Intense precipitation can have extensive economic outcomes, from disrupting outdoor activities to causing severe infrastructural damage, such as landslides, and endangering public safety. The urgency to mitigate these impacts underscores the need for improved early warning systems. Enhanced short-term weather prediction, or nowcasting, is critical for addressing these severe weather events effectively. Traditional meteorological forecasting methods, while foundational, are often constrained by simplistic physical assumptions and fail to capture the complex, nonlinear patterns of intense weather events. These methods also struggle with high computational demands and lack the resolution needed to detect crucial microscale atmospheric phenomena for accurate short-term forecasts.
To address these challenges, this research introduces a novel deep learning approach utilizing a streamlined architecture that combines a Vector Quantized Variational Autoencoder (VQVAE) and an Autoregressive (AR) Transformer. This model aims to predict weather conditions up to 180 minutes ahead, using data analyzed at 30-minute intervals. The proposed model displays comparable performance with the state-of-theart conventional methods and other deep learning nowcasting models in predicting precipitations and sometimes extreme events. This study seeks to enhance forecasting accuracy and efficiency, providing valuable contributions to the field of meteorological nowcasting.

In Dutch polders, numerous structures like bridges, weirs, culverts, and pumping stations have
been constructed over centuries to manage water levels. These structures play a crucial role in
maintaining water levels within predefined targets. The flat topography of the Dutch landscape
combined with the collective impact of head losses, induced by these structures may result in
flooding of polders during high runoff scenarios. Over time, culverts and bridges may
underperform due to alterations in the water system, increased pressure from climate change,
evolved design rules, insufficient maintenance, and shifts in land use.
A challenge is the potential hydraulic underperformance of structures and the need for their
premature replacement, which is costly. Waiting until the end of their technical lifespan may
contribute to floods. Therefore this thesis focuses on improving existing structures to mitigate
the need for replacement, specifically by streamlining inlet and outlet openings to reduce
energy losses. This leads to the research question of this thesis: “How can the head loss over
existing (too tight) culverts be minimised by adding an inlet or outlet profile and does this lead
to a substantial enhancement in the performance of these culverts, providing a practical option
to postpone the replacement of underperforming culverts?”
To answer this question, the problem is explored by looking into the fundamentals of energy
losses, including entrance losses, friction losses, and exit losses. This gives an understanding of
the conditions under which these losses manifest. However, these basic calculations have
inherent limitations due to their reliance on predefined coefficients. This renders them
inadequate for evaluating the effects of introducing new profiles onto an existing structure.
To overcome this, a flume experiment has been performed to verify whether it is possible to
measure water level differences for various profiles at the culvert entrance and exit. With a 3D
Computational Fluid Dynamics (CFD) model (OpenFOAM), flows around different culverts
are simulated. The results of the CFD model are compared to the flume experiment, after
which the CFD model is used to simulate a variety of scenarios, with different profiles, culvert
dimensions, velocities, and water depths.
As such, this thesis addresses challenges and uncertainties in quantifying head losses in culvert
structures through experimental methods and CFD modelling. Experimental setups struggle
with controlling all flow-influencing parameters, while CFD modelling offers flexibility but
requires careful consideration of uncertainties and limitations. The discussion emphasizes the
complexities of comparing experimental and model results, highlighting trade-offs and
uncertainties in each approach.
The conclusion answers the central research question, confirming that specific profiles added to
culverts can significantly reduce entrance losses up to 65%, thereby lowering headwaters for a
constant discharge.
The recommendations section outlines possibilities for further research, including optimizing
profile dimensions and conducting sensitivity analyses of influential parameters. Practical
recommendations involve aligning large-diameter concrete culverts with the socket end in the
flow direction and integrating groove or rounded profiles during construction for cost-effective
inlet loss reduction.

Recent Conflict Early Warning Systems have found little evidence of predictive power of drought indicators for conflict prediction. However, this may result from the context-specificity of the drought-conflict relationship, as stressed in the more recent climate-conflict literature.

The present thesis assesses the local role of meteorological drought indicators for communal conflict prediction in North-Western Kenya, as a region where the narrative of resource-scarcity driven conflicts exists.

A local-scale literature review on conflict dynamics followed by a fixed-effects logistic regression modelling approach stress the importance of the spatial dimension when analysing drought-conflict relationships. The role of cross-border transhumance in linking climate variability to conflict occurrence is stressed by the lower confidence intervals and more significant effects when moving the regression analysis from the spatial delimitation of administrative units to the agency level of ethnic groups.

