River in Turkey (10,275 km2). The added value of the data assimilation approach is relatively limited in the case of (i) and (ii), but more substantial for the data-sparse environment (iii) with only a limited amount of operational ground data.


Session R48, Data Processing: Data to Computations



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