Assimilation of Multiscale and Multivariable Hydrologic Variables over Pan European River Basins

Tuesday, 16 December 2014
Oldrich Rakovec1, Rohini Kumar1, Luis E Samaniego1, Juliane Mai1, Stephan Thober2 and Matthias Cuntz1, (1)Helmholtz Centre for Environmental Research UFZ Leipzig, Leipzig, Germany, (2)Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany
Understanding hydrologic model uncertainty and meaningful representation of hydrological processes leads to more reliable hydrologic forecasts, which can be in particular critical under extreme hydrometeorologic conditions. Therefore, hydrologic model development and evaluation should not only focus on the simulated streamflow (model output), but also on other key land surface variables. However, scale discrepancy between available observations and modeling resolution is often neglected.

In this study we introduce multiscale and multivariable parameter estimation approach to bridge the scaling gap. The basic components of this framework include the mesoscale hydrologic model (mHM 5.1, http://www.ufz.de/mhm) and the multiscale parameter regionalization (MPR) technique. This framework enables assimilation of various sources of information at their native spatial scales. Additionally, it allows to scrutinize model simultaneously at multiple spatial scales.

The application of this framework is demonstrated over 7 large European basins and evaluated for 350 smaller basins. Besides traditional calibration of hydrologic model against observed discharge, model parametrization is further constrained by a range of other variables available at different spatial scales: GRACE terrestrial water storage content (1° × 1° resolution), eddy flux data (≈500 m footprint) and ESA soil moisture product (0.25° × 0.25° resolution). Initial results shows that model parameterization constrained by streamflow only can not be outperformed. However, while parameterization based on complementary data sets leads to slight deterioration in streamflow performance, this marginal loss is balanced by improved simulation of other model states and fluxes. This becomes beneficial especially during the forecast applications, for which correct model initialization is crucial.