Differences in between ethnic groups in the obtained patterns of conflict behaviour in response to drought or water abundance are explained by their migratory behaviour along with a differentiated account of their relative drought vulnerability.

The lack of any considerable role of drought in the subsequently built quasi-replication of the WPS Global Early Warning Tool, is therefore assigned to the mismatch of administrative units as the spatial
unit of analysis in a pastoralist area, where herders frequently move their cattle to the other side of the border.

It is advocated for an ethnic-group centered approach to predicting conflict, which relaxes assumptions on spatial containment of conflict events. However, whether this alternative model specification leads
to a greater role of drought indicators in conflict prediction and better overall predictions, needs to be assessed in future work.

This thesis aims to assess the applicability of the CRCTool for evaluating the effectiveness of NBS for mitigating tropical urban floods in the city center of Paramaribo. Assumptions related to urban flood mitigation were tested, a sensitivity analysis for the most sensitive parameters was performed, and the hydrological behaviour of groundwater processes, evapotranspiration, and percolation were critically assessed. In addition, the model outcomes were compared to a hydrodynamic model, developed in D-HYDRO, to determine the importance of spatial effects such as flow routing and elevation. It was found that the current state of the CRCTool restricts its applicability to areas with low rainfall and runoff volumes. The continuous modelling capabilities are limited due to the way hydrological fluxes are implemented. Minor model adjustments can be made to make the tool more suitable for areas with larger rainfall volumes. This research recommends implementing these adjustments and thoroughly testing the tool's performance. Additionally, to minimize the impact of flow routing and elevation, it is advised to test the tool on smaller areas where the hydrological conditions are more clearly defined.

The Netherlands, Germany, and Belgium were hit by heavy and prolonged precipitation in July 2021. As time passed, weather warnings escalated, leading to evacuations due to predicted floods, including in the Rur catchment. It was difficult to forecast the flooding of the Rur, raising the question of which elements are crucial in a flood predictionmodel for the Rur river. This question is addressed by addressing both a hindcast of the 2021 flood event and creating forecasts based on the weather forecast of July 13, 2021.

The Rur river basin is characterised by topographic and geological variations, with the steep Eifel responding differently than the flat lowlands, and human intervention in the form of reservoirs and lignite mines. A hydrological Wflow_SBM model has been derived for the Rur river basin, encompassing these characteristics, along with a hydrodynamic ProMaIDes model for the downstream reach of the Rur. These models were compared to investigate various aspects: river routing, floodplain flow, tirbutary interactions, the influence of reservoirs, and the impact of reduced groundwater levels.

The results of the 2021 floods indicate that modelling flows in floodplains is crucial to shaping the flood wave, both in tributaries and the Rur itself. Additionally, the reservoir played a significant role in attenuating the flood wave, with the increase in the outflow of the reservoir primarily affecting the tail of the wave. The reduced groundwater level was simulated by adding a leakage termto the saturated subsurface zone, whose indirect effect is significantly greater than the leakage termitself. Moreover, the tributaries Worm and Inde, particularly, are influential in the Rur’s discharge. These characteristics are also evident in the simulated forecasts, although the spatial and temporal resolution is significantly lower for these meteorological predictions.

Finally, the characteristic response of the Rur demonstrates that not everymodel type is equally practical for flood forecasting. The dominant flow from the reservoirs is highly regulated and is unlikely to induce inundations downstream. Complex flow patterns in floodplains only become relevant in the Dutch Rur, which makes two-dimensional modelling particularly valuable here. Therefore, it is recommended to use a one-dimensional discharge model, incorporating delay effects from winter bed flows. When predicted discharges at the Stah station are exceeded, two-dimensional simulations may provide a solution, the model area reduced to the Dutch Rur, focussing on predictions where a critical value related to floodplain capacity (Qlimit = 300 m^3/s) is exceeded.

