Harrie-Jan Hendricks-Franssen has MSc degrees in soil science (1994, Wageningen Agricultural University) and atmospheric and climate sciences (2008, ETH Zurich) and a PhD degree in hydrogeology (2001, Technical University of Valencia, Spain). While being employed by the Agrosphere institute (IBG-3) at Forschungszentrum Jülich GmbH, he holds a W2-professorship on “Scientific Computation in Terrestrial Systems” at RWTH Aachen University. His research focuses on integrated hydrological modelling and the assimilation of measurements to improve predictions with land surface models and hydrological models. In this context, he (co)developed data assimilation frameworks which are currently operationally running in real-time for groundwater hydrological predictions. He works also on the development of measurement operators and better understanding of measurement data (e.g., cosmic ray probe data, eddy covariance data). He is co-coordinator of and PI in FOR2131 (“Data assimilation for Improved Characterization of Fluxes across Compartmental Interfaces”) and PI of a data assimilation project (C6) in the Transregional Collaborative Research Center TR32 “Patterns in Soil-Vegetation-Atmosphere Systems – Monitoring, Modelling and Data Assimilation”. Harrie-Jan Hendricks-Franssen (co-)authored 82 ISI-publications (state December 2017).
Prof. Dr. Harrie-Jan Hendricks-Franssen
Head of research group "Stochastic Analysis Terrestrial Systems"
Forschungszentrum Jülich GmbH
Institut für Bio- und Geowissenschaften (IBG)
Gebäude 16.6 / Raum R 3033
The research group Stochastic Analysis Terrestrial Systems performs simulations to calculate states of the terrestrial system and exchange fluxes of water, energy, carbon and nitrogen between different compartments of the terrestrial system, like for example land and atmosphere. The accuracy of model predictions is enhanced with data assimilation and inverse modelling methods where measurement are used to correct model predictions and estimate model parameters. Applications are at the field scale, catchment scale and continental scale.
Land surface models simulate the exchange of water, energy, carbon and nitrogen between the land and the atmosphere, as well as terrestrial system states like soil moisture and soil temperature, leaf area index, biomass of different plant organs, size of carbon and nitrogen pools in the soil, snow depth and groundwater depth. These models are applied at the scale of ecosystems. As the models are affected by large uncertainties we work on model improvements, like the representation of agricultural crops in models. We achieve better model performance by assimilating measurement data in the model to correct model states and model parameters. This is done with data assimilation methods that are implemented in the Parallel Data Assimilation Framework (PDAF), which is coupled to the simulation model. The data assimilation capacity is enhanced by extending the types of in situ and remotely sensed data which can be assimilated. Applications are at field, catchment, regional and continental scale for seasonal prediction of crop yield, impact of climate change and land use land cover change on water resources, the impact of future extreme events on (agricultural) ecosystems and the near real-time prediction for agricultural systems.