Evaporation, the transformation of liquid water to vapor, plays a crucial role in forested ecosystems by contributing significantly to the total evaporation through interception evaporation and transpiration. This process is critical in climate models used to forecast both immediate and long-term climatic changes. Yet, accurately measuring and partitioning evaporation in forests presents challenges due to the complex interplay of factors like canopy height and density, vegetation type, and soil characteristics. Properly segmenting total forest evaporation into its key components—interception evaporation, transpiration, and soil evaporation—is vital for enhancing hydrological and climate modeling. Simplifications in current global climate models, such as GLEAM or the EC-Earth3 used by the Royal Netherlands Meteorological Institute, often overlook the essential role of interception evaporation, focusing mainly on transpiration. This research examines a way to partition total evaporation into its fundamental segments. The study’s main objectives were to partition total evaporation into interception evaporation and transpiration, and further into canopy and forest floor interception evaporation. This was accomplished using
three methodologies: 1) Eddy Covariance (EC) systems positioned above the canopy to measure
total evaporation, with leaf wetness sensors distinguishing between wet and dry canopy states; 2) Analysis of leaf wetness data to quantify canopy interception evaporation; 3) The Bowen Ratio Energy Balance (BR-EB) method to assess overall evaporation and its split into canopy and forest floor components. Selected case days for analysis included scenarios following rain and dew events, with selection criteria based on minimum evaporation thresholds and weather conditions.
Results underscored the significant roles of transpiration and interception in total evaporation,
affected by environmental dynamics and sensor placement. Notably, sensors at higher canopy levels indicated faster drying and lower interception to transpiration ratios due to increased exposure to environmental factors. Despite employing diverse methodologies, the research did not uncover uniform patterns in evaporation partitioning, pointing to the intricate relationships between environmental conditions and canopy structure. The study pinpointed methodological constraints, such as in the assumptions related to leaf wetness sensor data, which might skew evaporation calculations. Future studies should integrate additional measuring techniques, like sap flow sensors and enhanced BR-EB methods, to improve data accuracy and deepen understanding of forest evaporation dynamics.

This thesis explores the application of Distributed Temperature Sensing (DTS) technology to observe temperature gradients and turbulent eddies within and above a forest vegetation layer. Atmospheric temperature gradients tie directly to heat transfer, a primary mode of which are turbulent eddies. The goal in studying these elements is to enhance the understanding of land-atmosphere coupling, which plays a role in climate and weather modelling. The data collection took place in the first two weeks of November 2022 at a meteorology study site in Loobos, the Veluwe. A fiber optic cable was installed, running from the forest floor to 1.5 times the canopy height along a tower. The DTS technology provided high-resolution temperature measurements at one-second intervals. Fluctuations in these profiles could potentially be associated with turbulent eddies. By analyzing temperature profiles over time and integrating meteorological data, the study provided insights into the temporal evolution of vertical temperature distribution at the test site and its sensitivity to weather conditions. Phenomena such as direct sunlight, rainfall, canopy effects, and inversion layers were identified in the DTS data and validated with other meteorological measurements. Notably, sharp temperature inversions observed on two nights exhibited strong correlations with wind speed and its variation. Increases in wind speed and variability were associated with lower inversion heights.

Water scarcity is a serious issue on many smaller islands, with population growth and the predicted impacts of climate change as driving factors. Bonaire is a small island located in the Caribbean Sea that has rural areas without grid connections to electricity and water. Agriculture, both livestock and hobby crop farming, in the Punta Blanku region used to rely on groundwater pumped from wells. Groundwater usage had to be discontinued due to salt intrusion causing the water to become brackish. Water for a large chicken farm that supplies almost all eggs for Bonaire now has water delivered by truck, but it is not reliable due to transportation issues and costs. Reverse osmosis (RO) is recommended as a reliable way to provide water to the Punta Blanku region. Water production can be powered by renewable energy and be more economically feasible with windmill power as the electrical energy source for the RO system. Surface seawater and brackish groundwater samples were tested to determine the best water source for the RO system. Total dissolved solids and electrical conductivity values determine the total power pumping need for the RO system. Using sample results and IMSDesigns, a reverse osmosis model designed by Hydranautics, it was determined that brackish water reverse osmosis (BWRO) was preferred over seawater reverse osmosis (SWRO). With Bonaire wind speeds, FreshWaterMill can easily power 200 cubic meters permeate production per day with BWRO. Additionally, less fouling is expected for BRWO than SWRO due to prefiltration by soil.

