Floating marine plastic debris has emerged as a major global environmental threat in recent years due to its persistence, long-distance transport, and harmful impacts on marine ecosystems. Understanding the key processes affecting plastic beaching is essential for accurately modelling plastic transport and predicting accumulation zones in nearshore marine environments. So far, research on plastic transport in shallow, nearshore waters is limited compared to deep ocean studies, resulting in significant uncertainties about wave-driven transport in these zones. While it is established that the impact of plastic density on the movement of floating plastic debris varies across wave zones, the dynamics in shallow water remain poorly understood. This research investigates how the density of finite-sized plastic particles influences the beaching dynamics under controlled, regular wave conditions in a laboratory flume simulating a nearshore environment using a sloped bathymetry. The densities relative to water of idealized spherical particles were systematically varied ranging from 0.09 to 0.93. Particles were released in the shoaling zone and tracked through the wave flume until beaching, allowing drift speeds to be analysed across different wave zones. It is observed that prior to breaking, in the shoaling zone, particles travel onshore with a speed close to the locally estimated Stokes drift regardless of the particles' relative density. In the breaking zone, density significantly affects particle drift speed: low-density particles accelerate strongly, nearing crest and phase speeds, while higher-density particles show only modest acceleration. Extending these findings to real-world coastal environments indicates that low density plastics tend to beach quickly, while denser particles remain suspended longer and thus may be affected more by lateral currents. While further research is needed to fully understand the role of density in plastic transport near the shore, this study clearly demonstrates that density significantly influences beaching dynamics—underscoring its importance in accurately modelling plastic transport in the nearshore environment.

Marine plastic debris has become an established concern as a threat to marine and coastal ecosystems. Despite progress in understanding plastic transport dynamics under deep-water conditions, the characterisation of these processes in the nearshore environment remains incomplete. This poses significant challenges in their parametrisation, essential for the accurate representation of coastal transport dynamics in predictive models.

In this study, experimental measurements of the plastic particles wave-induced transport in intermediate to shallow water depths are presented. The focus is put on the influence of wave steepness as a key parameter affecting the transport of marine plastic debris in the transition from deep water to the shoreline. Its potential as a predictive parameter is investigated through controlled laboratory experiments involving the generation of seven regular breaking wave conditions, characterised by varying offshore steepness, propagating in shallow water depth over a sloped bathymetry.

The results reveal a consistent increase in particle drift speed with increasing offshore wave steepness. While the exact functional nature of the observed positive relationship could not be definitively concluded, the trend appears more likely linear than quadratic, aligning with previous findings for particles deviating from perfect tracers undergoing deep water breaking conditions. Furthermore, wave breaking was observed to play an important role in enhancing particle drift speed. Finally, particle drift speeds were consistently underestimated by the Stokes drift and only partially captured by the wave crest speed estimates, progressively diverging from the former and approaching the latter as offshore steepness increased, though remaining consistently lower than crest speeds. This trend was most recognisable in the breaking zone across all the tested wave conditions.

Overall, the findings suggest offshore wave steepness as a robust predictor for marine plastic debris transport in the nearshore environment, proving its value as a classification parameter for future modelling efforts. By investigating how plastic particles respond to changing wave conditions in the nearshore environment, this study aims to contribute to a better understanding of their dynamics.

Hydrological modeling is used to estimate the states and fluxes in a system given inputs. This allows us to gain an understanding of the water system and make predictions such as flooding or drought. Models use parameters to describe a general process such as infiltration. These parameters can be measured, inferred or calibrated. Parameters represent local aspects such as infiltration into the soil and can differ for a catchment in rural Ohio versus mountainous Colorado. Even with the best local parameters, models will always fall short of reality since they contain simplifications. Data assimilation allows the uncertainty in the prediction of stream flow to be reduced or quantified. This thesis aims to answer the research question ’What do the adjustments in parameters and states imposed by data assimilation say about the deficiencies in the hydrological models of observed streamflow?’.

