This dissertation addresses the challenge of spring frost damage in fruit orchards. Frost events during the growing season can cause significant damage to blossoms and young fruit, leading to substantial yield losses. The study evaluates two protective strategies to mitigate such damage: 1) the use of wind machines and 2) the manipulation of surface vegetation. Both approaches are investigated through experimental observations and conceptual modelling..... ... Read more

Heavy rain events and flash floods, such as those in July 2021 in the Geul catchment, are projected to increase in both severity and frequency. In addition to traditional grey measures (e.g., dikes andreservoirs), rural nature-based solutions (NbS) that enhance the sponge function of the soil are beingconsidered. However, the effectiveness of rural NbS during extreme events remains contested.

This study simulates the potential of rural nature-based solutions for flood mitigation under extreme rainfall, with a focus on river peak flows in the Geul during the July 2021 event. To this end, the combination of a physics-based plot-scale model (SWAP) and a conceptual distributed catchment model (wflow SBM) is used. The work proceeds by: (i) comparing soil–water process representations in both models ; (ii) performing sensitivity analyses of soil parameters; (iii) designing a rural NbS scenario by modifying soil-hydraulic parameters; and (iv) evaluating the effects of these scenarios on flood hydrographs at the catchment outlet.

SWAP results show that simulated infiltration and overland flow are most sensitive to saturated water content (𝜃𝑠𝑎𝑡) and saturated hydraulic conductivity (K𝑠𝑎𝑡). A two meter sandy soil column would have accommodated the entire July 2021 rainfall, resulting in negligible overland flow. In wflow SBM, sensitivity analysis indicated that simulated flood hydrographs are most influenced by KsatVer, 𝜃𝑟, and 𝜃𝑠. Adjusting 𝜃𝑟 and 𝜃𝑠 in downstream subcatchments could significantly improve the model’s ability to match the observed hydrographs.

Scenario simulations in wflow SBM reveal spatially dependent effects. Downstream interventions have a damping effect on peak flow magnitude, whereas interventions in the upstream part of the Netherlandscan increase it. A catchment-wide rural NbS scenario reduces the downstream peak by approximately 50% and delays the peak by approximately two hours relative to the default model. This modelled delay lies within the observed five-hour offset between the Geul and Meuse peaks in July 2021, suggesting such measures are unlikely to increase flood risk at the confluence via peak synchronization. ... Read more

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.... ... Read more

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.... ... Read more

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. ... Read more

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. ... Read more

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. ... Read more

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.
... Read more

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. ... Read more

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. ... Read more

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. ... Read more

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. ... Read more

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. ... Read more

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. ... Read more

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.
... Read more

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. ... Read more

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... ... Read more

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. ... Read more

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. ... Read more

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. ... Read more