Climate change is intensifying hydrological extremes in Dutch lowland catchments, where shallow groundwater tables, active water management, and strong groundwater–surface water (GW–SW) coupling pose particular modelling challenges. This thesis investigates whether targeted structural modifications to the Wageningen Lowland Runoff Simulator (WALRUS) can improve its performance for the managed lowland catchment of the Marswetering in the Netherlands.

Two adaptations are proposed and evaluated against the baseline model. The first is a temperature- and direction-dependent groundwater resistance parameterization (cG), which is intended to represent streambed clogging asymmetry as well as seasonal variability driven by changes in water viscosity. The second is an adapted quickflow partitioning formulation (fQS), which is designed to prevent the complete suppression of land-surface quickflow during extended dry periods. Five model variants are calibrated using a multi-objective framework combining six hydraulic signatures including discharge, flow duration curve, autocorrelation, seasonal runoff coefficient, and normalized groundwater dynamics, and evaluated across a calibration period and two independent testing periods.

All model variants reproduce peak discharge with reasonable skill, but consistently underperform on low flows and summer groundwater dynamics. A structurally recurring opposite-sign error in summer groundwater behaviour, present across all variants and both testing periods, points to systematic underestimation of actual evapotranspiration or outward seepage or overestimation of the equilibrium storage deficit. The adapted fQS formulation produces a physically consistent but negligible improvement, largely absorbed by compensating changes in the quickflow reservoir constant. The direction-dependent cG formulation successfully reproduces asymmetric GW–SW exchange resistance, but introduces elevated summer discharge that worsens seasonal water balance closure. The temperature-dependent component of cG shows no identifiable calibrated influence. Additionally, the spatially uniform representation of managed surface water levels is identified as a source of uncertainty, given the heterogeneous water management across the catchment's sub-catchments.

The results indicate that improving summer unsaturated zone dynamics represents a higher priority for WALRUS development than further GW–SW exchange complexity. The multi-objective calibration framework developed here is directly transferable to comparable lowland catchment applications. ... Read more

Climate change poses prodigious challenges to water resource management. In addressing these challenges, traditional grey measures lack adaptability and long-term sustainability. In contrast, Nature Based Solutions (NBSs) offer flexible, sustainable and eco-friendly alternatives. The design and effectiveness of NBS-based adaptation strategies could be potentially assessed through integrated hydrological models, which simulate both surface and subsurface complex hydrological processes. However, the accuracy of models to efficiently simulate the hydrological processes significantly depends on the quality and availability of input data. Earth Observation (EO) datasets offer a wide range of hydrological data with comprehensive spatial and temporal coverage, yet their quality remains uncertain and requires evaluation across different scales and regions..... ... Read more

Vegetation activity plays a key role in the water cycle, with transpiration accounting for 50–80\% of total terrestrial evaporation globally. Yet many conceptual hydrological models neglect phenology, estimating transpiration as a function of relative soil moisture and assuming constant vegetation activity. This simplification leads to systematic errors, particularly in winter, when conventional models often overestimate transpiration.

Because transpiration is strongly correlated with sap flow, this study developed three methods to incorporate tree phenology into a semi-distributed conceptual hydrological model. The model structure distinguished between coniferous and deciduous trees. Sap flow dynamics were included either directly, using sap flow data, or indirectly, using temperature as a proxy for seasonal variation.

To address the limited availability of sap flow data, a Generalized Additive Model (GAM) was developed to predict normalised sap flow. Using temperature, relative humidity, incoming shortwave radiation, volumetric soil water content, and normalised accumulated growing degree-day as predictors, the model reliably reproduced sap flow dynamics for both tree types. In addition, the GAM framework enabled separate analysis of each predictor's relationship with sap flow, providing clearer insights into the underlying processes and the relative influence of individual variables.

The results show that including tree phenology improves the ability of conceptual hydrological models to represent transpiration seasonality and vegetation dynamics. Although discharge simulations were not substantially improved, the added internal realism reduces equifinality and makes the model more robust under changing conditions.

Among the three developed methods, the direct inclusion of sap flow dynamics resulted in the highest model performance, particularly in transpiration simulations. This highlights the added value of the sap flow prediction model itself, which provided a robust link between environmental drivers and vegetation dynamics, thereby strengthening the integration of phenology into conceptual hydrological modelling. ... Read more

Situated at the southern coast of Guatemala is the protected nature reserve of Biotopo Monterrico-Hawaii. This biotope contains a mangrove forest consisting of three tree species: the Rhizophora Mangle, Lagunculeria Racemosa and Avicenna Germinans. At the beginning of the 21st century, humaninduced fires destroyed parts of the mangrove forest. After this disturbance, the mangrove forest has shown signs of natural regeneration, although the extent of recovery remains low and spatially restricted. The area of Las Brujas currently shows one of the lowest natural regeneration rates of mangroves within the reserve. This study aimed to identify the main factors limiting mangrove regrowth in Las Brujas.

