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The main goal of TERENO was to create observation platforms on the basis of an interdisciplinary and long‐term aimed research program. By the aid of SoilCan‐lysimeters, an ideal network was built between the four different TERENO‐observatories with their highly instrumented test sites. Within the four TERENO‐observatories at 13 different experimental sites, in total 126 lysimeters were filled monolithically and the measurements were started in October 2010. The lysimeters have been cultivated as grassland or arable land with a standardized crop rotation: winter wheat – winter barley – winter rye – oat.

The main goal of TERENO was to create observation platforms on the basis of an interdisciplinary and long‐term aimed research program. By the aid of SoilCan‐lysimeters, an ideal network was built between the four different TERENO‐observatories with their highly instrumented test sites. Within the four TERENO‐observatories at 13 different experimental sites, in total 126 lysimeters were filled monolithically and the measurements were started in October 2010. The lysimeters have been cultivated as grassland or arable land with a standardized crop rotation: winter wheat – winter barley – winter rye – oat.

Urban drainage infrastructure has to adapt to challenges such as climate change, but also to trends such as population and settlement development. At the same time, the opportunities offered by digitization can be used to meet these challenges. In this thesis, therefore, two geoinformation-based models are developed, which use the advancing digitization to contribute to the adaptation of urban drainage to demographic and climate change. An important aspect of municipal flood protection are nature-oriented measures for the rainwater management as part of water-sensitive urban development. According to the recognized rules of technology, the overriding goal of rainwater management is to approximate the natural water balance. The estimate of this target value is difficult to determine. The potential offered by decentralized rainwater management measures to achieve or approach this target as well. One geoinformation-based model pursues these two goals. The first goal is the estimation of the water balance deficit. The second goal is a limitation of the potential measures for the best compensation of this deficit. For this purpose, structural, geological and hydrogeological restrictions are taken into account. The model works exclusively with geo-referenced data and thus offers direct localization of the results. Furthermore, the model combines the approach of the empirically determined water balance distribution functions of the DWA-A 102 and a GIS-based calculation. The advantages of this combination are the scaling of a selectable study area and the on the fly consideration of restrictions for the measures of decentralized rainwater management. The second geoinformation-based model is an artificial neural network (ANN) for calculating flood areas caused by heavy rain. The trained ANN is suitable for known areas, works in real time and substitute a conventional hydrodynamic model. Therefore, a supervised learning multi-layer feed-forward ANN was set up and trained. After that, it was then examined and assessed for its suitability. For the substitution of a hydrodynamic numerical model for calculation of flooding risks (flood areas or surface runoff) by an ANN, the ANN must 'learn' the hydrological processes of the conventional model. For this learning process, the ANN requires input values that essentially determine the hydrological processes (e.g. precipitation, digital elevation model, type of sealing, barriers and culverts, etc.). Moreover, the AAN requires target values. For the ANN, the target values are areas with a certain height water level caused by a precipitation event (flood areas with flood level). These target values were first calculated using a hydrodynamic numerical model.The developed model shows that ANNs are more suitable than conventional hydrodynamic numerical models for certain applications due to their computing speed. Even if the generalizability of the trained ANN to other model areas could not be successfully demonstrated, the ANN however was able to predict good results for a sufficiently known range of values.The biggest advantage of the ANN is the low computing time for predicting flood areas. In contrast to hydrodynamic numerical models, the ANN predicts flood areas with sufficient accuracy in a few seconds. The application possibilities of the developed model thus specialize in cases in which these strengths are indispensable. One indispensability is the prediction of flood area caused by spontaneously and spatially limited heavy rain. This indispensability is also a demand of the municipal flood protection. Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2021; Aachen : RWTH Aachen University 1 Online-Ressource : Illustrationen, Diagramme (2021). = Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2021 Published by RWTH Aachen University, Aachen

Urban drainage infrastructure has to adapt to challenges such as climate change, but also to trends such as population and settlement development. At the same time, the opportunities offered by digitization can be used to meet these challenges. In this thesis, therefore, two geoinformation-based models are developed, which use the advancing digitization to contribute to the adaptation of urban drainage to demographic and climate change. An important aspect of municipal flood protection are nature-oriented measures for the rainwater management as part of water-sensitive urban development. According to the recognized rules of technology, the overriding goal of rainwater management is to approximate the natural water balance. The estimate of this target value is difficult to determine. The potential offered by decentralized rainwater management measures to achieve or approach this target as well. One geoinformation-based model pursues these two goals. The first goal is the estimation of the water balance deficit. The second goal is a limitation of the potential measures for the best compensation of this deficit. For this purpose, structural, geological and hydrogeological restrictions are taken into account. The model works exclusively with geo-referenced data and thus offers direct localization of the results. Furthermore, the model combines the approach of the empirically determined water balance distribution functions of the DWA-A 102 and a GIS-based calculation. The advantages of this combination are the scaling of a selectable study area and the on the fly consideration of restrictions for the measures of decentralized rainwater management. The second geoinformation-based model is an artificial neural network (ANN) for calculating flood areas caused by heavy rain. The trained ANN is suitable for known areas, works in real time and substitute a conventional hydrodynamic model. Therefore, a supervised learning multi-layer feed-forward ANN was set up and trained. After that, it was then examined and assessed for its suitability. For the substitution of a hydrodynamic numerical model for calculation of flooding risks (flood areas or surface runoff) by an ANN, the ANN must 'learn' the hydrological processes of the conventional model. For this learning process, the ANN requires input values that essentially determine the hydrological processes (e.g. precipitation, digital elevation model, type of sealing, barriers and culverts, etc.). Moreover, the AAN requires target values. For the ANN, the target values are areas with a certain height water level caused by a precipitation event (flood areas with flood level). These target values were first calculated using a hydrodynamic numerical model.The developed model shows that ANNs are more suitable than conventional hydrodynamic numerical models for certain applications due to their computing speed. Even if the generalizability of the trained ANN to other model areas could not be successfully demonstrated, the ANN however was able to predict good results for a sufficiently known range of values.The biggest advantage of the ANN is the low computing time for predicting flood areas. In contrast to hydrodynamic numerical models, the ANN predicts flood areas with sufficient accuracy in a few seconds. The application possibilities of the developed model thus specialize in cases in which these strengths are indispensable. One indispensability is the prediction of flood area caused by spontaneously and spatially limited heavy rain. This indispensability is also a demand of the municipal flood protection. Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2021; Aachen : RWTH Aachen University 1 Online-Ressource : Illustrationen, Diagramme (2021). = Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2021 Published by RWTH Aachen University, Aachen