The research question addressed in this study is ”To what extent does the presence of a cloud forest canopy impact the Mestelá River catchment and how will this affect the various involved stakeholders?”. The study aims to investigate the importance of cloud forests in the Mestelá River catchment, Alta Verapaz, Guatemala, related to water security and the social impact of cloud forest conservation and management. The research methods used in this study were a combination of quantitative and qualitative methods.

Cloud forests play a vital role in regulating water flow in catchments. The Mestelá River catchment, where the NGO Community Cloud Forest Conservation (CCFC) is situated, is the focus of this research. The project’s primary aim was to establish a long-term canopy setup, ensuring future data collection. The project’s scope encompasses a range of methodologies, including the installation of a long-term measurement station in the canopy, computation of the Mestelá River discharge, the development of a rating curve, and the utilisation of a FLEX-Topo model to simulate the hydrological cycle in the catchment. Additionally, a stakeholder management analysis was conducted to understand the complex impact of cloud forests (conservation) on various stakeholders.

The study did not explicitly formulate any hypotheses, but the findings provide evidence for the impact of cloud forest canopies on river catchments and discharge. The study also has limitations, including the small sample size and the lack of long-term data. However, the study provides valuable insights into the importance of cloud forest ecosystems for water security and the social impact of cloud forest conservation and management. The stakeholder analysis reveals that for CCFC two methods of advocacy can be used. Whilst the CCFC is effective in bottom-up engagement with the community, in addition, a strip for small children was constructed. For top-down advocacy, using the FLEX-Topo
model for visualising water security in combination with cloud forest protection holds promise.

The implications of this work are substantial for cloud forest conservation and associated ecosystems. The findings offer valuable insights for developing effective conservation strategies that consider the canopy’s impact on the catchment and its stakeholders. It is important to note that the FLEX-Topo model is currently conceptual and requires further refinement and detail for the Mestelá River catchment. Nevertheless, this study contributes significantly to the understanding of cloud forest ecosystems and offers practical and theoretical applications for future research and conservation efforts.

Experiments using fiber-optical set-ups for distributed temperature sensing (DTS) were conducted in the LIAISE (Land surface Interactions with the Atmosphere over the Iberian Semi-arid Environment) field campaign during 15-30 July 2021 in the north-east of Spain. Three DTS set-ups were installed to measure temperature profiles along varying vertical scales; 1.6 - 40 m in the atmosphere, 0 - 1 m into the rapidly-growing alfalfa canopy and -0.5 - 0 m in the soil. Measurements were conducted at 5 s and 25.4 cm resolutions using a 1.6 mm Kevlar-reinforced fiber. The preliminary data of these three set-ups are described in the first part of this thesis, which display the potential of using DTS in a land surface campaign to capture vertical temperature structure in great detail.
A fourth fiber-optic set-up was installed with a horizontal extent of 70 m, measuring at four heights between 0.40 m and 2.05 m height. A thinner 0.5 mm cable was used here in an effort to obtain the fastest possible time response in order to measure temperature turbulence parameters using DTS. Measurements were made at 1 Hz and 12.7 cm resolution, however the actual sampling frequency appeared to be 0.15 Hz in the temperature spectrum, likely because of the long response time of the cable.
Despite the limited 0.15 Hz sampling rate it was possible to obtain turbulence information through the use of the structure parameter of temperature, C2T
. This parameter indicates the intensity of temperature fluctuations and was calculated over time, as is conventional. In a novel approach, it was also calculated over space, using the spatio-temporal dataset as obtained by DTS. Both the definition of C2T and the inertial range of the temperature spectrum were used to determine C2T. The spatial C2T obtained through
the definition method was found to have the best correlation with a sonic anemometer reference, with an R2 of 0.88. The temporal C2T lack the structure that is shown in the spatial C2T, which is likely due to 30-min averaged data for horizontal wind speed from the sonic anemometer or to Taylor’s frozen turbulence hypothesis not being a suitable assumption within the dimensions of this research. Determining C2T through the turbulent spectrum was successful for limited data points for the time series, and is currently inconclusive for the spatial series.
Recommendations for further research for using DTS in turbulence analysis are to investigate the effect of instrument noise and the limited sampling rate. Also a critical look into the current DTS calibration routines for atmospheric is recommended. This work provides a first step towards using DTS in capturing
turbulent information along spatial temperature series.

