Conditioning of a mesoscale hydrologic model with proxy soil moisture fields

Multiscale monitoring and data assimilation techniques are fundamental to improve the predictability of mesoscale distributed hydrologic models. In-situ measurements along with remote sensed information can be used to condition the parametrization of distributed models aiming at reducing their prediction uncertainty of both energy and mass balances. One of the key state variables responsible for the feedback mechanisms in the land-surface-atmosphere system is the soil moisture. This variable, on the contrary to other water fluxes, has a long memory and depends greatly on local conditions. The spatial distribution of soil moisture is therefore crucial to determine the spatial patterns of both surface runoff and actual evaporation. There are a number of proxies that can be used to describe the evolution of this state variable. They can be obtained at different resolutions, for example, the land surface temperature of the MODIS (NASA) sensor (1 x 1) km or the surface soil moisture (SSM) data based on ERS and METOP scatterometers (12.5 x 12.5) km. In this study we develop local-neighborhood estimators that help to constrain the spatio-temporal evolution of the top-layer soil moisture during a period of time. These estimators are included in the calibration process as a penalty function. The mesoscale hydrologic model (mHM) employed in this study is forced with (1 x 1) km daily meteorological variables such as precipitation, temperature and potential evapotranspiration. All parameters of mHM were regionalized with a multi-scale parametrization technique. The model was set up in the Neckar Basin in south Germany for the period 2001 to 2007, from which the first four years were used for calibration. The spin up period of the model was from 1992 to 2001. The search of good parameter sets was carried out with simulated annealing. Multiscale conditioning of soil moisture states in addition to the commonly used streamflow data lead to a significant reduction of the variability range of the model outputs (e.g. discharge) as well as that of the calibrated global parameters. The transferability of these parameters to ungauged locations and different scales lead to an improvement in performance of at least 10% compared with those results obtained with a control experiment (i.e. parameters not conditioned with proxy soil moisture fields).