Cluster B - Projects


Impact analysis of surface water level and discharge from the new generation altimetry observations

PD Dr.-Ing. L. Fenoglio
University of Bonn  |  +49 228 73-3575  |  [Email protection active, please enable JavaScript.]


Hydrological models still do not adequately represent surface water storage and river discharge, which quantify among other also the contribution of the continental hydrology to sea level change. We will integrate the new generation of spaceborne satellite altimeters, which include the Delay-Doppler, the laser and the bistatic SAR altimeter techniques, in the Integrated Monitoring System (IMS). These observations are denser and more accurate than conventional altimetry and will allow a better quantification of the impact of water use and climate change.



Towards a better understanding of moisture responses to radiative forcing

Jun.-Prof. Dr. S. Fiedler
University of Cologne  |  +49 221 4703693  |  [Email protection active, please enable JavaScript.]


The radiation budget plays a key role in climate changes. This project systematically assesses the soil moisture response to the radiative forcing of atmospheric composition changes and the influence of water management using CMIP6 models. With focus on the response of the soil moisture in Europe, the project separates contributions from water management, greenhouse gases, anthropogenic dust-aerosols, as well as aerosols from biomass burning and industrial pollution. It contributes to understand the possible future development of anthropogenic dust-aerosols in a warmer world.



Deep learning for satellite-based land use and land cover reconstruction

Prof. Dr. Ribana Roscher
University of Bonn  |  +49 228 73-60854  |  [Email protection active, please enable JavaScript.]


The goal of this project is the determination of land use and land cover from optical satellite data for specific points in time (as a snapshot) or for longer periods of time (e.g. one season). For this purpose, deep neural networks will be developed that take into account the specific biogeographical characteristics of the regions of interest in order to ensure a high generalization capability. Furthermore, spatiotemporal data gaps will be closed to improve the data basis for the developed methods and data and model uncertainties for the derived land use and land cover maps will be determined.


Probabilistic land use

Prof. Dr. Thomas Heckelei
University of Bonn  |  +49 228 73-2331  |  [Email protection active, please enable JavaScript.]

Dr. Hugo Storm
University of Bonn  |  +49 157 75745561  |  [Email protection active, please enable JavaScript.]


We aim to use Bayesian statistical approaches to merge a broad range of input data (existing land use maps, official statistics, sample data) to a land use map with uncertainty information. The map will provide a consistent and complete time series for Europe with an unprecedented combination of spatial resolution and crop coverage. Furthermore, we aim to develop a methodology that allows combining data driven models, such as deep learning methods applied to satellite imagery, with econometric land-use models considering economic and biophysical information. For combining these currently distinct approaches, techniques from the field of probabilistic programming shall be explored. Both activities are targeted to improve our knowledge of past land use (changes), which is crucial to model future land use and the implications for regional climate change within the cluster.


Towards a dynamic representation of irrigation in land surface models

Prof. Dr. Stefan Siebert
University of Göttingen  |  +49 551 39-24359  |  [Email protection active, please enable JavaScript.]


B05 will systematically collect spatial data providing the extent of irrigated crops in specific years for the Europe / Eurasia modeling domain and evaluate relationships with simulated crop specific irrigation requirement. The goal is to develop a dynamic representation of the extent of irrigated crops and to compare irrigation water use simulated with dynamic irrigated crop shares to those obtained with static crop shares. Improved implementation of irrigation water use will help to better quantify human impacts on the water cycle and on energy fluxes, in particular in dry years, and thus considerably contribute to the CRC’s key objectives.

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