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.

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.

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.

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.

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.

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.

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.

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, Bloschl, & 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, the 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 data set 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 if 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 be able 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 to calibrate and evaluate 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 stream flow response of the catchment satisfactory, 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 of 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 in 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.

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.

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.

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.

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.

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...

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.

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.

On the 13th and 14th of July 2021 a cold pit caused extreme precipitation along with record discharges in tributaries of the Meuse. These discharges caused floods in Belgium, Germany and the Netherlands.

Extreme precipitation occurrence and its intensity are expected to increase in the area due to climate change, increasing flood risk. Flood risk is the product of hazard, exposure and vulnerability. Research indicates all three are equally important drivers.

Planning of adaptation measures has been done with Decision Making in Deep Uncertainty (DMDU) methods to account for deep uncertainty. These methods lack natural and institutional context. Literature suggests both the natural system and the system governing it are complex. Therefore there is a need for a method providing climate change adaptation strategies with regard for the context and the notion that these systems are complex.

To address the research gap, a Framework is developed that combines natural and institutional aspects of climate change adaptation. A systems approach is used to find leverage points for wider systemic change. This can be used to spark autonomous adaptation, thereby also targeting exposure and vulnerability. The Framework provides a solution space of possible adaptation combinations and maps of spatial cooperation difficulty. By combining these possible combinations and locations are proposed. Systemic leverage points determine preferential strategies and additional policies. The Framework is tested in a case study in the Geul, Methods used are expert judgment, literature review, social network analysis and using open-source information.

The adaptive capacity is roughly 20 millimeters equivalent (6,680,000 m3) upstream of Valkenburg and 23.6 millimeters equivalent (7,882,400 m3) in the city itself. Thousands of combinations are possible. Depending on preference, this number is severely reduced.

The network governing flood risk is dense and polycentric. Cooperation quality is likely not sufficient between many actors to implement very intrusive adaptation measures that would be needed according to the solution space. When a certain cooperation is required, Flanders and the Netherlands are central players in the network. Germany and France are isolated in the network, causing the national governments of those nations to hold brokerage positions. Cooperation is the hardest in transboundary subcatchments especially when multiple nations are involved.

Depending on the preferences in the network, several adaptation strategies are possible in the Geul. In terms of cooperation difficulty, downstream solutions located in the Netherlands are preferential. Natural solutions would be preferable to harness self-organizational capacities of nature. Strategies that would cause secondary effects by intervening deeper into the systems should be highly visible. In addition, adaptation strategies should be accompanied by policies aimed at restoring the flow of information to citizens, restore or create financial incentives for autonomous adaptation of inhabitants and alter the paradigm of manageability of nature. Delays in planned adaptation response could be shortened by network interventions such as creating a central operational role in the network, implementing binding adaptation goals using the common European legislation or improving regional cooperation.

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

In mid-July 2021, heavy precipitation caused flooding for multiple tributaries of the Meuse and Rhine rivers which resulted in many deaths and exceptional damage. The Ahr in Germany, the Vesdre in Belgium and the Geul in the Netherlands suffered the greatest damage in each country. The first step in reducing such high damage from international, minor catchments is to build an understanding of the flood event.

The goal of this study was to analyse how extreme several hydrological aspects of the flood event of July 13-16 were for the Ahr, Vesdre and Geul. Analysing the extremity of an event requires a description of that event which helps with building knowledge. Its extremity places that knowledge into context. A flood with a 10-year return period will require a different approach than one with a 1000-year return period to balance the risk reduction and required effort. Three international catchments have been selected as case studies to allow an in-depth analysis of catchments and compliment current research on the flood event with a transboundary perspective.

The hydrological aspects considered for this study concern the forcing, flow mechanisms and response. For the forcing and response, a data analysis has been performed to study the extremity of the origin, magnitude, spatial and temporal variation of the precipitation, as well as the magnitude of the flow. Characteristics of both were used to determine the flood type. The data analysis on flow magnitude and seasonality has been supplemented by historical information to give more insight. The flow mechanisms have been derived from two models with varying structure and spatial variation by analysing parameter values and internal states.

The origin of the precipitation is considered as extreme since the cold core low, a low pressure area with a cold centre, was accompanied by several factors in a rare combination that likely contributed to the high precipitation amounts. The precipitation amount of July 14, the wettest day of the event, was of extreme magnitude according to the return periods of over 1000 years for several gauges. Although there was a large spatial variation in precipitation and thus their return periods, the spatial pattern showed a positive correlation of medium strength with the climatic pattern and is thus not considered as extreme. The temporal variation of the event is also not extreme. So during the event, only the amount of precipitation was extreme, but the precipitation amount in the preceding month was also extreme.