This thesis investigates the impact of a phenology model on conceptual hydrologic model. In conventional conceptual hydrologic models the evapotranspiration is partitioned into evaporation and transpiration by a combination of the potential evaporation and the availability of water. This way the seasonal differences in vegetation dynamics are not taken into account. These vegetation dynamics represent the presence of transpiring leaves in summer, changing to an almost complete absence of transpiration in winter for some vegetation types. Including information on these vegetation dynamics could potentially improve the way streamflow and evaporation is modelled by conceptual hydrologic models. To investigate the impact of a phenology model on conceptual hydrologic models the FLEXmodel is adjusted with a phenology model based on the ’Kc-ETo’ approach of the Food and Agricultural Organisation (FAO) of the UN. With the conventional FLEX-model and the FAOadjusted FLEX-model, 28 catchments from the USA are simulated in terms of streamflow and transpiration. The 28 catchments differ in terms dominant vegetation type and climate indices. The conventional model and the adjusted model are calibrated with streamflow observations and transpiration data from NASA Global Land Data Assimilation System (GLDAS) both separately and together. The conventional FLEX-model and the FAO-adjusted FLEX-model are compared to each other in order to determine under what climate conditions and for which vegetation types the phenology adjustment improves the performance of the model in terms of the ability to simulate streamflow and transpiration and the predictive capacity of the model. The streamflow simulation of the FLEX-model does not improve by the FAO-adjustment. The FAO adjustment does cause an improvement in the mean seasonal sum of the discharge in the spring months, which is structurally underestimated by the conventional model due to an overestimation in spring of the transpiration caused by the lack of information on vegetation dynamics. However the transpiration simulation of the FLEX-model does improve drastically by the FAO-adjustment. The large overestimation of the transpiration in early spring by the conventional FLEX-model is solved by the FAO-adjustment, improving the NS- and NSlogefficiencies for all catchments, regardless of their vegetation type of climate conditions.

Flash floods are a damaging and recurring problem in Cebu city, Philippines. Very little data is known about the intensities and precipitation amounts and the resulting river discharges. This research project firstly aims to gather as much data as possible on precipitation and river discharges that could cause the floods, it focuses on a small catchment in the city called the Mahiga catchment. Data is gathered by installing three tipping buckets and two trail cameras. The cameras were able to calculate the river discharges using an innovative open-source program called OpenRiverCam. Thanks to this program a hydrograph can be
made of the river for each precipitation event. The used cameras were trail cameras of the Brand Bushnell. During this project it was concluded that, due to their unreliability, using trail cameras with OpenRiverCam is really not recommended. Security cameras with a Raspberry Pi are more suited. Due to bad luck with the weather and faulty material only three different hydrographs could be made during our time abroad (10 weeks). These hydrographs however remained useful for the second part of this research project. The second part consists of modelling the discharge of the Mahiga catchment to different
precipitation amounts using HEC-RAS. HEC-RAS is a computer program meaning Hydrologic Engineering Center’s River Analysis System. The model has been calibrated using the gathered precipitation data from the tipping buckets and the discharge results from OpenRiverCam. Graphs have been made about discharges and accumulated volumes and rating curves. The accuracy of the model is reasonable but should be improved using more discharge events. What stood out was the high infiltration rate and the fast response time of the Mahiga catchment. In section three, the results from the HEC-RAS model are used to understand the impact gabion dams make on reducing the peak flow in the Mahiga creek.
The third part summarises the effectiveness of the gabion dams in preventing flash floods. Unfortunately there is no ’real’ flash flood event captured by the tipping buckets, so three precipitation events are used based on analog measurements of a tipping bucket nearby the catchment. The gabion dams are tested on a maximum precipitation intensity of 35 mm/h, 30 mm/h and 25 mm/h with a total amount of 40 mm. Higher amounts of total precipitation
are realistic, but have a larger time duration and are not considered flash floods anymore. The volume that gabion dams can retain is too little for these large amounts of precipitation and are therefore not in the scope of this report. The results show that with at least five gabion dams, the peak flow reduces for all above mentioned precipitation intensities, but for the 35 mm/h it is getting less effective. The model also showed that the effectiveness is very dependent on the volume that can be retained by the dams. Maintenance of the gabion dams is therefore of crucial importance especially with the large amount of sediments and
debris in the creek.

