Frost is a meteorological phenomenon that can cause significant damage to crops and fruit plants, leading to substantial economic losses in the agricultural sector. In 2017, spring frosts caused losses amounting to 3.3 billion euros across Europe (Lamichhane, 2021). In France, at least half of the 680 km2 of vineyards were damaged due to frost events in 2021 (Liubchenkova, 2021). Due to global warming, bud blooming tends to occur much earlier, while the probability of single frost nights in spring remains the same. Consequently, crop vulnerability increasingly coincides with the frost season. Effective mitigationmethods such as sprinkling, wind machines, and paraffin pots remain essential for preventing crop damage.
Among various mitigation methods, wind machines are potentially cost-effective and energy-efficient. However, little is known about the warming effectiveness of wind machines and the physical mechanisms behind the warming process. Therefore, the primary motivation of this thesis is to investigate whether wind machines are effective warming devices for future frost damage mitigation. However, current physical understanding of machine-inducedwarming remains limited. Experimental studies often face challenges with the coverage and resolution of measurements, which are typically insufficient to fully capture the temporal and spatial variability of machine-induced air mixing in orchards. Furthermore, there are only a few numerical studies available. The dynamic interplay between turbulent warming plumes and canopy structure, as well as the heterogeneous plant-air heat exchange in the orchard, has not been investigated numerically so far....@en

Transpiration is a crucial flux in greenhouses that directly affects plant growth. However, there are currently no direct measurements available. Sap flow is the most widely used method for quantifying transpiration, and calibration can enhance the accuracy of these estimates. Despite the existence of numerous recommendations for applying sap flow calibrations, few reports have employed them. Furthermore, plants frequently encounter various stressors during their growth and development, including drought, salinity, and reduced leaf area. These stressors can impede plant growth and, in extreme cases, can even result in plant death. Plant growth is contingent upon transpiration, and thus, stress conditions can also influence transpiration. However, there is a paucity of research on the effects of these stress conditions on transpiration.

The objective of this report is to calibrate the sap flow and subsequently investigate the effects of stresses, such as salinity, drought and leaf removal on transpiration in order to enhance irrigation strategies. Calibration was initially conducted by comparing daily sap flow with transpiration estimated by a water balance model using linear regression analysis. The corrected sap flow for the controlled and treated gutter was then compared to investigate the impact of stresses on transpiration.

The corrected sap flow coefficients were 2.30, 1.76, 1.64 and 3.58 for the controlled plants (1 and 2) and the treated plants (1 and 2), respectively. The corrected sap flow may be employed as an indicator of transpiration. It has been observed that salinity levels and a reduction in leaf area result in a decrease in transpiration, particularly when transpiration rates are already high. There was no significant difference in transpiration even when irrigation was reduced to 33% of the original amount. In the event of salinity, drought and leaf area reduction, it is recommended that the irrigation frequency be increased and the irrigation amount be reduced. Furthermore, it is recommended that salt leaching during the non-planting period be undertaken.

Water consumption reduction in greenhouse cultivation is a key objective for growers to optimize resource usage. Accurate estimation of transpiration, enabling growers to adapt water inputs to exact plant requirements, is vital for efficient water management. Various models have been developed to estimate transpiration. The Penman-Monteith and Stanghellini models are two of the most widely used models.
The Penman-Monteith model was originally designed for open-field conditions, while the Stanghellini model was specifically developed for greenhouse environments. In this study, the accuracy of these models in estimating transpiration was evaluated by comparing their estimated values with measured transpiration data. By affecting the VPD and stomatal resistance, temperature directly impacts the rate of transpiration in plants. This research also addresses a significant gap in previous studies by determining the optimal observation height for temperature data, which is essential for accurate modeling of transpiration.
Linear regression analysis was employed to assess the performances of the models. The results show that the Stanghellini model provides more precise estimations of transpiration compared to the Penman-Monteith model under greenhouse conditions. Moreover, using temperatures measured above the top of the plant canopy improves the accuracy of tran- spiration estimations in both models. Enhancing the accuracy of transpiration models in greenhouse conditions is critical for promoting efficient water management practices.