Forest biomass plays a crucial role in the quantification of the terrestrial carbon budget. Today, forest biomass is poorly quantified across most parts of the Earth surface due to the great difficulties in measuring biomass on the ground and consistently aggregating measurements across scales. SAR satellites can provide the required information globally and frequent in order to quantify current biomass state and the changes. Tandem-L is a German mission proposal for an innovative interferometric L-band radar mission that enables the systematic monitoring of dynamic Earth processes using advanced techniques and technologies. The mission is science driven aiming to provide a unique data set for climate and environmental research, geodynamics, hydrology and oceanography. Important application examples are global forest height and structure and the derivation of forest biomass. The Tandem-L mission concept consists of two cooperating satellites flying in close formation. The 3 D structure mode provides a unique data source to observe and quantify changes in forest biomass. This paper provides an overview of the potential of using multi-parametric SAR for forest structure estimation with the derivation of forest biomass and suggests a mission for a global acquisition.

Forest biomass plays a crucial role in the quantification of the terrestrial carbon budget. Today, forest biomass is poorly quantified across most parts of the Earth surface due to the great difficulties in measuring biomass on the ground and consistently aggregating measurements across scales. SAR satellites can provide the required information globally and frequent in order to quantify current biomass state and the changes. Tandem-L is a German mission proposal for an innovative interferometric L-band radar mission that enables the systematic monitoring of dynamic Earth processes using advanced techniques and technologies. The mission is science driven aiming to provide a unique data set for climate and environmental research, geodynamics, hydrology and oceanography. Important application examples are global forest height and structure and the derivation of forest biomass. The Tandem-L mission concept consists of two cooperating satellites flying in close formation. The 3 D structure mode provides a unique data source to observe and quantify changes in forest biomass. This paper provides an overview of the potential of using multi-parametric SAR for forest structure estimation with the derivation of forest biomass and suggests a mission for a global acquisition.

Climate models have continued to be developed and improved since the AR4, and many models have been extended into Earth System models by including the representation of biogeochemical cycles important to climate change. These models allow for policy-relevant calculations such as the carbon dioxide (CO2) emissions compatible with a specified climate stabilization target. In addition, the range of climate variables and processes that have been evaluated has greatly expanded, and differences between models and observations are increasingly quantified using ‘performance metrics’. In this chapter, model evaluation covers simulation of the mean climate, of historical climate change, of variability on multiple time scales and of regional modes of variability. This evaluation is based on recent internationally coordinated model experiments, including simulations of historic and paleo climate, specialized experiments designed to provide insight into key climate processes and feedbacks and regional climate downscaling. Figure 9.44 provides an overview of model capabilities as assessed in this chapter, including improvements, or lack thereof, relative to models assessed in the AR4. The chapter concludes with an assessment of recent work connecting model performance to the detection and attribution of climate change as well as to future projections.

Climate models have continued to be developed and improved since the AR4, and many models have been extended into Earth System models by including the representation of biogeochemical cycles important to climate change. These models allow for policy-relevant calculations such as the carbon dioxide (CO2) emissions compatible with a specified climate stabilization target. In addition, the range of climate variables and processes that have been evaluated has greatly expanded, and differences between models and observations are increasingly quantified using ‘performance metrics’. In this chapter, model evaluation covers simulation of the mean climate, of historical climate change, of variability on multiple time scales and of regional modes of variability. This evaluation is based on recent internationally coordinated model experiments, including simulations of historic and paleo climate, specialized experiments designed to provide insight into key climate processes and feedbacks and regional climate downscaling. Figure 9.44 provides an overview of model capabilities as assessed in this chapter, including improvements, or lack thereof, relative to models assessed in the AR4. The chapter concludes with an assessment of recent work connecting model performance to the detection and attribution of climate change as well as to future projections.

Originally, the Danube salmon (Hucho hucho) occurred in Bavaria and Austria in more than 250 rivers occupying more than 7,400 km of rivers. Nowadays, populations in »very good« and »good« status exist in only 0.7 % and 7.1 % of the original distribution. Therefore, the Danube salmon is classified as an endangered species. Due to ongoing stock declines the Danube salmon is running the risk to become a critically endangered species soon. The main reasons for the declines are river channelization and hydropower development. In addition, climate change may further contribute to stock declines in lowland river sections due to exceedance of water temperature limits of this cold-water species. Furthermore, Danube salmon and prey fish populations have lost their resilience to cope with re-established populations of fish predators (cormorant, goosander, fish otter) leading to ongoing population declines. Effective protection against further degradations such as new hydropower developments is required to safeguard the Danube salmon remaining populations. Furthermore, degraded rivers need to be restored and fish predators have to be managed to allow recovery of Danube salmon and prey fish populations. Due to the precarious situation conservation and restoration actions have to be implemented immediately.