Climate change poses increasing challenges to urban environments, with rising temperatures, sea-level rise, and altered precipitation patterns contributing to both flooding and drought. While much research has been dedicated to flood mitigation, urban drought has received comparatively less attention, despite its significant impact on vegetation, water availability, and overall ecosystem stability. The Paris Agreement of 2016 aims to limit global temperature increases, yet urban areas continue to experience extreme weather variations, necessitating a deeper understanding of their hydrological implications.

This thesis investigated the influence of urban environmental conditions on soil moisture dynamics, particularly during drought periods. The study aims to identify the relationship between green spaces in urban areas and soil moisture dynamics in relation to drought. It specifically examines how factors such as sun, shadow, vegetation type, and soil texture impact soil moisture retention and depletion. To achieve this research goal, the following three research questions are determined:

\begin{enumerate}[noitemsep]
\item How do urban environmental conditions affect soil moisture content in green spaces?
\item What is the effect of soil texture on soil moisture dynamics?
\item How do initial soil moisture conditions influence soil moisture dynamics during a drought period, as modelled using HYDRUS-1D?
\end{enumerate}

The study employs a combination of field measurements and HYDRUS-1D modelling, to analyse the soil moisture behaviour under different urban environmental conditions. Data collection took place at the campus of Delft University of Technology, where five distinct environmental conditions were selected as measurement points, sun, shadow, grass, trees and paved areas.

Soil moisture data was collected using the Profile Probe PR1 sensor (in combination with the HH2 Moisture Meter) at depths of 100mm, 200mm, 300mm, 600mm and 1000mm. Measurements were conducted manually over 10 weeks, 3 to 4 times a week, during the summer of 2024, with a total of 33 recorded sessions. Soil texture analysis was conducted using soil samples taken from depths of 150mm, 300mm, 500mm and 800mm. These samples were dried at 105$\degree$C for 24 hours and then sieved through six mesh sizes to determine the proportion of sand, silt and clay in each sample. The soil texture provided understanding of changes in soil moisture dynamics and was used for the critical input data for the HYDRUS-1D simulations.

HYDRUS-1D was employed to model the impact of initial soil moisture conditions and urban environmental conditions on the soil moisture dynamics during longer periods of drought. The model setup included defining soil hydraulic properties using the van Genuchten-Mualem equation and incorporating root water uptake parameters based on the Feddes approach. Monte Carlo simulations were conducted to optimize model performance, using 10.000 iterations to determine the best fitting parameters. The model was then applied to simulate soil moisture depletion during the extreme drought of 2018, to determine the effects of extremely wet or dry initial soil moisture conditions.

The measured soil moisture content results demonstrate significant variations in soil moisture retention among different urban environment conditions. Areas covered with vegetation exhibited higher moisture retention compared to paved surfaces. Within vegetated areas, shaded locations retained moisture for longer periods due to reduced evaporation rates. In contrast, measurement points in sunny areas experienced rapid soil moisture depletion, particularly in the upper 300mm of the soil.

Soil moisture at shallower depths, 100mm to 300mm, was highly responsive to precipitation events, while the deeper layers, 600mm and 1000mm, exhibited more stable moisture levels, indicating a stronger influence of soil texture and groundwater interactions. Statistical analysis using Kendall's and Spearman's correlation coefficients showed strong correlations between shaded grass-covered areas and tree-covered sites up to 300mm depth, but significant divergence beyond that, likely due to differences in root water uptake and soil texture.

Additionally, paved areas consistently maintained lower soil moisture levels across all depths due to limited infiltration, with only minor variations observed at the access tube edges where small gaps allowed some water entry. The modeling results confirmed that urban design choices, such as increasing permeable surfaces and optimizing vegetation placement, can significantly enhance urban resilience to drought.

Soil texture played a crucial role in soil moisture availability, with finer-textured soils (such as silt and clay) demonstrating superior retention capabilities compared to sandy soils, which facilitated rapid drainage.

Modeling results from HYDRUS-1D reinforced these findings, showing that initial soil moisture conditions had a notable influence on drought resilience. Wetter initial conditions resulted in prolonged moisture availability, while extremely dry conditions led to rapid soil desiccation. The impact of extreme initial soil moisture states was most pronounced in shaded grass areas and tree-covered locations, where higher retention capacity helped delay drought effects.

The study's findings align with existing literature on urban drought dynamics, confirming that meteorological, agricultural and hydrological drought factors interact to shape soil moisture behaviour. Meteorological drought, characterized by prolonged low precipitation, was found to be a primary driver of soil moisture deficits, with its effects compounded by urban environmental conditions. The research underscores the critical role of green infrastructure in mitigating the adverse effects of drought by preserving soil moisture.

