Agricultural businesses in Ghana heavily depend on rain-fed farming, but they are facing increasing challenges due to climate change and population growth. Accurate weather forecasts providing useful information on the onset of the rainy season are therefore of utmost importance. Unfortunately, the country still lacks reliable weather information and precise forecasts. This study aims to enhance and expand weather forecasting research in Ghana by running the regional Weather Research and Forecasting (WRF) model over Ghana and verifying its performance in forecasting the local onset of West Africa’s annual rainy season. Three experimental setups were conducted. The first experimental setup tested four different planetary boundary layer (PBL) schemes. Results showed that the schemes performed differently across the various agro-ecological zones of Ghana, highlighting the influence of spatial context on the model’s performance and the choice of PBL scheme. Further analysis within a smaller domain in the Forest zone revealed promising insights into accurately capturing categorized precipitation. In the third and final experiment, the WRF model was compared to the ECMWF’s operational forecast, demonstrating WRF’s capability of detecting local variations in rainfall, including heavier precipitation amounts, which the ECMWF model was not capable of detecting. This comparison underscored the added value of the WRF model over global models. Whether the WRF model can serve as an accurate weather prediction model to forecast the local onset of the rainy season in Ghana depends not only on its performance but also on the chosen definition of the onset. However, the results highlighted its qualities and robustness for multiple definitions, indicating a high potential for this purpose.

River discharge is traditionally required with a lot of measurements and data. However there are areas where discharge data is not available. In this research a tool is developed based on hydraulic geometry relations to estimate the discharge of a river in bank full state near Narok, Kenya. With the help of the Global Navigation Satellite System (GNSS) coordinates are converted in to cross-sections of the river in a bank full state. Discharges are assumed and verified through the hydraulic geometry relations of the cross-sections. These discharges are simulated in Super-Fast INundation of CoastS (SFINCS) to simulate the flash floods of Narok and an accurate estimate of the river discharge in a bank full state was obtained.

The Bui Dam, the second-largest hydropower dam in Ghana, plays a significant role in the sustainable energy mix of the country. It is managed by the Bui Power Authority (BPA) and has a capacity of hydro-clean generation of 404MW, contributing to 17% of the country’s total electricity generation. However, decision-making at the dam lacks the use of predictive models and meteorological measurements. This can lead, in the case of perceived flooding risks and high dam water levels, to valuable water being spilled and endangering the downstream areas. Balancing the tradeoff between energy production and safety can be effectively achieved by implementing predictive models that anticipate peak flows in advance.

Since its commissioning in 2013, the Bui Dam has experienced two instances of emergency spillage, resulting in significant financial losses, property destruction, and displacement of downstream communities. Currently, the reservoir management decision-making process uses two discharge stations upstream, with one of them yielding some unreliable outcomes for high flows. Therefore, it is crucial to prioritize the analysis and updating of rating curves to ensure accurate forecasting.

This research aims to address these limitations by recalibrating the rating curve using the reservoir balance in a conservative manner, i.e. leaning on the safe side to avoid overestimation. Additionally, a conceptual, semi-distributed model was developed simulating high flows, specifically focusing on the years 2019 and 2022 when spillage events occurred. Five different hydrological conceptual models, with three different structures: single, serial, and parallel structures, were tested. The serial model yielded the best results. Then the Black Volta Basin was divided into five sub-catchments, and each sub-catchment was lumped. In the absence of discharge data for the upstream sub-catchments, remote sensing data from GRACE and satellite altimetry (3 virtual stations with data from 2016 to 2022) were used to impose restrictions on the feasible model parameter sets, thereby improving accuracy.

The final model output was calibrated using discharge data obtained from the recalibrated rating curve, along with satellite altimetry data. In the calibrated benchmark case, the model effectively reproduced daily river flows, demonstrating an optimum Nash-Sutcliffe efficiency (NSE) of 0.85 for the period of 2018 to 2022. Subsequently, the model underwent extensive testing under various conditions, including an independent time period without recalibration, different precipitation input sources, transitioning from actual evapotranspiration (AET) to potential evapotranspiration (PET) input, and a change in the testing discharge location. Throughout these testing phases, the model consistently produced favorable results, with NSE values ranging from 0.74 to 0.86.

Furthermore, the model was tested for its progressive predictive capability in simulating the unexpected peak inflows that led to the spillage event in 2019, utilizing iv only precipitation data from the TAHMO precipitation stations, which are openly accessible with near-live timing. The model successfully predicted the occurrence of the large peak inflow, on October 22nd, which ultimately caused the spillage. The model anticipated the occurrence of the ”unexpected” second peak, to some extent, as early as October 12th, providing an 11-day predicting window.

Overall, this research enhances the understanding of the Bui Dam system by implementing a recalibrated rating curve and developing a conceptual model that incorporates remote sensing data. The results demonstrate the model’s capability to simulate past events accurately and predict future inflow patterns, thereby providing valuable insights for effective dam management and spillage prevention.

