There have been growing concerns over the decline of healthy river ecosystems and the severe consequences this decline could have on biodiversity, ecosystem services, and human well-being. These concerns have led to increased efforts in river restoration around the globe, which aim to improve the ecological health and functioning of rivers. The restoration is usually done by implementing strategies such as hydromorphological adaptation and flow management. These measures, nevertheless, do not guarantee the recovery of river ecosystems. This is because there are multiple factors contributing to the success of restoration projects, which can vary depending on the specific characteristics of each river system.
Habitat modeling, one of the most widely used ecological quality assessment tools for rivers, has been applied in the evaluation of restoration projects. An aquatic ecosystem is complex, and its dynamic nature requires a comprehensive understanding of the interconnections between biotic and abiotic components. These components also have a high degree of spatial and temporal variability. Therefore, it is crucial that approaches and modeling techniques be tailored to capture this dynamic. In the assessment of river restoration, for instance, habitat modeling needs to account for the changes in flow patterns, sediment transport, water quality, and habitat availability/quality for the key indicator species that result from the restoration efforts.
This study addresses the need for developing an integrated approach to habitat modeling, particularly for macrozoobenthos, an important indicator of river health that plays a crucial role in the functioning of aquatic ecosystems. The primary research objective is to improve the existing modeling framework (TRiMM) by focusing on three key aspects: 1) expanding the prediction factors of physical habitat that influence habitat suitability for macrozoobenthos; 2) integrating fuzzy algorithms in the suitability assignment process; 3) incorporating species' (re-)colonization capacity and habitat temporal variability into habitat connectivity assessment.
The model adopts the fuzzy logic method in the habitat module to account for the interactions between various factors described in the habitat template (Poff & Ward, 1990). Moreover, the model considers both spatial and temporal changes in habitat parameters by running a transient simulation over a specific time period relevant to the life cycle requirements of the target species. This allows for a more accurate representation of the dynamic nature of river habitats and provides valuable insights into how they may change over time. Additionally, the model incorporates species' (re-)colonization potentials into habitat connectivity analysis by considering their dispersal capabilities. This helps in understanding how changes in habitat parameters can affect the overall connectivity of river habitats, which is crucial for assessing the resilience and sustainability of the systems.
The proposed transient habitat modeling (TRiMM 2.0) is applied to two case studies of low-order rivers in Germany. The first case study focuses on a river that has been restored after a period of degradation. The habitat model was tested with sampling data, and the results reveal that the model improved when additional variables related to habitat were included. The second case study was a simulation of habitat suitability and connectivity in a hypothetical river reach. Hydraulic and morphological factors (water depth, velocity, temperature, and sediment) are simulated over a period of four years using SRH-2D. The simulation results showed that hydraulic and morphological factors had a significant impact on sediment characteristics, which in turn influenced habitat suitability and connectivity. This study also highlights the importance of considering multiple variables and their interactions when assessing river habitats. Additionally, the use of transient modeling provides information about long-term changes in habitat quality and connectivity.:Abstract
Kurzfassung
Contents
List of figures
List of tables
Nomencature
Acknowledgement
List of publications
1. General introduction
1.1. Research motivation
1.2. Statement of research objectives
1.3. Structure of the dissertation
2. Macrozoobenthos and stream’s ecology
2.1. Macrozoobenthos and their habitat
2.2. Factors influencing the distribution of macrozoobenthos
2.2.1. Food sources
2.2.2. Water quality
2.2.3. Physical habitat
2.2.4. Colonization process
2.2.5. Presence of other species
2.3. Spatial scale and temporal variability
2.4. Conclusion
3. State of the art in river habitat modeling
3.1. Habitat modeling and river ecology assessment
3.2. Habitat modeling principles
3.2.1. Habitat suitability curves method
3.2.2. Fuzzy logic method
3.2.3. Generalized additive models
3.3. Existing benthos habitat modeling
3.3.1. PHABSIM
3.3.2. RHYHABSIM
3.3.3. BITHABSIM
3.3.4. CASiMiR
3.3.5. HABFUZZ
3.4. TRiMM and further development
3.5. Conclusion
4. Basis for the modeling concept and methodological framework
4.1. Physical habitat template
4.1.1. Streamflow regime
4.1.2. Substrate regime
4.1.3. Thermal regime
4.2. Habitat connectivity
4.3. Species colonization and habitat connectivity
4.4. Analysis scales
4.5. Conclusion
5. Transient river habitat modeling for macrozoobenthos – TRiMM 2.0
5.1. Habitat model description
5.2. Input data preparation
5.2.1. Field survey
5.2.2. Hydro-morphodynamic models
5.3. Habitat suitability calculation
5.4. Patch-building and patch dynamics analysis
5.5. Habitat connectivity calculation
5.6. Conclusion
6. Model applications
6.1. Case study 1: Simulation of habitat suitability for macrozoobenthos in a small restored stream (Saxony, Germany)
Abstract
6.1.1. Introduction
6.1.2. Material and Method
6.1.3. Results
6.1.4. Discussion
6.1.5. Conclusion
6.2. Case study 2: Application of TRiMM 2.0 to simulate benthic habitat quality in a hypothetical reach of Zschopau river
6.2.1. Introduction
6.2.2. Methodology
6.2.3. Results
6.2.4. Discussion
6.2.5. Conclusion
7. Summary and future outlook
8. References
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:90334 |
| Date | 11 March 2024 |
| Creators | Thepphachanh, Sengdavanh |
| Contributors | Stamm, Jürgen, Berendonk, Thomas U., Egger, Gregory, Technische Universität Dresden, Univ.-Prof. Dr.-Ing. Jürgen Stamm |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:doctoralThesis, info:eu-repo/semantics/doctoralThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | urn:nbn:de:bsz:14-qucosa2-805378, qucosa:80537 |
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