The Miombo woodlands are characterized by a transition period which is defined as the dry period in which grasses wither, trees shed and flush their leaves a few weeks before the rainy season. It is difficult to measure evaporation, due to the influence of the plant water storage on the water availability during the dry season. The seasonal variation in the plant water storage makes it even harder, as the time lag between the plant water storage and the terrestrial groundwater storage varies between the 0 to 90 days depending on the vegetation density. (Tian et al., 2018). It is unclear why the trees prefer this early flushing strategy and what is triggering the shedding of old leaves and flushing of new leaves. It is difficult to remotely sense the variation during this period. In this report, the outputs of several evaporation products and the vegetation indices are studied and compared to see how models follow the transition period. Three models, that are chosen for the comparison use different methods to indirectly calculate the evaporation flux. The first model is the Surface Energy Balance System (SEBS), which is based on the Surface Energy Balance as the name suggests and calculates the evaporation through land-atmosphere relationships. The second model is the Global Land Evaporation Amsterdam Model (GLEAM); this model uses the water balance model to calculate the water stress factor and in return the actual evaporation. And lastly, the MODIS Terrestrial Evapotranspiration (MOD16A2) which uses the Penman-Monteith equation as a basis to separately calculate the evaporative fluxes such as transpiration, soil evaporation, and interception. The outputs of the evaporation models differ quite a lot, especially during the transition period. This is among other things due to the input variable such as vegetation indices. There are several vegetation indices such as the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Infrared Index (NDII) and the Leaf Area Index (LAI). The difference in the indexes is minimal and only small timing differences can be found. This means that the output differences are not influenced by the vegetation indices. MODIS follows the trends found by the vegetation indices best as it does not have the shortcomings that GLEAM and SEBS display. GLEAM overestimates the water stress because the model doesn’t account for plant water storage. While SEBS responds well until the start of July in which the model starts to oscillate. The oscillations could be due to the slash and burn culture which happens around this time which could impact SEBS substantially due to its dependency on temperature and radiation. Even if the MODIS output is the most similar to the vegetation output, it is limited by the fact that it is average values and daily variations might be lost. As this research is based on mostly satellite images supplemented by field observations, no statements can be made about it how accurate the models are. But based on this research, the MODIS model seems to be the preferred evaporation output.

Kathmandu is a prime example of a city exhibiting both rapid metropolitan region expansion and a population boom. This growth leads to a water stress and pollution of the surface and groundwater. The lack of proper waste management and sanitation results in the ongoing deterioration of the water quality in the Kathmandu Valley. The situation is worsened by the lack of a complete and safe drinking water network leading citizens to pivot on conventional water sources like stone spouts and bore wells. Consequently, groundwater extraction, from the aquifer under the Kathmandu Valley, is expected to keep increasing and the groundwater table to drop as the recharge is less than the extraction rate. This research aims to address the influences of land-use on the quantity and quality of water sources in the Kathmandu Valley and how the necessary data can be collected by citizen science in the future. The fieldwork took place in August 2017 when groundwater level, water quality and land-use data were collected from seven watersheds within the Kathmandu Valley in Nepal. At the same time, existing citizen science precipitation data were processed and juxtaposed with respective satellite data, namely IMERG GPM. On the one hand, the results confirm our initial assumption that the quality of the river water dramatically deteriorates (except for nitrite, nitrate and phosphate) while flowing through the agricultural and urban areas. Another observation is that spout water quality (except E. coli, turbidity and iron) is also negatively influenced by human activities. On the other hand, there is no clear link between land-use and wells’ water quality. A strange finding is that spouts and wells water quality do not behave similarly showcasing that they do not originate from the same aquifer. A health risk analysis was conducted with the results indicating that some water quality parameters have values exceeding the WHO standards. Regarding the option of implementing satellite data to facilitate citizen science, the contingency analysis shows that satellite products can detect the general temporal precipitation pattern although they perform poorly when it comes to estimating the correct amounts of rainfall. An extended network of rain gauges within the Kathmandu Valley is vital to establish a strong linear relationship between ground and satellite data which is, in turn, essential to enable GPM to enhance the temporal resolution of the citizen science measurements. Furthermore, the implementation of land-use and water quality measurements into citizen science shows increasing potential especially when taking into account the contribution of ODK. The accuracy of the citizen science ground truthing was found to be 33% when compared to the experts. Proper training of the citizen scientists is essential to improve performance. During research temperature, EC and turbidity were labelled as indication parameters. Those parameters are used to indicate the range of other water quality parameters which cannot be measured easily. The indication parameters, however, can be measured with affordable equipment and together with water quality strips prove to be powerful tools in the hands of citizen science. Finally, the groundwater recharge for the monsoon period was determined, but it is expected to be drastically reduced the rest of the year. A solid conclusion about the relation between recharge and land-use can’t be made unless a more substantial number of wells is regularly sampled.