One significant discovery regarding the character of the Black Volta River at the Bui Dam is the limitation of the prediction period to a strict maximum of two weeks. While the model proves effective within this time-frame, it is advisable for future research to consider incorporating weather predictions to extend this window further. Doing so would enhance the model’s forecasting capabilities and provide even more valuable information for dam operators and decision-makers.

Hydrological and meteorological research are of great value to comprehend the complex Eastern Africa system, and more specifically the Kenyan Rift Valley, as impacts of climate change and indisputable changes in the hydrology of the lakes have become more apparent in recent decades. Lakes in the Kenyan Rift Valley region, spanning from the south to the north of Kenya, have been experiencing an increase in their water levels resulting in severe flooding in some cases. The flooding has been a relatively gradual but steady process throughout the 2010s. However, since 2020 the lakes have been rising more rapidly. This research aims to understand the main drivers behind the overflowing of Lake Nakuru as one of the lakes in the Kenyan Rift Valley and specifically examines the hypothesis that increased precipitation in the catchment area is the primary cause of this phenomenon. The study utilises various methodologies to analyse spatio-temporal variability in precipitation, investigate the dynamics of Lake Nakuru, and identify the origin and driving forces behind the changing precipitation patterns.

A change point analysis is performed for weather station data throughout the catchment including lake precipitation and catchment precipitation. A water balance model is built to analyse the impact of precipitation on the lake through precipitation bin analysis and different catchment interactions such as catchment runoff, lake evaporation, and sub-surface catchment storage. Multiple linear regression has been applied to identify key catchment characteristics for the variability of the lake volume. Through moisture tracking as part of this research with WAM2layers it was possible to identify changes in moisture sources and climate drivers of precipitation in the catchment.

The findings of the research reveal significant changes in precipitation patterns, with a notable increase in precipitation since 2010. Change point analysis indicates that this increase coincides with the rise in lake volume, suggesting a strong correlation between precipitation and the overflowing of the lake. The water balance model employed in the study further emphasises the impact of precipitation on the lake, highlighting the complex interactions between precipitation and the catchment. Moisture tracking and climate driver analysis provide insights into the origins of the precipitation variability. The study identifies the western Indian Ocean region, particularly east of Madagascar, as an increasingly important moisture source for the catchment area. Moreover, the study highlights a negative correlation between El Niño and September precipitation, indicating the influence of climate oscillations on the changing precipitation patterns. These findings suggest that future climate changes, including increasing sea surface temperatures and intensifying La Niña and El Niño events, may further contribute to the likelihood of flooding in the area.

Overall, the research strongly supports the hypothesis that the overflowing of Lake Nakuru is primarily caused by changes in precipitation. The study emphasises the need for further research and monitoring to better understand the complex interactions between climate, precipitation, and the lake system.

Water is essential for life on earth and is vital for numerous sectors of our society. Pressures arising from climate change, growing populations, and the shift towards clean energy accentuate the importance of effective water management. To make decisions about water resource allocation, hydraulic modeling studies have been undertaken on large African rivers. These studies employ global terrain models, utilize remotely sensed water surface elevation data, and often involve estimating river channel bathymetry. Typically, these models also require an estimation of the river channel bathymetry. However, observed bathymetry data is seldom available and crucial for the hydraulic model performance. It has resulted in a key challenge of modelling large rivers in this data-sparse context.
This research aims to model a medium to large sized river with wide floodplains in three dimensions by integrating discharge data and a highly accurate bathymetry. The primary objective is to quantify the friction coefficient and establish a reliable rating-curve for the river system. By utilizing these key components, the study seeks to provide a comprehensive understanding of the hydraulic behavior of the river, contributing to improved water flow predictions and management strategies. The bathymetry data is acquired through two different methods. The dry bathymetry is obtained using an UAV (DJI Phantom 4) and photogrammetry (WebODM). The wet bathymetry data is collected using both, sonar with the Deeper Chirp+ and spatial referencing with the RTK-GNSS from ArduSimple. These methods are cost-effective and require minimal manpower, making them practical options for acquiring accurate bathymetric information. The discharge data is acquired using the open-source software, OpenRiverCam. OpenRiverCam uses Large Scale Particle Image Velocimetry (LSPIV) to determine the surface velocities and combines the results with the bathymetry data to calculate discharges, providing an efficient solution for assessing river flow characteristics. LSPIV has the advantage that it is a non-intrusive method of measuring the flow velocity and does not require physical probes or instruments in the water. The bathymetry data and discharge data are integrated into the Delft3D FM Suite to assess the accuracy of the measurements and estimate the friction coefficient in both the river and the floodplain. This modeling approach enables a comprehensive analysis of the hydraulic characteristics of a medium to large sized river and supports the evaluation of flow resistance in the study area.
The data acquisition took place at three study sites close to the Bui Dam, in the Black Volta Region, Ghana. The Bui Dam is the second largest hydro-power dam in Ghana managed by the Bui Power Authority (BPA). The Bui Bridge and Bamboi Bridge study sites are positioned downstream of the Bui Dam, allowing for accurate quantification of the discharge and the bathymetry measurements. The third study site, Chache, is positioned upstream of the dam, where daily water level measurements are taken. BPA has observed that the rating curve at this location is outdated. Therefore, efforts are made to update the rating curve and quantify the friction coefficient at this site in both the river and the floodplain.
This research has made significant progress in developing a three-dimensional discharge model and rating curve for medium to large rivers using advanced data collection methods and integration techniques. The study successfully combined photogrammetry and sonar measurements to effectively determine the bathymetry of the river, overcoming challenges related to high water velocities and dense vegetation. The LSPIV technique and OpenRiverCam were utilized to integrate surface velocities and discharge measurements, leading to a more comprehensive understanding of river dynamics. However, limitations were encountered in assessing the accuracy of the model at the Bamboi Bridge site due to the LSPIV results. This highlights the importance of obtaining more comprehensive data and observations to enhance the model’s accuracy. The comparison of rating curves at the Chache site resulted in positive results. Although, further verification during the wet period is required through velocity and discharge measurements to determine the accuracy. Overall, this research contributes to a better understanding of river behavior and provides valuable insights for water flow prediction in an efficient, cost-effective manner with minimal intensive manpower, ensuring a non-intrusive approach.

