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Virtuelle Forschungsumgebungen (VRE), sind Plattformen auf Basis von Informations- und Kommunikationstechnologien (engl. ICT) mit ganzheitlichem Ansatz, die das wissenschaftliche Arbeiten im Team und insbesondere dessen Kommunikation und Organisation unterstützen sollen. Sie haben zum Ziel, schneller und besser zu nachvollziehbaren Ergebnissen zu gelangen, als bisher. Projekte in der ökologischen Langzeitforschung (engl. long-term ecological research, im Folgenden als L-TER bezeichnet) sind durch den Langzeitaspekt und Raumbezug in der Regel umfangreiche Projekte mit mittleren und großen Datenmengen (Medium Data und Big Data) sowie interdisziplinären, örtlich verteilten Forschungsgruppen. Sie sind daher prädestiniert für den Einsatz von VREs. Die vorliegende Arbeit untersucht die spezifischen Aspekte und Anforderungen an virtuelle Forschungsumgebungen von L-TER Projekten im Kontext der Umweltinformatik. Dazu wird der Begriff „L-TER VRE“ eingeführt. Grundlage hierfür ist der Stand des Wissens zu allgemeinen VREs, Datenmanagement und ICT, die interdisziplinär in Verbindung gebracht werden mit den Zielen, Methoden und Daten der L-TER (z.B. Analyse von Zeitreihen). Es zeigt sich, dass sich die Anforderungen an L-TER VREs – aufgrund ihres Einsatzbereiches für langfristige L-TER Projekte – in der Nachhaltigkeit durch ihre Persistenz (Daten, Infrastruktur), Flexibilität (projektspezifische Rahmenbedingungen), der eingesetzten ICT und vom Faktor „Mensch“ mit seinem gesellschaftlichen Umfeld von VREs für andere Aufgaben unterscheiden. Aus den Erkenntnissen wird ein innovatives Baukastenkonzept mit dem Namen „Rapid L-TER VRE“ zur Planung, dem Aufbau und Betrieb von L-TER VREs vorgestellt. Es basiert auf Open-Source-Software und kann durch die gemeinsame Basis der Bausteine auch für allgemeine VREs Verwendung finden. Da VREs oder ähnliche ICT-Infrastrukturen im Rahmen von „e-Science“ als Rückgrat für die Organisation der Projekte prädestiniert sind, ist der Aufbau solcher Infrastrukturen oft schon zur Voraussetzung bei der Antragstellung von Projekten geworden. Ein Prototyp sollte daher bereits vor oder bei der Antragstellung existieren. Spätestens zum Projektbeginn muss eine Produktivumgebung zur Verfügung stehen. Vor diesem Hintergrund ist das Konzept „Rapid L-TER VRE“ speziell für eine schnelle (engl. rapid) Entwicklung von VRE Prototypen mit einer kurzen Übergangsphase in den Produktivbetrieb ausgelegt. Das Baukastenkonzept wird auf verschiedene existierende Projekte im Umfeld des Fachgebietes „Environmental Informatics“ und der Fakultät „Umwelt und Naturwissenschaften“ der BTU Cottbus-Senftenberg konzeptionell, sowie in der Form von Prototypen angewendet und dessen Anwendbarkeit allgemein bewertet. Aus den Erkenntnissen werden allgemeine Schlussfolgerungen in Bezug auf den Einsatz von Technologien und die Nachhaltigkeit für den erfolgreichen Betrieb von L-TER VREs gezogen – dabei steht der Mensch als „maßgeblicher“ Faktor im Mittelpunkt.