In this study, suitable rainwater harvesting techniques are investigated for smallholder farmers in northern Ghana, in order to reduce the impact of dry spells during the growing season. Dry spells and water shortage during the growing season have a negative impact on food production in northern Ghana, where 95% of the farms are rainfed. A potential solution for rainwater harvesting for smallholder farmers is adding organic materials to the soil. Here, we conduct a local field experiment comparing two plots in a maize field, one treated plot with added sheep excrement and one untreated plot as control plot. Results show twice as large grain yield for the treated plot, while observed soil moisture levels are not significantly different between the two plots. The behaviour of water in the maize field is analysed with a water balance model, which reproduces the overall dynamics of soil water storage, but overestimates peaks in response to precipitation events. The model confirms that soil water levels remain well above the wilting point in both plots during the growing season. The measured soil moisture is also used to calibrate an AquaCrop model, which is an FAO crop model giving yield outputs. The crop output of the AquaCrop model shows no difference between a field with and without manure, indicating again that soil moisture in this case is not the determining factor for yield differences found in the field experiments. A possible explanation for the difference in yield is the release of nutrients from the manure throughout the season, which were not yet captured in the soil sampling. Besides that, no dry spells occurred during the growing season, so the effects on a real dry soil could not be measured. A multi-criteria analysis is used to assess the suitability of rainwater harvesting solutions that are suitable for smallholder farmers in northern Ghana. It is found that multiple in-situ and micro-scale rainwater harvesting technologies are suitable, like deep tillage, adding manure and conservation agriculture. It should be noted that having the required knowledge to apply these rainwater harvesting technologies successfully is essential.

Weather and climate models require knowledge about the surface temperature and surface heat flux to make predictions for climatology, farming, wind power prediction, etc. Most current models make use of Monin-Obukhov Similarity Theory (MOST). This theory expresses multiple turbulent fluxes in terms of their mean gradients with height. MOST results in an accurate universal form for the non-dimensional wind, temperature and humidity profiles from just above the surface and up to the top of the Atmospheric Surface Layer. The description of the near-surface temperature profile above a rough surface is, however, non-robust and ill-defined in the lowest layer of the atmosphere, the Roughness Sublayer. This thesis presents a new flux-gradient framework based on surface geometry. We apply it to the near-surface temperature profile of high-resolution experimental data from fibre optic measurements over a grass surface. The framework was developed to universally compare 3 different types of models at the surface: a Direct Numerical Simulation in a smooth pipe validated model; a constant surface gradient over grass model and a tall canopy model. Where the temperature is scaled with the already in use turbulent heat flux scale, the new length scale scales the height based on a finite surface gradient which is observed in experimental data. The experimental data can in this framework collapse onto one universal profile with the use of a reference temperature taken at the top of the grass. The framework thus extends MOST with a universal non-dimensional temperature profile in the roughness sublayer.

Rain-fed agriculture is the main source of food in Ghana therefore improving quantitative rainfall estimates is essential for local farmers to predict crop growth using vegetation models. Rainfall dynamics in the tropics is an ongoing topic of research due to their complexity and sub-grid precipitation variability. At the same time, tropical areas such as Ghana are the most affected by a lack of proper rain gauge network coverage. Traditional methods rely on empirical assumptions and statistical theories that require continuous calibration and still struggle to accurately represent local variability. The aim of this paper is to demonstrate the potential of a Deep Learning (DL) approach using bi-spectral information of water vapor imagery (WV) and thermal infrared (TIR) as a starting point to develop an effective alternative to the Cold Cloud Duration (CCD)
method which is a widely applied statistical technique by satellite rainfall products like Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and Tropical Applications of
Meteorology using SATellite data (TAMSAT) that are specifically designed for Africa.
WV inhibition of low-level features assures the correct depiction of strong convective motions usually related to heavy rainfall which is crucial in tropical areas where convective rainfall is dominant. The addition of WV 7.3m is particularly beneficial in North Ghana as tropical systems
are advecting dry air from the nearby Sahara desert creating discontinuities in precipitation events which translates into dry intrusions and dry slots seen in the images of the WV channel.
The developed Deep learning model showed strong performances in binary classification where it outperformed IMERG-Final false alarms count resulting in lower rainfall overestimation (FBias < 2.0), although further research is needed to overcome the very poor relation between GEO-IR images and actual rainfall estimates at the surface.