Regional climate model (RCMs) simulations are used in hydrological (climate-change) impact assessment studies. However, RCMs exhibit noticeable deviation from observation, and can show large variation in ensemble projections (biases). The objective of this study is twofold, first to assess the robustness of two high-skill bias-correction methods; empirical quantile mapping (QM), and scaled distribution mapping (SDM) in improving the hydrological modelling of the Rhine River. The second is to assess the potential impacts of climate change on low flows at Lobith based on RCP8.5 scenario. The two correction methods are applied to correct the systematic bias from five climate datasets from the Coordinated Downscaling Experiment in Europe (EURO-CORDEX) covering the Rhine domain, using high resolution gridded datasets (1 km2) spanning from 1979 to 2019.
The bias-corrected simulations from the hydrological model provided more accurate discharge estimates than the wet biased simulations, with an average error of less than 100 m³/s at Lobith. The correction methods are also capable of correcting unprecedented temperature and precipitation values, making them useful in climate assessment studies in the Rhine river. However, it appears that the accuracy of the bias correction depends on the parent GCM, performance of the raw RCM and the skill of the hydrological model in estimating discharges at the point of interest. In addition to that, the drizzling effect could not be reduced using these methods.
Noticeable climate change impacts at Lobith are found using the bias-corrected projections. These projections suggest that low flows are going to be more frequent and longer in the coming 38 years. Unprecedented discharges (< 700 m³/s) are projected to occur at least 50 times between 2020 - 2060. This is coupled by a decrease in the long-term mean annual flow by 100 m³/s and a slight shift in the seasonality of low flows (2 weeks shift).
The general hydrograph at Lobith is set to change due to climate change for the period (2020 – 2060), with relatively higher discharges from early June to end of August followed by relatively lower discharges in the last four months of the year. Water levels are projected to decline in average by 20 cm (early June to the end of August) and increase in average by 30 cm (end of August till to end of December). The study recommends the need of combining bias correction, the feedbacks in the climate system (land use changes) and climate adaptation strategies to study these effects further.

Disasters like inland floods and landslides are a cause of extreme rainfall. To increase the time to take early measures against such disasters it is of great importance to have access to accurate prediction of the rainfall. For the prediction of floods, Quantitative Precipitation Forecasts (QPFs) are used as input for hydrologic models. Numerical Weather Prediction (NWP) models are commonly used to generate QPFs, but for short lead times (less than 6 hours) the NWP forecasts are not accurate enough. For very short-term forecasting the nowcasting method is used. Nowcasting rainfall is a computational process of extrapolating the most recent rainfall observations and has great potential for lead times up to 6 hours. A source for opportunistic sensing to generate rainfall estimations is Commercial Microwave Links (CMLs). Signals for telecommunication purposes will travel along a CML (link) path from one station to another. When a rainfall event occurs, the signal attenuates. This attenuation can be used for the estimation of path-averaged rainfall intensity estimations. This thesis investigates the possibilities of using CMLs for estimating and nowcasting the rainfall. The study area is Sri Lanka, a country without access to radar-based rainfall estimation. A 3.5-month CMLs data set with 2560 unique links from Dialog Sri Lanka is used and compared to 8 hourly and 12 daily rain gauge stations. The studied period is from September 12 to December 31 in 2019. To generate rainfall estimations from the CMLs, the algorithm RAINLINK is used. RAINLINK includes a set of default parameters, which was optimized for the Netherlands. For Sri Lanka, two new optimal parameters are derived. For the optimal parameters daily CML estimations are calibrated with the 12 daily rain gauges. With the new optimal parameters, the rainfall estimations are calculated and used in Pysteps, to generate nowcasts for 20 rainfall events in the studied period. Deterministic and probabilistic nowcasts are generated. These nowcasts are compared with a benchmark nowcast, called Eulerian Persistance (EP). RAINLINK gives more accurate rainfall estimations in Sri Lanka with the new parameters of the wet antenna attenuation and the coefficient compared to the default parameters. Longer path lengths tend to have lower wet antenna attenuation and a higher value for the coefficient. Nowcasts were made for the 20 selected rainfall events. The accuracy was calculated with two verification methods: the Fraction Skill Score and the Critical Success Index. From these results, the deterministic nowcasts with S-PROG and benchmark EP are accurate for lead times up to an hour. The deterministic nowcasts give the most accurate results, compared to the EP and the probabilistic nowcasts. The probabilistic nowcasts show the least accurate results, and are with a lead time of one hour only accurate for low rainfall intensities (1 mm/hr). Overall, the nowcasting results are showing that with CML rainfall estimations, Pysteps can make accurate nowcasts in Sri Lanka. To improve the nowcasts even more, a combination of rainfall estimations from CMLs, rain gauges, and satellite data could be considered.