The limitations of the study are acknowledged, including the constraints of the HYDRUS-1D model, which assumes only vertical water flow and does not account for lateral redistribution. Additionally, the generic settings of the Profile Probe PR1 resulted in soil moisture values not specifically related to the soil type. Finally, only one drought event has been evaluated in the model, limiting the generalizability of the findings.

This research provides valuable insight into the interplay between urban environmental conditions and soil moisture dynamics during drought periods. The findings highlight the importance of green spaces in urban resilience to drought, demonstrating that shaded and vegetated areas significantly enhance moisture retention compared to exposed, paved environments. Soil texture and initial moisture conditions were identified as key determinants of drought response, with finer-textured soils and wetter initial states offering greater resilience.

The study emphasizes the necessity of integrating soil moisture considerations into urban planning and climate change adaptation strategies. Enhancing green infrastructure, optimizing soil management practices, and incorporating soil moisture monitoring into urban water management can help mitigate the impacts of future drought events. Future research should focus on expanding the dataset across multiple urban locations and improving model accuracy by integrating groundwater dynamics and lateral water flow processes.

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.

In recent years, Sustainable Urban Drainage Systems (SUDS) have gained popularity for managing stormwater in urban areas. However, effective asset management of these systems remains challenging due to the widespread reliance on reactive maintenance. This thesis examines the condition of dry swales in Utrecht, Netherlands, and their role in managing stormwater under the municipality’s current maintenance framework.

A comprehensive visual inspection of 210 dry swales was conducted, alongside an evaluation of the area characteristics influencing component failures. The results indicate that only 67% of the swales function properly, with overflow failures being the most common issue, particularly due to clogging; 5% of swales were found to be in a failure state. Conversely, the vegetation layer emerged as the component requiring the most continuous maintenance, with only half of the swales sustaining a well-functioning vegetation layer. Notably, complete clogging issues in swale overflows were linked to a high percentage of impermeable areas within the catchment and a smaller length-to-width scale.

The analysis further revealed that filter basins exhibited the highest percentage of functioning components, emphasising the critical role of the infiltration process. However, even minor deterioration in these basins could significantly impact overall swale performance. The study also identified that a larger impervious area within a swale’s catchment correlates with increased failure likelihood in various components. Factors such as tree density in a catchment area, impervious area, and the age of the swales were shown to contribute to issues like standing water and sediment accumulation in filter basins.

The investigation into sediment accumulation at overflows indicated that sediment tends to build up more in catchment areas with higher percentages of impermeable surfaces, although this phenomenon is influenced by multiple factors that warrant further research.

The findings underscore the necessity of improving the current reactive asset management of swales. By establishing a comprehensive database of inspection data, future research can better inform predictive asset management applications, incorporating parameters such as impervious area percentage, tree density, and swale age into machine learning models to predict swale failures.

Ultimately, the research emphasizes the importance of effective asset management for SUDS, not only to enhance swale efficiency but also to mitigate urban flooding risks. Regular inspection and monitoring are essential to understanding swales’ functionality and degradation over time, informing design improvements and promoting the adoption of sustainable urban drainage systems. This study contributes to the broader goal of creating more resilient and sustainable urban environments through robust asset management of SUDS.

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.

Urban areas are prone to extremely high temperatures as a result of climate change and the urban heat island effect. Urban micro-climate modelling has become an important tool to evaluate the effect of heat mitigating measures and develop climate-sensitive urban designs. However, many models lack the accurate fine scale simulation of evapotranspiration influence and soil moisture conditions to predict human thermal comfort (HTC).
This works objective is to better understand the coupling of the heat and water balance for micro-climate predictions of urban squares and its influence on the HTC, especially under the influence of greening. Therefore, a literature review on recent model developments was conducted. Additionally, a case study was performed for the Heat Square in the Green Village using the model VTUF-3D, chosen based on the literature review.
Five urban micro-climate models work on the fine scale water balance representation, namely ENVI-met, i-Tree Hydro+, Solene-Microclimat, ST, and VTUF-3D. ENVI-met offers comprehensive analysis options of greening solutions and user-friendliness, while VTUF-3D excels in detailing soil and plant characteristics. Given the detailed water balance simulation and the accurate trend prediction for the latent heat flux, it is expected that greening effects on the urban micro-climate of the Heat Square can be predicted accurately with VTUF-3D, if not for spatial and human thermal comfort assessment.
The reviewed models require further model development towards a comprehensive water balance representation and related greening analysis options. Additionally, further efforts towards model applicability are needed, including validation and user-friendliness. VTUF-3D requires improvement of spatial variability representation and HTC prediction.
Note, the literature review is no holistic assessment and does not provide a conclusion on the overall performance of the models. The case study was limited in model feature configuration.