Scientific literature points to several models used to calculate the transpiration of vegetable crops inside a greenhouse but the results are hard to compare since different experiment setups were used. This study aimed to analyse the accuracy of four different transpiration models, Penman-Monteith, Stanghellini, Priestley-Taylor, and Takakura for the calculation of tomato transpiration inside a soilless greenhouse. By addressing this knowledge gap the outcome of this study provides a step towards more autonomy in vegetable horticulture. During the summer a three month experiment was conducted in a Venlo-type soilless greenhouse in The Netherlands. The air temperature, relative humidity, wind speed and net radiation were measured inside a tomato greenhouse. Hourly transpiration data measured using a combination of sap flow sensors and lysimeters, was compared to the four different models. The Penman-Monteith and Priestley-Taylor models, intended for outdoor use, had a similar accuracy as the Stanghellini and Takakura models that are developed for Venlo-type soilless greenhouse. When comparing the models error range to the average hourly transpiration, it is found that the models are not accurate enough for irrigation scheduling. A model sensitivity analysis illustrates how changes in net radiation, temperature, humidity and wind speed affect modelled tomato transpiration. A recommendation on methods to improve model accuracy is made.

As the intensity and frequency of urban heat wave increases in Europe, evaporative cooling from watering pavements has been considered as a promising strategy to regulate urban temperature. The aim of this research is to include evaporative cooling modelling and enhance the surface temperature model SURF-TEMP, which is in development at the Swiss Federal Institute of Aquatic Science and Technology (Eawag), to predict the optimal use of water resources towards mitigating urban heat. In order to test various evaporation models found in literature, we first developed a simple Python model to evaluate ten evaporation models. Differences among the simulated evaporative heat fluxes and surface temperature reduction can be around two folds under the same simulation condition for different evaporative cooling models. Besides the evaporation models, three equations to obtain the convective heat transfer coefficient were tested. We tested their uncertainties and determined the equation that provides the most neutral estimation on convective heat transfer coefficient. We proceeded to conduct various simulations to understand the sensitivity of these evaporation models with respect to four factors (wind speed, relative humidity of air, initial surface temperature, and initial water height). These factors influence the evaporative cooling efficiency and therefore the optimal watering rate. Two evaporative models that represent the higher and lower limit of the simulated evaporative heat flux are incorporated into SURF-TEMP. Results show that the model that estimates the highest evaporative heat flux is in good agreement with the lab measurement published by (Parison et al.,2020) when watering rate is equal or above 1 mm per hour. When watering rate falls below 0.75 mm per hour, the models studied underestimate the evaporative heat flux and surface temperature reduction. Discrepancies during low watering rate are caused by the absence of modelling of water conduction and infiltration with the pavement, non-linear relation of evaporation rate with vapor pressure difference, and the inherent error between simulation and experimental results even under dry condition.

Large parts of the world rely on rainfed agriculture for their food security. In Africa, 90% of the agricultural yields rely only on precipitation for irrigation purposes and approximately 80% of the population’s livelihood is highly dependent on its food production. Parts of Ghana are prone to droughts and flood events due to increasing variability of precipitation phenomena. Crop growth is sensitive to the wet- and dry spell phenomena during the rainy season. To support rural communities and small farmer in their efforts to adapt to climate change and natural variability, it is crucial to have good predictions of rainfall and related dry/wet spell indices.
This research constitutes an attempt to assess the dry spell patterns in the northern region of Ghana, near Burkina Faso. We aim to develop a model which by exploiting satellite products overcomes the poor temporal and spatial coverage of existing ground precipitation measurements. For this purpose 14 meteorological stations featuring different temporal coverage are used together with satellite-based precipitation products.
Conventional machine-learning and deep-learning algorithms were compared in an attempt to establish a link between satellite products and field rainfall data for dry spell assessment. The deep-learning architecture used should be able to efficiently manipulate spatial data. Hence, Convolutional Neural Networks were used in order to detect spatial patterns in the satellite data.
Using these models we will attempt to exploit the long temporal coverage of the satellite products in order to overcome the poor temporal and spatial coverage of existing ground precipitation measurements. Doing that, our final objective is to enhance our knowledge about the dry spell characteristics and, thus, provide more reliable climatic information to the farmers in the area of Northern Ghana.

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.