The Dual-Frequency Precipitation Radar on board the Global Precipitation Measurement (GPM) mission core satellite has been providing precipitation products across the globe for over 6 years, thereby even supplying precipitation estimates for areas on Earth where surface-based precipitation measurements are not possible, like in remote regions, as well as seas and oceans. In this study, the GPM DPR Ku-band Level 2A instantaneous observations are compared with continuous measurements from a surface network in the Netherlands for high-intensity precipitation events during summer months. The aim of the research is to gain more insights in the temporal and spatial correspondence of the DPR’s measurements with precipitation occurring on the surface. The surface network consists of 2 C-band weather radars and an automatic gauge network of 33 gauges (approximately 50 km apart). Radar reflectivity factor is one of the prime parameters used for precipitation retrievals from radars and is thus the parameter used for anal- ysis of both the DPR and the weather radar network. Precipitation intensity is the parameter retrieved from the automatic gauge network. Space-borne and surface measurements have vary- ing characteristics, for instance in spatial and temporal resolution, and thus many challenges arise to perform an accurate and qualitative comparison. In this research, data is funneled by selecting high-intensity gauge data with a threshold of 15 mm/h within 40 minutes from GPM scan time within a 2.5 km range from the nearest DPR footprint. Ultimately, 26 high-intensity gauge measurements spread over 18 dates were used in the comparison analyses, selected out of data ranging from May - October for the years 2018 - 2021. The analyses showed that DPR and WR data correspond best in time (average correlation of 0.785) when DPR scantime is within 3 minutes of WR scantime. Also, results showed that instantaneous DPR data can be related to high-intensity gauge observations within a 40 minute time range by use of wind direction and wind speed. Furthermore, it was observed that precipitation performance of DPR is found satisfactory for intensity ≥ 0.5 mm/h (POD between 0.7 - 0.9 based on distance WS-IFOV), but unsatisfactory for intensities ≥ 15 mm/h (POD of 0.18 - 0.25). The results imply that DPR measurements don’t have an obvious temporal and spatial lag with respect to the observations made from the surface and thus form a prospect for global usage, if more follow-up research is conducted.

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.

Rain gauges are a powerful tool to measure rain entering a watershed. When water flow through a watershed is modeled, these rainfall measurements are used as inputs. Hydrological models have become increasingly complex as they more accurately represent the physical processes occurring. This is mostly done by increasing the spatial and temporal resolution of the model. As this resolution is increased, the inputs also need to increase. This thesis looks into if rain gauges are in the right place when used as inputs for hydrological models. This has been done by analysing four factors which literature showed to have affect rain gauges. The four considered factors are: the distribution of rain gauges, the steepness of the slope they are on, the location on that slope and their location within a watershed. For each of these factors algorithms have been developed in Python which compute relevant information on a given station. These algorithms have been applied to 368 gauges across the United Kingdom (UK), available from an open data source. The rain gauges are well distributed across different altitudes matching the distribution of heights across the UK. Above 400 m there are no gauges and this area is therefore underrepresented. The spacing of stations is good, a few close together and some isolated gauges on islands. The steepness of slopes varies strongly, when a steepness of 25% is used as a threshold only around 3% are on too steep of a slope. A fair amount of gauges are on ridges. Especially those near the coast have steep seaward slopes and thus will suffer from underestimating the actual rainfall. Within watersheds gauges are often near rivers causing other areas of the watershed to be underrepresented, especially areas of higher elevation. In future research it is recommended to use more gauges in the data set. Secondly focusing on a baseline comparison can help identify which stations are placed incorrectly. Lastly it is recommended to vary the resolution of elevation data and the spatial area considered, focusing on watersheds.

Indonesia has a severe problem of fossil fuel dependency, making it become one the world’s larger carbon emission contributor. At the same time, the growing population will increase the energy demand in the future. Thus, in order to meet the energy demand and decrease the carbon emission, the government together with PLN as the state electricity company will committed to implement carbon neutral targets by 2060. Additional of 413 GW installed power capacity will be necessary in which 308 GW generated from renewable energy. Indonesia has many renewable energy alternatives that can be utilized such as solar, wind, biomass, and hydropower. Among various renewable energy alternatives, hydropower has emerged as one of the means to achieve the aforementioned targets. Indonesia has a hydropower potential of around 75 GW from large hydropower and 19.4 GW from small hydropower, meanwhile, due to a number of barriers, hydropower contributed only 7% from installed large-scale hydropower and 2% from installed small-scale power plants.