The assessment of potential evaporation or reference combined evaporation and transpiration is among the most important components for many hydro-climatic applications such as irrigation design and management, water balance assessment studies, and assessment of aridity classification indices. Aridity classification indices such as UNEP, Thornthwaite and others are usually employed at large scale applications and require respective estimations of potential or reference combined evaporation and transpiration. The major problem in such applications is not only the limited availability of stations per se but also the limitation of many stations to provide data for a complete set of parameters (i.e., precipitation, temperature, solar radiation, wind speed, humidity). A complete set of climate parameters is prerequisite for accurate estimations of potential or reference combined evaporation and transpiration using the most advanced methods, which are expressions of energy balance (e.g., ASCE-standardized method, successor method of Penman-Monteith FAO-56). Unfortunately, large scale applications of aridity indices suffer from this limitation and the common solution is to use temperature-based formulas. The most popular and historical temperature-based formula is the one of Thornthwaite, which was developed to support the respective aridity classification index. The popularity of this formula is based on the minimum requirement of mean monthly temperature and latitude at the location of interest. Considering the above, this study aims to develop a global database of local correction factors for the original Thornthwaite formula that will better support all hydro-climatic applications but mostly to support large scale applications of aridity indices, which are highly prone to data limitations. The hypothesis that is tested in this work is that a local correction factor that integrates the local mean effect of wind speed, humidity and solar radiation can improve the performance of the original Thornthwaite formula and to convert it at the same time to a formula of reference combined evaporation and transpiration for short reference crop. The global database of local correction factors was developed using gridded climate data of the period 1950-2000 at 30 arc-sec resolution (~1 km at the equator) from freely available climate geodatabases. The correction factors were produced as partial weighted averages of monthly ratios between the benchmark ASCE-standardized method for short reference crop versus the original formula of Thornthwaite by giving more weight to the warmer months and by excluding colder months of Epr<45 mm month-1 where monthly ratios are highly unstable with unrealistic values. The validation of the correction factors was made using raw data from 525 stations of Europe, California-USA and Australia that cover periods mostly after 2000 and up to 2020. The validation procedure showed significant improvement in the estimations of reference combined evaporation and transpiration using the corrected Thornthwaite formula that led to a 19.4% reduction of RMSE for monthly and a 55% reduction of RMSE for annual estimations compared to the original formula. The variation of the correction factor was also investigated in different major Köppen climate classes and it was found that tends to increase in drier and warmer territories. The five major Köppen groups were ordered as follows B > C > A > D > E considering the magnitude of the correction factors values. The corrected and original Thornthwaite formulas were also evaluated by their use in UNEP and Thornthwaite aridity indices using as a benchmark the respective indices estimated by the ASCE-standardized method. The analysis was made using the validation data of the stations and the results showed that the corrected Thornthwaite formula increased by 18.3% the accuracy of detecting identical aridity classes with ASCE-standardized method for the case of UNEP classification, and by 10.4% for the case of Thornthwaite classification in comparison to the original formula. The performance of the corrected formula was extremely improved especially in the case of non-humid classes of both aridity indices. The overall results showed that the correction factors produced in this study can improve the performance of the original Thornthwaite formula providing better estimations of the aridity classification indices.

Root zone storage capacity Sr is a significant variable for hydrology and climate studies, as it strongly influences the hydrological behaviour of a catchment. A climate-derived method (water-balance between precipitation and transpiration) was applied for estimating Sr values for 113 catchments in Australia. Various climate, hydrological and vegetation characteristics were compared with Sr, and the relations between them were analyzed. The eucalyptus forests ,evaporation and the seasonal pattern of climate were determined as more important variables. Principal Component Analysis and K-Means clustering method were applied for clustering these catchments, which indicating the co-evolutionary impacts of climate and vegetation on root zone storage capacity.