In this thesis, a data assimilation framework was developed for the eWaterCycle platform. This framework takes care of the intricacies of data assimilation for the user. Any model implemented with the Basic Model Interface can make use of this, models within eWaterCycle are focused on FAIR hydrology. The framework was implemented in such a way that any data assimilation scheme can be used, following the FAIR principles. The framework was verified to work with synthetic observations for both the HBV (Hydrologiska Byråns Vattenbalansavdelning) and the Lorenz model. The framework also makes applying data assimilation within eWaterCycle easier and scalable.

To answer the research question, the five-year hydrological response of 671 catchments in the United States of America was modeled. These catchments are from the CAMELS dataset, which is a collection of hydrological data with characteristics for catchments across the United States of America. The data assimilation scheme particle filtering was applied to the conceptual hydrological model HBV using eWaterCycle. The parameters and states of each experiment were stored and analysed. The data assimilation scheme requires hyperparameters. These hyperparameters were optimised using the first 26 catchments in the dataset. This single combination of hyperparameters was used across the 671 catchments.
Taking the best prediction of stream flow at every time step, the data assimilation experiment outperformed the calibrated model in 461 of these catchments. Part of this is due to the bias introduced through the use of one set of hyperparameters. Analysis shows that catchments of the same characteristics perform similarly. Catchments with a low mean stream flow, have a high spread of predicted observations causing very good predictions when selecting the best. Due to this bias, no real correlations can be drawn about the relation of background characteristics and model deficiency. Analysing three catchments on a catchment scale showed that step changes in parameters often occur around flood peaks. The data assimilation scheme adjusts the working of the model to better capture the observed streamflow.

To further incorporate the framework in eWaterCycle more testing should be done on 2D models. Deficiencies in the HBV model can be further analysed by optimising hyperparameters per catchment type or flow regime.

Predicting near-surface temperature profiles is an essential, yet often challenging aspect of modeling boundary layer meteorology. The surface temperature is commonly inferred from similarity relationships. These predict the vertical profiles of both wind and temperature at some height above a surface with roughness elements, such as grass. These profiles have a logarithmic shape along the vertical. Due to experimental limitations, there are very few observations in the region close to the surface where roughness elements are present. As a consequence, the logarithmic profiles are commonly extrapolated down to the surface. However, this approach is physically inconsistent at its core. Temperature gradients become infinite as the surface is approached, as a result of the logarithmic properties of the similarity profiles. One consequence is that these profiles are extremely sensitive to small perturbations close to the surface, which is a major source of uncertainty when extrapolating temperatures. To combat this, new physical models are being investigated in an attempt to describe these profiles in a more accurate and physically rigorous manner.
A broader goal in the field of atmospheric science is to study a way to unify internal canopy dynamics with the dynamics above the canopy to yield temperature profiles that are valid from the surface, through the canopy, up into the atmosphere. In working towards these goals, a key element still lacks; precise, high resolution temperature measurements through the canopy-atmosphere interface. Novel measurement techniques such as distributed temperature sensing (DTS) have advanced the quality of datasets significantly, yielding temperature profiles with a resolution and accuracy on the order of centimeters. However, it has thus far not yielded sufficient accuracy for conclusive model comparison and for studying internal canopy temperature profiles. Therefore, there is a need for a more accurate, high-resolution dataset.
To this end, an experiment was designed to gain detailed insight into these regimes. A helical frame structure was designed, built and combined with the method of distributed temperature sensing (DTS) to attain a high resolution temperature profile along the vertical. The setup was installed at Cabauw where several weeks of data were acquired. Preliminary data analysis shows that the resulting data is well suited for the aforementioned purposes. Strong gradients near the surface can be identified as a result of the high measurement resolution on the millimeter scale. Furthermore, close to 80 data points are located within the 10cm canopy, allowing for the investigation of internal canopy transport dynamics. Finally, the data may be used in the future to validate any future models that aim to combine the canopy and atmospheric regimes.

In July 2021 severe flooding occurred in the South of Limburg, Belgium, and Germany due to heavy precipitation. Extreme precipitation events like this are expected to occur more often in our changing climate. Urbanization is thought to be another contributing factor to the July 2021 flood event. The Netherlands is expected to increase urbanization as a solution to its housing shortage. To make room for urbanization, while minimizing the effect of climate change, the government wants to make ”water and soil leading”, Water en Bodem Sturend, in the decision-making about the layout of the Netherlands.