In a Ghana project, field measurements at the Black Volta aimed to enhance hydrological predictions for the Bui power dam. Validating devices and exploring floodplain contributions, such as with GNSS reflectometry and cameras, provides crucial insights for dam management and model improvement.

In this multidisciplinary project, conducted at the Black Volta in western Ghana, the focus is on addressing challenges related to data scarcity in hydrological predictions, particularly for the Bui power dam. The dam has faced issues during flood events, leading to spillages and subsequent damages. To enhance predictions, field measurements were carried out, employing innovative methods for remote areas within the TEMBO Africa project. The project aimed to validate devices measuring river parameters and improve the understanding of floodplain contributions to river discharge. Key methods included GNSS reflectometry for water level measurement and a camera-based approach for discharge determination. The GNSS-reflectometry device was successfully tested, validated, and installed at the Black Volta, automating water level measurements. The camera-based method demonstrated success on smaller streams but faced challenges on wider rivers. Despite this, it was installed at the Black Volta for daily discharge measurements, promising a reduction in rating curve uncertainty when combined with water level measurements. Field observations were utilized to enhance an existing hydraulic model, refining the floodplain's representation. The study also delved into determining the floodplain's roughness coefficient, involving manual measurements and attempts at automation using remote sensing techniques. The roughness coefficients were implemented into the Delft3D model, showing contributions of floodplains to river discharge. Despite challenges, including equipment installation timing and location disruptions due to bridge construction, the project provides valuable insights for improving hydrological models and preventing dam spillages in the future.

This research addresses how Ricanau Mofo, a low-lying village in Surinam, can become a more water-adaptive and sustainable village, while it faces land erosion, river bank erosion, changing rainfall patterns and sea level rise. It is urgent to intervene, as these issues are expected to increase in occurrence due to climate change. Constraints and limitations that are important to take into account are cultural preservation, maintaining accessibility to the Cottica River, the limited availability of financial resources and the need for a low-maintenance intervention.

Three strategies are proposed. The first focuses on addressing land erosion. Planting vegetation on critically eroding areas is a short term measure, while the long-term involves constructing footpaths with drainage channels. This not only mitigates soil erosion, but also regulates water and is relatively cost efficient. The second strategy targets the river bank erosion, which includes wooden bulkheads with vegetation and stones for short term implementation. For the long term, a river bank protection system with groynes is designed, to break waves, slow down the stream velocity and in time causes land gain. As the long term plans require external financial aid, a business case is set up and shared with the captain of Ricanau Mofo, STEORR, the District Commissioner and the Ministry of Public Works. The third strategy addresses water damage in the urban environment. Short-term it consists of providing building guidelines of where to build more water adaptive, and how. This is placed on an informational board in the village. The long-term contains a flood early warning system and recommended equipment.

This project's significance lies in identifying interventions that are effective against erosion and water damage while being locally implementable in the rural areas of Surinam. It can be seen as a pilot project that is scalable to other villages along the Cottica River or in the whole of Surinam. However, there are limitations to the project. The most important is the lack of data quantity and data quality. This caused implications for dimensioning the interventions and their financial impact. Another limitation is that the project does not create ‘dry feet’ for the village; it creates a way of mitigating water damage while living next to the Cottica River. In addition, there is a limitation in the financing of follow-up projects. Therefore, a business case is also being delivered to the Ministry of Public Works, the District Commissioner of Marowijne South-West and the captain of Ricanau Mofo. They can use it to apply for funds from international organisations and include it in future policy plans.

To summarise, Ricanau Mofo can become more water adaptive by regulating how to build and where, by continuing the prototype of the bulkheads by planting more vegetation and, by requesting financial aid for the river bank protection long term. As Figure 0.2 shows, it not only contributes as a report, but also in a tangible form of a prototype and in educational information boards in the local language to enhance the continuity and help the village.

