B01
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 |
Summary
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.
B02
Towards a better understanding of moisture responses to radiative forcing
Jun.-Prof. Dr. S. Fiedler
University of Cologne | +49 221 4703693 |
Summary
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.
B03
Deep learning for satellite-based land use and land cover reconstruction
Prof. Dr. Ribana Roscher
University of Bonn | +49 228 73-60854 |
Summary
The goal of this project is the reconstruction of land use and land cover from optical satellite data using deep learning. To this end, we will develop spatio-temporal deep neural networks that consider the specific biogeographical characteristics of the regions of interest in order to ensure a high generalization capability across the study region. Furthermore, predictive uncertainties for the derived land use and land cover maps will be determined and the model’s capabilities in the context of multi-task learning will be studied. Lastly, we will also explore the potential of generated data to fill spatial and temporal data gaps.
B04
Probabilistic land use
Prof. Dr. Thomas Heckelei
University of Bonn | +49 228 73-2332 |
Dr. Hugo Storm
University of Bonn | +49 228 73-60828 / +49 157 75745561 |
Summary
Agricultural land use practices, such as crop choice and irrigation decisions, influence the temporal and spatial distribution of water and energy fluxes. In B04 we generate crop and irrigation maps that cover Europe at a 1x1 km resolution from 1990 - 2020 and that help model those fluxes. Importantly, the generated maps are consistent with existing knowledge, for example on regional crop production quantities. We apply Bayesian statistical approaches and Machine Learning to incorporate various types of information sources, such as soil and climate maps, survey data, economic statistics, and remote sensing data. The maps are probabilistic in nature, meaning that they transparently reflect data and model uncertainty.
B05
Towards a dynamic representation of irrigation in land surface models
Prof. Dr. Stefan Siebert
University of Göttingen | +49 551 39-24359 |
Summary
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.