Hydrological models are designed to represent the interactions between the
physical process and the water storage in short-term or long-term forecasting. The hydrological models in climate models (also know as Land Surface Models) aim to represent hydrological processes and interactions at a global scale. In this research, one Land Surface Model, HTESSEL is introduced. Previous studies have shown that HTESSEL is not reproducing hydrological fluxes well at a catchment scale.
In this study, the problems in representing discharge in HTESSEL can be summarized in three aspects: mismatch in peaks, slower recession,and the monthly delay. Thus, the aim of this research is to investigate the reasons of the poor performance in HTESSEL, and provide possible suggestions for a better fit. Therefore, the hydrological models HBV and GR4J (that operate on catchment scales) are introduced to identify the problems in HTESSEL by model comparison.
HTESSEL, HBV and GR4J models are used to simulate river discharge in 15 catchments are compared in terms of structure and parameterization. HTESSEL use tabulated parameter values, while HBV and GR4J are calibrated to match observations firstly. In order to investigate the influence of different model processes and parameters, a second calibration of the HBV and GR4J parameters is applied. Here, the model parameters are calibrated to the HTESSEL model output. Comparing the two calibration results, the parameter differences can be identified.
The results show that the soil column in HTESSEL is a key factor that influences the surface and subsurface runoff. On the one hand, HBV and GR4J can reproduce the slower falling limb in humid region by increasing their slow reservoirs. On the other hand, the top 50 cm of soil column is the effective depth that influences maximum infiltration rate. Thus, the changing of effective depth and the parameterization of orography variable b, which influences the fast runoff in HTESSEL, are necessary in temperate and mediterranean catchments.
According to this study, to solve the problem of mismatch in peaks this 50cm should be a spatial variable firstly. The increase of effective depth could overcome the overestimation in some places and the decrease of effective depth could overcome the underestimation of peaks in other places. In addition to effective depth, optimizing parameter b is also necessary, because it influences the fast runoff. Moreover, for the problem of slower recession and monthly delay, decreasing the size of soil column in HTESSEL is one way to get a better fit. Thus, in future, more study could focus on the interplay of the soil infiltration capacity and the fast runoff parameters. It might be helpful to improve the simulation.

In dit rapport zal er onderzocht worden wat de dagelijkse ruimtelijke spreiding van regen in Delft is op basis van de neerslagdata van het Delft Meet Regen project. In totaal hebben er 90 vrijwilligers meegedaan aan dit project en zijn er dus ook 90 meetpunten. De neerslagdata die gebruikt is voor dit onderzoek is de neerslagdata in de periode van 1 augustus 2021 tot en met 17 september 2021. De neerslagdata is vervolgens vergeleken met de data van KNMI station 449, door de dagelijkse gemiddelden normaal en cumulatief met elkaar te vergelijken. De KNMI data heeft een goede overeenkomst met de neerslagdata van het Delft Meet Regen project.
Hierna is het onderzoeksgebied ingedeeld in de volgende vier gebieden: noordwest, noordoost, zuidwest en zuidoost. Van deze gebieden zijn de dagelijkse gemiddelden, aantal metingen, standaard afwijkingen en de dagelijkse gemiddelden cumulatief met elkaar vergeleken. Dit is gedaan doormiddel van grafieken, staafdiagrammen en ruimtelijke plots. Hieruit is geconcludeerd dat er sprake is van gemiddelde dagelijkse neerslagverschillen tussen de gebieden, maar dat er geen sprake is van een patroon. Het aantal metingen per gebied verschilt per dag en dit heeft invloed op de nauwkeurigheid van de resultaten. Hoe meer metingen er beschikbaar zijn, hoe nauwkeuriger de resultaten zullen zijn...

In het project Delft Meet Regen zijn er ongeveer 90 inwoners in Delft die een eigen handmatige regenmeter hebben gekregen en elke ochtend het aantal millimeters regen meten dat gedurende de ongeveer 24 uur daaraan voorafgaand is gevallen. De data van deze regenmeters gedurende een periode van ongeveer 6 weken zijn vergeleken met die van het automatische weerstation van het KNMI bij Rotterdam Airport, de regenmeter van het vrijwilligersnetwerk van het KNMI bij de Botanische tuin in Delft, en de automatische weerstations die verbonden zijn met het WOW-NL netwerk. De data van het automatische weerstation van het KNMI bij Rotterdam Airport zijn vergeleken met de gemiddelde waarden van de data van het Delft Meet Regen project, waarbij de verschillen te inconsistent zijn om dit aan een systematische fout te wijten. De buien in Delft verschillen van de buien in Rotterdam. De neerslagdata van de regenmeter van het vrijwilligersnetwerk van het KNMI volgen bijna dezelfde patronen als de regenmeter van het Delft Meet Regen project, met een afwijking van enkele millimeters. Er zijn vijf meetpunten waar de handmatige regenmeters van het Delft Meet Regen project zijn vergeleken met de automatische weerstations die zijn verbonden met het WOW-NL netwerk die op dezelfde locatie staan. Op een na, volgden de data van de handmatige regenmeter bijna dezelfde patronen als het automatische weerstation. Hierbij was er in de meeste gevallen een afwijking van enkele millimeters, waarbij sommige regenmeters een grotere afwijking vertoonden dan andere.