Ethiopia’s energy demand is expected to increase sevenfold in the coming 30 years, resulting in increased variable renewable electricity (VRE) production by solar PV and wind. Energy storage acts as a buffer that mitigates the effects of over- or under-capacity in production by VRE. With 97% of global bulk energy storage, pumped hydro storage is the most widely used and mature energy storage method. With its long operational life, high round-trip efficiency (80%) and stable cost trajectory, it is a beneficial option for many energy systems. However, drawbacks of pumped storage include heavy technical, site-specific restrictions, long construction times and high initial capital investment requirements.

This study investigates if Ethiopia’s energy pathways benefit from adding pumped storage, where to build it, and if storage increases system resilience. The long-term energy planning tool OSeMOSYS is used, which allows for detailed investigation into system dynamics whilst parallelly minimising costs. OSeMOSYS enables the investigation into Ethiopia by looking at an extensive host of techno-economic
specifications and supply and demand dynamics from the electrification of transport and integration of variable renewables to residential cooking demands.

This report discusses thirteen scenarios which are separated into three main categories: Base Case (3), Emission Penalty (EMI) (6) and Varying Wind Capacity and Seasonality (WND) (6). The base case introduces pumped storage to the energy pathways. The EMI scenario characterises three pathways for carbon pricing. In the WND scenario, wind power’s capacity factor and seasonality are altered to
investigate the potential effects of using more accurate local data or prioritising some supply zones on the energy system configuration. Additionally, the most favourable locations for solar PV and wind are combined with potential PHS locations to find optimal sites for storage construction.

The results of the research show that pumped hydro storage is adopted into the energy system in all scenarios, following both a diurnal and seasonal (dis)charge pattern. Variable renewable integration increases by an average of 10% from the addition of storage (78 GWh). The emission penalty increases the electrification of residential cooking demand and boosts VRE penetration but does not integrate
storage integration further than the base case due to reaching the upper limit of the storage capacity set in the planning experiments. Lastly, the changes in capacity factor and seasonality have a marginal effect on the energy pathways.

Pumped hydro storage increases the energy system’s resilience to climate-driven seasonal uncertainties and prices due to fossil fuel and carbon price uncertainties by making it less dependent on fossil fuels, decreasing vulnerability for potential emission penalties and seasonal capacity fluctuations. The introduction of PHS does not increase overall system costs, making it a prime candidate for large-scale energy storage in Ethiopia, combined with the stable levelised cost of storage and high maturity

The Dual-Frequency Precipitation Radar on board the Global Precipitation Measurement (GPM) mission core satellite has been providing precipitation products across the globe for over 6 years, thereby even supplying precipitation estimates for areas on Earth where surface-based precipitation measurements are not possible, like in remote regions, as well as seas and oceans. In this study, the GPM DPR Ku-band Level 2A instantaneous observations are compared with continuous measurements from a surface network in the Netherlands for high-intensity precipitation events during summer months. The aim of the research is to gain more insights in the temporal and spatial correspondence of the DPR’s measurements with precipitation occurring on the surface. The surface network consists of 2 C-band weather radars and an automatic gauge network of 33 gauges (approximately 50 km apart). Radar reflectivity factor is one of the prime parameters used for precipitation retrievals from radars and is thus the parameter used for anal- ysis of both the DPR and the weather radar network. Precipitation intensity is the parameter retrieved from the automatic gauge network. Space-borne and surface measurements have vary- ing characteristics, for instance in spatial and temporal resolution, and thus many challenges arise to perform an accurate and qualitative comparison. In this research, data is funneled by selecting high-intensity gauge data with a threshold of 15 mm/h within 40 minutes from GPM scan time within a 2.5 km range from the nearest DPR footprint. Ultimately, 26 high-intensity gauge measurements spread over 18 dates were used in the comparison analyses, selected out of data ranging from May - October for the years 2018 - 2021. The analyses showed that DPR and WR data correspond best in time (average correlation of 0.785) when DPR scantime is within 3 minutes of WR scantime. Also, results showed that instantaneous DPR data can be related to high-intensity gauge observations within a 40 minute time range by use of wind direction and wind speed. Furthermore, it was observed that precipitation performance of DPR is found satisfactory for intensity ≥ 0.5 mm/h (POD between 0.7 - 0.9 based on distance WS-IFOV), but unsatisfactory for intensities ≥ 15 mm/h (POD of 0.18 - 0.25). The results imply that DPR measurements don’t have an obvious temporal and spatial lag with respect to the observations made from the surface and thus form a prospect for global usage, if more follow-up research is conducted.

This research aims to explore sediment transport behavior and accumulation caused by run-of-river weirs in order to examine there feasibility for sustainable sediment mining purposes in the White Volta River. Weirs are used to raise water levels up-stream to increase water levels and storage, but their is little information regarding sediment continuity and transport over a weir. Observational research studies suggest that weirs do not have a 100% trapping efficiency, however, due to the reduced flows this study hypothesizes that sediment will settle upstream of the structure.
Based on data from the White Volta River, a 1D Flume model is created using
HEC-RAS which tests several sediment transport functions and bed roughness parameters with constant bed slope, suspended and bed-load gradation in order to estimate sediment settling. The results suggest that during peak discharge events settled sediments are flushed up and over the weir. As peak discharges decrease, settling occurs based on particle diameter causing increased sediment  storage up-stream until the next flushing event. The volume of sediment stored ramp is highly variable with respect to bed roughness and the used transport function, suggesting river calibration is required for concrete estimates. Based on the model results, approximately 1 − 2% of the White Volta’s yearly suspended load settles in-front of the structure before the discharge flushing event.

