Impacts of climate change and land use change on the water resources of the Mula and Mutha Rivers catchment upstream of Pune, India

Water scarcity is one of the most challenging problems the world is facing in the 21st century. Population growth and economic development often lead to an increase of water demand, whereas climate change and land use change have an impact on water availability. The assessment of the impacts of climate change and land use change on the water resources is highly relevant as it is a prerequisite for water management adaptation and for the development of suitable mitigation strategies, especially in regions with scarce water resources, high climate sensitivity, and a rapid socio-economic development. This thesis aims at the development of a hydrologic model to analyze the impacts of climate change and land use change on the water balance components in the meso-scale (2036 km²) Mula and Mutha Rivers catchment upstream of the city of Pune, India. To this end, the hydrologic model SWAT (Soil and Water Assessment Tool) was modified and adapted to the study area. By combining generally available data, locally available data, field measurements, expert knowledge, and data preprocessing methods in this hydrologic modeling approach, the problem of limited data availability was addressed. A focus was set on the spatial interpolation of sparse rainfall data, as rainfall is one of the most important inputs for hydrologic models. It was found that the applied modeling approach is suitable for data scarce regions. Furthermore, the methodology is transferable to tropical and sub-tropical regions. In particular, the use of a TRMM rainfall pattern as a covariate for spatial rainfall interpolation was very promising. Climate change impacts were analyzed using regional climate model data based on IPCC emission scenario A1B. A new downscaling approach was developed that is based on representing the scenario data by rearranging historically measured data in order to link the coarse resolution regional climate model data to the catchment scale. The hydrologic model was run for the scenario period from 2020 to 2099 using the rearranged weather data. The developed downscaling technique provided a consistent weather input for the scenario period, but was limited by the range of measured temperature values. Hence, climate change impacts at the end of the scenario period were likely to be underestimated. The climate change scenario resulted in higher evapotranspiration, particularly in the first months of the dry season. Thus, water availability was decreased more rapidly and earlier in the dry season. In addition, more frequent dry years led to repeated low water storages in the reservoirs at the end of rainy season. Past land use changes between 1989 and 2009 were identified with the help of three multitemporal land use classifications which were based on multispectral satellite data. Two model runs were performed and compared using the land use classifications of 1989 and 2009. The main land use changes in the past two decades were an increase of urban area and cropland, while semi-natural land use decreased. Urbanization in the eastern part of the catchment resulted in a shift of cropland towards the west. On the catchment scale the impacts of these land use changes upon the water balance canceled each other out. However, at the sub-basin scale, urbanization led to an increase of the water yield and a decrease of evapotranspiration, whereas the increase of cropland resulted in an increase of evapotranspiration. These changes yielded a change of the intra-annual course of runoff, so that runoff increased in the rainy season due to urbanization, and decreased in the dry season due to increased irrigation water demand. Climate change and land use change pose challenges to the diverse water users inside and outside of the catchment. In particular, the indicated decrease of water availability in the dry season exacerbates the imbalance of water availability and water demand at this time of the year. Overall this thesis substantially enhances the knowledge of global change impacts on the water resources in the study area, which provides a means to mitigate future impacts by adapting water management. Furthermore, the developed and improved methods for hydrologic modeling in data scarce regions are transferable to other study areas and applicable in future research.