An extensive literature review was conducted, key stakeholders were interviewed, and field measurements were taken to investigate precipitation data, tidal influence, flow dynamics, ground level, and physical-chemical parameters. Results indicate that visually, the precipitation patterns have been undergoing small changes in the past years. These changes elongate the time during which young mangrove saplings are submerged by water, increasing their risk of drowning. Tidal influence in Las Brujas is minimal. The flow is driven mainly by wind and river discharge. Moreover, flow velocities are low, not exceeding 0.12 m/s, and are stagnated by invasive species. This reduces nutrient, sediment, and propagule transport, and therefore stresses mangrove regrowth. Satellite data reveal no significant changes in ground level, yet sediment flow was observed, indicating that natural heightening of the ground level is possible. A higher ground level could reduce the mortality rate of young saplings by decreasing the risk of drowning. The physical-chemical parameters were within optimal ranges for mangrove growth, suggesting that water quality is not limiting mangrove regeneration during the wet season.

The most critical parameters influencing natural regeneration during the wet season appear to be water depth and flow dynamics. Submergence of saplings for more than 15 days results in high mortality due to oxygen deprivation. In addition, reduced flow conditions limit the dispersal of propagules and restrict nutrient exchange within the water column. To better understand these processes, further hydrological and hydrodynamic investigations during the dry season are recommended. ... Read more

In the future, dry and wet extremes are projected to intensify, while the time between the extremes is expected to shorten. Therefore, both extremes should be analyzed and modelled together. Antecedent soil moisture strongly shapes catchment response: at low levels, more storage reduces run-off, but once a threshold is exceeded, run-off rises sharply in a non-linear way. Conversely, dry conditions may induce water repellency, leading to increased run-off even with low soil moisture levels. It remains unclear whether this non-linear relationship changes during drought–flood transitions, forming the first knowledge gap of this study. To address this, the study examines how soil moisture correlates with peak discharge during such transitions.

Furthermore, both hydrological and hydrodynamic studies have a modelling gap, as initial drought states are rarely included and modelling of compound extremes remains scarce. To address this, the 3Di hydrodynamic model is used, which has been widely applied for floods but not yet for droughts. This study examines how well 3Di simulates peak flow after drought using effective precipitation as input, and whether incorporating soil moisture conditions through recharge further improves the results.

The research is conducted in the Hupselse Beek as a case study. The standardized streamflow index (SSI) is a drought index that is used to assess the hydrological droughts. Criteria are set in order to find drought events, of which three are selected for further modelling within 3Di: one for calibration and two for validation. The correlation analysis is performed by analyzing run-off against the antecedent soil water content for all drought events.

In addition, a 3Di model of the study area is developed containing the domains of surface water and groundwater. Measurements are compared with model results to test performance. The simulations cover a one-week period that includes the extreme rainfall event. Horton infiltration values, effective porosity and hydraulic conductivity values are calibrated in a sequential way to see if the results can approach the observations, with performance assessed through key metrics.

The selected drought dataset contains 38 events, demonstrating a positive non-linear relationship between soil-water content and run-off. Furthermore, a threshold in soil water content is observed around 0.22 mm3/mm3. It is concluded that the Hupselse Beek remains responsive to soil moisture, even under hydrological drought conditions.

For 3Di, the groundwater results show relatively good key metric performance while the surface water deviates strongly in response to effective precipitation. For the recharge simulations, the results in performance are worse. The Horton infiltration needs to decrease to compensate for the decrease in input. The incorporation of soil moisture conditions, and the effect of not representing them therefore needs to be researched further for 3Di modelling. At the same time, the groundwater results highlight the potential of 3Di for modelling peak flows during hydrological drought. ... Read more