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.

This work provides a water balance-based approach to improve understanding of drought trends by calculating root storage deficits in the United States. To do this, data is ultimately compiled from 1125 watersheds for the years 2001 to 2016. The root storage deficit is then determined using iteratively optimized transpiration values minus the water available to plants and compared to the latent heat of the FLUXCOM energy fluxes data. The stimulus for this work is a better understanding of desiccation processes. Answers to this are given only indirectly. For example, no concrete consequences can be derived from the calculated root storage deficits. Further-more, it turns out that the water cycle-based approach provides clearly interpretable results only for catchments with sufficient water supply. Thus, increasingly arid catchments do not satisfy the general conditions required for the calculations. Catchments in which the root storage deficit exceeds one meter are declared as ever-growing deficit. In these catchments an increased dry-ing process is to be assumed. In all other catchments, temporally congruent developments are shown for both applied methods. Due to the increased dependence on incoming precipitation, calculations performed with the CHIRPS dataset were repeated with the CRUNCEP dataset. This turns out to be generally smaller but confirms the drying processes suggested by the CHIRPS dataset. In contrast, the extents of drying suggested for both methods differ by up to 972 mm for the CHIRPS precipitation dataset, with holistically calculated root storage deficit maxima differing by an average of 178mm. This work is nonetheless useful because otherwise only grid dataset-based and thus statistically adjusted approaches are common in climatological science. Thus, the water cycle-based approach provides a concrete application of the raster values to point measurements.

Many conceptual hydrological models do not include any information on plant phenology, which represents the dynamic behavior of vegetation, to partition evaporation in a catchment. Such models often use potential evaporation to quantify evaporation from the interception reservoir and what remains afterward to quantify transpiration, or the other way around depending on the modeler’s decisions. Such defined orders are the opposite of what is perceived in reality. Vegetation is alive, continuously developing, and impacts the partitioning of evaporation through its ability to transpire. Hence, vegetation is able to constantly change this defined order and thus the ratio of transpiration over interception evaporation over time.

This study aimed to include information from plant phenology, representing the dynamic behavior of vegetation, to partition evaporation in a catchment in conceptual hydrological models. This study hypothesized that the conceptual hydrological models that do include this information provide more reliable hydrological responses. To test this hypothesis, this study used two conceptual hydrological models, which are the lumped FLEX and GR4J models, to simulate daily values streamflow and evaporation for two distinct catchments in the United States.

To include information from plant phenology, this study applied three separate modifications to the conventional structures of the two conceptual hydrological models. The first modified structure uses a part of the Jarvis model, in which stomata respond to temperature, to partition evaporation. The second modified structure uses a method similar to the crop evaporation method of the Food and Agriculture Organization. Lastly, the third modified structure uses a combination of the other two structures.

This study concludes that conceptual hydrological models that include information from plant phenology, as in the three modified model structures, are able to provide streamflow simulations as good as models that do not include plant phenology. The streamflow simulations of conceptual hydrological models, such as the three modified model structures, are therefore not necessarily more reliable. However, conceptual hydrological models that do include information from plant phenology might still benefit hydrological studies that take into account the effects of long and short-term changes to the vegetation in a catchment, such as the effects of climate change, land-use change, and forest fires. Before plant phenology should be used in the models of such studies, challenges such as the uncertainties in the simulations of the evaporation components need to be addressed.