Accra and Kumasi are the two major cities in Ghana. Spatial urban expansion has been experienced in transforming different non-urban Land Use Land Cover (LULC) types into urban/built-up areas with a potential direct relationship to temperature rise in the cities. Thus, this dissertation aims to establish the relationship between urban spatial expansion and temperature in Accra and Kumasi metropolis. Multi-source datasets such as remote sensing images, different GIS vector layers, reference maps and historical temperature datasets were used for this retrospective study. This research was grouped under three components: environmental science, environmental technology, as well as environmental management and planning. From the environmental science component, LULC maps were produced for different years to assess the trend of temporal change in the various LULC classes in the two metropolises. Remote sensing indices and land surface temperature were retrieved from the remote sensing images to determine their correlations. Temperature time series was analysed by calculating temperature indices and determining temporal trends to reveal changes in air temperature to detect urban warming and its impacts. From the environmental technology, the novel random forest algorithm was utilised to classify the satellite images of both cities since previous works have utilised other traditional classifiers. The satellite imageries were used for point-based estimation of temperature to determine Urban Heat Islands (UHIs) hotspots. For environmental management and planning, spatial urban expansion techniques were utilised to ascertain trends in urban/built-up areas, especially in both cities' sub-metropolitan zones. For prescient purposes, future LULC modelling was implemented to provide insights into the proportions of the various LULC changes in 2025. The analysis identified two salient findings: increased urban/built-up areas at the expense of agricultural and forestlands throughout the study period and the positive correlation between spatial urban expansion and temperature. This indicated warming up of urban temperature in both cities. The major findings in this dissertation provided evidence of how integrated datasets and research techniques can be utilised for LULC changes to determine the relationship between spatial urban expansion and temperature at local scales. Institutions such as metropolitan assemblies and policymakers may adopt the concepts demonstrated in this work to rapidly assess urban environments and investigate the relationship between spatial urban expansion and temperature.
Process-based hydrological models, which simulate nitrogen load from its sources to the receiving waterbody, play an important role in supporting catchment management. The reliability of those models, such as the representative model SWAT used in this study, is determined by a sound calibration and the analysis of the prediction uncertainty. The multi-objective calibration approach prevails the classic single-objective calibration on the spatial parameterization of specific processes. However, the requirement of additional observations and practical procedures limits its application. Moreover, the prediction uncertainty of nitrogen load is inevitable and should also be quantified. This study is a scientific contribution to catchment management by overcoming the challenge with a systematic, well-founded concept of multi-objective calibration and uncertainty analysis for nitrogen load simulation in data-scarce catchments. The concept is tested and proofed by its practical applicability on the Yuan River Catchment (YRC) in China and leads to a generalized, recommended procedure for catchment management application as valuable progress in this field.
The study proposed to apply three groups of objectives, multi-site, multi-objective-function, and multi-metric. The applicability and the advantages of two multi-objective calibration approaches, Euclidean Distance and Non-Dominated Sorting Genetic Algorithm-II were analyzed. To quantify the prediction uncertainty, the study proposed to use the simulations with the highest or the lowest percent bias to represent the uncertainty band of the nitrogen load from the critical source areas (CSAs) and to the stream. The data-scarcity of the YRC was overcome by metrics obtained from open-access satellite-based datasets and metrics extracted from the existing discharge observations. Results show that multi-objective calibration has ensured the model’s better performance in terms of the spatial parameterization, the magnitude of the output time-series and the water balance components, in comparison to single-objective calibration. The predicted CSAs showed that 50% of the total nitrogen (TN) loading to the stream was from 26.3% to 37.1% of the area in the YRC. Meanwhile, over 50% of those TN were from the paddy field. Recommendations for application go to the multi-objective calibration considering all three groups of objectives. Approaches to obtain multi-metric objectives in the YRC are also applicable for catchments with data-scarcity. Recommendations for nitrogen management in the YRC is to emphasize the CSAs identified, especially the paddy field.
Since Z3, the first automatic, programmable and operational computer, emerged in 1941, computers have become an unshakable tool in varieties of engineering researches, studies and applications. In the field of hydroinformatics, there exist a number of tools focusing on data collection and management, data analysis, numerical simulations, model coupling, post-processing, etc. in different time and space scales. However, one crucial process is still missing — filling the gap between available mass raw data and simulation tools.
In this research work, a general software framework for time series scenario composition is proposed to improve this issue. The design of this framework is aimed at facilitating simulation tasks by providing input data sets, e.g. Boundary Conditions (BCs), generated for user-specified what-if scenarios. These scenarios are based on the available raw data of different sources, such as field and laboratory measurements and simulation results. In addition, the framework also monitors the workflow by keeping track of the related metadata to ensure its traceability.
This framework is data-driven and semi-automatic. It contains four basic modules: data pre-processing, event identification, process identification, and scenario composition. These modules mainly involve Time Series Knowledge Mining (TSKM), fuzzy logic and Multivariate Adaptive Regression Splines (MARS) to extract features from the collected data and interconnect themselves. The extracted features together with other statistical information form the most fundamental elements, MetaEvents, for scenario composition and further time series generation. The MetaEvents are extracted through semi-automatic steps forming Aspects, Primitive Patterns, Successions, and Events from a set of time series raw data. Furthermore, different state variables are interconnected by the physical relationships derived from process identification. These MetaEvents represent the complementary features and consider identified physical relationships among different state variables from the available time series data of different sources rather than the isolated ones. The composed scenarios can be further converted into a set of time series data as, for example, BCs, to facilitate numerical simulations.
