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 |
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.
Towards a better understanding of moisture responses to radiative forcing
Jun.-Prof. Dr. S. Fiedler
University of Cologne | +49 221 4703693 |
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 |
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.
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 |
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.
Towards a dynamic representation of irrigation in land surface models
Prof. Dr. Stefan Siebert
University of Göttingen | +49 551 39-24359 |
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.