The Volkerak-Zoommeer is a former estuary located the Rhine Meuse Delta in the southwestern part of the Netherlands. After the completion of the Volkerakdam, Philipsdam and Oesterdam as part of the Delta Works, this estuary was cut of from the Oosterschelde and a fresh water lake was formed in a sea water environment. This new lake became the Volkerak-Zoommeer: a lake that acts as a fresh water supply for the regional water systems of the water boards Hollandse Delta, Scheldestromen and Brabantse Delta as well as providing a tidal free shipping connection between Antwerp and the Rhine. The operational water manager of the lake is Rijkswaterstaat. They manage the water level and water quality by letting fresh water in from the Hollandsch Diep at the Volkeraksluizen and release it at Bath into the Westerschelde. This mechanism enables Rijkswaterstaat to flush the system and adhere to the target levels, as well as managing a maximum chloride concentration of 450 mg Cl/L during the growing season. In 2018, the Netherlands experienced one of the most severe droughts since the beginning of weather measurements started. Low discharges at the Rhine resulted in a decreasing water supply and more salinization in the Rhine Meuse Delta. When the discharge of the Rhine at Lobith reach below 800 m3/s, it is no longer allowed to let in fresh water through the Volkeraksluizen. This could lead to problems in the fresh water supply to the regional system of the water users of the Volkerak-Zoommeer. The question came up whether creating a fresh water buffer by temporarily heightening of the water level could be used to overcome a period with no fresh water supply from the Hollandsch Diep. In autumn 2020, the dynamics of the system were researched during a practical trial. The water level was heightened up to +0.15 m NAP. Next, the inlet was closed and it was measured how long it would take until the water level reached -0.10m NAP with normal flushing operations at Bath. During a second trial, the outlet at Bath was closed too. Based on the insights of these trials, a prototype for a Decision Support System was created. This system is able to give insights in the development of the water level at the Volkerak-Zoommeer according to operational water management decisions. This Decision Support System was used to evaluate the impact of delta and climate scenarios in 2050 and 2085 on the water level of the Volkerak-Zoommeer. The maximum possible duration for a period without a fresh water supply was researched per scenario within the target levels of -0.10 m NAP and + 0.15m NAP. Different flushing regimes at Bath were evaluated. For normal flushing operations, this duration is 5 till 7 days. For flushing every second low tide, this period could be lengthened to 8 to 13 days. When the outlet at Bath is closed, the maximum duration is 20 till 60 days. From the trials, it was found that the chloride concentration will gradually increase in this period. Unfortunately, sufficient insights in the dynamics of the chloride concentration lack at the moment to sufficiently incorporate the chloride concentration in the prototype. From historical data, the maximum duration of a period with discharges lower than 800 m3/s measured at Lobith was 16 days during the growing season and 82 days for all year data. Within the current target levels, this duration cannot be reached. It was therefore researched what initial water level theoretically could be achieved based on historical data. This turned out to be +0.50 m NAP. Here, the Volkerak-Zoommeer was considered as a closed system, neglecting the open connections with the Dintel and the Vliet. With an initial water level of +0.50 m NAP, the duration without fresh water supply could be significantly lengthened. With normal flushing operations, the period can take between 12 and 19 days. For flushing operations every second low tide, this period takes 20 till 34 days. When the outlet at Bath is closed, a period up to 120 days could be bridged.

Due to climate change, drought-flood alternations have become more frequent and more severe in many countries in the world. However, little research has been done on the relationship between flood and drought. Thus, this research aims to investigate whether a relationship exists between multi-year droughts and the floods that follow in the post-drought period. The hypothesis is that vegetation decreases as a result of long-term drought, which decreases evaporation and increases the proportion of precipitation that becomes runoff, and thus, increases flood.

To test this hypothesis, the United States is chosen as a case study area. Hydrological data and remote sensing data are collected for 671 basins in the case study area, from around 1980 till 2014. A list of criteria is established to detect the occurrence of long-term droughts, verify the water balance, and ensure data availability. 83 basins fulfill all criteria and these basins are investigated further.

These 83 basins are plotted inside the Budyko space and Fu's equation is fitted to find the free parameter that indicates the runoff ratio. It is found that in general, no increase in runoff ratio is detected after drought. However, looking at the basins individually, changes in the runoff ratio do exist. Further investigations into the cause of these changes are carried out by looking at the effects of drought on the partitioning of hydrological fluxes and separating the basins' movements into climate effects and residual effects. Vegetation changes are also investigated to see whether the runoff changes are related to vegetation changes. The results show that although strong correlations exist between the runoff ratio, evaporative index, and residual effects, no relationship is found between the runoff ratio and the vegetation-related variables. It is concluded that using the method in this research, no significant relationship is found between the multi-year droughts and floods that occur after drought.