Since the mid-19th century global temperatures have increased significantly, so does the intensity of extreme rainfall. Previous studies have suggested that by the end of the century extreme rainfall intensity would increase by 10.5% and 28.2% for the low and high emission scenarios, and the centennial extreme rainfall would increase by 13.5% and 38.3%. For this model-based result, our aim is to verify it with observed historical data and find out the growth rate of extreme rainfalls. In this study, the MEV method was used to analyze the changes in the intensity of daily extreme rainfall in the 1-year, 10-year, and 100-year return periods at 29 stations and linearly fit them to determine the trends, and finally found that: the extreme rainfall showed an increasing trend in 70% of the stations in all data years, and the average growth rates in the 1-year, 10-year, and 100-year return period are 9.73%, 8.06% and 12.00%, re- spectively. From 1950 to the present, 56% of the stations have an increasing trend of extreme rainfall and the growth rate is 6% and 4% for 1-year and 10-year events, 2.4% for the 100-year events. For the growth rate per decade, the 1-year and 10-year events grow faster after 1950 than data from 1850, and the growth rate of the 100-year events becomes smaller.

This report provides a flood risk assessment of the Isiolo River Basin, in collaboration with Nelen & Schuurmans (3Di, FIS Tool) and Zephyr Consulting (SLAMDAM). This flood risk assessment includes a study of the current flood risk management in Kenya, and in the Isiolo River Basin in particular, because the need for proper flood management is urgent: various climate studies predict an increase in rainfall and an increase in flood risk as a result of the effects of climate change.

Current flood risk management is inadequate. Kenya has defined 21 flood-prone areas whereof one of them is Isiolo Town. Isiolo Town is located in the ENN basin which is, relatively, the most prone to the effects of climate change compared to the other basins. Furthermore, the ENN basin currently has the highest poverty rate and avoidance of further enlargement in poverty rate is important, so there is a need to mitigate flood risks. Since Isiolo Town is located in the Isiolo River Basin, this basin has been chosen for an in-depth study.

The Isiolo River Basin is an Arid Semi-Arid Land region which is often prone to flash floods. Isiolo Town is a flat area located downstream of mountainous area, the rain which falls upstream flows fast downstream and converges into town, often resulting in inundation. Many hazards, both natural and others, are increasing the flood risk in the basin and specifically Isiolo Town.

This flood risk demands flood risk mitigation measures. One possible measure is the SLAMDAM. The SLAMDAM is a movable water-filled flood-barrier. One dam has a length of 5 meters and a height of 1 meter and the dams can be connected to a desired length. The water stored in the dam can be used afterwards for irrigation or other uses.
To recommend effective areas to implement the SLAMDAM, 3Di and the FIS Tool are used. 3Di is a hydrodynamic model and it creates flood maps for different rain events. These flood maps are used as input for the FIS Tool. The FIS Tool calculates the benefits for deploying the SLAMDAM at a certain location for a particular length. The locations which result in the highest benefit are recommended to deploy the SLAMDAM in case of particular rain events. However, a site visit is required to see whether the modelled situation aligns with the real-life situation and to see whether boundary conditions are met.
The SLAMDAM is also compared to other flood risk mitigation measures. Some were analysed using the FIS Tool, whereas others are evaluated based on five self-formulated ranking criteria. These criteria form the base of a scoring matrix where each relevant mitigation measure is scored on.

The performed research has shown the SLAMDAM to rank the best compared to other mitigation measures, both when using the scoring matrix and when using the FIS Tool. However, it is highly recommended to use the SLAMDAM in combination with a Flood Early Warning System. In this way the community downstream can be warned in time to deploy the SLAMDAM. The FIS Tool is found to be especially valuable in finding proper locations for deployment and the dam can be stored close to these locations, enabling fast deployment of the dam in case of need.

