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 delves into glacier dynamics using the 2D Shallow Ice Approximation enriched by satellite remote sensing data on glacier surface velocities and ice thickness, aiming to refine empirical laws for better predicting glacier movements. The integration of such data has been pivotal, markedly enhancing model calibration despite challenges like steady-state assumptions necessitated by data scarcity. This underscores the critical role of high-quality, temporally resolved data in modeling glacier dynamics accurately.
A significant advancement was the implementation of spatial stratification, which notably improved model performance—reducing Root Mean Square Error (RMSE) by up to 30% and elevating the coefficient of determination (R²) by 0.2 to 0.4 across different regions. This highlights the potential of fully distributed inversions to capture the complex variability of glaciers. Employing Julia for its computational efficiency proved effective for large-scale modeling tasks, setting a promising foundation for future research aimed at understanding and predicting glacier responses to climate change. It is recommended to utilize geostatistical interpolation methods for inverting glacier characteristics from sparse data, in order to acquire these characteristics across the entire glacier area.

Interferometric Synthetic Aperture Radar (InSAR) stands as a widely adopted technique for monitoring displacements on the Earth's surface, providing millimeter-level precision. Several InSAR studies show the efficacy in retrospectively identifying hazardous situations, such as the failure of a structure. The next imperative step is to detect and identify anomalous points proactively. This necessitates robust and repeatable displacement estimates to avoid misinterpretation and instill confidence in the results.

Many InSAR studies use a batch estimation process requiring a robust algorithm to obtain reliable results. Here we propose a test recipe and introduce metrics to assess the robustness of the InSAR displacement estimates quantitatively, comparing the batch-estimated results of varying SAR acquisition inputs. Robustness characterizes the stability of displacement estimates in the face of disturbances and uncertainties, demonstrating resilience against changing conditions and input.
Our quantification of robustness involves three core metrics to assess InSAR displacement estimates.

Case studies conducted over the city center of Amsterdam and a coastal region at the North Sea reveal the useful insight provided by robustness testing in identifying ambiguities and fallacies in the applied algorithm. Notably, the main challenges arise from the estimation of atmospheric delay, which emerges as a sensitive step with ample room for enhancement. A robust atmospheric estimation appears very dependent on the use of a sufficiently large area of interest while the estimation is sensitive to first-order network changes.

Through the implementation of appropriate measures, an average metric improvement of a factor of four can be achieved, reducing the likelihood of a misinterpretation of the InSAR time series. This underscores the effectiveness of the proposed test recipe in improving existing InSAR software.

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.

This project is a consulting project for Community Cloud Forest Conservation (CCFC) on how to obtain and communicate to relevant stakeholders an understanding of the impact of land use change and climate change on the hydrological balance of the cloud forest ecosystem in the Sierra Yalijux. The outcomes of the project will be used by CCFC and partners in four areas: Rural water committee capacity building with municipal and village leadership groups, environmental education with the ministry of education, reforestation, and conservation carbon/water credit prioritization with the national forestry institute, and to create thesis topics for bachelors level students with Universidad Rafael Landívar and Universidad de San Carlos. In order to achieve this goal, we divided our efforts in four areas: First, a description of the situation and a review of literature to identify gaps in scientific and practical understanding of local cloud forest hydrology (Chapter 2). Second, an analysis of the situation at a regional scale using publicly available historical data such as remote sensing data and data from the national meteorological authority (Chapter 3). Third, identifying important hydrological processes in the Cloud Forest micro-climate (Chapter 4) and prototyping and testing measurement setups (Chapter 5). Fourth, making suggestions on how to apply the results to the intended impact areas that CCFC has (Chapter 6). Our recommendations to CCFC for capacity building with water committees are based on a literature re view, we found that the presence of Cloud Forest is expected to increase base flow in springs due to its ability to capture additional hydrological inputs in the dry season, increase moisture recycling after heavy rain events, and store water in the soil. We recommend working with water committees to outline the recharge zones of their springs, run some simple calculations on water availability based on precipitation, and develop manage ment plans for the area. Our recommendations for further research are based on the research approaches we describe at the regional scale and the prototyping of field methodologies that we tested. A more permanent setup for data collection is being developed jointly with the Universidad de San Carlos at CCFC’s nature preserve.

Providing farmers with the tools to monitor their crops continuously and reliably can aid the scaling of global food production to meet the ever-growing demand. The optical Normalised Difference Vegetation Index (NDVI) is commonly used to monitor crop greenness as an indicator for biomass, but it is limited by clouds and signal saturation. Synthetic Aperture Radar (SAR) imagery, which is not hampered by clouds, can be used to complement the NDVI. The Biomass Proxy (BP) combines the NDVI and SAR data, but spatial biomass estimations still strongly depend on the NDVI and therefore face the same limitations.
This study aims to assess the potential value of spatial SAR data to approximate the in-field biomass distribution. The SAR signal from Sentinel-1 and the NDVI signal from Sentinel-2 were analysed temporally and spatially for fields of maize, barley, oat, and spring wheat in the Dutch province of Flevoland. It was assumed that consistent SAR patterns in the spatial signal correspond to biophysical changes in the monitored crops. A framework to detect these patterns and include them in the BP was created based on combining cluster detection with spatial autocorrelation. The components of this framework demonstrate that backscatter intensity, phenological stage and crop type influence the probability of consistent patterns and that consistent patterns could not be observed from the spatial NDVI signal. Moreover, it was found that the BP’s sensitivity to the input signals depends on crop type. With the knowledge of when and where consistent patterns occur, targeted research can be done to understand the spatial SAR signal better and, thereby, optimally use all available information.

This report proposes improvements of the land surface model (LSM) used in the turbulence-resolving Dutch Atmospheric Large-Eddy Simulation model (DALES). Important changes include the infiltration of precipitation, the parametrization of the soil hydraulic functions and the formulation of the soil water extraction by vegetation roots. The performance of the improved LSM version is validated using offline simulations and observations from the CESAR meteorological observatory in Cabauw (Netherlands), during the year of 2015. An optimal parameter set for this location set is obtained from both satellite retrievals and in-situ observations. In particular, the seasonal dependence of both the Leaf Area Index (LAI) and the roughness lengths for heat and momentum is prescribed to the model.

The offline simulations determined that the improved version of the LSM is able to model the surface fluxes during the year of 2015 with an accuracy of 10 W/m2. This accuracy was found to be strongly dependent on the chosen land surface parameters such as the LAI, the roughness lengths, the soil textures and the vegetation root profiles. Furthermore, the lower boundary for soil water transfer, set to free drainage, was found to cause too much vegetation stress after an extended simulation time.

Finally, the LSM is tested in a fully-coupled mode during three different meteorological events observed at the CESAR observatory. Firstly, stratocumulus simulations confirm the importance of the surface evaporation on the development of the stratocumulus layer, which is considerably reduced compared to simulations that assume the surface to be saturated with water. Secondly, the growth of the clear convective boundary layer was found to be greatly affected by the partitioning of the surface fluxes, as the sensible heat flux plays a key role in entrainment. Although the modelled sensible heat flux was found to be in agreement with the observations, the growth of the boundary layer was still substantially underestimated. Lastly, it was found that it is possible to model the main development stages of radiation fog in DALES, despite the fact that the modelled fog layer appears to be too persistent.