Therefore, the goal of this research is to investigate the best suitable subcatchment for the construction of new residential houses within the Geul catchment, in terms of flooding. The July 2021 flood event is used as a reference. The first step was to investigate the hydrological response of the Geul catchment. Secondly, this hydrological response was modelled by the semi-distributed hydrological models HBV coupled to D-RR and by the distributed model Wflow_sbm. HBV and D-RR are set up in this research, while Wflow_sbm is adopted from Klein (2022) and Bouaziz (2022). The hydrological models are coupled to the Geul hydrodynamic model D-HYDRO of Hulsman, Weijers, Verstegen, and Goedbloed (2023). The building plans in the Geul catchment were investigated and scenarios were constructed. These scenarios were simulated in the hydrological models. This method resulted in a workflow that can be found in Idsinga (2024). The workflow can be applied on analyses of different land cover types.

The modelled hydrographs showed differences between the hydrological models. Each model better describes one part of the hydrological response compared to the other. HBV and D-RR better represent the subsurface flow and describe the hydrological response during consecutive precipitation events. Wflow_sbm represents the overland flow flux better and therefore describes the hydrological response during the July 2021 flood event. The modelled flood extents during the July 2021 flood event are also compared to the estimated extent by Slager, de Moel, and de Jong (2021). Wflow_sbm showed better similarity to the measured flood extent than HBV and D-RR. The Province of Limburg wants to build 18,730 new houses in the South of Limburg. This results in an increase of 6 km2 paved area. In this research, this increase is applied to different locations in the Geul catchment. Next, the impact of completely paved subcatchments was investigated. The relatively small 6 km² increase in paved area did not result in different discharge behaviour and the total area of the flood extent showed a small difference. However, it impacted the flooded paved area. Building far from the river on the hills resulted in no increase in the flooded paved area. New houses in the valleys, close to the river, are more exposed to flooding. In the Meerssen subcatchment, the added paved area was responsible for 95% of the total increase in the flooded paved area. This was also the case in the Gulp subcatchment, where about 90% of the increase in flooded paved area came from the added paved area.

The Meerssen subcatchment is the most vulnerable to flooding. This subcatchment contains the most paved area and more runoff will result in a more flooded paved area. A completely paved Gulp subcatchment results in a less flooded paved area than building 6 km2 close to the Geul in the Meerssen subcatchment. When the Belgians build new houses in the Sippenaeken subcatchment, the Netherlands will receive more water during an extreme event such as in July 2021.

The letter Water en Bodem Sturend states that new houses must be built in sensible locations. In this research, the location of new houses is found to be important for the hydrological response. Building close to the river results in a more flooded paved area than building far from the river. The Gulp subcatchment is the least vulnerable to flooding and can be considered the best building location for new houses among the three investigated subcatchments.

