This study analyzed the relationship between late Harmattan dust in February-March and subsequent early rainfall in April-May in West Africa, focusing around the high cocoa-producing regions. The impact of Harmattan was evaluated using high-resolution Sentinel-5P TROPOMI data. Strong positive correlations were found in the southwestern regions of Côte d'Ivoire and southern Liberia where cocoa production is prominent. This is consistent with anecdotal evidence obtained from a local farmer in Gagnoa, which falls under the positively correlated area found in this study. The findings suggest that late Harmattan dust may serve as a potential indicator of early rainfall. This could help better agricultural planning for local farming communities, given the significant influence of rainfall amount on cocoa growth.

Small and medium reservoirs are of vital importance to the local communities in Ghana. Since these smaller reservoirs are especially sensitive to problems such as evaporation and sedimentation, and these reservoirs react strongly to seasonal fluctuations in temperature and precipitation patterns, continuous monitoring is of great importance. Sentinel-1 SAR imagery offers a reliable manner in which these reservoirs can be delineated and monitored, but the wind-induced phenomenon of Bragg scattering poses problems when we want to further increase automation of the monitoring process. In this thesis, a classifier has been developed that uses Sentinel-1 imagery, Gray Level Co-Occurance Matrix (GLCM) features and a Simple Non-Iterative Clustering (SNIC) to differentiate between surrounding shore, unaffected water and Bragg scattering patches. The combination of these features and methods, together with a Random Forest classifier showed to be accurate in differentiating between the different classes and delineating the final reservoirs, even when Bragg scattering is present on the SAR imagery.

Accurate water-level monitoring is vital for effective river management, flood prevention, and environmental conservation efforts. The increasing frequency and severity of flooding events, driven by climate change, highlight the critical need for reliable, robust, and sustainable water-level measurement systems. Such systems are particularly necessary in remote and resource-constrained settings where conventional infrastructure is lacking or insufficient.

Traditional water-level measurement methods, including radar, ultrasonic, pressure sensors, and conventional imaging systems, encounter significant limitations. These include high acquisition and maintenance costs, susceptibility to environmental damage, vandalism risks due to conspicuous placement, and dependence on stable, continuous power sources. Consequently, there remains an unmet demand for affordable, resilient, and autonomous monitoring devices capable of functioning reliably in challenging and off-grid environments.

To address this gap, this study presents the development and validation of an innovative water-level monitoring prototype as a prove of concept. The proposed system integrates a low-cost Raspberry Pi-based imaging sensor equipped with infrared illumination to enable accurate measurements both during the day and at night. An onboard processing unit autonomously captures and analyses images in real-time using one of two distinct image-processing algorithms: the mean-difference method and the Kolmogorov–Smirnov (KS) test method. These algorithms quantify water levels by detecting pixel-intensity contrasts, thus eliminating the need for direct physical contact with the water body.

The prototype underwent field validation in a natural river environment, demonstrating robust and consistent performance. The system achieved an accuracy within ±4.6 cm at a 95% confidence interval. Notably, it exhibited stable performance under varying environmental conditions, with moderate bias differences between daytime and nighttime scenarios, and minimal sensitivity to precipitation effects. The mean-difference algorithm demonstrated superior precision, while the KS-test method offered enhanced robustness against environmental variability.

This research underscores the practical feasibility and significant potential of low-cost, autonomous camera-based systems for sustainable water-level monitoring. Such solutions can substantially improve disaster preparedness and environmental management, especially in remote or economically disadvantaged regions lacking traditional monitoring infrastructure. Future research directions include integrating renewable energy solutions such as solar power to enhance operational autonomy, exploring advanced image-processing algorithms for increased accuracy, and conducting extended validations across a broader range of hydrological and environmental scenarios.

In Ghana, flash floods are often triggered by severe storms. Flood Early Warning Systems (FEWS) can mitigate flood impacts but require accurate, near real-time rainfall data. Implementing FEWS in Ghana is challenging due to sparse ground-based data and a lack of accurate, rainfall data with short latency. While satellite-based rainfall products offer a promising alternative, they often show significant discrepancies compared to ground observations, limiting their use for effective FEWS.

The Meteosat satellite images provide a valuable source of data for near-real-time applications, due to its short latency (within 15 minutes) and relatively high temporal (15-minute) and spatial resolution of (3 x 3 km at sub-satellite point). This study explores the use of Earthformer, a space-time transformer model, to improve rainfall intensity estimates with minimal latency, using data from the Meteosat satellite. By evaluating the potential of Earthformer to create rainfall estimates, this research aims to contribute to more reliable FEWS, ultimately strengthening disaster risk management in flood-prone regions of
Ghana.

The Earthformer model is trained on IMERG-Final, a satellite-based rainfall product known for its relative high accuracy but with delayed availability of several months. With the application of FEWS in mind, it was investigated whether the model could be adapted to improve the estimation of higher rainfall intensities. Two model setups were tested: one using a mean squared error (MSE) loss function during the training of the model and another with a balanced weighting loss function to emphasize higher intensities.