Over recent decades, climatic changes and significant variations in all key components of the hydrological cycle have been observed in many regions worldwide, profoundly altering water availability, river flow regimes, and the concentration of nutrients and pollutants. Ecosystem is a key component of the terrestrial hydrological cycle as it shapes the hy drological functioning of catchments by regulating the long-term average partitioning of water into drainage and evaporative fluxes (i.e. latent heat). In response to a changing environment, ecosystems continuously adapt to allow the most efficient use of available energy and resources. However, direct quantification of how ecosystems adapts to climatic variability over long time periods and the mechanistic drivers thereof at the catchment scale is missing so far. As a consequence, it remains unclear how climatic variability, such as precipitation regime or canopy water demand, influences the partitioning of water fluxes, the hydrological response, and hydrological processes and transport mechanisms at the catchment-scale. Therefore, the overarching objective of this thesis is to address the follow ing main research question: How does climatic variability affect the hydrological response and transport mechanisms in a temperate-humid basin over multiple decades? All analysis in this thesis is carried out in a large river basin, the Neckar basin, Germany. A unique long-term dataset is used for this basin, consisting of 70 years of hydrometeorological and tracer data. Hydrological and transport processes in the basin are quantified using a state-of-the-art semi-distributed hydrological model that (i) includes spa tial heterogeneity in topography, vegetation and precipitation, (ii) accounts for ecosystem adaptation to climate variability via a time-varying root zone water storage capacity, and (iii) uses StorAge Selection (SAS) functions to account for mixing of tracers and to estimate time-varying water age distributions at catchment scale. Multi-objective calibration of the hydrological model using the long-term hydrometeorological and tracer dataset pro vides the basis for investigating how climatic variability affects hydrological and physical transport processes in the Neckar basin. The first research question focuses on ecosystem adaptation to climate variability via changes in root zone storage capacity. The root zone storage capacity is a critical factor reg ulating latent heat fluxes and thus the moisture exchange between land and atmosphere as well as the hydrological response and biogeochemical processes in terrestrial hydrological systems. To be survive, root systems of vegetation and the associated vegetation-accessible water storage capacity respond to the ever-changing conditions of its environment. How ever, as these changes occur at landscape scale and are mostly reflected by changes in the composition of plant species present in a specific spatial domain, fluctuations in root zone storage capacity occur largely at time-scales that reflect the life-cycles of individual plants. However, it remains unclear whether root zone storage capacity adapts to climatic variability and evolves over time, thereby reflecting ecosystem adaptation to changing conditions. The thesis investigates this for the Neckar basin by quantifying long-term changes in root zone storage capacity using two different methods, i.e. hydrological model calibration and an independent water balance estimation method. The analysis provides quantitative mechanistic evidence that root zone storage capacity significantly changes over multiple decades reflecting ecosystem adaptation to climatic variability. However, the analysis also suggests that accounting for temporal evolution of root zone storage capacity with a time-variable formulation of that parameter in a hydrological model does not sig nificantly improve its ability to reproduce the hydrological response and may therefore be of minor importance to predict the effects of a changing climate on the hydrological response. The second research question investigates the use of different isotopic tracers to estimate water age distributions, i.e. age distributions of water fluxes (“transit time distri butions”, TTD) and water stored in catchments (“residence time distributions”, RTD) as fundamental descriptors of hydrological functioning and catchment storage. These distri butions provide a way to quantitatively describe the physical link between the hydrological response of catchments and physical transport processes of conservative solutes. However, water age distributions cannot be directly observed, and instead have to be estimated with tracer-aided models. Stable isotopes (𝛿18O) and tritium (3H) are frequently used as tracers in environmental sciences to estimate age distributions of water. It has previously been argued that seasonally variable tracers, such as 𝛿18O, generally and systematically fail to detect the tails of water age distributions and therefore substantially underestimate water ages as compared to radioactive tracers, such as 3H. Early approaches often relied on simple lumped sine-wave or lumped parameter convolution integral models under the assumption that water storage in catchments is at steady state. Here, these methods are compared with the more realistic StorAge Selection (SAS) functions embedded in the dynamic hydrological model used in this thesis. By comparing water age distributions inferred from 𝛿18O and 3H with several different transport model implementations, this thesis demonstrates that previously reported underestimations of water ages are most likely not a result of the use of 𝛿18O or other seasonally variable tracers. Instead, these underestimations can be largely attributed to choices of model approaches and complexity. It is therefore strongly advocated to avoid the use of steady-state model types in combination with seasonally variable tracers and to instead adopt SAS-based or other time-variant model formulations that allow for the representation of transient conditions. The third and final research question investigates the effects of temporal variability of the hydrological response on physical transport processes over a spectrum of time scales from daily to multiple decades. Due to limited availability of tracer records over longer durations in many catchments, most previous studies focused on daily time scales to analyse temporal variability of water ages as metric of physical transport and the underlying drivers. To improve understanding of long-term transport dynamics, this thesis quantifies the variability in water ages, identifies the associated dominant controls from daily to multi-decadal time scales, and analyses the associated temporal evolution of water ages of streamflow and evaporation. It is shown that there are no major long term dynamics in water ages, driven by either internal processes or external transport variability. Consequently, the physical transport dynamics in the upper Neckar basin, and potentially in other basins with similar water age characteristics, are inferred to exhibit near-stationarity over multiple decades. Concluding, this thesis provides sufficient evidence that long-term varying root zone storage capacity significantly reflects ecosystem adaptation to climatic variability. However, the temporal evolution of root zone storage capacity does not control variation in the partitioning of water fluxes and has no significant effects on fundamental hydrological response characteristics of the studied semi-humid river basin in the near future under a changing climatic condition. In addition, the thesis suggests physical transport processes can be assumed to be near-stationary and predictable across multiple decades under either internal (i.e., time-variant root zone storage capacity) or external transport variability (i.e., climatic variability), which contrasts with the frequently reported fractal pattern in stream water solute dynamics. This finding is crucial for management of subsurface water quality and the design of restoration interventions for groundwater affected by legacy contamination such as nitrate. ... Read more

This study examines the spatiotemporal changes in snow regimes across High Mountain Asia (HMA), with a focus on snowfall, snowmelt and snow water equivalent (SWE) trends and their relationship with elevation, temperature, and precipitation. Utilizing ERA5-Land data, the analysis reveals a general decrease in snowfall and SWE, as well as snowmelt in low to mid-elevations. Notably, high-elevation zones, despite also experiencing declining snowfall, exhibit increased snowmelt due to the persistence of deep snowpacks. However, the overall reduction in snowmelt across all basins, and significant decline of total snowmelt in the Brahmaputra, Indus, and other major river basins, underscores the critical impact of climate change on water resources. Correlation analyses further highlight complex interactions between temperature, precipitation, and snow regimes, varying by season and elevation. The study acknowledges limitations in ERA5-Land's accuracy, particularly in high-elevation regions, and calls for future research to incorporate multiple data sources and uncertainty analysis to improve the reliability of findings. The implications of declining snowmelt for water availability in major river basins are significant, warranting continued investigation. ... Read more

