Land surface and sub-surface data assimilation
Dr. Anne Springer
University of Bonn | +49 228 73-6149 |
Dr. Carsten Montzka
Forschungszentrum Jülich | +49 2461 613289 |
In C01 we will develop a coupled multi-scale, multi-source data assimilation (DA) system at the continental scale, where remotely sensed surface soil moisture, total water storage changes from the GRACE/-FO missions and land surface temperature data will constrain a coupled reanalysis from groundwater to the land surface. We will evaluate to what extent the reanalysis exhibits skill to represent observed trends, reproduce interannual variability, and to simulate extreme events. A special focus is laid on investigating strategies to correct for anthropogenic impacts on the hydrological system (irrigation, groundwater abstraction) through DA.
Modulation of soil water fluxes by changes in vegetation properties and management
Prof. Dr. Frank Ewert
University of Bonn | +49 228 73-2041 |
Dr. Thomas Gaiser
University of Bonn | +49 228 73-2050 |
Prof. Dr. Guillaume Lobet
Forschungszentrum Jülich | +49 2461 61-9013 |
We will investigate the impact of changes in agricultural and forest management during the last decades on land surface – atmosphere interactions. We will improve the parameterization of land surface model CLM so that it can reproduce the effects of changes in management on regional heat, water and carbon fluxes. Therefore, we will link a suite of models starting from mechanistic single plant models that couple carbon and water flow within the plant with the external environment over specific crop models at the field scale to the Community Earth System Model CLM.
Towards ecosystem reanalysis by coupling of water and carbon cycles
Prof. Dr. Harrie-Jan Hendricks-Franssen
Forschungszentrum Jülich | +49 2461 614462 |
Prof. Dr. Wulf Amelung
University of Bonn | +49 228 73-2780 |
Prof. Dr. Jürgen Kusche
University of Bonn | +49 228 73-2629 |
In project C03 we investigate the impact of uncertain ecosystem parameters and the representation of soil respiration in a land surface modelling approach on the simulation of the coupled water, energy and biogeochemical cycles over Eurasia, focusing on the variability of water and carbon fluxes in space and time. This is particularly relevant for simulating the impact of land use and land cover change on these cycles. We will set up an ensemble of land surface models, all with (slightly) different inputs of atmospheric forcings and soil and vegetation parameters, to represent the uncertainty we have in these forcings and parameters. We will investigate which sources of uncertainty are more important and whether the land surface model is able to reproduce the observed values taking into account these sources of uncertainty. Using measurement data from well-equipped sites across Europe (e.g., from the ICOS and eLTER networks), we will modify model parameters so that model simulations better reproduce measured values. At the same time, based on experimental data across Europe, the representation of soil respiration in the model will be improved, in particular the dependence of soil respiration on soil temperature and soil moisture. In addition, an ensemble of land surface model runs over Eurasia will be performed using these updated parameters and the improved representation of the respiration process. We will test whether the implemented changes have improved the performance of the land surface model by using a completely independent data source, the monitoring of continental water storage changes by the GRACE satellite. We hypothesize that the continental-scale net ecosystem exchange (NEE) from a reanalysis with improved ecosystem parameters and improved representation of soil respiration (SR) will be better correlated with observed total water storage (TWS) variability.
Snow data assimilation and its impacts on hydrological cycle and atmospheric fluxes
Dr. Bibi S. Naz
Forschungszentrum Jülich | +49 2461 619717 |
Prof. Dr. Gabrielle J. M. De Lannoy
KU Leuven, Belgium | +32 1637 6713 |
The project C04 aims to develop and implement a snow data assimilation scheme into the fully coupled Earth system model to optimally merges snow product observations with model simulations for improving continental-scale snow estimates. Further, we will investigate the impacts of improvements in snow estimates on the hydrological processes and on the atmosphere through the snow albedo effect and soil moisture-precipitation feedbacks. This project contributes to the development of assimilation strategies based upon remotely sensed observations to improve snow estimates in fully coupled models.