Abstract
In the present study, we focus on the assimilation of satellite observations for Surface Soil Moisture (SSM) in a hydrological model. The satellite data are produced in the framework of the EUMETSAT project H-SAF and are based on measurements with the Advanced radar Scatterometer (ASCAT), embarked on the Meteorological Operational satellites (MetOp). The product generated with these measurements has a horizontal resolution of 25 km and represents the upper few centimeters of soil. Our approach is based on the Ensemble Kalman Filter technique (EnKF), where observation and model uncertainties are taken into account, implemented in a conceptual hydrological model. The analysis is carried out in the Demer catchment of the Scheldt River Basin in Belgium, for the period from June 2013–May 2016. In this context, two methodological advances are being proposed. First, the generation of stochastic terms, necessary for the EnKF, of bounded variables like SSM is addressed with the aid of specially-designed probability distributions, so that the bounds are never exceeded. Second, bias due to the assimilation procedure itself is removed using a post-processing technique. Subsequently, the impact of SSM assimilation on the simulated streamflow is estimated using a series of statistical measures based on the ensemble average. The differences from the control simulation are then assessed using a two-dimensional bootstrap sampling on the ensemble generated by the assimilation procedure. Our analysis shows that data assimilation combined with bias correction can improve the streamflow estimations or, at a minimum, produce results statistically indistinguishable from the control run of the hydrological model.
Highlights
Sources of errors in model estimations are generally of two kinds, input data errors and model errors
The situation is very different in the lower soil layer, where no assimilation is taking place, but the moisture values are calculated by the SCHEME model after assimilating satellite data at the upper layer
The purpose of this study is to explore the possibilities to improve streamflow estimation from a hydrological model by assimilating soil moisture data
Summary
Sources of errors in model estimations are generally of two kinds, input data errors (including initialization) and model errors. One method to decrease deviations of the estimated model states from observations is to optimally “assimilate” observed data of relevant model variables into the model. Data assimilation has a long history as a modeling tool, and in the present article, we will focus on applications to hydrology, with emphasis on soil moisture and streamflow. Soil moisture is a variable that plays an important role in a hydrological system by regulating several water exchanges and energy fluxes. The data from in situ SSM measurements are invaluable in calibrating land surface and hydrological models, while long-term time series can reveal trends due to climate or land use changes. In situ soil moisture measurements still remain very sparse, especially compared to the high spatial variability of this variable
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