This research evaluated the performance of Land Surface Models (LSMs) in simulating droughts, examining Land-Hist offline simulations from the Land, Surface, Snow and Soil Moisture Intercomparison Project (LS3MIP). It is well known that LSMs possess uncertainties and biases due to oversimplifications or the absence of certain physical processes (e.g., groundwater interactions and lateral connectivity). Therefore, the objective of this research was to identify the strengths and weaknesses of various LSMs and how this relates to the performances in simulating soil moisture droughts. To address this objective, eight LSMs were evaluated: CESM2, CMCC-ESM2, E3SM-1-1, EC-Earth3-Veg, HadGEM3-GC31-LL, IPSL-CM6A-LR, MIROC6, and UKESM1-0-LL. Two reference evaporation data sets (DOLCE V3 and an ensemble of FLUXCOM-RS, BESS and PML) and a reference soil moisture data set (SoMo.ml) were utilized for the evaluation. After a global analysis on the LSM evaporation characteristics, six climate diverse study areas were selected for further investigation. A long-term analysis was performed by examining the water balance and implementing the LSMs into the Budyko Framework. Subsequently, soil moisture deficits were calculated for the driest periods in time, and the resulting accumulated deficits were compared with the reference evaporation data. The timing and progression of the deficits were evaluated utilizing the reference soil moisture data. Finally, the sensitivity of the model was evaluated by examining the response of evaporation anomalies to precipitation anomalies and comparing this with the reference evaporation data. The results showed that there was a large spread in output and performance among the LSMs across all parts of the evaluation. The greatest contrasts among the LSMs were found in the dry to wet transition zones within the tropics. In this latitudinal range, the worst performing LSMs overestimated the accumulation of soil moisture deficits and the severity of droughts, while the opposite was found for the extratropical regions. Additionally, the models showed, in general, to be overly sensitive to precipitation anomalies. When ranking the implemented model bases in the LSMs based on their performance during droughts, the findings showed that the Community Land Model (implemented in CMCC-ESM2, E3SM-1-1 and CESM2) was predominantly the best performing, followed by ORCHIDEE (IPSL-CM6A-LR) and HTESSEL (EC-Earth3-Veg). MATSIRO (MIROC6) and JULES (HadGEM3-GC31-LL and UKESM1-0-LL) were the least performing model bases. From a hydrological perspective, the findings of this research could be linked to some known limitations of LSMs. Oversimplified soil and vegetation dynamics could contribute to the LSMs being overly sensitive to precipitation anomalies while the contrasts between the tropical and extratropical regions could be attributed to the representation of the soil moisture-evaporation coupling, which plays a greater role in the tropical study areas. Ultimately, this research could contribute to LS3MIP and the Land Surface Modeling community, as the results highlight the strengths and weaknesses of the LSMs in simulating soil moisture droughts. From there, this research could contribute to improving LSMs, understanding drought mechanisms, and addressing climate change impacts, especially in drought-prone regions.

Kelp Blue is a company whose top priority is the well-being of the planet. Through the cultivation of giant kelp on offshore farms, they create several sustainable products, new job opportunities in regions where they are needed, enhance biodiversity in the water, and above all, sequester tons of CO2 from the air. The start-up is still in its research and development phase, but plans to be building farms on a large scale in just a few years. Despite their knowledge in engineering, the company still needs consulting on certain elements. Therefore the company invited a group of students from the Delft University of Technology to Lüderitz, Namibia for a consultancy project. The project involved creating a procedure for the company to scale up in a sustainable manner. The students decided that this complex problem should be divided into sub-problems. One workstream focused on reducing the carbon emissions during upscaling, while the other workstream focused on analyzing and improving the company’s current design and installation of the farms. Following, both parts of the project are shortly summarized: Part I: Improving the company’s current design Kelp Blue is currently in the pilot phase, in which they’re installing their first large giant kelp farms. Before, they were focusing on the complete installation, including planting the kelp on the submerged netting structure, and the review of this. For the company’s commercial phase, where they want to be able to place farms daily on a large scale, designs were still developed and analyzed. For the commercial phase, this workstream made a new design and installation method. It required an installation where buoys would need to be submerged, the structure locked in place at the desired depth, without the use of scuba divers or remote operated vehicles. Despite the fact that these requirements were challenging, an outstanding result was achieved. The main problem was divided into subproblems, and for each of those a suitable solution was created. Hopes are that the company will consider the given advice as helpful and maybe implement some parts of (or the whole) new design. Part II: Reducing carbon emissions during upscaling First, interviews and desk research were conducted to get a good idea of the challenge of sustainability. This included reading reports, speaking with employees, policy makers and experts with experience within the area. With this information, the challenge could be mapped out and the solution space became clear in terms of legislation and technical possibilities. Climate information was also requested that could later be used to run simulations. During the determination of the possible solutions, research was done on the realistic possibilities, where eventually the use of either solar or wind energy was most appropriate. After conducting a multi-criteria analysis that was put together with the management of Kelp Blue, investing in a solar plant proved to be the most appropriate solution.

