Data assimilation of satellite-based actual evapotranspiration in a distributed hydrological model of a controlled water system

Abstract Advances in earth observation (EO) and spatially distributed hydrological modelling provide an opportunity to improve modelling of controlled water systems. In a controlled water system human interference is high, which may lead to incorrect parameterisation in the model calibration phase. This paper analyses whether assimilation of EO actual evapotranspiration (ETa) data can improve discharge simulation with a spatially distributed hydrological model of a controlled water system. The EO ETa estimates are in the form of eight-day ETa composite maps derived from Terra/MODIS images using the ITA-MyWater algorithm. This algorithm is based on the surface energy balance method and is calibrated for this research for a low-lying reclamation area with a heavily controlled water system: the Rijnland area in the Netherlands. Data assimilation (DA) with the particle filter method is applied to assimilate the ETa maps into a spatially distributed hydrological model. The hydrological model and DA framework are applied using the open source software SIMGRO and PCRaster-Python respectively. The analysis is done for a period between July and October 2013 in which a high discharge peak followed a long dry-spell. The assimilation of EO ETa resulted in local differences in modelled ETa compared to simulation without data assimilation, while the area average ETa remained almost the same. The modelled cumulative discharge graphs, with and without DA, showed distinctive differences with the simulation, with DA better matching the measured cumulative discharge. The bias of simulated cumulative discharge to the observed data reduced from 14% to 4% when using DA of EO ETa. These results showed that assimilating EO ETa may not only be effective in the more common applications of soil moisture and crop-growth modelling, but also for improving discharge modelling of controlled water systems.