The peak discharges have been estimated to be around 990, 600 and 90 $[m^3/s]$ at the downstream gauges of the Ahr, Vesdre and Geul. The higher peak discharges coincide with sharper hydrographs and a stronger relief and impermeable soils. The return periods of the peak discharges were over 1000 years. Such extreme return periods are not surprising as such discharges had never been measured before. However, historical reconstructions show that similar flood events have occurred at least once for the Ahr. The assumed rare occurrence of summer floods in the regions was also debunked by historical information as 21 to 44\% of the historical floods occurred during the summer half-year. Another assumption by reports on the flood event was the flood type identification of a flash flood. The large spatial and temporal scale, as well as lag times for downstream gauges of 13 to 24 hours, do not resemble a flash flood, but rather a long precipitation event with a fast response.

Two models, a lumped, simple model based on the HBV model and a distributed more complex model based on the SBM model, have been calibrated for several years preceding the flood event, resulting in a good performance. Their performance for the flood event was low due to a strong underestimation of the peak discharge. This underestimation was resolved by a re-calibration. The re-calibrated parameters and internal states of the models show an increase in fast responding mechanisms, up to unprecedented magnitudes. The SBM model includes more processes and shows a rare amount of overland flow due to saturation excess. The fast response mechanisms, both in magnitude and relative runoff contribution, were unprecedented and can be considered as extreme.

The magnitudes of the precipitation, flow mechanisms and discharge were extreme for the 2021 flood event. The extreme flows were possible due to fast flow mechanisms, most likely overland flow due to saturation excess, which can be linked to the extreme pre-conditions. The strong relief and impermeable soils also seem to play a role in this extreme response. All catchments are capable of a fast and strong runoff response and although the forcing of the event was extreme, a new flood event in the future can not be excluded and so a higher level of preparedness and protection is required.

This study should be considered as an exploratory first step for action on the flood event. In order to move forward and increase the protection of the region, more detailed research is required. Such research can focus on several areas. Varying the study regions could be helpful for specific purpose. Several examples are an analysis of the entire region for a precipitation analysis, one catchment study for flood measures, or a catchment comparison for detecting hydrological influences. The anthropogenic influences can also provide more insight and opportunities and could be included in these future researches. The use of models was limited in this research but their further development is essential for flood risk management. Uncertainty is another topic that deserves more attention. An important factor in decreasing the uncertainty is to construct robust and extensive measurement infrastructure. Nevertheless, without more international cooperation and data accessibility the additional data will not reach its full potential. International cooperation also provides more value to the previous recommendations. This research shows that the transboundary catchments of the case study can be compared and give more insights to the overall behaviour.

Seasonal snow is the major water resource of more than a billion people around the world. In a plethora of regions in the Northern Hemisphere, agricultural, industrial, and drinking water supply are highly dependent on seasonal snow. In addition, the melting of seasonal snow regulates the magnitude and timing of high and low flows, controls the length of the growing season, and determines land surface warming via the albedo feedback. The aim of the study is to quantify the local and regional dynamics of snow cover in the Taurus mountain range and to identify the main climatic drivers responsible for the detected snow cover variability. A data-driven approach based on satellite observations is followed for the systematic analysis of large-scale snow cover in the region of interest. Compared to various remote-sensing snow cover studies that focus either on small/catchment scales, with limited spatial context, or on continental scales that cannot provide detailed insights into a specific region, this study investigates local and regional spatial patterns and temporal dynamics of snow cover across a specific mountain range of interest. The objectives of this study are: (a) to quantify the snow cover temporal variability in the different sub-regions of the Taurus mountain range, (b) to analyze the trends and to identify the regional differences, and (c) to examine the sensitivity of annual snow cover duration to inter-annual climatic variability. The Taurus Mountain Range is divided into sub-regions, using the WWF HydroSHEDS Basins Level 3 dataset and the Köppen–Geiger climate classification map, and in100-m elevation bands. The temporal variability of snow cover is quantified by the Regional Snowline Elevation (RSLE). The Regional Snowline Elevation (RSLE) is estimated in Google Earth Engine (GEE) using the methodology developed by Krajci et al. (2014). The temporal trends of the annual number of snow cover days (Dsc), for the different elevation zones in each sub-region, are derived from the RSLE time series and are analyzed using a modified Mann-Kendall (MK) non-parametric test. The sensitivities of Dsc to the inter-annual variability of winter temperature and precipitation and the snow cover duration trends are estimated in each tile and for all elevation bands in all sub-regions. In general, the analysis carried out, considering its limitations and uncertainties, found that there is no reason to be concerned about future water shortages in the area of the Taurus Mountain Range due to the decrease in the amount of water stored as snow. This is a positive outcome that indicates that there are no significant future changes in snow cover patterns and, as a result, the availability of water in the region will not be at risk.