Recent reports show that deforestation in Latin-America has been severe over the last decades. Especially Brazil is subject to an alarming rate of forest loss, which will remain a factor in the coming decades. In addition to deforestation, there is an increasing amount of hazards like floods and mudslides. These hazards result in major damage to human life and nature. This research analyzes the relation between deforestation and river discharge for observed data, a simple conceptual model and complex model WFLOW for 30 catchments subject to deforestation in Brazil. These three studies use satellite data provided by \cite{GEE} to determine annual cumulative deforestation per catchment. In the study with observed data the relation between deforestation and data from CAMELS-BR \citep{CAMELS2020} is researched by analyzing annual values of two discharge driven parameters: Runoff coefficient and recession coefficient. The second study consists of a simple self-made conceptual model. In this second study, the relation between recession coefficient $\alpha$ and annual deforestation is analyzed by calibrating the model per year for parameter $\alpha$. Lastly, a study is conducted on the performance of WFLOW in catchments subject to deforestation. During this study, several input parameters are changed to observe the response of hydrological processes in WFLOW.

The study with observed data shows both increasing and decreasing trends in coefficients for several degrees of deforestation. Literature shows that landcover type after deforestation is a major factor in the interpretation of these results. However, a lack of quality annual landcover data prevents better research in the non-masked impact of deforestation on discharge. The results of the simple model study show no significant relation between deforestation and recession coefficient $\alpha$. The simplicity of this self-made conceptual model is the weak and strong point of this sub-study. The simplicity makes the results less suitable for analyzing the exact impact of deforestation on discharge, but it is useful for observing general signals and is easily scalable to different catchments. The simulated discharge by WFLOW show a steep overestimation of discharge during peak flow in comparison to observed discharge by CAMELS-BR. Therefore it is not possible to analyze how WFLOW reacts to deforestation. Instead, an in-depth analysis on the cause of this poor performance is conducted by analyzing timeseries for hydrological factors like unsaturated zone depth. The results of this analysis indicate the overestimation of discharge is caused by a lack of outflow from soil layers. In addition, the difference between the Budyko framework of different data sets used in this research, show that uncertainty in quality of input data is a plausible factor on the output of WFLOW.

In conclusion it is observed that deforestation does not necessarily lead to higher runoff coefficients and recession coefficients for measured data in this study area. In addition, the conclusion of the simple model study is that no significant relation between deforestation and recession coefficient $\alpha$ is observed for this conceptual model. Finally, the performance of WFLOW is considered too poor to analyze the impact of deforestation on discharge. The root of this poor performance is considered to be a combination between lack of groundwater modelling and uncertainty on the quality of input-data.

Rain gauges are a powerful tool to measure rain entering a watershed. When water flow through a watershed is modeled, these rainfall measurements are used as inputs. Hydrological models have become increasingly complex as they more accurately represent the physical processes occurring. This is mostly done by increasing the spatial and temporal resolution of the model. As this resolution is increased, the inputs also need to increase. This thesis looks into if rain gauges are in the right place when used as inputs for hydrological models. This has been done by analysing four factors which literature showed to have affect rain gauges. The four considered factors are: the distribution of rain gauges, the steepness of the slope they are on, the location on that slope and their location within a watershed. For each of these factors algorithms have been developed in Python which compute relevant information on a given station. These algorithms have been applied to 368 gauges across the United Kingdom (UK), available from an open data source. The rain gauges are well distributed across different altitudes matching the distribution of heights across the UK. Above 400 m there are no gauges and this area is therefore underrepresented. The spacing of stations is good, a few close together and some isolated gauges on islands. The steepness of slopes varies strongly, when a steepness of 25% is used as a threshold only around 3% are on too steep of a slope. A fair amount of gauges are on ridges. Especially those near the coast have steep seaward slopes and thus will suffer from underestimating the actual rainfall. Within watersheds gauges are often near rivers causing other areas of the watershed to be underrepresented, especially areas of higher elevation. In future research it is recommended to use more gauges in the data set. Secondly focusing on a baseline comparison can help identify which stations are placed incorrectly. Lastly it is recommended to vary the resolution of elevation data and the spatial area considered, focusing on watersheds.