The stress on global food security is expected to increase. Hence, crop and drought monitoring will become increasingly important in the future. Synthetic Aperture Radars (SAR) are able to penetrate clouds and thus reliably provide data. Despite the extensive literature that can be found on the use of high spatio-temporal resolution SAR data, a study investigating the influence of drought on Sentinel-1 data over agricultural crops has yet to be conducted. This research aims to bridge this knowledge gap by utilizing Sentinel-1 data. The Sentinel-1 data is acquired and processed in Google Earth Engine and afterwards, data analysis is performed using Python. This results in parcel level SAR (VV, VH and VH/VV) data. This research is focused on maize, sugar beet, potato, onion and barley parcels in study areas in the Netherlands during the 2017, 2018 and 2019 agricultural summer season, of which 2018 and 2019 were impacted heavily by drought. 
This research demonstrates that phenological changes are reflected in Sentinel-1 data with increasing backscatter intensities during leaf development and stem elongation phases. Subsequently, saturation occurs which halts the rapid increase of backscatter. During harvest, the VH/VV ratio decreases rapidly. Time series of barley behave differently due to its unique vertical structure.The results show that VV and VH backscatter values are 2.5, 2 and 1 dB lower during the 2018 drought compared to 2017 for maize, sugar beet and potato parcels, respectively. Furthermore, the seasonal VH/VV ratio cycle for maize, onion and barley is shorter in a drought year and shortest in 2018. The VH/VV ratio cycle in 2018 was 30, 10 and 20 days shorter compared to 2017 for maize, onion and barley, respectively. Lastly, significantly lower VH/VV ratio values are observed during the vegetative stages in 2019. The percentage of individual parcels that show responses similar to aggregated responses ranges from 68% to 100%. Moreover, the results show that the overpass time has a large influence on drought response. Morning passes show significant increase in the magnitude of the VV and VH backscatter drop during the drought periods, especially for sugar beet and potato.The regional variability was assessed by comparing parcel backscatter from the northern part of the Vechtstromen water board, the Scheldestromen water board and the Flevopolder. Generally, drought impact is found to be most extreme in Vechtstromen. However, onions in 2018 were impacted most in Scheldestromen according to yield data. This clearly translated into lower VH backscatter and VH/VV ratio values during and after the drought period. Also, regional differences in maize time series caused by irrigation are observed. The results show that areas in which irrigation was allowed with ground and open water had a longer VH/VV ratio cycle in 2018, compared to areas in which irrigation was allowed only with groundwater.Overall, the usage of Sentinel-1 data for drought monitoring purposes shows tremendous potential. This gives a promising outlook on the use of dense C-band SAR data for the detection of crop drought stress.

In this report the hydrological response to multiyear droughts has been researched. Long-term droughts have resulted in an increase in tree die-off in affected areas. As the hydrological response is related to the vegetation in a catchment, it is of interest to find out whether catchment response regarding rainfall-runoff is different after a long-term drought relative to the situation before the drought. It is important to know this, because a good prediction of water availability is required for making decisions regarding water resources. The research is conducted in Australia, where a long-term drought took place from 1997 up to 2008. The two research questions that have been investigated during this research are: 1) Is there a change in runoff ratio (the fraction of precipitation that becomes runoff) in catchments after a long-term drought relative to the situation before the drought? 2) Is there a change in the root-zone storage capacity (the amount of storage available in the root-zone of the soil) determined with the mass curve technique following a multiyear drought? In this research, the runoff ratio observed after the drought has been compared to the runoff ratio before the drought. This is done to determine whether a change has taken place in the rainfall-runoff relationship of the catchment. In the catchments where the rainfall-runoff relationship shows a change from the situation before the drought, a decrease in runoff ratio is mostly observed. In 54 out of the 196 analyzed catchments, the runoff ratio is lower than the 10 percentile of the runoff ratio before the drought. In 27 catchments, the runoff ratio after the drought is above the 90 percentile of the runoff ratio before drought. In most of the studied catchments no clear difference in the rainfall-runoff relationship can be found due to a long-term drought. Factors that were found to play a role in the differences between the responses to a drought were the drought severity, which is a combination of the drought length and the intensity of the drought, and the seasonality of the precipitation. Catchments that had an increase in runoff ratio were more likely to have a longer drought period and a summer-based precipitation pattern. For the second research question, the root-zone storage capacities have been determined using an earlier derived mass curve technique method, combined with a method that has been developed for recovering ecosystems. In order to compare root-zone storage capacities before and after a drought, a variable is introduced that describes the differences between the values for the root-zone storage capacities before and after the drought. Catchments that showed an increase in the runoff ratio were also more likely to have a negative change in the root-zone storage capacity. This indicates that the root-zone storage capacity decreases in catchments that show an increase in the runoff ratio after drought.