The model’s accuracy was first evaluated by comparing its outputs with IMERG-Final on a test dataset, and secondly with ground station observations from the Trans-African Hydro-Meteorological Observa-tory (TAHMO), and Ghana Meteorological Services (GMET) for the year 2022. IMERG-Early was used as a benchmark for near real-time performance. The comparison with IMERG-Final revealed that both Earthformer models outperformed IMERG-Early for lower rainfall intensities in terms of probability of detection (POD), success rate (SUCR) and the Critical Succes Index (CSI) and that the balanced loss model also outperformed IMERG-Early for higher intensities. However, as rainfall intensity increased, the performance of both Earthformer models and IMERG-Early decreased.

Further comparisons with ground station data highlighted weak correlations at 30-minute intervals between all satellite rainfall estimates and ground observations (including IMERG-Final, IMERG-Early and both Earthformer models). However, when the data was aggregated to daily intervals, correlations improved significantly, suggesting that timing errors could play a role, however, further investigation is needed to quantify their impact.

Additional analysis revealed that peak rainfall was often underestimated, while lower intensities were overestimated. This discrepancy could be explained by several factors: the coarser spatial resolution of satellite estimates compared to gauge stations, the displacement of rainfall from observed clouds, and difficulty in capturing warm rain processes. Additionally, it was concluded that capturing spatial variability within Mesoscale Convective Systems (MCSs) is challenging. This is potentially due to anvil cloud tops obstructing the satellite’s view, similar cloud-top temperatures for different rainfall intensities, and strong wind shear increasing the risk of rainfall misallocation.

This research demonstrates the potential of a space-time transformer model for near real-time rainfall estimation as it shows improved performance for most intensities when compared to IMERG-Early, with IMERG-Final set as the reference truth. However, the reduced performance at higher intensities and discrepancies with ground observations of all satellite based products underscore the need for further model development to improve extreme rainfall detection and better align satellite estimates with ground truth data. These improvements are essential for the model’s utility in FEWS and contributing to more
effective disaster risk management in flood-prone regions.

Recent shifts in climate conditions have disrupted rainfall patterns in the Sudanian region of West Africa, causing local farmers to struggle with adjusting their farming strategies (Biasutti, 2019; Gbangou et al., 2019; Tzachor et al., 2023). This has led to the phenomenon known as the 'false onset' or 'false start' of the rainy season, which is characterized by erratic rain events at the beginning of the season, followed by intermittent dry spells of various lengths (Laux et al., 2008; Silungwe et al., 2019). Given that farming in the region is predominantly rainfed, and seasonal rainfall patterns largely guide planting decisions, traditional sowing strategies need to be reconsidered given these changing conditions. This research aims, therefore, to analyze the impact of changes in the dynamics of rainfall patterns in West Africa on crop yield for rainfed agriculture. Specifically, it will examine the effect of several different sowing strategies on crop yield using a crop simulation model.

First, it provides an overview of local farming practices in the Sudanian region of West Africa, alongside farmers' perspectives on climate change and the adaptive measures they use. The findings are based on interviews with local farmers in the region and discussions with research institutes and regional agencies involved in climate change, adaptation, and agricultural water management. Two main approaches were used during the interviews: semi-structured individual interviews based on a questionnaire and group discussions. The investigation revealed that farmers are acutely aware of the changing climate and its negative impact on crop production. The traditional practice of sowing after the first two rainfalls has become increasingly unreliable under current climate conditions. There is a consensus among farmers that the rainfall total has decreased, the duration of the season is shortening, and there is a high frequency of dry spells throughout the rainy season. Additionally, there is a notable lack of climate-based services to support farmers' decision-making processes. Consequently, smallholder farmers expressed a strong interest in receiving not only weather forecasts but also climate risk information, which is crucial for making agriculture in the region more resilient to climate change.

Chapter 3 examines various sowing strategies used in the rainfed farming system of West Africa, where intermittent dry spells during the rainy season exacerbate challenges and affect crop yield response. In the context of a changing climate, we hypothesize that the traditional focus on the onset of the season to start sowing leads to crop losses in years of high rainfall intermittency. Using AquaCrop, an FAO crop model, we simulate the crop response to varying water availability to sowing dates ranging from the 1st of May to the 30th of November at 20 locations in WA. The crop we focus on is maize (Zea mays L.). The analysis revealed that sowing directly after the first rains carries a higher risk of yield loss due to insufficient buildup of soil water storage to overcome dry spells. Based on three years of data per station on average between 2016 and 2020, various sowing strategies were evaluated. We identified safe sowing windows across the Sudanian region that secure optimal yield in 97% of all cases. We find that delaying sowing to mid-June (savanna and western part of the region) and to July (semi-arid region) ensures optimal yields. Of the three commonly applied local onset approaches covered in our evaluation, only the local onset LO10mm, defined as four consecutive days with 10 mm/day, achieves a similar yield result. The advantage of the safe sowing window approach is that it is easily accessible for smallholder farmers, who in many cases do not have access to local rainfall information.