In this thesis, an easily reproducible modeling approach was developed for assessing the climate change impact on streamflow. This approach was tested by using it to assess the impact of climate change on streamflow in 5 different contrasting catchments across the United States. Many studies show that climate change is expected to influence streamflow regimes all over the world. However, these studies are often difficult to reproduce because the modeling approaches used are usually only locally applicable. In the approach used in this study, hydrological model calibration and validation were done using open-accessible ERA5 forcing together with observed streamflow data provided by the GRDC. The model performed best in a mountainous catchment, while the worst performance was found in a dry catchment and a catchment containing several lakes. The low performances here are mainly caused by imperfect forcing data used for calibration and the neglection of lake processes. The climate change impact analysis used forcing from two CMIP6 models with the SSP245 and SSP585 scenarios. The projections showed significant changes in streamflow in colder regions, which are most likely related to changing snow melt processes. The main finding in warmer regions is that streamflow is generally expected to decrease in the drier periods. Changes of streamflow in these regions are most likely related to changes in precipitation and evaporation processes. However, results remain very uncertain due to disagreements between climate models and sometimes doubtful performance of the hydrological, caused by oversimplification of the model and imperfect ERA5 calibration data. The designed modeling approach facilitates reproducibility of climate change impact analyses in a wide range of catchments using different climate models and scenarios. Its use makes it easier to expand similar analyses to a large ensemble of these aspects.

Hydrologic model performance evaluation depends on streamflow observations that are accurately positioned in the landscape. For distributed hydrologic models, this means that the streamflow observation need to be mapped to a location along the model streamflow network that represents the location of the observation station in a hydrologic system. However, the gridded representation of the modelled area causes a spatial mismatch between the hydrologic system and hydrologic model. In this study we aimed to develop a Machine learning-based method to improve matching between streamflow observations and streamflow simulations. The setup of this method was implemented in two steps: (1) a dataset was created consisting of streamflow characteristics of simulations and observations and (2) a Machine learning algorithm was trained with the created dataset. Three data sources were used for the creation of the dataset: (1) 595 streamflow observations were retrieved from the Global Runoff Database Centre (GRDC), (2) streamflow simulations were extracted from the European Flood Alert System (EFAS) and (3) we were provided with a manually created and checked dataset by European Centre for Medium Range Weather Forecasts linking each GRDC observation to the correct EFAS grid cell. To link 60% of the observations in the dataset with the correct grid cells, the observations required to be moved away from the cell corresponding to the geolocation of the observations. The method developed in this study anticipated this by creating a search window around the initial location of each observation. The streamflow simulations were extracted from the grid cells in the search window and compared with the streamflow observation. The algorithm aimed to select the streamflow simulation that best reflected the characteristics of the streamflow observation. The characteristics were described with streamflow signatures. Four Machine learning algorithms, a Logistic Regression, Random Forest, Support Vector Machine and K Nearest Neighbours algorithm, were trained with a Kfold Cross Validation procedure to match streamflow simulations with streamflow observations based on streamflow signatures. Their performance was compared with four benchmark algorithms: a Center Cell benchmark which places the observations on their initial location, and the Root Mean Squared Error, Kling-Gupta Efficiency and Nash-Sutcliffe Efficiency benchmarks that compare the streamflow observation with the streamflow simulations. We identified the Logistic Regression and Random Forest algorithms as the best performing algorithms. However, neither outperformed all benchmarks. Despite these results, we show the potential to automate matching between streamflow observations and streamflow simulations with a ML-based approach in this study.

The knowledge deficit model assumes that people make more informed decisions when they are presented with more information. This model is often used in communication strategies while it has received quite some critique from science communicators and is not well supported by social science theories. One of these critiques comes from the observed Dunning-Kruger effect, where individuals unskilled in a certain area do not possess the skills to realize their incompetence. This effect has not been researched extensively yet in relation to climate change science communication and is the topic of this paper. By means of an online questionnaire (316 respondents), respondent’s knowledge and estimated knowledge on climate change is tested. The Dunning-Kruger effect has been detected for this group and suggests a critical re-evaluation of the knowledge deficit model, however additional research is necessary. An initial analysis into the influence of factors like age, gender and highest completed education level on actual and estimated scores and the discrepancy between these is also done to provide leads for further research.