A software prototype of this framework was designed and implemented on top of the Java and R software technologies. The prototype together with four prototype application examples containing mathematical function-generated data, artificial model-synthetic hydrological data, and measured hydrological and hydrodynamic data, are used to demonstrate the concept. The results from the application examples present the capability of reproducing similar time series patterns from specific scenarios compared to the original ones as well as the capability of generating artificial time series data from composed scenarios based on the interest of users, such as numerical modelers. In this respect, it demonstrates the concept’s capability of answering the impacts from what-if scenarios together with simulation tools. The semi-automatic concept of the prototype also prevents from inappropriate black-box applications and allows the consideration of the knowledge and experiences of domain experts. Overall, the framework is a valuable and progressive step towards holistic hydroinformatics systems in reducing the gap between raw data and simulation tools in an engineering suitable manner.
In most studies on flood risk in Nigeria, priority is given to urban flood risk than rural flood risk. Smallholder farmers who mostly inhabit rural areas and flood plains are known to be at great risk of flooding, but to date, there have been no studies on the impacts they experience and how they cope with these impacts.
To establish the existence of significant change in precipitation in the study area, statistical analysis of precipitation data was conducted using linear regression and non-parametric Mann Kendall test. Landsat satellite images for 1986, 2003 and 2016 were obtained from the United States Geological Survey’s (USGS) website and used to collect and analyze data related to land use.
To determine flood risk in the context of land use change, the HEC-HMS model version 3.5 was used. Land use maps corresponding to 1986, 2003, and 2016 LULC conditions were analysed and prepared for the calculation of CN values using Soil Conservation Service (SCS-CN) method. CN map was prepared by integrating the maps of hydrologic soil groups and land use in ArcGIS software.
Based on selected morphological parameters, a flood risk map was developed for the study area. The morphologic flood risk showed that most parts of the basin face moderate flood risk. Higher runoff values were observed under 2016 LULC conditions mainly due to intense deforestation relative to those of 1986 and 2003. The results indicated that runoff and peak discharge are significantly affected by deforestation instead of changes in the rainfall pattern. Flood inundation maps of the study area were prepared for the flood events of 2012 and 2015. Maximum depth for the 2012 and 2015 flood events was calculated as 10.58m and 7.39m, respectively.
The socioeconomic aspects of the research were conducted using household survey method. Using snowball sampling techniques, 400 flood-affected households were selected. The results show that the adaptive capacity of smallholder farmers in the study area remains very low and hampered by several factors such as poverty and weakened social networks. The synopsis of the examined variables leads to the following conclusion: Instead of climatic factors – as one indicator of vulnerability – it is social inequality and poverty in the region as well as the structural economic differences between various groups that mainly drives vulnerability in the study area.
There is an increased focus on interdisciplinary research in hydroinformatic related projects for applications such as integrated water resources management, climate change modelling, etc. The solution of common problems in interdisciplinary projects requires the integration of hydroinformatic models into hydroinformatic systems by coupling of models, enabling them to efficiently share and exchange information amongst themselves.
Coupling of models is a complex task and involves various challenges. Such challenges arise due to factors such as models required to be coupled together lacking coupling capabilities, different models having different internal data formats, lack of a coupling mechanism, etc. From the perspective of physics, different models may use different discretisations in space and time, operate on different scales in space and time, etc.
A model coupling concept using a coupling broker, that is independent from the coupled models, has been developed in this work and been implemented as a prototype for a software framework for coupling hydroinformatic models. It is based on the approach of tensor objects and the ideas of the OpenMI standard for model coupling. Tensor objects are a complete representation of physical state variables including dimensions, units, values, coordinate systems, geometry, topology and metadata. They are autonomous entities that can adapt themselves to the requirements of coupled models through operations such as scaling, mapping, interpolation in space and time, etc. The central entity in coupling is the Tensor Exchange Server, which acts as the coupling broker. It is responsible for defining the coupling mechanism, brokering the communication between the models and adapting the information to the requirements of the coupled models by taking advantage of the functionality provided by tensor objects. By fulfilling these roles in coupling, the coupling broker concept goes one step further than tools such as the OpenMI standard and facilitates the task of coupling models since each coupled model doesn't individually need to be adapted to be able to perform these tasks on its own.
