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Significance of storm spatiotemporal variability and movement in flood hydrodynamic modelling
(2024)
This dissertation aims to explore the impact of various storm properties on computational flood modelling and to propose a method for integrating them into flood risk assessment. It focuses on three key areas:
Firstly, it examines within-storm variability, investigating how variations in storm intensity, duration, and spatial distribution affect runoff dynamics. Through a systematic theoretical approach, the study generates synthetic rainfall signals with different hyetograph variabilities and applies them to a hydrodynamic model. The results highlight the significant influence of storm spatiotemporal variability on runoff response, affecting peak discharge, hydrograph shapes, and flooded areas. Temporal variability emerges as particularly dominant, overshadowing spatial variability and other storm properties such as return period and volume. The study emphasizes the spatial dependency of these effects within the drainage network.
Secondly, it investigates the role of storm movement in flood modelling, recognizing its potential to alter runoff dynamics and inundation patterns. By generating synthetic rain hyetographs traversing the catchment area at varying velocities and directions, the study shows that storm movement significantly impacts runoff response and flood extents. Higher storm velocities lead to more pronounced peaks and faster runoff onsets, resulting in larger flooded areas. The direction of storm movement plays a crucial role, with storms aligning with the average stream direction causing the highest peaks and flooded areas. The magnitude of this influence varies depending on the location within the catchment.
Finally, the dissertation introduces a dynamic spatiotemporal rainfall model aimed at preserving storm event properties. Using an event-based approach, dynamic precipitation events are identified and regenerated, ensuring the conservation of storm spatiotemporal variability and movement characteristics. This facilitates the generation of more physically plausible and spatiotemporally coherent precipitation time series. The model prioritizes user accessibility, offering a practical tool for integrating storm properties into flood computation modelling and risk analysis frameworks. By addressing these aspects of storm behaviour, the dissertation contributes to enhancing the accuracy and effectiveness of flood modelling, thereby improving flood risk mitigation efforts.
The Accra Metropolis of Ghana has experienced rapid urban expansion over the past decades. Agricultural and forest-lands have been transformed into urban/built-up areas. This study analysed urban expansion and its relationship with the temperature of Accra from 1986 to 2022. Multi-source datasets such as remote sensing (RS) and other ancillary data were utilised. Land use land cover (LULC) maps were produced employing the random forests classifier. Land surface temperature (LST) and selected d(RS) Indices were extracted. Regression techniques assessed the interplay between LST and remote sensing indices. The LULC maps revealed increasing trends in the urban/built-up areas at the expense of the other LULC types. The analysis from the LST and the RS indices revealed a direct relationship between temperature and urban/built-up areas and an inverse relationship between temperature and vegetation. Thus, spatial urban expansion has modified the urban temperature of Accra. The integrated utilisation of RS and GIS demonstrated to be an efficient approach for analysing and monitoring urban expansion and its relationship with temperature.
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.
Temperature variability may have direct and indirect impacts on the environments of the Accra and Kumasi Metropolises in Ghana. This study analysed temperature and trends in temperature in both cities using in-situ measurements from one meteorological station in both cities from 1986 to 2015. The temperature indices were computed using the RClimdex package from the Expert Team on Climate Change Detection Monitoring Indices (ETCCDMI). The temperature time series was pre-whitened before the Mann–Kendall trend and Sen’s slope estimator analysis were applied. Initial analysis revealed minimal variation in temperature in both cities. The results from the analysed temperature indices revealed an increase in warm days and a general rise in the minimum temperature compared to maximum temperatures. Mann Kendall and Sen’s slope revealed significant trends in the annual and seasonal (dry and wet seasons) in minimum temperature in both cities. These might lead to an increased rate of heat-stressed diseases and an overall rise in urban warming in both cities. The analysis of temperature, indices and trends provided comprehensive insights into the temperature of Accra and Kumasi. The results highlight the essence of evaluating temperature indices and trends in light of Climate Change concerns. It is recommended that urban green and blue spaces should be incorporated into land use plans as these policy directions can aid regulate the temperature in both cities.
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.
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.
This thesis focuses on computer modelling issues such as i) uncertainty, including uncertainty in parameters, data input and model structure, ii) model complexity and how it affects uncertainty, iii) scale, as it pertains to scaling calibrated and validated models up or down to different spatial and temporal resolutions, and iv) transferability of a model to a site of the same scale. The discussion of these issues is well established in the fields of hydrology and hydrogeology but has found less application in river water quality modelling. This thesis contributes to transferring these ideas to river modelling and to discuss their utilization when simulating river water quality.
In order to provide a theoretical framework for the discussion of these topics several hypotheses have been adapted and extended. The basic principle is that model error decreases and sensitivity increases as a model becomes more complex. This behaviour is modified depending if the model is being upscaled or downscaled or is being transferred to a different application site.
A modelling exercise of the middle and lower Saale River in Germany provides a case study to test these hypotheses. The Saale is ideal since it has gained much attention as a test case for river basin management. It is heavily modified and regulated, has been overly polluted in the past and contains many contaminated sites. High demands are also placed on its water resources. To provide discussion of some important water management issues pertaining to the Saale River, modelling scenarios using the Saale models have been included to investigate the impact of a reduction in non-point nutrient loading and the removal and implementation of lock-and-weir systems on the river.
Die zeitliche Variabilität des Niederschlages auf Skalen von Jahren bis hin zu Ereignissen ist von wesentlicher Bedeutung für die Untersuchung hydrologischer Prozesse. Langjährige Reihen von Niederschlagsdaten mit einer hohen zeitlichen Auflösung stehen nur selten zur Verfügung. Eine Möglichkeit, die Einflüsse der Variabilität von Niederschlägen auf die hydrologischen Prozesse dennoch zu quantifizieren, ist es, stochastisch generierte Reihen als Eingangsdaten für hydrologische Modelle zu nutzen. In dieser Arbeit wurde ein stochastischer Niederschlagsgenerator der aus zwei Stufen besteht, entwickelt. Zunächst erfolgt die Generierung von Niederschlagsereignissen durch ein Poisson-Rectangular-Pulse (PRP) Modell, anschließend werden diese Ereignisse durch eine mikrokanonische multiplikative Zufallskaskade disaggregiert. Die Methodik wurde in die frei zugängliche Software R implementiert.
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.