To validate rainfall intensity in urban areas measured by satellite a first prototype proof of concept is introduced and tested. Using low-cost electronics an umbrella is converted into a mobile acoustic rain gauge which can be used in urban areas to measure rainfall intensities. A reed switch is placed in the umbrella to measure whether the umbrella is open or closed. Using a piezoelectric sensor and a Sparkfun sound detector rain droplets are detected and using a Pycom WiPy send over Bluetooth to an application which saves it on an online server. Tests during a laboratory experiment to see how the output data evolves shows that the data collected have an output range of about 10 % compared to its mean value. During field evaluation, to compare its output data with rainfall intensities as measured with radar, it is shown that the output data follows the radar measurements within acceptable bounds.

Big data sources can play an important role in revolutionizing the field of water resources research. Time series data with high temporal and spatial dimensions encapsulates with itself numerous factors essential for coming up with robust decisions. In this thesis, we assess one such big data source for efficient water management in the Oum Er Rbia basin, Morocco. The surface water detection technique furnished used in this thesis is found to be accurate in detecting the surface water sources and its temporal and spatial dynamics.
The remotely sensed time-series data of reservoir area was used to come up with Level-Area-Storage(LAS) relationships for the five main reservoirs in the Oum Er Rbia basin. These curves were able to approximate the present set of LAS curves well. Hence, were used in place of the local LAS curves in a water allocation decision model called RIBASIM. Thus, we had two scenarios one where the local LAS curves were used to optimize reservoir operations and the other where remotely sensed LAS curves were used instead of the local LAS curves.
The operating rule curves in the water allocation decision model were then optimized for the two scenarios. The optimization was done to maximize the performance of the system across three objectives: a)public water supply, b)irrigation and c)hydroelectricity generation. A trade-off between the three objective functions was then shown using parallel and scatter plots. It was observed that for the same set of LAS curves the performance across all three objectives improved post-optimization of the operating rule curves. This showed that there were rooms for improvement in the existing reservoir operating rule curves. The operating rule curves for the water allocation decision model with remotely sensed LAS curves were then optimized. The best set of operating rule curves that we got from the second optimization were then used with the local LAS curves to see how the system would perform with these operating rule curves. This gave us an idea of the feasibility of using remotely sensed data to come up with water management decisions and also to assess the benefits of using remotely sensed time-series data. Though the performance over the three objectives was not as good as the results we got by optimizing the system with local LAS curves, it was better than the system performance across the three objectives with the existing set of operating rules and local LAS curves. Thus, it can be used when there is a dearth of proper LAS curves.
Besides, optimizing operating rule curves, the remotely sensed time-series data of reservoir surface area was used to assess the effects of sedimentation in the reservoir storage. It was observed that for larger reservoirs the percentage change is not much as compared to the smaller reservoirs. Apart from the size of the reservoir, more study is required to make a detailed analysis of how factors like topography and soil texture influence the rate of sedimentation. Despite its limitations, the remotely sensed time-series data of reservoir surface area can be used to perform a qualitative analysis of the rate of sedimentation and can give reservoir authorities an idea of the need for bathymetry. This can help in avoiding unnecessary bathymetries which are infeasible both economically and physically.