In 2018 The Netherlands experienced the most severe drought since 1976. In response to this, the Minister of Infrastructure and Water Management established the 'Beleidstafel Droogte'. An external scientific committee took part in the Beleidstafel Droogte. One of their key recommendations was to introduce other drought indicators since the currently used indicator (precipitation deficit) is not sufficient due to limitations. This research investigates what drought indicators can be used in The Netherlands to support anticipative drought management. For water managers, it is crucial to know when and where drought occurs, and how severe this drought is. To reveal the start, severity, and ending of a drought, drought indicators can be used. However, to define drought, regional differences need to be incorporated. Therefore, this research used a case study area to find out what drought indicators can be introduced. Within the boundaries of this area, three different analyses were conducted: 1) hydrological analysis, 2) stakeholder analysis and 3) policy analysis. According to the results, a set of potentially relevant and suitable drought indicators was selected. These drought indicators were tested with focus groups to determine whether they can be used to support anticipative drought management. The area of water board Vallei & Veluwe was selected as the case study area. Concerning the hydrological analysis, multiple methods were applied to obtain a better understanding of the water system and essential hydrological processes. Because of the relative nature of drought, several stakeholders that are directly affected by drought were interviewed. Through semi-structured interviews, information concerning drought was obtained about the stakes and information needs for different stakeholders. Since measures can be taken to anticipate drought or to mitigate drought problems, the policy on drought was considered. In this way, the interrelation between policy on drought and drought indicators was taken into account. The results of the analyses mentioned above led to the following selection of drought indicators: SPI-1 & SPI-3, soil moisture, stream discharge, groundwater levels/isohypse, NDVI and EVI. To test the selected indicators can support anticipative drought management, two focus groups session were organised. For these focus groups, a dry year was reconstructed, which was explored by the participants. Based on the information that was presented to the participants, they were asked whether they would advise taking measures and what their level of alertness was. One group received information from the selected indicators, whereas the other group received information which is used nowadays for drought monitoring. After evaluating and comparing the focus groups, conclusions were drawn. The participants that received information from the selected drought indicators acted more pro-active, because: 1) measures were advised more rapidly, 2) more measures were advised, and maintained applicable for a more extended period, and 3) the level of alertness was significantly higher. Based on these findings, it was concluded that the selected drought indicators support anticipative drought management.

In this study, an unsupervised classification approach is used to investigate and characterize the spatial and temporal variability of MetOp-A ASCAT backscatter (σ◦) and the TUW SMR vegetation parameters across mainland France between 2007 and 2017. Currently, soil moisture data is retrieved from ASCAT backscatter measurements using the TU Wien Soil Moisture Retrieval (TUW SMR) approach. To correct for the influence of vegetation on soil moisture, two so-called ’vegetation parameters’ are also estimated from the backscatter measurements. These vegetation parameters are the slope (σ′) and curvature (σ′′) of a second-order Taylor polynomial which describes the incidence angle dependence of backscatter. Recently, Steele-Dunne et al. (2019) showed that the slope and curvature contain significant information about vegetation phenology and vegetation water dynamics across the North-American grasslands, suggesting that the vegetation parameters are a potentially valuable source of information on vegetation dynamics. This study further investigates the value of the vegetation parameters as a source of information about vegetation dynamics for land cover types present in France. 3492 ASCAT grid points were clustered using agglomerative hierarchical clustering based on σ′ and subsequently analysed. The results show that clusters based on σ′ are contiguous and can resemble distinct land cover features; areas like Paris and the Alps are clearly visible in cluster maps. While the clusters differ in terms of σ′ – which follows from a clustering based on σ′ – the results show that the clusters generally also have distinct σ◦ and σ′′ characteristics. This suggests that the clusters represent ’scattering surfaces’ that differ in terms of their seasonal scattering characteristics. It was found that grid points with a heterogeneous land cover footprint tend to have noisy seasonal backscatter signatures, while homogeneous land cover footprints have more recognizable seasonal behavior. Additionally, certain backscatter signatures tend to correspond to certain land cover footprints; in particular the agricultural area around Paris produced clear σ◦ , σ′, and σ′′ signatures corresponding to specific growth stages of wheat and the rapid land cover change during the agricultural season. In general, the results are consistent with the existing assumptions that σ′ is a measure for vegetation density and σ′′ is a measure for the relative dominance of ground-bounce and direct scattering from vertical vegetation constituents. Finally, clustering was performed on ten years of dynamically estimated σ′ and a measure for robustness was introduced to quantify the clustering certainty for each grid point. Robust grid points are found in areas that have relatively stable land cover like the Alps or Paris, suggesting that these areas exhibit predictable seasonal backscatter behavior with low interannual variability. Poor robustness scores are mainly found in north-west France, where land cover is heterogeneous and seasonal backscatter behavior is highly variable. This study confirms that the TUW SMR vegetation parameters contain valuable information about vegetation phenology across different land cover footprints. Furthermore, it was shown that unsupervised classification methods based on the vegetation parameters are able to identify areas with similar scattering characteristics, and are able to show how these areas change over time.

