Cluster B - Projects

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  |    This email address is being protected from spambots. You need JavaScript enabled to view it.


 

Summary

Surface water level and river discharge are key observables of the water cycle and among the most sensible indicators that integrate long-term change within a river basin. The new generation of spaceborne altimeters includes Delay Doppler, laser and bistatic SAR altimeter techniques. The central hypothesis of B01 is that these new observations outperform conventional altimetry (CA) and in-situ measurements providing (a) surface water levels and discharge of higher accuracy and resolution (both spatial and temporal), (b) new additional parameters (river slope and width) and (c) better sampling for flood event detection and long-term evolution, providing valuable new information to the CRC.

Phase 1 addresses two research questions: „How can we fully exploit the new missions to derive water level, discharge, and hydrodynamic river processes“ and „Can we separate natural variability from human water use“? 

For this, B01 monitors with space observations water height change and water exchange between rivers, lakes and reservoirs and the impact of natural and human disturbances. River discharge Q is the  primary product of B01, that is made available in the CRC modelling  and for assimilation in the IMS.

 

B02

 

Towards a better understanding of moisture responses to radiative forcing


Prof. Dr. S. Fiedler
GEOMAR Kiel  |    +49 431 600-4054 |    This email address is being protected from spambots. You need JavaScript enabled to view it.


 

Summary

The radiation budget plays a key role in climate change. This project systematically assesses the soil moisture response to the radiative forcing of atmospheric composition changes and the influence of water management using the Coupled Model Intercomparison Project Phase 6 (CMIP6) models. With a focus on the response to 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  |    This email address is being protected from spambots. You need JavaScript enabled to view it.


 

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  |    This email address is being protected from spambots. You need JavaScript enabled to view it.

Dr. Hugo Storm
University of Bonn  |    +49 228 73-60828 / +49 157 75745561  |    This email address is being protected from spambots. You need JavaScript enabled to view it.


 

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  |    This email address is being protected from spambots. You need JavaScript enabled to view it.


 

Summary

In B05 we will systematically collect spatial data providing the extent of irrigated lands (total and for specific crops) in specific years (around every three years) for the Europe / Euroasia modeling domain from 1990 to 2020 mainly at sub-national levels (NUTS 2); then the new annual irrigation database at the period of 1990 to 2020 will be generated via the combination of process-based model simulation and ground survey observations. The new annual irrigation database will be applied in community land model (CLM) to quantify differences in dynamics and trends of irrigation water use across the region compared to the use of the current static data products. We will analyze the impact of climate variability on the extent of irrigated crops, irrigation water requirements, and irrigation water use. The impact of the dynamic irrigation data on spatial patterns, dynamic trends of water storages, and related drying and wetting patterns across the project regions will be analyzed as well.

 

 

 

 

 

Collaborative Research Centre (SFB) 1502 - DETECT

Kekuléstr. 39a
53115 Bonn

+49 228 73 60585 / 60600

Coordination Office

logomosaik slim Universität Bonn Forschungszentrum Jülich Geomar Georg-August-Universität Göttingen Deutscher Wetterdienst