Agriculture is an important source of income for many countries in the Global South, where it may account for as much as 25% of GDP. Precipitation is crucial for agriculture in countries like Ghana, where ~95% of farming is rainfed. Accurate rainfall observations are limited in Ghana. The sparse rain gauge network and the lack of weather radars make remote sensing methods a potentially attractive alternative source of rainfall data. Radar satellites, such as Sentinel-1, emit radiation that passes through the atmosphere and is scattered back to the satellite by the Earth’s surface. The backscatter measured by the satellite is correlated with the wetness of the soil but the existence of vegetation hinders straightforward quantification of soil moisture. By choosing sites with a simple and, more or less, constant phenology, it may be possible to eliminate the effect of vegetation on backscatter. Soccer field may qualify as sites with such a simple and constant phenology. The main objective of this study is to use the Sentinel-1 data over soccer fields and assess them as rainfall detectors. A machine learning approach will be used to reach this objective. This research assessed the stability and the generalization capabilities of a classification model (rain/no rain). The model was trained with and applied to different locations and periods (2019 & 2020). Ground observations from 53 Ghanaian (TAHMO) and 1 Greek stations were used. Soccer fields in Ghana and Greece were selected and their suitability as rainfall detectors was checked based on the correlation between modeled soil moisture and backscatter strength. The rain/no rain classification of the soccer fields was made with a stacked classifier that was trained and validated with both spaceborne and ground data. The classifier was tested on six different datasets from Greece and Ghana 2019 and 2020. The stability of the model was assessed by a Leave-p out cross-validation approach. The generalization in space was tested by using different environments. The generalization in time was tested by using different time periods. The results showed that the classification was stable. The minimum and maximum performances for the different testing datasets were 0.43 to 0.85. The median performance of the algorithm in Ghana for 2020 is 67%. The stacked classifier was found to have the best performance compared to other classifiers. Finally, the performance of the stacked classifier was competitive in comparison with the performance of the well-known IMERG algorithm. The study showed that there is a potential for using radar backscatter from suitable fields to detect rainfall. The classifier is stable and can be generalized in time and space under certain conditions.

Human interventions in the White Volta River in Ghana increase the flood risk in living spaces of the local communities, causing great challenges in the area. Burkina Faso has built a hydropower dam near the border with Ghana which has a large impact on the discharge extremes of the White Volta River. As a result of poor water management of this dam, water is spilled during the wet season, causing more floods in northern Ghana. Several communities, living near the White Volta River, depend on the river water for their drinking water demand and use the sand in the river bed as construction material. This puts them in a vulnerable position when changes in the natural flow regime of the river occur.

This thesis studies the impact of constructing multiple small dams in the White Volta river in order to decrease the flood risk in villages located close to the river and create an opportunity for controlled sand mining as a result of sedimentation.

Tamale is, with 672.000 inhabitants, the largest city in this area and struggles with the consequences of the floods as well. The drinking water company has trouble meeting the drinking water demand during both the wet and the dry season. When the area is flooded, the water intake point shuts down, and during low water levels, the pumps can get clogged. This clogging occurs even more often as a result of increased illegal sand mining from the river banks by the local communities. This has broadened the river, resulting in lower water levels and a higher turbidity.

A possible solution to ensure the water intake in Tamale and reduce the flood risk in northern Ghana is the construction of multiple small dams in the river bed to flatten the discharge peaks and slow down the water in the White Volta River. At the same time, these dams can create an additional advantage by causing upstream sedimentation that provides a possibility for controlled sand mining in the river bed. To research the effectiveness of this solution, the impact of the dams on flood risk and sedimentation, near seven villages in northern Ghana, was modelled.

A base case of the flood of 2003 was compared to a scenario in which seven dams were implemented at locations close to villages that are often exposed to floods. This was done by building a hydromorphological model of the White Volta River, using the D-HYDRO 1D2D software.
The results of the morphological simulation show that in total, the sedimentation upstream of the dams can fill 81 trucks with sand per day. However, extracting the sediment at the most upstream dams decreases the possibilities for sedimentation near the downstream dams. The results of the 1D2D hydraulic model show that the proposed dams are not able to reduce the flood extent in villages and can even increase inundation depths, resulting in more flood damage.

It is recommended to increase the 2D grid of the hydraulic model in order to receive more realistic backwater curves.

