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.

For cotton cultivation, large amounts of water are necessary. One of the countries facing water challenges related to cotton cultivation is India. India experiences a monsoon weather pattern causing large amount of rain from June to October and no rain during the other months. One of the challenges India’s small holder cot- ton farmers face is the lack of water after the monsoon. A solution to this problem could be the construction of (small) reservoir structures and renovation of currently present structures. One of the main questions arising here is ‘how can a hydrological model estimate new reservoir locations for small holder cotton farmers and what is the potential of these new structures and current structures in Maharashtra India with Ghatanji block and Hinganghat block as case study areas?’. Estimating potential reservoir locations as well as determining which current reservoir structures have the potential to be renewed could increase the water availability for small holder cotton farmers in Maharashtra, India.
Four focus areas are chosen, these areas are Yavatmal, Nagpur, Wardha and Amravati. Within these fo- cus areas, Ghatanji block and Hinganghat block are used as case study areas for developing and validating different models used for analysing current and suitable potential locations.Remote data based tools used to locate, validate and analyse the potential of current and potential suitable structures are QSWAT (hydrological model), Google Earth Engine (land cover analysis), Python coding, Zoom.Earth (locating current structures), socio-hydrological model (yield and benefit analysis).
Out of the initial 692 potential locations, 315 locations in Ghatanji block and 349 locations in Hinganghat block are classified as suitable potential reservoir locations. A location was marked suitable if a minimum size (200m3) reservoir on this position has the ability to fill up completely at least once a year for an average monsoon season. Additionally, the structure was positioned in a location with more than 30% crop coverage and less than 25% urban coverage. Out of a total of 2212 current structures, 840 locations in Hinganghat and 970 locations in Ghatanji have a high potential of collecting water if renovated and in use. This high potential is a result of small distance to the nearest stream and suitable land cover on the position of the reservoir. Existing structures were classified as suitable if they adhere the same prerequisites as stated above regarding the coverage and for the additional requirement that a structure should be located less than 200 meters from the nearest stream point.
The results of this investigation show that a hydrological model can estimate new potential reservoir locations and that the use of a hydrological model is beneficial for locating these potential locations when com- pared to current or recently constructed locations. The benefit of using a hydrological model is that it can identify the surface runoff paths of water during and after a precipitation event. Furthermore, can remote data based tools help in identifying the suitability of both potential as well as current structures.

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.