In order to reach the government’s goals, further study is needed to better understand the hydropower potential in Indonesia. Hence, the aim of this research is to quantify the potential of hydropower for Indonesia to find the possible location based on the economic consideration and to understand the positive influence of hydropower application. The analyses will be done using GIS-based modelling approach based on three DEM sources with 3 different resolutions, namely DEMNAS (0.27 arcseconds), USGS (1 arcsecond) and MERIT (3 arcseconds). The gross theoretical potential will be calculated based on the river discharge and the head of every pixel of the DEM. Further, the technical potential could be obtained by eliminating the output of theoretical potential with contraints area. Subsequently, the cost components (e.g investment and operational cost) will be added to the model to quantify the levelized cost of electricity (LCOE). The potential location that has LCOE lower the cost of power generation.

Based on the analysis, the theoretical potential in Indonesia ranges for approximately 159 GW to 182 GW, or in annual energy production amounts to 1400 TWh to 1600 TWh. Subsequently, the technical potential after eliminating the constraints area decreased to around 550 TWh (63 GW) – 700 TWh (80 GW). On the other hand, based on the technical potential results, the LCOE ranges from 1 to 69 cent USD/kWh. However, only around 45% of the total technical potential is economically feasible. Thus, the hydropower potential lowered to 240 TWh (10 GW) – 690 TWh (38 GW). According the results, hydropower could cover 9% to 25% of the total required additional capacity planned by PLN and could reduce the carbon emission around 90% compared to the carbon emission of fossil fuels. Since this study used three different DEM resolutions, the output of the analyses varies depending on the DEM used. Based on the results, higher resolution DEM could delineate river shape better and thus the location of estimated hydropower potential location could be more accurate. However, DEM with larger pixel size could detect better the medium and large hydropower potential

Extreme rainfall brings substantial threats to lives, infrastructure, and the economy in cities. Radar rainfall nowcasting was proven able to provide forecasts up to 2 to 3 hours in advance on a catchment scale. However, an extensive evaluation of nowcasting skills for urban areas has not been performed yet. In this study, we selected 80 extreme events that occurred in 5 main Dutch cities (Amsterdam, The Hague, Groningen, Maastricht, and Eindhoven) from 2008 to 2021. We evaluated the performance of probabilistic nowcasts with 20 ensemble members applying short-term ensemble prediction system (STEPS) from Pysteps for these cities, focusing on analyzing the dependence on rainfall characteristics and city sizes. Nowcasts in Eindhoven (96 km2) and Maastricht (67 km2) had higher errors because the rainfall intensity of their events was higher. Besides, nowcasts at small areas showed higher error, especially when the size was below 100 km2. We found that forecast errors were higher and the forecast was less reliable for the 1-h event durations than for 24-h durations. Despite these differences, skillful lead times measured by Pearson correlation in all the cities were about 20 to 24 minutes for both the 1-hour and 24-hour events. CARROTS (Climatology-based Adjustments for Radar Rainfall in an Operational Setting) adjusted the bias in real-time QPE and QPF, but QPF still reduced with increasing lead time. Also, CARROTS did not adjust the rainfall spatial distribution much, so the skillful lead time did not change much. The skillful lead time in this study was shorter than the counterparts on the catchment scale because small areas are more sensitive to the displacement of forecast rainfall. Still, such lead time is similar to the findings in other research on short convective rainfall over small areas. Future research could try to apply machine learning, 3-dimensional nowcasting, or blending numerical weather prediction in the nowcasting process to better forecast the growth and decay of rainfall at a longer lead time.

Over the last years the Netherlands has often had to deal with droughts and water shortages during summer. This problem is caused by long periods without rain but with high evaporation rates and is enhanced by groundwater extraction for drinking water. Due to climate change, these droughts are expected to occur more often and become more severe. A possible strategy to mitigate drought in the eastern part of the Netherlands is to scale down the groundwater extraction, thereby limiting the groundwater depletion. In this case however, an alternative drinking water source has to be created. This research explores the option to use the former river bed of the Rhine near the Dutch-German border, the Rijnstrangen, to create this alternative drinking water source, answering the following question: How can retention of Rhine water in the Rijnstrangen contribute to drought mitigation in the eastern part of the Netherlands?