The usefulness of the coupling broker concept for coupling models is demonstrated with the help of three application examples: firstly, a subsurface-flow model coupled with a model simulating metabolism in the hyporheic zone, secondly, a subsurface-flow model coupled with a surface-flow model and finally, an information management system presenting the results of a hydrodynamic simulation of a section of the river Rhine. These examples demonstrate the extensibility and flexibility of the presented coupling concept, which can be used to couple multiple hydroinformatic models in hydroinformatic systems.
Improvement of flood risk assessment under climate change in Ho Chi Minh City with GIS applications
(2014)
Ho Chi Minh City is the largest city in VN. The city is the most important center of economy, society and culture in the southern region of Vietnam. However, due to characteristics of natural conditions with low topography and borders touch the sea so that since the late 20th century with the rapid economic and urban development there are environmental problems have arisen. One of the problems is flooding issue caused by high tide. With these natural conditions and sea level rise of climate change in the future, Ho Chi Minh City is considered as one of the most affected and damaged cities in the world.
Therefore, many policies have been set out from the national to local levels in Vietnam to prepare for adaptability of impacts and risks of the sea level rise and the climate change. And this has also been considered in Ho Chi Minh City as the development policies of the city authorities have to consider in the context of the sea level rise and the climate change. A number of researches have been conducted to assess the impact of climate change to Ho Chi Minh City in the future. However, these researches are still need to be enhanced further.
The flooding problem is a major issue of the sea level rise in Ho Chi Minh City. And to make a good result, the flood model needs a lot of requirements that ensure fine quality of input data, suitable model and a relied procedure. In the available research, the input data is not really the highest quality in the available context in Ho Chi Minh City. As the flood model is implemented, one of the input data requirements of the model is information detail of elevation in the flooded area. And this is more necessary than in geophysical urban areas such as Ho Chi Minh City. And to assess fully flood risk issues for flooding caused by tidal phenomenon in Ho Chi Minh City, the determination of many characteristics of flood model is very useful for users who need to apply the results of the model for the planning development in Ho Chi Minh City in the future. Besides adopting a uniform environment as GIS for managing all the data of flooding problem and making conditions for the development of decision support systems is very necessary for flood management in the future.
The research has been carried out and its results have been generated on the flood risk assessment associated context of the sea level rise due to climate change with high emissions scenario A1FI in 2030 for the current houses, the population and the land use types. The results have shown a lot of the areas where are inundated in the future with the increasing flooding duration, depth and frequency even though they are not flooded at the current because there are some protective structures. This will be helpful for suggesting a forecast of the development direction to decision makers in Ho Chi Minh City for next time period.
And the last part is the proposal for decision and policy makers, authorities and planners as well. Moreover, the results of this research can be used as the references and the foundations for further researches and so that the problems that Ho Chi Minh City may be encountered due to flood risk caused by climate change to the economic and social aspects of the development in Ho Chi Minh City will encounter. And the problem of adaptation to climate change will be more completed and more thoroughly so that it is to minimize the damage of climate change for the city.
Ungauged and data-scarce coastal river catchments, small and medium-sized ones in particular, are popular worldwide, especially in developing countries in Southeast Asia. In general, coastal river catchments are exposed to various coastal hazards such as fluvial floods, typhoons, and sea-level rise. In addition, climate change and socio-economic development will substantially change global flood risks. However, in scientific research, flood risk and future flood risk assessments in such coastal river catchments have not been significantly considered. Therefore, the overall objective of this thesis is to propose an innovative methodological framework for modeling and evaluating flood risk in such river catchments in the future under the impacts of climate change, sea-level rise, and land-use change based on using numerical models (hydrological models and land-use change models) and remote sensing data (satellite images and precipitation) in combination with other state-of-the-art methods, tools and models, and new approaches, such as flood risk assessment methods, regionalization methods, and statistical downscaling methods. The proposed framework was adopted, applied, and validated successfully for a case study - Tra Bong River Catchment - on the south-central coast of Vietnam as a test application. The main conclusion of this research is that the proposed framework can provide valuable information and projections of changing future flood risk associated with climatic and socio-economic changes in ungauged and data-scarce coastal river catchments. The proposed framework is designed with free model and method choices, meaning that it can apply different methods and tools for each step depending on the natural characteristics of river catchments, data collection, tools, and understanding of models, etc. Finally, this research can be used as a technical reference for designing and building national, regional, or local frameworks/guidelines for assessing future flood risk in ungauged and data-scarce coastal river catchments in Vietnam and other countries in Southeast Asia.