Conceptual models in hydrology are widely used, allow for easy interpretation and require little data. Machine learning models in hydrology often outperform conceptual models but lack the ease of interpretability, require large amounts of data and and do not obey physical laws. Hybrid approaches aiming to combine the advantages of both approaches are becoming more popular. A Neural Ordinary Differential Equations approach is introduced to combine a differential equation-based conceptual model with a neural network. Additionally, conceptual models and LSTM models are used as benchmarks. The models are tested using the LamaH-CE dataset as well as the E-OBS dataset. In many cases the hybrid models outperform the conceptual model. However, to further improve the performance of hybrid models more research is needed to make the models more computationally efficient and optimized training strategies are required to explore the full potential of the approach.
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Driven by global temperature increase and changing precipitation patterns, the intensifying hydrological cycle is expected to introduce more frequent and severe streamflow droughts. These changes will have a profound impact on agriculture, ecosystems and water management. This study analysed the spatial and temporal changes in streamflow droughts across 386 Mediterranean river basin from 1970 to 2019, focusing on their drought characteristics: duration, severity, intensity, maximum deficit, inter-arrival time, recovery rate and decline rate. The river basins were categorized into seven climatic regimes, and each was analysed for changes in these drought characteristics. Streamflow droughts were quantified
using the Threshold Level Method, defining drought events as periods in which daily streamflow is below a set threshold. To identify patterns, trends were assessed across four fixed time periods, along with multi-temporal analyses for both annual and seasonal changes. The analysis showed that annual streamflow droughts, although becoming longer, are getting slightly less severe and intense across the majority of river basins. However, distinct seasonal and regional variations were found. Between 1988 and 2019, winter and spring streamflow droughts experienced a decrease in duration and severity (up to -30% for winter) across the entire study area. On the other hand, regional differences were observed for summer and fall. During this same time period, summer and fall streamflow droughts in the Pyrenees, Southern France, Italy and Croatia have become much longer, more severe and more intense (20-40% for summer), whereas Central and Southern Spain experienced decreasing duration and drought deficit volumes. The implications of increasing streamflow drought conditions on the water availability in Mediterranean river basins is severe, which highlights the need for better understanding and water management of streamflow droughts. ... Read more

The applicability of the FLEX-Topo and FIESTA models in simulating the impacts of land cover change on the streamflow dynamics in the tropical montane cloud forest of the Mestelá catchment, Alta Verapaz, Guatemala was investigated. Understanding these impacts is crucial for effective water resource management in regions vulnerable to deforestation, where cloud forests play a critical role in regulating hydrological processes, and in areas prone to flooding and droughts.

The FIESTA model was integrated to generate spatially distributed meteorological inputs, tailored to unique spatial characteristics of the tropical montane cloud forest region. These inputs informed the FLEX-Topo model, which was adapted to conceptualize the dominant hydrological processes in the study area for simulating streamflow in the Mestelá catchment. The model was calibrated to represent distinct land use classes within the catchment using multi-criteria calibration with different objective functions and constraints to account for parameter uncertainty. Scenario-based simulations, including deforestation, reforestation and the conversion of agricultural land to pine plantations, were conducted to quantify their effects on streamflow dynamics, water balance components and extreme hydrological
events.

The integration of the FIESTA model provided spatially distributed inputs, however further evaluation is needed to assess the accuracy of this distribution. Its spatial variability enabled the inclusion of fog interception into the water balance, representing a key hydrological process in cloud forests. Calibration of the FLEX-Topo model was achieved by optimizing parameters for distinct land use classes and using dynamic land use fractions. Scenario analysis revealed that deforestation potentially increased peak flows by 18.5% (±1.4%), while restoring forest cover reduced extreme flows by 39.5% (±1.9%), highlighting the role of reforestation in flood mitigation. Replacing agriculture with pine trees on steep slopes also reduced extreme flows, while additionally addressing landslide risks.

The combined application of the FLEX-Topo and FIESTA models offers valuable insights into hydrological responses to land use changes, particularly in cloud forest regions, highlighting their potential for informing policy decisions related to land conservation and water management in tropical montane cloud forests. ... Read more

Vegetation strongly influences evaporation from land by transporting water from the subsurface to the atmosphere through root water uptake. The amount and timing of this water flux depends on the aboveground (e.g., the amount of leaves) and belowground (e.g., the root extent) characteristics of the vegetation. Although these vegetation characteristics vary strongly both in space and time, there is lack of adequate representation of this vegetation variability in large scale hydrological and land surface models. This causes deficiencies in representing the associated variability in modeled water and energy states and fluxes, which introduces uncertainties in future predictions of the hydrological cycle, including hydrological extremes such as droughts and floods. To address this issue, this research aims to develop more realistic model representations of spatial and temporal vegetation variability, and explore their potential for improving modeled water fluxes in large scale hydrological and land surface models.