Heavy rainfall, combined with expanding (unplanned) urban settlements in flood prone areas, expose Dar es Salaam (Tanzania) to the risks of flooding. The urbanisation is so rapid in many areas that it is not beneficial to carry out expensive surveys which are quickly out of date. The work carried out by community-mapping project Dar Ramani Huria (Swahili for "Open map") aims to make a detailed map of Dar es Salaam, to enable the hydrologic models to approach the real situation more closely. However, the surveying methods used until recently are not sufficiently accurate. However, an alternative emerges in the form of community members using a low-cost, dual-frequency global navigation satellite system (GNSS) receiver during surveys. However, before this receiver can be implemented a detailed research has to be done. In this thesis the horizontal and vertical performance of the U-blox ZED-F9P receiver in Delft (the Netherlands) and Dar es Salaam is studied. The research is divided into two parts: performance and case study. For the performance study a series of post-processed kinematic (PPK) experiments were conducted in Delft and Dar es Salaam. The experiments have been designed in order to provide a variety of location, antenna-performance, baseline length, software package and movability. In addition, two re-initialisation experiments were conducted to measure how fast the interrupted GNSS signal is regained by the receiver. The case study focused on the desirability and feasibility, mainly focussing on accuracy, of implementation in the project of Dar Ramani Huria. Structured and unstructured interviews with employees of the Humanitarian OpenStreetMap Team (HOT) Tanzania were held to find out the requirements of implementation. The positioning performance of the receiver varies between the different experiments. The conclusions regarding the positioning performance are based on the scatter plots in the horizontal plane and the positioning over time for the three separate directional components; East, North and Up. The values for the horizontal performance (RMS East, RMS North) and for the vertical performance (RMS Up) of the fix solutions insofar as they fall inside the 95% confidence ellipse are decisive. Only the relevant experiments, namely those who can map a larger area with a single reference station are taken into consideration. The horizontal positioning performance ranges from 1.13 till 16.83 However the latter, high value is from the 9 baseline Dar es Salaam experiment with a very low percentage of fixed solutions. If we disregard the experiments with low percentage of fixed solutions then the horizontal positioning performance ranges van 1.13 till 9.42. The vertical positioning performance shows less accuracy ranging from 3.56 till 14.75. If we compare this performance with the requirements for Dar Ramani Huria’s project, even the strictest of 2cm, the performance is more than adequate according to the "few cm accuracy" requirement. The experiment with the high-end antenna shows with values 2.44mm (RMS East) and 3.42mm (RMS North) the best horizontal and with the value 3.75$mm(RMS Up) the best vertical performance. Another factor influencing the performance is the location, in particular the aspect of atmospheric delay that varies between Dar es Salaam and Delft. This research thesis concludes that the implementation of the receiver in Dar Ramani Huria's project is well possible and that the performance of the receiver is adequate. This conclusion is confirmed by what is actually occurring in the field: HOT Tanzania and Dar Ramani Huria already started using the GNSS receiver and carrying out surveys with this receiver.

Fog harvesting is a sustainable drinking water solution for arid climates. Previous studies have developed an analytical model to predict the fog water collection efficiency as a product of aerodynamic and deposition efficiency as independent factors. In this study, we tested the assumption that deposition efficiency stays unchanged when the geometry of the fog catcher is adjusted. We assessed the collection efficiency of both straight and curved fog water collectors using computational fluid dynamics models and performed controlled experiments in a climatic wind tunnel on sample fog water collectors. The analytical model disregards convex fog harvesters because their lower drag coefficient reduces the aerodynamic efficiency of the fog harvester. The results of the CFD models show that efficiency can be doubled if fog catchers are built convex facing the wind. The wind tunnel experiments support the results from the CFD models. The results of this study show that for convex fog harvesters, although less fog passes through the net, the deposition efficiency increases resulting in a net increase of water collection.

Water level data is a key factor in water systems research. Frequently measuring water levels in areas with tidal influence is especially desirable because it enables observations to follow the rise and fall between minimum and maximum water level. However, a majority of developing countries are not able to measure water levels at the desired time stamp and spatial scale with currently available water level measuring instruments, predominantly due to limited financial resources. Countless reliable instruments exist that measure water levels, although low-budget instruments are few and far between. Literature study showed that the price of water level instruments are not representative of the water level measurement accuracy acquired.
This research focuses on the need for a water level measuring instrument that is low cost, automatic, reliable and suitable for use in developing countries, specifically in Myanmar. In Myanmar, automated water level data collection remains challenging due to limited financial resources. This data collection limitation inhibits Myanmar's ability to optimize the distribution of water resources, potential consequences of poor water resource management include floods and droughts. Key requirements for a water level gauge to be considered suitable for use in developing countries include, simple to operate and repair, made from off the shelf components and operational in remote areas.
We developed an automatic water level gauge incorporating an acoustic distance sensor, which is the type of sensor used for parking assistance in modern cars. To validate the applicability of our instrument, field trials were undertaken in The Netherlands and Myanmar.
Our research objective was achieved and therefore we demonstrated it is possible to build a water level measuring instruments that is cost-efficient, automatic, reliable and suitable for use in developing countries. Although tests results from the Netherlands are promising, further optimization is needed for deployment in Myanmar.