Water challenges the livelihood of all living beings through its scarcity and its abundance. Due to climate change these extremes will intensify in the future. As mankind continues to cluster in economic centre points, housing areas as well as infrastructure increases. This situation forces the water to run off rather than infiltrate. Inundated areas place a threat to health and mankind will be forced to increase the resilience in urban areas. Adaptation to more extreme weather situation on private terrain is necessary as private space contributes 50% of the area. In order to stimulate inhabitants to adapt different measures it is essential to get an idea of their receptivity. The receptivity model of Jeffrey and Seaton (2009) offers a framework to understand with which of the 4 A’s (Awareness, Association, Acquisition and Application) residents are struggling most. A sequential mixed method of combining questionnaires and focus groups was used to collect data on residents’ receptivity and perceived effectiveness of potential rainproof measures. Additionally, the hydrologic expected effectiveness of selected rainproof measures was analysed with help of the Adaptation Support Tool and compared to the perceived effectiveness. Although residents are developing all 4 A’s simultaneously to different extend and speed, it appears that residents are struggling with Awareness. Their experience with flooding and water damage reveals awareness of the issue itself; however, hesitation to implement countermeasures was noticed. The awareness of solutions is missing for more than half of the respondents. The association with their own agenda and recognising the benefits is not yet widespread among residents. The acquisition requirement of receptivity – the knowledge about implementation, operation and maintenance – is missing. It is evident that the application requirement cannot be accomplished as acquisition is absent. Residents demand additional information. The development of the 4 A’s, specifically Association and Acquisition, require more attention. Residents recognise their own responsibility for the private lot, but also shared responsibility and thus community actions are expected. Residents insist that the municipality and Waternet fulfil their tasks within their responsibilities. Transparency and communication with residents are important features, whereby provision of tailor-made advice has high importance. Recognising responsibilities and also dependencies between different stakeholders as well as knowledge requests reveal lacking receptivity and further encouragement is required. Frontrunners can hereby play an important role. The effectiveness of countermeasures is an important consideration for increasing resilience. Evidently respondents indicated rainwater barrels as possible measure to temporarily store water. Green roofs were also well known. The perceived effectiveness scored relatively high for all temporary water storage measures. The Adaptation Support Tool (AST ) calculated effectiveness between 2% and 42%, where this is a percentage of a 60mm precipitation storage target. The highest effectiveness of 42% is achieved by intensive green roofs, whereas the costs are relatively high. A rainwater pond on the other hand has low investment and maintenance costs and scored an effectiveness of 22%. Green gardens reveal higher costs, but also greater effectiveness. It is expected that all gardens already have some green spaces and a simple intervention as reducing tiles could contribute up to 30% of the target. The calculations demonstrate that none of the measures could achieve the target alone. A combination is required, whereby the order of implementation can be guided by their effectiveness and costs. For improving receptivity of residents, several points should be considered. First of all connect residents with similar issues, they possibly motivate each other and develop an urge to action. Tailor-made information improves information distribution. Residents are reluctant due to unfamiliarity with alternatives for their specific situation. It is also important that residents are aware of the effectiveness and the costs of measures for when they need to chose. The AST calculations give insight in the effectiveness and costs of measures. Transparency of tasks, responsibilities and choices of the municipality, Waternet and Amsterdam Rainproof could provide a positive mindset towards the implementation of rainproof measures. Communication about (possible) barriers might be helpful in order to prepare residents. It is important that the municipality promotes climate adaptation throughout the entire legal and organisational levels.

Gathering accurate and reliable precipitation data is essential in developing countries, since it applied to a wide range of applications, from improving short term weather models to global climate change research. The most common way of acquiring rainfall measurements is the rain gauge. However, this traditional measurement equipment requires frequent visits from researchers, due to the clogging risks as well as the risk of being easily tampered with by unauthorized people. This makes it impractical and expensive to create a extensive network of these rain gauges, whilst the demand for the precipitating data remains high. A measurement equipment type which is more suitable for this remote and independent requirement for precipitation measurement in developing countries is GPS, since an aspect of GPS measurements is the possibility to use the equipment in relatively remote conditions, with little human interference necessary. In addition to this, due to the nature of GPS measurements, rain is expected to be an important component in GPS data, as a disruptive to the signal, a variable found in the Signal-to-Noise ratio (SNR). Considering the above, GPS seems like a good alternative to the traditional rain gauge for precipitationmeasurements. During this additional thesis, the question whether GPS can be used reliably for precipitation data, will be answered. During a measuring campaign in Uganda from September to November in 2018, GPS data was gathered near precipitation measurement locations from TWIGA’s School-2-School initiative. For the processing, graphs of the SNR and precipitation of the same days and locations were created and were visually inspected to see if a relationship or correlation was present. From these graphs this relationship between SNR and precipitation was not immediately clear. The nature of the SNR can be caused by a multitude of reasons and variables and unless the correlation is very strong between variables, this correlation will not be very clear from just a visual inspection of these SNR and precipitation graphs. To untangle these variables, correlationmatrices were used, where the variables can be looked at in pairs and instead of as quadruplets (or more). From the correlation matrix of the Ugandan data is is clear that correlation between precipitation and SNR is not significant, as it is similar to the correlation with a randomly generated variable. A slight correlation is present, however, with the GPS elevation angle. During processing, data from Cabauw (the Netherlands) was used as an extra data set to compensate for the small amount of rain in the Ugandan data. This processing followed the same procedure as was applied with the Ugandan data; visual inspection of the SNR and precipitation graphs and generating a correlationmatrix. In the Dutch data the relationship between the SNR and the precipitation was still small, however, larger than in the Ugandan data. Nonetheless, the correlation between the SNR and the elevation was very strong. In conclusion, from both the Ugandan and Dutch data, the correlation between precipitation and SNR is not strong enough, or in other words, using GPS data to approximate rainfall, is not achievable using the methods applied and resources used during this additional thesis.

