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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 this first volume of the series ‘Ecosystem Development’, the geographic situation of the artificial catchment 'Chicken Creek', its construction and fundamental structures, as well as an outline of the comprehensive monitoring program are introduced. Established in autumn 2005 thanks to financial support from the Brandenburg University of Technology and the State of Brandenburg itself, the monitoring program was set up in order to document the highly dynamic development of this initial-phase system and describe the newly emerging structures both qualitatively and quantitatively. The data provided by the central monitoring project can also be used as the fundamental basis for a number of other scientific projects. The objective of this series is to document this important research site, including the changes occurring during the ongoing initial stages of ecosystem development. Whereas this volume presents the site itself and the general concept of the monitoring investigations, the following volumes in the series will present the results gathered during each phase of the investigative period.
This volume summarizes the monitoring activities and results at the ‘Chicken Creek’ catchment for the period 2005 to 2010. The development in all ecosystem compartments is assessed, classified, and compared to more mature systems with regard to functional relevance and succession stage. In a final synopsis, the whole catchment is evaluated with regard to ecosystem development and an outlook is given on the expected trends both in the short and the medium term.
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.
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.
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.
This report introduces the monitoring installations of the artificial catchment 'Chicken Creek' and summarizes results of the measurement in the period 2005-2008, covering various aspects and compartments from meteorology and hydrology, soil and soil solution chemistry, vegetation and soil fauna to limnology and surface patternsfor the period 2005 to 2008. This volume is the beginning of a series with results of the ongoing Chicken Creek monitoring program.
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.
In anthropogenically heavily impacted river catchments, such as the Lusatian river catchments Spree and Schwarze Elster in Germany, the robust assessment of potential impacts of climate change on the regional water resources is of high relevance for water resources management. Large uncertainties inherent in future scenarios may, however, reduce the willingness of regional stakeholders to develop and implement suitable adaptation strategies to climate change.
This thesis proposes the use of an integrated framework consisting of i) an ensemble based modelling approach and ii) the incorporation of measured and simulated meteorological and hydrological trends to consider uncertainties in climate change impact assessments. In addition, land use, as the most responsive catchment characteristic to buffer potential climate change impacts, is considered as one suitable trigger for climate change adaptation.
The ensemble based modelling approach consists of the meteorological output of four climate downscaling approaches (DAs): two dynamical and two statistical. These DAs drive different model configurations of the two conceptually different hydrological models WaSiM ETH and HBV light. The objective of incorporating measured meteorological trends into the analysis was twofold: trends in measured time series can i) be regarded as harbinger for future change and ii) serve as a mean to validate the results of the DAs. In order to evaluate the nature of the trends, both gradual (Mann Kendall test) and step changes (Pettitt test) are considered as well as temporal and spatial correlations in the data. The suitability of land use change as an adaptation strategy to climate change is evaluated in the form of different land use change scenarios: i) extreme scenarios where the entire catchment is parameterised as coniferous forest and uncultivated land and ii) scenarios of changes in crop cultivation and ii) a combination of a change in crop cultivation and forest conversion. As study areas serve three almost natural subcatchments of the Spree and Schwarze Elster (Germany).
The results of the ensemble based climate change impact analysis show that depending on the type (dynamical or statistical) of DA used, opposing trends in precipitation, actual evapotranspiration and discharge are simulated in the scenario period (2031 2060). While the statistical DAs simulate a decrease in future long term annual precipitation, the dynamical DAs simulate a tendency towards increasing precipitation. The trend analysis suggests that measured precipitation has not changed significantly during the period 1961 2006. Therefore, the strong decrease in precipitation simulated by the statistical DAs should be interpreted as a rather dry future scenario. The dynamical DAs, on the other hand, are too wet in the reference period and needed to be statistically bias corrected which destroys the physical consistency between the parameters. Concerning temperature, measured and simulated trends agree on a positive trend. The uncertainty related to the hydrological model within the climate change modelling chain is comparably low when long term averages are considered but increases during low flow events. The proposed framework of combining an ensemble based modelling approach with trend analysis on measurements is a promising approach to gain more confidence into the final results of climate change impact assessments and to obtain an increased process understanding of the interrelation between climate and water resources.
In terms of climate change adaptation, land use alternatives can have a considerable impact on the water balance components as the analysis of the extreme scenarios revealed. The scenarios of changes in crop cultivation in combination with forest conversion show, however, that the impact on the long term annual water balance is comparably low. An intra annual shift in the water balance components can be triggered which makes these scenarios suitable to reduce low flow risks during the summer. Overall, land use change can serve as one part of an integrated climate change adaptation strategy. Such as strategy needs, depending on the severity of the climate change impact, to include other, especially technical measures of water resources management, such as additional water storage, different strategies to manage the existing and new reservoirs. It may also consider additional water transfers from neighbouring, more water rich, river catchments. Regional adaptation planning needs also to consider problems related to water quality which are a consequence of the long term mining activities in the Lusatian river catchments. Last but not least, adaptation strategies should not only consider climate but also other aspects of global change.
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.