This report describes the development of an early warning model for thunderstorm occurrence around Lake Victoria. It is the first model to use the TAHMO lightning sensor data to predict thunderstorms. This study hopes to contribute to the TWIGA focus area of increasing disaster resilience through forecasting and early warnings and could be used to reduce the impact of thunderstorms for the communities around Lake Victoria, and to aid the flights of Zipline’s medicine-delivery drones.

Due to global warming, temperatures are expected to rise and, with it, the increase in moisture holding capacity of the atmosphere. Changes in precipitation extremes with temperature are governed by the Clausius-Clapeyron relationship (CC), which states that precipitation increases 7% per degree of warming. While global models and observations are consistent with CC, research at hourly observations have showed deviations in the CC relationship, showing steeper increase up to 14% per degree (super-CC) and <7% per degree for larger temperatures (sub-CC). Studies have shown that scaling rates can be influenced by several factors, such as the length of observations, rainfall percentiles or the timescale of rainfall, among others. Apart from methodological limitations, research on scaling rates is not readily available in all parts of the world, with scarce studies carried out in the African continent. In this context, there is a need for better understanding the relationship between precipitation and temperature as well as the implications for future extremes. To address these challenges, this research investigated 3 main topics. Firstly, existing methodologies were compared and analysed for 353 rain gauges in Germany. Using as extreme rainfall indicators wet percentiles (80th-99th quantile), 3 methods were tested and statistically compared to estimate scaling rates with dew point temperature (DPT): (i) binning methods, (ii) quantile regression and (iii) change-point models at an hourly timescale. Secondly, the presence of super-CC was evaluated for storm events instead of fixed hourly intervals. For that, data from Germany at 10-min resolution was used. The last step involved the analysis of scaling rates for a dense network of newly available rain stations in 4 countries in sub-Saharan Africa: Ghana,Kenya, Tanzania and Uganda. The results from measuring the scaling of extreme precipitation with DPT shows that two regimes arise for Germany: for DPT below 13ºC- 15ºC the scaling rates are close to the CC rate across all quantiles. For warmer DPT of 15ºC or more, scaling rates above the CC are observed, with increasing slope for the highest quantiles. The study of storm events shows that, for mean intensity and peak rainfall intensity, there is a strong sensitivity to DPT, with higher DPT associated with higher rainfall. The analysis of storm duration indicates that shorter events are more frequent for larger temperatures than for shorter ones. The study in sub-Saharan countries shows consistent negative scaling rates with dew point temperature. In addition, for large surface air temperatures (SAT), there is a decrease in relative humidity, suggesting that there is a general lack of moisture availability at the higher SAT ranges. To better understand the physical processes that influence these results, the use of alternative predictors that describe the atmospheric moisture are proposed. In a future warming world, current literature indicates that the greatest rainfall increases will be more visible for short-duration storms rather than increases in annual mean precipitation. As such, within this project, a novel analysis on scaling rates for storm properties shows the potential of these approach to obtain detailed information on the storm properties and their relationship with temperature, which could be used in storm simulations and projects characterising current or future climate.

In this research, the flood damage caused by heavy rain and plastic blockage in the neighbourhood of Atonsu, Kumasi is quantified. Rapid urbanisation and poor waste management result in floods with an increasing impact in Kumasi, the fastest growing city of Ghana. This rapid growth of the city results in uncontrolled urbanisation of the floodplains and induces damage to homes during a flood event. Furthermore, due to a poor waste management system around thirty percent of the waste produced in Kumasi gets dumped in the streets and forms blockage of the water flow in the river. Moreover, the river is used as a dump not only by citizens but also by factories, which results in contaminated river water. As this research aims to give a first insight into the actual flood damage in Kumasi, simplified methods are applied, as they do not require a lot of data. To quantify the tangible damage, expressed in monetary values, depth-damage curves are constructed for different types of land use in the study area, to show the relation between the inundation depth or inundated area and the monetary damage. Besides that the intangible damage is quantified in the form of the number of lives lost and people exposed to waterborne diseases, both depending on the total number of people affected. The damage is quantified for floods induced by four different rain events, all with different peak precipitation, duration and frequency. The results indicate that the larger the rain event, the more damage it causes. It is furthermore estimated that plastic blockage of the flow of the river results in an increase of monetary damage of up to 5.7 percent and the number of people that are affected by the flood increases with up to 17.4 percent. Finally, the results of this research show the magnitude of flood damage in Kumasi and could be used to show the urge of measures that need to be taken to reduce the flood damage.