Accurate weather forecasting plays an important role in predicting precipitation events. With the warming climate the precipitable water vapour in the atmospheric is increasing. Since weather parameters as precipitable water vapor have a high spatial variability, interpolation of water vapor data over an area of hundreds of kilometer does not have a sufficient quality for weather prediction applications. Nowadays, researchers are investigating if the precipitable water vapour can be quantified using GPS transmitted signals in a densified GPS network. An accurate quantification of the ionospheric delay is important to efficiently calculate the precipitable water vapour. Moreover, the ionospheric delay is the biggest error and limitation of the GPS signal. It is important to understand how the ionospheric delay varies spatially and in time. Therefore, variability in the ionospheric delay is an interesting factor in weather forecasting and climate change. To monitor the ionospheric delay a high temporal (in minutes) and spatial resolution (in km-grid) is needed, because the ionospheric delay changes spatially and throughout the day. A possibility to achieve this is to densify GPS networks. Previous research has shown that it is possible to measure the ionospheric delay with dual frequency receivers. In developing countries this densification of GPS networks cannot be achieved with expensive dual-frequency receivers. This study investigates if a higher receiver network density can be achieved with the help of low-cost single frequency receivers. Therefore, a densified GPS network of dual and single frequency receivers is set-up in and around Kampala, Uganda. This research demonstrates how the Satellite-specific Epoch-difference Ionospheric Delay model (SEID) can be used to compute the ionospheric delay for a single frequency receiver through time. The SEID model creates a second frequency for a single frequency receiver which is used to resolve the ionospheric delay. The intensity of the ionospheric delay depends on the electrons in the ionosphere. The number of free electrons in the path of a signal is expressed as the total electron content. This research shows how to compute the total electron content in the ionospheric layer of the atmosphere. After computing the second frequency for the single frequency receivers the observations need to be processed using Precise Point Positioning (PPP) to compute the precipitable water vapour. As a case study Uganda is chosen, because it is located on the equator. The ionospheric delay fluctuates more at the equator so this is an interesting region to investigate the variability. The analysis shows that an high accuracy of the GPS signal is needed to create desirable results. Therefore, field campaigns with single frequency and dual frequency receivers should incorporate antennas with noise reduction. In order tot assess the accuracy of the ionospheric delay obtained by using single and dual frequency receivers, future research should be focus on better network set-up and getting the right equipment with better noise reduction

Sub-Saharan Africa is one of the most vulnerable regions in the world to climate change. This is largely driven by the dependence on rain fed agriculture for food production. At the same time African climate observational networks have been in decline since the 1990s. A new kind of rainfall sensor (the intervalometer), which counts the arrival of drops at a piezo electric element, is tested during the Tanzanian monsoon season alongside tipping buckets and an impact disdrometer. Rainfall rates are derived from rainfall arrival rates using Marshall and Palmer’s (1948) exponential parameterisation. This parameterisation is defined independently of a notion of scale and therefore implicitly assumes that rainfall is a homogeneous Poisson process. Testing of the Poisson assumption shows that 22.5% of the total drops observed can reasonably be considered Poisson and that the main reason for Poisson deviations are non compliance with the stationarity criterion (36.7%) and the presence of correlations between drop counts (14.3%), particularly at higher arrival rates (ρa > 500 [m−2.s−1]). The total rainfall amount [mm] calculated from intervalometer measurements overestimates the tipping bucket value by a factor of approximately three. The overestimate is most likely due to poor calibration of the minimum detectable drop size (Dmin). A correction is applied to constrain the overestimates of mean drop size by the intervalometer parameterisation to the observed disdrometer measurements. The correction results in an improvement in the estimate of the total rainfall amount to within 10% of tipping bucket measurements. The total rainfall amount [mm] calculated from disdrometer rainfall arrival rates is within 5% of co-located tipping bucket measurements. The form of the mean drop size relation with arrival rate appears stable in time and space. The intervalometer shows good potential for use as a rainfall measurement instrument or to derive estimates of mean drop sizes.

Europe is a continent with diverse climatic conditions. The dominant climates are the Oceanic, the Mediterranean and the Continental ones. The western part of Europe has an oceanic climate, southern Europe has a Mediterranean climate and eastern Europe has a continental climate. Because of such heterogeneities, a vast range of extreme climatic events might occur in different areas. We define extreme climatic events the droughts, floods and heavy snowfall. Those events will be generically referred to in this research as anomalies. The purpose of this study is the identification of these extreme climatic events in the area of Europe, with the use of Special Sensor Microwave Imager (SSM/I) data at 37GHz frequency. The data that are used are Brightness Temperature (TB) values. The detection of the events will be achieved with the Polarization Difference Brightness Temperature (PDBT). The PDBT values can be related to changes to surface wetness and the surface geometry. It could be used as an indicator of an anomaly, because the higher the values of PDBT the higher the surface wetness. The methodological steps of the work consist in a statistical analysis of the SSM/I time-series, in the design of a detection algorithm of the anomalies under investigation and on the debate of its performance. The analysis of the temporally long SSM/I data will provide a first understanding of the data sensitivity to events under investigation and of their distribution for the statistical modelling of the Normalized Polarization Difference Brightness Temperature (NPDBT) indicator. The calculation of the NPDBT exploits the same principles as the well-known z-score index. The detection of the anomalies will be then achieved through thresholding the NPDBT index. Further information for the detection of anomalies is provided by the soil moisture time series from the Soil Moisture Active Passive (SMAP) sensor and the precipitation data from the Global Satellite Mapping of Precipitation (GSMAP). The soil moisture data appear to be more useful for the dry events, whereas the precipitation data for the flooding and the heavy snowfall events.