Chapter 2 focuses on model representations of spatial and temporal variability of aboveground vegetation characteristics based on satellite remote sensing data. Interannual variability of land cover and leaf area index (LAI) from latest global remote sensing datasets are integrated into the land surfacemodel Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (HTESSEL). Furthermore, datasets of LAI and the fraction of green vegetation cover are used to develop and integrate a spatially and temporally varying model parameterization of the effective vegetation cover. The effects of these three implementations on simulated hydrology are evaluated using offline (land-only) model simulations. The results show that the enhanced variability of aboveground vegetation characteristics considerably improves the simulated variability of evaporation and near-surface soil moisture. These improvements are connected to a framework that describes how the implemented vegetation variability influences internal model interactions between vegetation, soil moisture, and evaporation.

Chapter 3 evaluates how climate-controlled root zone parameters influence water flux simulations with the land surfacemodel HTESSEL. To this aim catchment scale root zone storage capacity Sr (mm), defined as the maximum volume of subsurfacemoisture that can be accessed by the vegetation roots, is estimated using the memory method. In this method Sr is derived from soil water deficits, reflecting the ability of vegetation to adapt to the local climate conditions by sizing their roots in such a way to guarantee continuous access to water, keeping memory of past water deficit conditions. Climatecontrolled Sr is estimated with the memory method for 15 catchments in Australia to adequately represent the spatial variability of the vegetation roots. These estimates are integrated into HTESSEL, replacing the static root representation based on soil types and uniform soil depth. The results of offline model simulations show that climatecontrolled Sr representation significantly improves the timing of modeled discharge in the study regions. This suggests that a climate-controlled representation of the model Sr has potential for improving water flux simulations by land surface models in a global context.

Chapter 4 presents the influence of irrigation on the estimation of Sr with the memory method. The memory method Sr is derived from the seasonal patterns of root zone water input and output. Besides precipitation as input, irrigation supplies additional water to the root zone in irrigated agricultural fields. However, the influence of irrigation on the memory method Sr estimates has not been assessed previously. In this study two methods based on different globally available irrigation datasets are developed to account for irrigation in the memory method for estimating Sr. The Sr estimates fromthese two methods are compared to a case without considering irrigation for a large sample of catchments globally. The results show, for the first time, that irrigation considerably reduces Sr in regions with extensive irrigation, highlighting the relevance of irrigation for adequately estimating ecosystem scale Sr.

Chapter 5 investigates the influence of climate, landscape, and vegetation variables on Sr globally. So far, there is limited insight on the controls of global-scale root development and their spatial variation. A random forest model is used to predict Sr as estimated with the memory method based on 21 variables for a large sample of catchments globally. The results indicate that hydro-climatic variables are the dominant, but spatially varying, driver of ecosystemscale Sr, while landscape and vegetation play aminor role. Based on the importance of the various drivers, a reduced parsimoniousmodel using four variables is used to predict Sr on a global scale. These predictions largely resemble other global estimates of root characteristics based on more complex methods and datasets. This indicates that the here developed parsimonious model to estimate global scale Sr based on four simple globally available variables adequately represents the spatial variability of Sr globally. Together with the results from Chapter 2, it can be concluded that integration of these estimates into large scale hydrological and land surface models has potential to improve model water fluxes.

The findings of this dissertation directly contribute to the large scale hydrological and climate model communities by providing methods to adequately represent spatial and temporal vegetation variability. The results demonstrate the potential of these methods to improve modeled water fluxes by large scale hydrological and land surface models, with major implications for the accuracy of hydrological and climate predictions. This dissertation lays the foundation for future research aimed at further improving the realism of model vegetation variability. ... Read more

