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  |  [Email protection active, please enable JavaScript.]


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.

 

 

07 | PhD student
in the field of geodesy

100% (E13 TV-L) | All positions are fixed-term, initially for a period of either 3 or 4 years, with the possibility of an extension

Will be published soon on the website of the university

In project B01, we are looking for a PhD student in the field of geodesy, with a background in geodesy, hydrological modelling and satellite altimetry. Programming, Skills in GIS and modelling experience would be considered an asset.

B02

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.]


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  |  [Email protection active, please enable JavaScript.]


Summary

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.

 

09 | PhD student
in the field of remote sensing and data science

100% (E13 TV-L) | All positions are fixed-term, initially for a period of either 3 or 4 years, with the possibility of an extension

Will be published soon on the website of the university

In project B03, we are looking for a PhD student in remote sensing and data science, with a background in machine learning, and land use and land cover remote sensing.

B04

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.]


Summary

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.

 

10 | Two PhD students
in the field of agricultural economics

75% (E13 TV-L) | All positions are fixed-term, initially for a period of either 3 or 4 years, with the possibility of an extension

Will be published soon on the website of the university

In project B04, we are looking for two PhD students in the field of agricultural economics, with some experience in quantitative analysis/data science and an interest in (Bayesian) Econometrics/Machine Learning. One PhD student, supervised by Prof Heckelei, will focus on the Bayesian approach to merge existing data sources. The second PhD student, supervised by Dr Storm, focuses on combining data driven deep learning approaches with econometric approaches considering economic or biophysical information.

B05

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.]


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.

 

11 | PhD student
in the field of agronomy

75% (E13 TV-L) | All positions are fixed-term, initially for a period of either 3 or 4 years, with the possibility of an extension

Will be published soon on the website of the university

In project B05, we are looking for a PhD student in the field of agronomy, with a background in crop modeling, land surface modeling or irrigation science and skills in programming

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