Snow in mountainous areas is of great importance for the water supply in many catchments. To get data on snow cover, ground station data is not enough and, in many catchments, not available. Therefore, satellite data is used to measure snow cover. In this thesis the MODIS daily snow cover dataset (MOD10A1) is used. These images are obstructed by clouds. In order to create a complete dataset, the Regional Snowline Elevation Method is used which uses the elevation of the snowline to interpolate over the missing data. This method is accurate but is computationally demanding. Using Google Earth Engine, it is attempted to improve the method. The method developed combines grid cells with daily images and computes the RSLE for each cell, for each day. The results are exported to a CSV file, reducing the downloaded data from 150GB to 41.6 MB for the 0.50° resolution and 168MB for the 0.25° resolution. The computation time however was not improved with this method. The developed code was used on the Caucasus area. After the data from Google Earth Engine was downloaded, trends on yearly snow cover duration were computed using the Mann-Kendall test. It followed that 17% of the trends in both resolutions were significant and, except for one location, were all decreasing trends. The decreasing trends show a decline of snow cover duration of 1-4 days per year. Looking at regional differences it becomes clear the greatest number of trends can be found in the south-west. The Google Earth Engine code was able to compute the required data however, it took a long time doing so. Therefore, a more sophisticated code has been developed, making used of the ability to reduce the resolution of an image, and computing the value of pixels at the same time. This code runs quicker, but is at this moment unusable, due to problems with the thresholds and export. Being unable to export an image collection is one of the shortcomings of Google Earth Engine. Others include: download tasks that run out after twelve days without raising an error when starting the task, limits to the amounts of bands used, and sensitivity of the computation time to busyness on servers. Things that need to be improved and need further research are: the filters applied to smoothen the dataset, the use of other trend analyses, the effect of snow cover trends on the area, and the resolution in combination with the cloud threshold value. The analyses took six hours to run, which can be improved by using function-based programming, instead of process-based.
In the end the goal to develop a more efficient method has partly been met by decreasing the amount of downloaded data significantly, even though the running time has not been improved. Using the data from the developed method, decreasing trends were found in snow cover duration over the entire Caucasus but mainly in the South-West, which can greatly influence the water supply to a large part of the Caucasus and its surrounding areas.

During three months information about the sewer system of Maputo was gathered, mostly at DNA, DAS, CRA, AdeM, AIAS and at the Municipality of Maputo. The information, consisting of reports, papers, maps, presentations and websites, was used to estimate the potential amount of wastewater in the sewer system of Urban Maputo. This wastewater could be available for reuse in Maputo, at the WWTP, being this the main purpose of the project “Sustainable freshwater supply in urbanizing Maputo, Mozambique” led by TU Delft, UNESCO-IHE and the Mozambican University UEM.
The sewer network consists of system one and system two. System one was built by the Portuguese in the 40s as a drainage system, but nowadays it functions as a combined sewer and it discharges directly into the bay. System two, built by DHV, a Dutch consultancy firm, in the 80s consists of sewer lines, a WWTP and two pumping stations. These pumping stations are also supposed to pump a part of the water of system one to the WWTP. However, because of sand in the pipes the pumping stations are not being operated.
The billed amount of drinking water was used to calculate the flow in systems one and two. These data were obtained per neighbourhood and multiplied by 0.8, a guideline in Maputo for the amount of drinking water ending up in the sewers. For the water flowing in the sewer network, three cases are estimated, the actual status, system two completely working and the total volume of system one and two. The actual flow into the WWTP is 3957 m3/day with 20,665 m3/day being directly discharged into the bay. If the pumping stations of system two were operating, 10,266 m3/day would flow to the WWTP and 14,357 m3/day would be directed into the bay (Figure 1). By measuring the amount of influent at the WWTP, using the existing Venturi meter, the calculations were validated. The measurements show a flow arriving to the WWTP in the order of the magnitude of the calculations .This influent is generated by approximately 38,000 users that are connected to the sewer system.

The sewer network of Maputo has a few critical parts which should be repaired as soon as possible, and better maintained in the future. First of all the pumping station of system two should be turned on. Before this is possible the sand in the sewers in front pumping station two must be removed and the pipes should stay clean. Sand and plastic bags ends up in the sewer system through drains or open manholes. To overcome clogging, drains and manholes have to be better maintained.
Another recommendation is to collect the wastewater being discharged by system one and convey it to the existing WWTP or to a new one. The municipality has plans for this but lacks financing.
All the water which is collected by system two is conveyed to the WWTP, but the WWTP is not functioning well. There is white slime in the effluent and colourful tarnish, which is a sign of bacteria being present in the effluent. The effluent is either directly used for irrigation of crops, which poses a risk for human health, or directed to the estuary.
At the moment there are detailed plans to introduce a sanitation fee, which is necessary to improve, operate and maintain the system. CRA has been working on introducing the fee for several years already and they expect to introduce it within the coming years.