Predicting streamflow in a changing climate poses significant challenges for traditional hydrological models. Static parameter sets result from model calibrations over historical data that increasingly encounter the non-stationary impacts on the hydrological system. Endeavouring toward non-stationary model parameters by incorporating time-adaptive ecosystem-scale root zone storage parameters shows promise for modelling systems under change. The ongoing CATAPUC project aims to further develop, refine, and implement this adaptive modelling approach. This paper continues to build upon this body of work by investigating the evidence for land cover change impacts on root zone storage capacity, focusing specifically on uncertainties in the evaporative balance. Combining long-term hydrometeorological data from the primary study area, the Meuse basin, with the large-sample CAMELS datasets (GB and US) analyses were performed across 283 catchments. Applying the vector operations from Velde et al. (2014) and Jaramillo et al. (2018) to decadal changes within the Budyko framework, we separate climate-related evaporative changes. We isolated the residual component of evaporative change, which is the unknown component affecting parameter estimations. Our findings indicate that this residual component is twice as prominent in evaporative change as the climate component. The aim is to test if land cover changes have contributed significantly to the residual component of evaporative change. A multi-scale approach to land cover change analysis is adopted to bridge the data gap from 1984 to 2019. Implementing ensemble machine-learning methods on Landsat imagery with Google Earth Engine, we develop annual timeseries of (30m) high-resolution multi-class data for this period with accuracies up to 86% for 117 catchments (Meuse and GB). Resulting land cover change estimates suggest that Meuse Basin urbanisation rates may have been significantly underestimated for this period. The Meuse Basin over the most recent 20-year period is found to deviate anomalously in actual evaporation compared with the large sample. A distinct spatial pattern reveals a concentration of deviations in the east of the basin. Calculated by Tempel (2023), the anomaly in the basin corresponds with a relative median error in root zone storage capacity change of −14%. Low flow analysis is performed to remove the possibility that deviations are affected by anomalous contribution to streamflow from additional subsurface flow. We observed that the increase in low flow variability over the same period exhibits a spatial pattern similar to the Meuse Basin anomaly. We found no meaningful causal relationships linking multi-scale interdecadal changes in forest, agriculture, and urban land classes to the observed deviations in the large sample datasets. The implication of this study is that land cover change is likely not a significant driver of evaporative changes, specifically, errors, throughout the record available.
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Climate change-induced changes in weather patterns call for the development of hydrological models that perform well under increasingly extreme and varied conditions. Multiple research studies have demonstrated that hydrological models perform poorly when applied to climate conditions that differ from those during the calibration period of the model (Duethmann, Blöschl, & Parajka, 2020). The need for robust hydrological models was emphasised after the Geul catchment, and large parts of Belgium and Germany flooded in July 2021. During this flood, hydrologists from Waterboard Limburg (WL) indicated that the current model would not have been able to correctly forecast the flood under such an extreme rain event anyway (Expertise Netwerk Waterveiligheid, 2021). The underlying assumption in this research is that hydrological models cannot perform to the same standard during changing weather conditions because they are overfitted during the calibration period, meaning that the parameter values are the mathematical best fit for streamflow predictions, but do not represent the internal hydrological processes between precipitation and discharge. One way of trying to improve the internal processes of a hydrological model is to create more relations between these processes and external measurements (Kirchner, 2006). In this research, a secondary calibration dataset is used, namely evaporation data. There are two goals in this thesis. First, building a hydrological model of the Geul which represents the streamflow response of the catchment in a meaningful way. Second, comparing whether a difference in calibration methodology, specifically comparing calibration on discharge with calibration on both discharge and evaporation, would improve the predictive power of the model. These goals are set up to answer the main research question of this thesis: Is the predictive power of a discharge-calibrated hydrological model of the Geul catchment in the Netherlands greater than the predictive power of an evaporation-calibrated model in addition to discharge? The methodology used in this thesis for calibration and evaluation is Generalised Likelihood Uncertainty Estimation (GLUE), with NSE efficiency as objective function. The first goal, building a hydrological model of the Geul which represents the streamflow response of the catchment satisfactorily, is successfully reached. The model calibrated on streamflow (Model Q) and the model calibrated on both streamflow and evaporation (Model QE) were able to predict streamflow satisfactorily, with DeQ scores between 0.632 and 0.649 across all runs. The second goal was to compare the performance of Model Q and Model QE. Model QE outperformed Model Q on monthly runoff coefficient, monthly average evaporation, and cumulative evaporation, increasing the NSE score of Model Q for the monthly runoff coefficient from 0.774 to 0.801, the NSE log score of the monthly average evaporation from 0.884 to 0.891, and the cumulative evaporation from 0.760 to 0.774. However, Model QE did not outperform Model Q on streamflow. Therefore, it cannot be concluded that additional calibration on evaporation data increases the predictive power for the streamflow of the model. However, the model now more accurately represents the observed evaporation data without trading this for less predictive power of streamflow. Therefore, Model QE can be assumed to be a more accurate description of the hydrological processes in the Geul catchment than Model Q. ... Read more

Hydrological models are commonly used to predict future streamflow. However, the assumption of stationary model parameters obtained through calibration on past conditions may not accurately represent non-stationarity in hydrological system characteristics. Evidence suggests that vegetation adapts its root zone storage capacity in response to changing climate, emphasizing the need to account for non-stationarity in hydrological models. This study examines the effects of long-term climate variability on root zone storage capacity and its consequences on hydrological response. By using the method of Fu (1981), we determine whether the root zone storage capacity has significantly changed and evaluate the sensitivity of hydrological model predictions to these changes. We explain the changes in root zone storage capacity and evaporation using various climate indicators. For two large sample datasets CAMELS GB and CAMELS USA, we confirm that the Fu method can be used with the same omega parameter when transitioning from one decade to the next, indicating small differences in root zone storage capacity. However, for hydrometeorological data from the Meuse basin in Northwest Europe, we observe a trend where the actual evaporation is smaller than expected. This suggests that caution should be exercised when applying the Fu-method. In four different scenarios, we have implemented historical changes in evaporation as altered root zone storage capacity in the wflow flextopo model. The scenarios based on the evaporation trends observed in the Meuse data, result in less evaporation during the summer months (May, June, July) within a range of 0 to -22%, and an increase in streamflow during the autumn months (September, October, November) between 0 and +48%. On the other hand, the scenarios based on evaporation trends observed in all combined data (Meuse, CAMELS GB, CAMELS USA) result in changes around zero of evaporation during summer months between -7% and +5%, and of streamflow in autumn months between -11% and +10%. This study represents a step towards a more reliable and robust estimation of root zone storage capacity in hydrological modelling, thereby enhancing our ability to predict future streamflow. ... Read more

