The political interest in fish habitat suitability and, consequently, of fish populations has increased. The fish habitat suitability is a key factor for successful ecological restoration, for example via dam removal and implementation of fish passage. Furthermore, the fish population composition determines the ecological quality of water bodies within the Water Framework Directive (WFD).

The aim of this thesis is to develop an ecological model for fish habitat suitability in the river Rotte basin in the Netherlands that can be used in policy development for ecological restoration. The model serves as a decision support system to explain differences in fish population and evaluate the impact of management actions to modify fish habitat suitability. The river Rotte suits perfectly for this case study, because recently a fish passage has been realised between the Rotte and Nieuwe Maas to facilitate fish migration and expand living conditions. Furthermore, the river Rotte is a designated WFD water body, but the current status of the river varies between "poor" and "moderate" due to an unbalance between plant-loving and benthivorous fish species.

The model developed in this thesis is a Bayesian Belief Network model that predicts habitat factors for food preference and preference for habitat structure. The model is based on machine learning with a set of cases from monitoring data and predicts the probability distribution for fish habitat suitability for plant-loving and benthivorous fish species. The model has been applied to assess the impact of local conditions on fish habitat differentiation and to evaluate the impact of management actions on fish habitat suitability. The research shows that the Bayesian Belief Network model is very useable for policy making and facilitates the participation of various stakeholders. However, the current version of the model shows inadequate prediction accuracy and relies heavily on sampling data. This can be improved by expanding the scope of the model to include other water bodies in the Netherlands and by using metamodels for specific model variables.

Overall, the Bayesian Belief Network model is functional and usable for policy making, but further improvements are needed to enhance its prediction accuracy. This can be achieved through expanding the model's scope, evaluating its performance, and including more habitat factors in the model structure.

Freshwater biodiversity is in a state of crisis with an annual decline rate of 3.9% compared to 1.1% decline in terrestrial biodiversity (Living Planet Index). The actual decline rate may be higher than estimated as many of the species are data deficient or not yet assessed or became extinct before assessment. We have far surpassed the sustainable or natural limit of reductions in populations of freshwater species. With an unprecedented increase in global energy and water demand, planetaries boundaries for sustainable use of freshwater may be overstepped in the near future, as presently visible at a local scale. A staggering decline in freshwater biodiversity is a first warning signal of a looming global water crisis. With continued pressures like river fragmentation, flow regulation, overextraction of surface or groundwater, pollution, invasive species and climate change, on freshwater habitats, this crisis may shift one level higher and impact human species severely.

Environmental flows forms the link between ecological health of a river and the ecosystem services we derive from it. It can be a great tool to achieve twin objective of maintaining the ecosystem integrity of freshwater habitats and deciding trade-offs for ensuring sustainable water management in a river basin. This study focuses on developing a holistic methodology for preliminary assessment of ecosystem integrity or ecological health of a river basin, which can be easily adapted to other river basins using open source global datasets. The proposed methodology was applied to Kaveri basin to test its applicability and identify the limitations of available global datasets. A widely accepted regional environmental flow assessment framework, ELOHA (Ecological Limits of Hydrological Alteration) was adapted by using global datasets. A global river classification dataset was used to identify the river classes in Kaveri basin. Monthly hydrological alteration in magnitude at the location of gauge stations was calculated, using PCR-GLOBWB data as reference for natural flow conditions, in absence of records for natural flow in a highly modified Kaveri basin. An ecosystem integrity indicator framework was developed to assess the hydrologic, geomorphic and ecological modifications in the river basin. Indicators grouped under four main categories - Connectivity status, Land Use, Biodiversity, Water Quality were adopted using exiting global datasets and values for all the sub-basins of Kaveri basin were estimated.

Finally an attempt to derive flow alteration-ecological response was made. Threatened fish species percentage, quantified using IUCN spatial dataset, showed an increase in value with increase in alteration in flow magnitude. No clear relationship was observed when data for other taxonomic groups like plants, molluscs, odonata, shrimps and crabs were used. Hence, species of concern (IUCN red list category - CR, EN,VU) data can be useful in deriving preliminary flow alteration-ecological response relationship. An attempt to find linkage between flow alteration and floodplain gross primary productivity was also made. In dry season an inverse relationship was observed at few gauge stations but in general other climatic factors like rainfall and evapotranspiration had greater influence on gross primary productivity. Impact on gross primary productivity due to flow alteration could not be isolated using existing datasets because of coarse resolution.