This research assesses the impact of plastic waste accumulation on flood events with citizen observations in Kumasi, Ghana. In Kumasi, the fastest growing city in Ghana, flooding events have become more intense due to rapid urbanisation and land use intensification. Additionally, rapid population growth, urbanisation and lack of organised waste collection, brings along tonnes of solid (plastic) waste on the streets, riverbeds and -ways. Plastic waste creates blockages in drains and rivers, which causes flooding of open areas, streets and houses. To what degree plastic waste accumulation aggrevates flooding in Kumasi remains poorly understood. Therefore, this study investigates to what extent plastic accumulation impacts flood severity, in response to rainfall events in the city of Kumasi, Ghana. This is done by modelling different rainfall scenarios in a 1D-2D model developed with the software HEC-RAS. This model was forced with static and dynamic data available from open source databases and collected during fieldwork. Data collection took place during six consecutive weeks in May-June 2019 and consists of bathymetry and discharge measurements, a flood- and plastic waste survey performed together with citizens. The impact can be determined using different model scenarios varying in rainfall magnitude and in obstruction of riverways, serving as plastic accumulation. Flood severity was analysed in terms of extent, depth, velocity, local hydraulic impact and flow distribution. The model has been validated with two events, one during which velocity and stage was measured, which results in the same order of magnitude, modelled slightly lower than measured in the field. The second event was a historic rainfall event, which could be validated with community reported flood depths. From this validation it became clear the flood extent of the event was underestimated and depths close to the streams were overestimated. The results show that the selected rainfall events without blockage cover the studied area between 11-25% with water depths higher than 10 cm. The inundated areas are adjacent to the river streams and inundate flat and low lying areas. Comparing this to the scenarios where bridges where obstructed to 33% and 67% individually, the total inundated area remains more or less the same, varying between 0-3% in inundated area compared to the scenario without blockage. However, in terms of flood distribution, areas around the blocked bridge are impacted more, thus inundation areas shift within the observed study area. Comparing this to the rest of the area, upstream or downstream less flooded areas arise, making the change in total flood extent only change slightly. Furthermore, the two bridges that are obstructed have different impact on the flow in the study area, as the scenarios of obstructing the second bridge with 33% produce a numerical instability and therefore incorrect results. From the plastic waste survey it followed that plastic waste is very much abundant in the study area, thus the occurrence of plastic accumulation at the bottleneck locations seem inevitable. The results of this research have shown the main contribution to flooding is flow from upstream areas exceeding river capacity, while local blockages mainly aggravate flooding around the blocked locations.

This report describes the development of an outfall structure in Panama City. At this location a Matasnillo river enters the bay along the Cinta Costera. Since this river is polluted it causes a bad water environment and smell at the river mouth area such that it is unable for recreation. Boskalis wants to improve the area in co-operation with local authorities. To do so a solution had to be found for the problems caused by the river, while the peak river discharge during rainstorms will not cause flooding in the area. Several concept designs were developed and in the end a hybrid design, consisting out of an outfall pipeline and culvert, was selected as most viable solution. This design discharges the polluted water through the outfall pipelines several kilometers offshore. During rainstorm events the surplus in river discharge is discharged through the culvert structure. The design is substantiated by functional and structural design calculations. In the end a cost analysis was made.