This study aims to establish a framework for the growing number of trend analyses that have been performed on river flows in Europe. Most studies apply statistical trend test to fixed periods with relatively short records. Fixed periods are crucial for trend testing, as they provide an appropriate visualisation in a geographical sense. However, short-term changes can often be inconsistent with long-term trends. This study adopts two methods of trend testing. First, a trend analysis is performed over five fixed periods with an identical end year, on a varying number of stream gauges based on record length availability. Afterwards, a temporal sensitivity analysis is performed, whereby trends are estimated on all combinations of start and end year. This method is performed on stream gauges with at least 60 years of record length. These two methods are performed on an encompassing set of hydrological signatures, with the intent to capture the temporal sensitivity in all aspects of the streamflow regime. The results of the fixed period analysis display distinct but coherent spatial patterns for each signature. Furthermore, the results reveal a considerable amount of temporal variability in all signatures. This temporal sensitivity is elucidated further by the results of the temporal sensitivity analysis, which shows that trend analyses are extremely sensitive to the choice of both the start and end year of the considered period. The study provides a reference for comparison of both past, present and future studies. The temporal sensitivity analysis is shown to be a powerful tool and is recommended for future trend analysis studies to contextualize the short-term trends. ... Read more

In order to evaluate measures to increase water availability in and around reservoirs it is necessary to have reliable reservoir water storage models. For that reason, it is necessary to assess the validity of these models. The aim of this research is to do this assessment of two models in data scarce semi- arid regions. The Nakamb ́e catchment is used as a case study. The study covers important aspects of hydrological modelling, such as model input (data) selection, hydrological model choice, calibration, model performance testing, parameter sensitivity, and reservoir water storage simulation.
For the selection of the optimal model forcing data diverse precipitation products are reviewed: CHIRPS, ERA5, and local measurements. Among the evaluated datasets, CHIRPS emerges as the superior choice validated against the local measurements. With respect to the potential evaporation, the combination of ERA5 and local measurements results in the most suitable potential evaporation data, leveraging the temporal and spatial aspects of ERA5 and the absolute values of the local measurements.
Comparing a lumped hydrological model (HBV) with a distributed model (SBM Wflow) in simulating river discharge reveals that the HBV model outperforms its counterpart in simulating discharge. This contrast in performance is attributed to potential overparameterization in the Wflow model, coupled with the complexities of parameter estimation in data-scarce areas. The HBV model, while bearing simplifications, benefits from a more comprehensive calibration process. The model performance is strongly influenced by the calibration efficiency, where the significantly shorter simulation time of the HBV model facilitates an extensive Monte Carlo sampling-based calibration, in contrast to Wflow’s time consuming manual parameter adjustment.
Additionally, the sensitivity analysis showed that in the HBV model, the parameters affecting actual evaporation are the most sensitive one. This emphasizes the importance of accurately simulating this component for the proper model performance. The Wflow model exhibits strong equifinality due to the many parameters within the model. The complexity of this model made it impossible to test all parameters and therefore only some parameters are tested.
Both reservoir water storage models studied, the HBV Reservoir Water Storage Model (HBV RWSM) and the Wflow reservoir module, can effectively simulate reservoir water storage fluctuations, although they differ in how the components are calculated. Due to data limitations, it is impossible to determine which, if any, of the models is correct. However, based on the downstream discharge the HBV RWSM displays a more promising performance.
In conclusion, the HBV model outperformed the SBM Wflow model in simulating discharge due to its simplicity and ease of calibration. Sensitivity analyses highlighted the significance of accurately representing actual evaporation. Both water balance models, the HBV RWSM and the Wflow reservoir module, performed similarly concerning the NSE values. The fluxes contributing to the water balance in the two reservoir water storage models differ significantly. The lack of data on these fluxes makes it impossible to determine which models performs best. Data limitations remain a significant hurdle in model evaluation, emphasizing the need for additional data collection, particularly upstream and downstream of the reservoir, to enhance reliability and reduce uncertainties.
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In today's world, extreme flooding and drought events are becoming increasingly common globally. These challenges arise from various factors, with climate change and changes in land use being among the most significant contributors.

While numerous studies have explored the combined impact of climate change and land use on streamflow, there is a research gap when it comes to analyzing historical data for changes in the magnitude and timing of discharge peaks and low-flow periods. To address this gap, this MSc Thesis investigates river discharge in five European countries: Belgium, Germany, France, Luxembourg, and the Netherlands.

To analyze potential patterns and variations in magnitude changes, trend analyses were conducted for annual and monthly mean daily flows. Non-parametric methods such as Sen's slope and the Mann-Kendall test were employed to calculate trends in average, maximum, and minimum daily flows at both yearly and monthly levels. The issue of autocorrelation in discharge flows was also addressed by using a modified version of the Mann-Kendall test for stations with autocorrelated data. Furthermore, the possible shifts in the timing of discharge peaks and low-flow periods were examined. We employed statistical tools such as statistical entropy, Kullback-Leibler divergence, and various descriptive statistics to determine if there have been changes in the month with the highest flow over the years.

The study's results generally align with existing research. Regarding annual discharges, for average and maximum analyses, stations with decreasing trends were predominantly found in the North, East, and central parts of the study area (Germany), while the North-West exhibited stations with significant increasing trends in most cases (North France).