The TU-Wien developed a soil moisture retrieval algorithm that uses the incidence angle dependence of backscatter to obtain soil moisture estimates (Wagner et al., 1999). The core of this algorithm is a second order Taylor expansion with which the backscatter is normalized at a reference angle. Studies have shown that the first and second order derivative within this Taylor expansion, known as slope and curvature, are somehow related to the wet biomass and structure of vegetation. The general approach to forward model satellite observations with land surface variables in a data assimilation framework is through a radiative transfer model (Albergel et al., 2017). However, this requires plenty of assumptions about the vegetation canopy (such as stem height, shape, size, orientation etc.) and is therefore relatively inefficient for understanding the impact of soil moisture and vegetation dynamics on backscatter on a large scale. This study investigates the possibility of using support vector machines as a surrogate model instead of a radiative transfer model to link the TU-Wien normalized backscatter and slope to land surface variables soil moisture and leaf area index. The land surface variables are simulations from the CO2-responsive ISBA-A-gs land surface model. Support vector machines have the advantage of providing implicit kernel functions, which make them very useful for non-linear problems. The ISBA-A-gs data is provided by Météo-France. In total, 1324 support vector machines have been optimized through a cross validated grid search. The optimized hyperparameters were shown to have spatial consistency and look promising as an initial approach to forward modelling backscatter and slope. The SVM performances are further investigated through corresponding land cover types of grid points and the land surface variables.

The purpose of this thesis is to conduct research on increasing flood resilience and, albeit to a lesser extent, drought resilience of New Orleans, Louisiana, United States of America. Focusing on the Orleans Lakeside sub-basin, this project builds on previous work through a joint design-engineering approach. This project seeks to address two shortcomings of existing proposals such as the Greater New Orleans Urban Water Plan: consideration for extreme weather events in the design framework as well as focusing on the integration of technical adaptation measures with the built environment. Hence, the main research question answered in this report is “How can Orleans Lakeside be made more flood and drought resilient by integrating multi-layered resilience capacities of the built environment, whilst safeguarding consistent design of technical adaptation measures within the urban space?”. The development of a design framework and detailed design interventions follows from a synthesis of earlier proposals, supplemented with a vulnerability analysis that focuses on the different capacities of the built environment in dealing with weather events of various magnitudes. For this analysis, the threshold (preventing damage), coping (mitigating damage), recovery (recovering from damage) and adaptive (flexibility to change) capacities of the built environment are investigated. The multidisciplinary approach taken in this project culminates in an urban water management design for Orleans Lakeside which is focused on introducing open water in the urban environment. This design proposal offers a high potential of meeting the required water storage capacity, with the intention to reintroduce the lost identity of wetlands to the suburbs of New Orleans. Typological and urban design across scales aim to integrate water system elements into the urban fabric, and to merge a new water-based urban environment with traditional desired American neighborhood qualities.

The Amazon rainforest is among the most vital ecosystems on earth, holding about a quarter of the global terrestrial carbon sink. Since 2005, three 100-year return period droughts have occurred, the likes of which have the potential to turn the forest from a carbon sink to a source – meaning disastrous consequences for the planet. Monitoring of the Amazon is hence, indispensable. This study explored avenues for monitoring the canopy water dynamics in the region through ASCAT backscatter and dynamic vegetation parameters. These dynamic vegetation parameters are slope and curvature - the first and second derivatives of a second-order Taylor polynomial describing the incidence angle dependence of ASCAT backscatter data. A 10-year length of data from 2007-16 was used to find spatial and temporal patterns in the backscatter, slope, and curvature over Amazonia, and related them to climatic variables such as radiation and precipitation to find the driving force behind the parameters. The findings suggest that the spatial patterns of the ASCAT parameters match the distribution of major land cover types in the region, with significant differences between the major cover types. The evergreen forests have high mean backscatter and low mean curvature compared to other cover types, and weak seasonal variations. The savannas, on the other hand show much stronger amplitudes of seasonal changes. The wetlands, as well, have strong seasonality with especially high ranges in curvature. They also show a change in the backscatter-incidence angle relationship during flooding seasons, thus demonstrating potential for forest flood detection and monitoring. Consistent diurnal differences were observed especially in the backscatter of all regions. These diurnal differences are shown to be an interaction between vegetation phenological activity and precipitation seasons. In the dry season, the morning values are generally higher as the vegetation transpires water through the day. Water stress and a consequent decrease in backscatter are also detected during the Amazon droughts of 2010 and 2015. The spatial distribution of these negative backscatter anomalies matched that of precipitation deficits during the droughts. The anomalies were also seen to be strongest during the peak drought months. These findings indicate that ASCAT backscatter can detect water stress and droughts in the Amazon vegetation. Seasonal cycles in backscatter and dynamic vegetation parameters are visible in all regions. While backscatter follows the moisture availability in the canopy, the slope and curvature are related to variables of moisture demand (such as radiation and humidity) through a strong influence of vegetation phenology. In the radiation-limited Amazon vegetation, the slope peaks with the period of photosynthetic activity following the radiation maximum, while the curvature peak covers the leaf-flushing season. The ASCAT parameters show a relation to the vegetation water dynamics in all major cover types in the Amazon. There is, thus, a solid prospect for the use of ASCAT backscatter and vegetation parameters for long-term monitoring of the Amazon with respect to canopy water dynamics in a variety of land cover types, as well as events such as droughts.