In the Netherlands, the Delta Programme aspires to adjust spatial planning climate-proof and water-resilient, in order to be prepared for extreme weather in 2050. To achieve this ambition, municipalities, provinces, regional water authorities and central governments conduct stress tests to map out the vulnerabilities in their areas of authority by no later than 2019. The stress tests comprise four themes: pluvial flooding, drought, heat and floods. In addition, the Delta Programme 2019 acknowledges the mitigation of and adaptation to land subsidence as an important tasking. The municipality of Rotterdam faces the challenge of adding land subsidence as stress test theme and assessing its influence on pluvial flooding. Contrary to the stress test pluvial flooding, no consended methodology exist on how to map out vulnerabilities concerning land subsidence. In addition, only few studies have numerically investigated the spatial-temporal effect of land subsidence on pluvial flooding in urban areas. However, the advent of techniques to measure ground level (LiDAR) and land subsidence (InSAR) and advances in high resolution flood modelling (3Di) enable the numerical modelling of urban pluvial flooding influenced by land subsidence. This research explores the investigation of the influence of land subsidence on pluvial flooding in Rotterdam by supplementing the conducted stress test pluvial flooding with a land subsidence assessment. The conducted stress test pluvial flooding in Rotterdam is based on a 3Di-simulation of standardised rain events, based on a DEM 2016.

To asses the current influence of land subsidence on pluvial flooding, a Digital Elevation Model (DEM) that approximates the sub-neighbourhood Tuinenhoven at design level is created and used as 3Di-input. When comparing 3Di-results based on this design DEM to the stress test pluvial flooding, it becomes clear that the total volume of water during extreme rainfall stored on the streets is not affected by land subsidence. The bathymetry of the DEM does affect the water's distribution however. The Tuinenhoven case-study demonstrates that currently land subsidence increases the severity of the impact of pluvial flooding but that the main cause of pluvial flooding during extreme rainfall is the limited capacity of the drainage system.

Land subsidence in Rotterdam complex. The conducted land subsidence analysis based on an InSAR data-set supports this complexity. It illustrates that the subsidence behaviour of Rotterdam is influenced by foundation type, land use classification, the presence of dredge in the anthropogenic layer and top soil type. This respectively indicates the occurrence of pole rot and shallow foundations, anthropogenic compression and compaction of shallow soft layers caused by loading, landfill subsidence as a result of land fillings that contain dredging spoil and consolidation of the Holocene clay layer as a result of drainage. However, the land subsidence analysis failed to identify location-specific dominant land subsidence processes. This failure was primarily caused by the limitation to only one linear subsidence rate between 2009 and 2014 per point. To demonstrate how land subsidence can be translated to pluvial flooding based on a land subsidence analysis, land use classification was selected as the most dominant influencing factor and used in a linear land subsidence prognosis until 2030. The linear assumptions largely obstructs results to be interpreted location-specific.

The IJsselmonde case-study shows that land subsidence is expected to decrease the passability of roads and decrease the risk per building in the future. These decreases are caused by the fact that roads relatively subside fast and buildings relatively slow. The biggest influence on the risk per building classification is the assumed threshold value per building. Simulated road maintenance results in an increase of the passability of roads and an increase of buildings at risk of water nuisance. The loss of the water-storing function of the road after reconstruction increases the water levels in gardens and puts buildings at an increasing risk.

In conclusion, the most challenging part of investigating the influence of land subsidence on pluvial flooding is the crucial identification of the different occurring land subsidence processes. It is demonstrated that the possibilities with InSAR-data are promising, when used with sufficient competency, although the available InSAR data should be divided in shorter intervals to detect the subsidence rate trends. Land subsidence rate trends are crucial in the identification of land subsidence processes and assessing influences like groundwater variations and increased loading due to maintenance or construction works. When the land subsidence analysis is improved, so will the land subsidence and threshold height per building prognosis. When the relative prognosed decrease of the threshold value per building is improved, it can be quickly assessed whether buildings classified at risk in the stress test pluvial flooding are at future increasing risk during extreme rainfall, without conducting a full 3Di-simulation.

Bioswales contribute to climate resilience, as they positively impact the urban water infrastructure by improving the water balance and water quality. However, current bioretention design ignores the facts that different designs in different soils and climatic locations produce different performance results.
The effect of polder conditions on the hydraulic performance, as well as the effect of different storm-types and initial conditions will be researched in this report. To this end, 5 bioswales located in Rotterdam, The Netherlands are monitored on their hydraulic behaviour. The discharge of the drain, groundwater levels in and at the edge of the bioswale and the water level in the bioswale are monitored for 4 of the bioswales. One bioswale only has groundwater and surface water measurements. The polder conditions resulted in a low volume reduction and faster and stronger reacting drain. However, the peak reduction and peak delay were still quite good due to the low permeability of the top-soil. For smaller storms, even the goal for volume reduction could be met. It was found that the structural porosity governed the infiltration, but large plants can increase the permeability too much. In addition, the textural porosity should start high enough to allow for vegetation development.