In yearly minima discharge flows, the patterns were aligned with average and maximum analyses; however, additional stations showed decreasing trends, which were located in Belgium. In the monthly analysis, positive trends were primarily observed during winter months (February, December, and January), while April and March showed decreasing trends in most cases (monthly average and maxima analyses), with a few exceptions in minimum daily flows.

Notably, more than 50% of the stations exhibited shifts in the month when they experienced maximum and minimum discharge, particularly between 1980-2000 and 2000-2021. This finding suggests potential avenues for future research. ... Read more

For the development of regional landslide early warning systems, empirical-statistical thresholds are of crucial importance. The thresholds indicate the meteorological and hydrological conditions initiating landslides and are an affordable approach towards reducing people’s vulnerability to landslide hazards. This thesis defined different landslide hydro-meteorological thresholds in Rwanda and evaluated their predictive capabilities. Chapter 1 identifies the landslide problem to society, opportunities for possible solutions, overview of the previous research and knowledge gap. It defines the research concepts, research objectives and outlines... ... Read more

Fluvial flooding poses a major threat to mankind and annually leads to major economic losses with many casualties worldwide. The consequences of this can be mitigated when accurate and rapid predictions are available when the water will arrive at which location. Current numerical simulations take a significant amount of time due to their computational demand, which is not affordable during a calamity where each second can make the difference between life and death. Literature has shown promising results in fluvial flood forecasting by using a deep learning model, which generally takes only a fraction of the computation time compared to conventional models. However, these models have thus far only been trained on static landscapes with changing boundary conditions, which prevents the model’s application elsewhere.

This research explores therefore the possibilities of creating and training a generic non-location bound deep learning model which can predict the spatial distribution of fluvial flood arrival times per grid cell. The architecture of the created deep learning model consists of five parallel encoder-decoders, which takes the elevation, slopes, elevated elements, land roughness, and initial water levels into consideration, depending on the dataset. The model is trained, validated and tested on four unique datasets, which consists of 30,000 flooding samples. The degree of complexity within a sample increases with each dataset number.

The average error for the four consecutive test datasets were 0.91, 1.41, 1.25, and 1.76 hours per cell. The differences in the predicted and the groundtruth are relatively small although the deviation tends to become larger at the end of the simulation, in regions with a strong gradient in arrival time, and in hilly and complex landscapes.

In addition, the model has been tested on various benchmark landscapes to examine specific flow phenomena, as well as a realistic test scenario in the Dutch dike ring 48. The model shows satisfactory performances for landscapes other than those present in the dataset, untill it encounters a feature on which the model was not trained for, such as undershots or irregularly shaped waterways.

This research has shown the potential of deep learning in predicting fluvial flood arrival times on unseen before landscapes. Recommendations for further studies include the use of an active dike breach and a variable flood location.
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The future hydrological response of a river system is often predicted using hydrological models that are calibrated on past observations. By using static model parameters it is assumed that hydrological systems do not change in time. For long-term predictions, this is in clear contrast with the notion that vegetation actively adapts its root-system. Moreover, it neglects the possible impact that climate change might have on vegetation species and land-use. The root-zone storage capacity (S_r) is a key component in hydrological models as it has a critical function in the partitioning of water fluxes. This research (1) proposes a regression relationship using climate analogy to estimate the time-dynamic root-zone storage capacity for the Meuse basin, and (2) quantifies the impact of this time-dynamic root-zone storage capacity on the change in hydrological response under future climate change.

A selection of catchments from large sample data sets have been used in the analysis. For each of these catchments we calculated 27 catchment descriptors and the S_r using the water balance method. The regression relationship that is developed based on climate analogy and Multi-Linear Regression analysis showed that the S_r value can be estimated based on 4 catchment descriptors, namely Holdridge Aridity Index, phase shift of precipitation, seasonal amplitude for the potential evaporation, and sand fraction. This regression relationship has an adjusted R² value of approximately 0.70.

We considered three model scenarios. Each scenario consists of a part that models the historical hydrological response which is forced using the simulated historical climate data, and a part that models the future hydrological response which is forced using the simulated 2K climate data. The differences between these model parts indicate how the hydrological response of a system will change in the future. The benchmark scenarios use a static S_r value which is estimated for the simulated historical climate data using different methods. We considered the benchmark scenario for the water balance method and for the regression relationship. The third model scenario is called the dynamic regression scenario. For this scenario the regression relationship is used to estimate the S_r of the simulated historical and simulated 2K climate data. These values are then used for the historical and future part of the model, respectively.

Comparing the benchmark scenarios shows that the regression relationship only has a minimal impact on the change in hydrological response. The impact of the time-dynamic S_r on the change in hydrological response of the system is quantified by comparing the benchmark scenario for the regression relationship with the dynamic regression scenario. The comparison indicates that a time-dynamic S_r results in an increase of absolute evaporation during the summer months (+6.6%), while at the same time resulting in lower values for the streamflow (-8.6%) and groundwater storage (-4.8%) during the winter months. The root-zone storage capacity shows an increase throughout the whole year (maximum +23.6%). In other words, the time-dynamic root-zone storage capacity has a significant impact on the seasonality of the change in the hydrological response. The results also show that the regression relationship is promising and suggests that it might be a good way to estimate the root-zone storage capacity for climate projections in temperate climates.
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