Sustainable floodplain management and hydropower
The project team developed tools and indicators to quantify, predict and monitor the consequences of adaptive flow management and other restoration actions in floodplains downstream of hydropower schemes.
Project description (completed research project)
Hydropower production is an important component of the Energy Strategy 2050. Its sustainable development is mandatory, and application of adaptive flow management, reactivation of sediment dynamics and restoration of rivers and floodplains is becoming more rigorous and obligatory.
Aim
The overall goal was to provide tools and indicators to sustainably develop hydropower production while evaluating and optimising restoration actions such as adaptive management of flow and sediment regimes. For this, the development and testing of indicators linking hydromorphological structure (e.g. habitat diversity) and ecological function (e.g. biodiversity, microbial activity) applicable for the evaluation of restoration actions across different floodplains and impacts, comprehensive hydraulic modelling to quantify and predict the ecological potential of restoration actions, and remote sensing techniques for spatially explicit monitoring at the floodplain scale was integrated.
Results
Based on the linkage between structure and function, macroinvertebrate and microbial assemblages could be separated by a hydromorphological regime of different floodplains (residual flow, hydropeaking, near-natural). Structural (habitat-) diversity and hydrological dynamics increased macroinvertebrate/microbial diversity and thus fostered process heterogeneity (nutrient cycling).
The numerical models developed led to improvements in the Hydro-Morphological Index of Diversity (HMID) and evaluation of geomorphological properties (change estimation in floodplain heterogeneity) of restoration measures. Under residual flow conditions, the limits and potential of the HMID have been identified.
Based on floodplain change assessment, the project provided recommendations for the acquisition and processing of unmanned aerial systems (UAS) data to obtain a robust estimate of land cover dynamics. Optimal observation of dynamics should focus on the gravel-water interface. The use of imaging spectroscopy showed that the hydrological regime influences vegetation properties, making alluvial trees potentially less resistant to stress, for instance drought. Current models of plant strategies do not account for a large variance within the same tree species.
The results and their applications were tested in a large-scale field experiment of an artificial flood released in September 2016. Structural changes from the flood had significant effects on floodplain function via benthic and microbial communities. Structural changes were quantified using the HMID in combination with sediment replenishment. Other ecomorphological changes were assessed at the landscape scale using UAS. The combination of developed methods allowed a comprehensive evaluation of the (positive) impact of this flood and its use as a tool for the sustainable management of rivers and floodplains.
Relevance for research
The results contribute to the important research question of how multiple disturbances and structural-functional linkages interact in floodplain ecosystems, which is directly linked to different restoration actions such as artificial floods and sediment replenishment. Numerical models combined with remote sensing products allowed quantification of floodplain change, an important prerequisite for evaluating ecosystem impacts and management adaptations.
Relevance for practice
The combination of structural and functional indicators for evaluation, modelling for prediction, and remote-sensing for monitoring is well-suited to quantifying floodplain alterations in an integrative management framework from hydropower production. It serves as an effective tool to evaluate and optimise restoration measures in an eco-economical view.
Original title
Hydro-Ecology and Floodplain Sustainability in Application (HyApp)