Due to increasing global population and increasing food demand, crop yield needs to be improved to forestall potential food shortages. To achieve optimal crop yield, irrigation is applied to replace water losses due to evapotranspiration (ET). Information on ET should be applied to optimize water allocations and water use. In this study, the correlation between ET and yield will be investigated on field-scale level to assess the potential of high resolution ET products as a tool to detect yield variation. The variability of crop yield and transpiration are caused by the variability in the topography, groundwater and soil properties.
Agricultural practices and field-scale water management demand high resolution (in meters) and high temporal resolution (daily to sub-daily) remote sensing products. With the arrival of new satellite platforms, such as Sentinel-2, the aforementioned remote sensing data can be improved significantly in spatial and temporal resolution. In order to compare the functionality of different remote sensing products, an assessment is executed for two satellite derived vegetation indices: normalized difference vegetation index (NDVI) and normalized difference water index (NDWI), and two satellite derived evaporation products: WaPOR (Water Productivity through Open access of Remotely sensed derived data) and a newly developed evaporation algorithm from VanderSat. Within this research, the focus lies on assessing which dataset is able to observe the spatial difference and temporal patterns on field-scale level. Using a large sugarcane plantation in Xinavane, Mozambique, as a case study, we demonstrate how the spatial variability of the remote sensing results are correlated to the sugarcane yield. To assist irrigated agriculture we demonstrate that a high resolution evaporation product is needed to incorporate spatial variability in evaporation estimates. The analysis shows that the high resolution satellite derived vegetation indices are related to the spatial variability of yield. Our results indicate that NDWI has a strong positive correlation of 0.73 with yield, but NDVI has only 0.64. The actual evapotranspiration estimates have a moderately positive correlation with yield of 0.5 for WaPOR and 0.57 for VanderSat. Evaporation estimates should be related to yield to control irrigation properly. WaPOR and VanderSat use NDVI as a input for crop stress, these existing evaporation algorithms should incorporate high resolution spatial imagery as NDWI instead of NDVI to assist irrigation adequately. In order to use the satellite derived evaporation algorithms for agricultural practices and field-scale water management, future research should be focus on improving the relation between satellite derived evaporation algorithms and yield.

The Miombo woodlands are characterized by a transition period which is defined as the dry period in which grasses wither, trees shed and flush their leaves a few weeks before the rainy season. It is difficult to measure evaporation, due to the influence of the plant water storage on the water availability during the dry season. The seasonal variation in the plant water storage makes it even harder, as the time lag between the plant water storage and the terrestrial groundwater storage varies between the 0 to 90 days depending on the vegetation density. (Tian et al., 2018). It is unclear why the trees prefer this early flushing strategy and what is triggering the shedding of old leaves and flushing of new leaves. It is difficult to remotely sense the variation during this period. In this report, the outputs of several evaporation products and the vegetation indices are studied and compared to see how models follow the transition period. Three models, that are chosen for the comparison use different methods to indirectly calculate the evaporation flux. The first model is the Surface Energy Balance System (SEBS), which is based on the Surface Energy Balance as the name suggests and calculates the evaporation through land-atmosphere relationships. The second model is the Global Land Evaporation Amsterdam Model (GLEAM); this model uses the water balance model to calculate the water stress factor and in return the actual evaporation. And lastly, the MODIS Terrestrial Evapotranspiration (MOD16A2) which uses the Penman-Monteith equation as a basis to separately calculate the evaporative fluxes such as transpiration, soil evaporation, and interception. The outputs of the evaporation models differ quite a lot, especially during the transition period. This is among other things due to the input variable such as vegetation indices. There are several vegetation indices such as the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Infrared Index (NDII) and the Leaf Area Index (LAI). The difference in the indexes is minimal and only small timing differences can be found. This means that the output differences are not influenced by the vegetation indices. MODIS follows the trends found by the vegetation indices best as it does not have the shortcomings that GLEAM and SEBS display. GLEAM overestimates the water stress because the model doesn’t account for plant water storage. While SEBS responds well until the start of July in which the model starts to oscillate. The oscillations could be due to the slash and burn culture which happens around this time which could impact SEBS substantially due to its dependency on temperature and radiation. Even if the MODIS output is the most similar to the vegetation output, it is limited by the fact that it is average values and daily variations might be lost. As this research is based on mostly satellite images supplemented by field observations, no statements can be made about it how accurate the models are. But based on this research, the MODIS model seems to be the preferred evaporation output.