Chapter 4 focuses on identifying sowing strategies that minimize yield losses at the regional level. Climate variability poses great challenges to farmers in the region who are heavily dependent on rainfall for farming. Hence, we investigate three sowing strategies available in the Sudanian region of West Africa (WA) to assess their ability to ensure crop yields for smallholder farmers under a changing climate. AquaCrop-GIS, the GIS version of the FAO crop model, is used to simulate the yield response of maize (Zea mays L.) to varying sowing dates (1st of May to 30th of November) throughout the rainy season across WA. Based on an average of 38 years of data per grid cell from the Global Precipitation Climatology Centre (GPCC), we identify safe sowing windows across the Sudanian region that secure at least 90% of maximal yield. The analysis revealed that the current sowing strategies, based on minimum thresholds for rainfall accumulated over a period, widely applied in the region, carry a higher risk of yield failure than the safe sowing window strategy, especially at the beginning of the rainy season. It also appears that delaying sowing for a month to mid-June in the central region (east of Lon 8.5°W), and to early August in the semi-arid areas is a safe strategy for smallholder farmers that ensures optimal yields. A long-term comparison between the periods 1982-1991 and 1992-2019 reveals earlier sowing dates for both LO10mm and LO20mm, indicating a shift towards a wetter regime relative to the reference period. In contrast, the safe sowing window strategy shows later sowing dates, underscoring the need for caution due to erratic early-season rainfall and variability beyond the control period defined by local sowing practices. The precipitation-based strategies hold a higher risk of yield failure compared to the safe sowing window strategy, which is more accessible to smallholder farmers and better suited to the current climate conditions.

The key findings of this dissertation contribute to regional efforts in building a climate-resilient agricultural system by equipping smallholder farmers with simple yet effective sowing strategies. The research recommendations offer practical, easily implementable solutions for smallholder farmers across West Africa. Given the dynamic nature of rainfall distribution and the uncertainty in future climate projections for West Africa, it is essential to strengthen regional initiatives that improve access to rainfall data and climate services. These efforts will equip farmers with the tools and knowledge needed to make informed decisions. As our understanding of rainfall patterns evolves, updating these recommendations will be crucial to mitigating the negative effects of dry spells and enhancing crop productivity across the region. Additionally, regional efforts should prioritize enhancing and expanding climate and weather monitoring infrastructure to improve our understanding of the region’s complex weather systems at a finer scale. This will enable more precise modeling of the intricate weather-crop-soil interactions and provide localized information to smallholder farmers. Leveraging crop simulation models will facilitate the exploration of both current and future scenarios, helping to identify the most efficient farming practices. This will ultimately improve water use efficiency and contribute to closing the food security gap.

West African food and economic safety are heavily reliant on agriculture, most of which is rainfed. Changing rainfall patterns induced by global warming jeopardize yields by unpredictable water availability. At the same time, a rapidly growing population leads to rising demands for food production. Accurate rainfall information is essential for farmers to adjust their crop management practices and avoid yield, thereby improving the overall resilience of the region. However, this information is largely lacking due to a sparse rain gauge distribution, limited resources and data transmission challenges. Added to this, existing satellite rainfall products show a particularly poor correlation with ground observations in West Africa.
This dissertation aims at improving rainfall information for farmers in the Sudanian Savanna bioclimatic region of West Africa. The Sudanian Savanna stretches across Africa as a broad belt, covering roughly from southern Mali in the north to northern Ghana in the south. Its West African area expands from Senegal in the west to Chad in the easte. Improving rainfall information will support climate resilience and food and economic safety in the region. This research leverages on the unique potential of Earth Observation satellites to provide rainfall information everywhere, due to their global coverage and ability to track atmospheric processes. To tackle the general poor performance of existing rainfall information products, this work proposes an alternative avenue. Particularly, it investigates the potential of Deep Learning (DL) methods to extract relationships between meteorological variables and raw satellite data that might be overlooked by traditional satellite rainfall retrieval methods....

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.

This thesis explores risk-aware operational decision-making methods to support the integration of Renewable Energy Sources (RES) into the energy system by enhancing energy flexibility under operational uncertainty. Amidst the urgent global shift towards RES to combat climate change, this work identifies and addresses the challenges posed by the intermittent and uncertain nature of renewable energies, such as wind and solar power, to grid stability and energy reliability....

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

Improved understanding of historical precipitation extremes is important to better explain their behavior, predict future occurrences, and inform planning and engineering design. The intensity, seasonality, and timing of these extremes have far-reaching consequences, and require a comprehensive analysis of both historical trends and projected future changes. By integrating historical observations, statistical methods, and climate model projections, this research provides valuable insights into precipitation extremes on the global domain.

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.