With this study, it is shown that the Rijnstrangen realistically can contribute up to 100 Mm3/y to the drinking water production in its region. This is up to 75% of the drinking water production of the Dutch province Gelderland, in which the Rijnstrangen is located. The exact maximum extraction volume from the Rijnstrangen depends on policy choices such as the maximum accepted water level in the Rijnstrangen and the maximum accepted average extraction from the region around the Rijnstrangen.

From a water quantity point of view, the maximum extraction volume of up to 100 Mm3/y indicates that utilizing the Rijnstrangen as a retention reservoir is a promising option to contribute to drought mitigation in the eastern part of the Netherlands. Therefore, further investigation of this idea is relevant.

For hundreds of years, ditch water levels in Dutch agricultural peatlands have been lowered to increase the loading capacity of farming parcels. This lowering results in groundwater levels in the middle of the parcels that fall a few decimeters under the ditch levels. When the groundwater level in a peat soil is lowered, newly uncovered peat is exposed to oxygen and begins to oxidise. This process emits greenhouse gasses (GHG), releases nutrients; like nitrogen and phosphorus that stimulate eutrophication, dries out the top subsurface layer and causes the land to subside. This study focuses on an island polder in Warmond, the Zwanburgerpolder, with a clay - peat subsurface. Half of the Zwanburgerpolder belongs to the Eenzaamheid, a biological cheese farm with the ambition to transition to a regenerative cheese farm. One of the key objectives to achieve this regenerative goal is to limit the GHG emitted from the farm. An analysis of the current (ground)water system of the Zwanburgerpolder was done which focused on limiting land subsidence by raising the groundwater level and thereby reducing the emission of GHG, improving (ground)water quality and stimulating biodiversity. First, four research components were examined through field experiments and literature studies: 1) water quantity, 2) water quality, 3) GHG emissions and 4) land surface displacements. The obtained results were used to understand how the Zwanburgerpolder works and identify the relationships between the four components. Then, two groundwater models were developed to represent the current groundwater level and flows in the Zwanburgerpolder: one in iMOD and one in FlexPDE. The iMOD model was used to get a visual representation of the groundwater level variations across the island and to see the groundwater response to precipitation and evaporation. The FlexPDE model was used to get an overview of the phreatic groundwater level drop between two ditches over the summer. To find the most suitable way to limit land subsidence in the Zwanburgerpolder, the models were adjusted to represent possible future situations. Instead of using the collected climate data of 2021 as input parameter, the projected climate data of 2065 was used. The future models consisted of a base scenario, in which no changes were made to the polder compared to the current situation and adapted scenarios, in which the following five water management measures were tested: 1. Adding ditches, 2. Installing horizontal drains, 3. Temporarily inundating parcels, 4. Installing vertical drains and 5. Raising the summer ditch water level. Temporarily inundating parcels performed the best quantitatively and qualitatively during the comparison of the measures. However, this measure is agriculturally unfavourable because as a consequence, parcels cannot be used for grazing during a significantly long period of time. For the Eenzaamheid, where regenerative practices and agricultural capacity take center stage, the recommendation for limiting land subsidence is to combine and adjust two measures: temporarily inundating parcels and adding ditches. Gutters that currently run down the center of the parcels should be enlarged and inundated during the summer period, by siphoning water from the boezem. Inundation can take place during the whole summer, since grazing is still possible alongside the gutters. It is recommended to start off by only applying the measures in the most critical parcels in order to use it as a testing ground to check the possibly negative effects of the measures besides the desired positive effects of limiting land subsidence, reducing GHG emissions and improving the (ground)water quality. Further, the results discussed in this report provide an interesting addition to peatland subsidence studies. They were obtained through field experiments done on a much smaller budget than other studies done so far with expensive measurement setups. The recommendation towards such peatland subsidence studies is therefore to apply a large network of lower cost measurement setups, instead of a few costly ones, in order to get an extensive representation of the behaviour of different Dutch peatlands. The main limitations in this study are the uncertainties linked to the parameters used to build the iMOD and FlexPDE models and the time constraint on the field experiments. The parameter uncertainties mean that the final model outputs fall within a certain error range. To minimize the error range, a model sensitivity and uncertainty analysis should be done. Without the field work time constraint, it would have been possible to identify the seasonal patterns in the groundwater level fluctuations compared to the other water bodies and in the land surface displacements.