The Meuse river basin covers an area of 33,000 km2, touches five countries and is a major communication route in Europe. It is one of the catchments with longest streamflow records, with daily measures of discharge dating back to the beginning of the previous century. Attempts to model streamflow with standard hydrological models revealed that average streamflow was consistently overestimated by the model in the period 1933-1968. Different attempts to explain such anomaly can be found in the literature. In this work we hypothesise that this anomaly could be resolved by considering a time varying root zone storage capacity, represented by a model parameter (Su,max), which has affected the partitioning between precipitation and streamflow. Vegetation is in fact believed to adjust root zone storage capacity to overcome droughts with a return period of about 20 years. To test our hypothesis, a semi-distributed conceptual model, based on the FLEX modelling approach, was used. A time varying Su,max was obtained with two approaches: by calibration of the model parameters in a moving time window, and by derivation of Su,max directly from climate variables. The results show that adding time dependency to Su,max improves the mean flow simulation, however not to a degree that it fully explains the observed anomaly. Deriving Su,max directly from climate variables delivered a better fit to the average streamflow than calibration, which confirms the feasibility of a climate derived root zone storage capacity in hydrological modelling.

Near-surface wind speed is typically only measured by point observations. The so-called Actively Heated Fiber-Optic (AHFO) technique, however, has the potential to provide high-resolution distributed observations, allowing for better understanding of different processes. However, before it can be widely used, its performance needs to be tested in a range of settings. Therefore, in this work, experimental results on this novel observational wind-probing technique are presented. We utilized a controlled wind-tunnel setup to assess both the accuracy and the precision of AHFO as well as its potential for outdoor atmospheric operation. The technique allows for wind speed characterization with a spatial resolution of 0.3 m on a 1 s time scale. The flow in the wind tunnel is varied in a controlled manner, such that the mean wind, ranges between 1 and 17 m/s. Comparison of the AHFO measurements with observations from a sonic anemometer shows a high overall correlation, ranging from 0.94-0.99. Also, both precision and accuracy are greater than 95 %. As such, it is concluded that the AHFO has potential to be employed as an outdoor observational technique in addition to existing techniques. In particular, it allows for characterization of spatial varying fields of mean wind in complex terrain, such as in canopy flows or in sloping terrain. In the future the technique could be combined with regular Distributed Temperature Sensing (DTS) for turbulent heat flux estimation in micrometeorological/hydrological applications.

Climate change and human influences are widely investigated. However, the processes of aerosol-cloud interactions are still not adequately known and the associated lack of knowledge causes uncertainties in climate change prediction. Therefore this study presents different approaches to investigate those interactions, in particular the Twomey-effect, which states that an increase in aerosol loading leads to an increase in cloud drop number density and a decrease in cloud drop effective radius, considering constant liquid water path.
The data analysed was obtained during the ASCII campaign 2016 at Ascension Island. Cloud and aerosol measurements were done by an ultra-violet (UV) lidar during the month September 2016. The cloud microphysical properties - cloud drop number density and cloud drop effective radius - were retrieved using the cloud property inversion retrieval algorithm. The cloud effective radius varied between 1.88 and 4.48 $\mu m$. The cloud drop number density had values in the range of 228-1690 $cm^{-3}$. Furthermore, the total aerosol profiles for clear sky scenes and the aerosol profiles below clouds were retrieved, solving the boundary-value-problem using the ’Klett’ approach. For the aerosol profiles below clouds an extra factor was introduced, accounting for multiple
scattering inside the clouds. The aerosol loading arrived at Ascension Island came mainly from the South (Atlantic Ocean) in the lower 1200m or from the East (African continent, biomass burning events) above 1200m. The aerosol-cloud interactions were examined for both the clear sky and the below cloud aerosols with the cloud properties. Both approaches gave evidence for the Twomey-effect.
Those results suggest that the UV-lidar is a suitable instrument for investigation of aerosol-cloud interactions. Future projects can use those approaches to gain more knowledge over the interactions, enabling a major improvement of climate change predictions.