The non-linear relation between Evapotranspiration (ET) and the atmospheric moisture demand in terms of vapor pressure deficit (VPD) has been widely reported. Observations point towards a diverse range of potential drivers; however, without uncovering why the identified factors are found to control diurnal ET-VPD hysteresis. Modelling efforts have laid a theoretical foundation to unravel the compound effect of surface and atmospheric states on this hysteresis. However, so far these models have been unable to realistically incorporate the non-linear feedbacks between atmosphere and surface, and lack a vegetation representation that reflects the underlying biological mechanisms. To unravel in what manner the characteristics of ET-VPD hysteresis are controlled, multi-scale observations spanning biology, meteorology, and hydrology from the CloudRoots campaign are combined with modelling in a novel manner. For the first time a proof-of-concept is delivered for reproducing ET-VPD hysteresis with a model that incorporates both surface-atmosphere feedbacks as well as a mechanistic vegetation component. Observations are used for parameter initialization and evaluation to ensure that the modelled hysteresis curve and underlying processes represent a realistic case. Next, this calibrated model is used for a sensitivity analysis. The results show that both the early morning state of the surface and the state of the atmosphere influence the characteristics of the hysteresis loop with respect to its width, height, and initial slope. Via control of the stomatal aperture, soil moisture stress and the vegetation’s capacity to assimilate CO2 for photosynthesis affect the height and width of the curve. The height of the hysteresis loop is significantly affected by entrainment of warm dry air, while its width is minorly impacted. The characteristic fingerprint of entrainment is distinctly different to that of soil moisture stress and the capacity for CO2 uptake. The sensitivity analysis applied to our observationally inspired case, appears to facilitate quantification of the strongest model responses to changing external forcings and parameters. Therefore, the presented approach offers a promising tool, allowing further research into the controlling mechanisms of ET-VPD hysteresis.

In this study, deep neural networks are employed to act as a surrogate model between the Meteo France land surface model and Advanced SCATterometer (ASCAT) satellite observations. This provides a measurement operator for the assimilation of ASCAT satellite derivations into this model. Currently, TU Wien uses ASCAT measurements to retrieve soil moisture from backscatter. Next to backscatter signal, two additional vegetation parameters are extracted from the TUWien SoilMoisture Retrieval Approach. These parameters are slope and curvature and describe the second order Taylor polynomial, which explains the incidence dependency of backscatter. A recent study showed that slope and curvature could contain valuable information about vegetation water dynamics and biomass phenology. The new explored relationship gives an unique opportunity to relate land surface variables with these observation parameters. This is significant as it could be used to create a climatological data set of high quality and temporal consistency. The surrogate model avoids the need to use a Radiative TransferModel (RTM) to relate the land surface model to the ASCAT observations. RTM’s require complex input variables such as size, shape, height, thickness and orientation of the canopy but also the dielectric properties. Additionally, RTM’s are not based on the actual output of the land surface model (LSM), as the LSMs simulate vegetation parameters such as leaf area index, soilmoisture, gross primary production, temperature and respiration. Thismakes RTMs less suitable to act as a measurement operator. The suggested method to simulate ASCAT observations are deep neural networks. Deep neural networks are able to capture every highly non-linear relationship by using only the outputs from the LSMs. For this study a regular feed forward deep neural network is used to simulate the backscatter signal of the ASCAT instrument, whereas slope and curvature are simulated by a deep convolutional neural network. The results show that the deep neural network is able to simulate the seasonal and inter-seasonal variation of backscatter. Concerning slope the model was capable to capture the seasonal trends and some of the interseasonal variations. Curvature shows the worst model performance, as the model is only able to capture the timing of the seasonal changes but not the right magnitudes. In general, the performance depends on the variation of the observation and land surface data. This suggests that the model structure needs to be adapted according to the complexity of the investigated grid point. A black-box interpretation model, called DeepSHAP, is used to extract the most important features for each observation simulation. This feature importance allows a physical interpretation of the ASCAT observations. The most relevant feature for backscatter is soilmoisture, which is consistent with previous research and gives confidence to the feature importance extraction method. The slope signal is mostly related to the gross primary production and therefore biomass assimilation. Curvature shows the highest correlation for LAI. Both results confirm the previous assumptions that curvature and slope are, respectively, related to structural and phenology changes. The results of this research are substantial as they allow to the first time to relate actual vegetation parameters to the slope and curvature signals. It additionally proves that deep neural networks are a possible choice to act as a surrogate model between ASCAT observations and a land surface model.