Dam failures are rare but highly catastrophic events which can be devastating for both infrastructure and the population living downstream. The recent failures of Abu Mansour dam in Libya (September 2023) and Kakhovka dam in Ukraine (June 2023) have shown that large number of fatalities are associated to these events. It is therefore important to understand the hydrodynamic behaviour of dam break waves to ultimately identify the risk they pose, especially for dams which do not have detailed data information for modelling studies. The objective of the present study is to determine the hydraulic characteristics of the waves and to predict the potential loss of life in case of a dam failure event using a simplified screening method which can be applied to a larger dam dataset. The approach of the research was to utilise the original theory of Ritter (1892) who presented an idealised solution to the Saint-Venant equations. These equations were modified by incorporating the effect of friction which eventually had a reducing effect on the idealized dam break wave celerity. Using this simplified hydraulic model the dam break wave depths and velocities were able to be determined from the required input of dam height. From additional past dam failures, the proposed limits of fatality rates in the curves of Reclamation Consequence Estimation Methodology (RCEM 2014) were validated. To determine the Population at Risk (PAR) the study utilised Geographic Information System (GIS) tools and buffer methods were tested. For a smaller dataset of 22 dams, selected based on range of dam heights from the Global Reservoir and Dam (GRanD) database, the results showed that a buffer with downstream distances determined by one hour travel time and a 1000 m width gave the most consistent estimates of the exposed population when compared to the method incorporating Digital Elevation Models (DEM). When the analysis was extended to the complete dataset of GRanD, a new buffer method was proposed because of the limitation of GIS analysis tool. This buffer method with downstream distance of 25 km was tested against three different inundation widths. Results showed that using a buffer with fixed downstream distance of 25 km and an inundation width of 1000 m gave the most closest estimates when compared with the DEM method. The study was able to highlight the total population at risk on a global scale and also zoomed into the region of Europe. Comparison between the dams allowed identification of high risk dams in terms of the exposed population. Specifically, the total potential fatalities were determined for the top ten PAR dams of Europe. Furthermore, to demonstrate the usability of the risk screening method the analysis was extended to two developing countries, Pakistan and Madagascar, where Multi Hazard Early Warning Systems (MHEWS) do not tend to exist. The results showed the difference in PAR and fatalities for each dam and provides valuable insights to develop preliminary risk management and evacuation plans both at local and national level. Despite its limitations, the study provides a quick risk screening method which can serve as an alternate to the resource intensive numerical modelling approach. Future research might be needed to reduce uncertainties and achieve more accurate estimates.

The Eastern Nile Basin is a highly populated region dealing with inadequate energy systems and water scarcity issues. Moreover, its population is expected to increase in the following decades, leading to even higher electricity and water demands.
In order to sustainably meet this demands and guarantee access to electricity and water for all, new technologies and careful energy planning can play an important role.
In this context, floating solar power is a relatively new technology with promising advantages, such as the synergies between solar and hydropower resources, the exploitation of already existing infrastructures, and the reduction of evaporation rates and land use. These become even more relevant if seen in the context of the Eastern Nile Basin countries, where the need for efficient energy sources and solutions to the water scarcity issues are vital.

In this work, the role of floating solar power in the sustainable fulfillment of the increasing energy demand of the region is explored. The novelty of this study consist in the introduction of floating solar power in a long term regional energy system cost-optimization model (OSeMOSYS-TEMBA) at a single plant resolution. To do so, the single hydropower plants are also explicitly modelled, allowing both the spatial disaggregation of floating solar power plants and the connectivity between the countries via the Nile river. The regional approach is further enhanced by the presence of electricity trade links between countries, which connect the energy systems of the single countries directly.
Finally, the role of floating solar power on the energy system's footprints is evaluated in terms of CO2 emissions, land use and water savings. To this extent, a new methodology for land use accounting and pricing is proposed, and findings from previous studies are brought together to assess the evaporation reduction rates caused by the floating solar power plants.
This extended modelling framework is then used to analyse different scenarios, exploring hydrological regimes under different climate change projections and policy decisions such as the introduction of taxes for carbon emissions and land use change.

The results show that floating solar photovoltaics are a cost-optimal technology since early stages in the modelling horizon, and their full assumed potential is developed under every scenario. Their role in satisfying the energy demand of the whole region reaches 3\% of the generation mix in the reference scenario, but it increases to 4.3\% with the introduction of taxes on carbon emissions and land use. Moreover, the introduction of such policies cause an anticipation of floating solar power's capacity expansion. On the other hand, the tested climate change projections do not affect the results relevantly.
The sensitivity analyses, however, prove that the obtained results are very sensitive to the assumptions behind capacity constraints and costs of these technologies, which need more dedicated research.
As far as the energy system's footprints are concerned, the results show that the implementation of floating solar power can help reduce the total emissions and land use slightly, and cause evaporation reduction rates up to 376 million m\textsuperscript{3}/y (approximately 2\% of the total evaporation from hydropower reservoirs).
The optimal locations to invest in this technology are identified to be the largest hydropower plants in the system (Lake Nasser, the Grand Ethiopian Renaissance Dam and Merowe reservoir), but the reason of this choice relies in the very large size of these plants, which emerge for highest
FPV capacity deployment and water evaporation savings at the large scales considered.
Future research is still needed to reduce the uncertainty behind the key parameters (costs, capacity constraints), improve the representation of hydropower production, improve the evaporation assessments and investigate the effects of implementing floating solar power at smaller spatial and temporal scales.

Suriname is highly vulnerable to hazards that are climate change related, such as droughts and floods. Knowledge about future precipitation in Suriname is needed for the population of Suriname in order to adapt to climate change effects. By analysing 32 CMIP6 models, this study investigates the impact of climate change on precipitation in Suriname. The intermodel spread of the projected change in precipitation in 2100 compared to the reference period (1991-2020) is large, ranging from -45% (-2.0 mm/day) to +10% (+0.5 mm/day). Drivers of climate change in Suriname are the Intertropical Convergence Zone (ITCZ), Atlantic Meridional Overturning Circulation (AMOC) and EL Niño Southern Oscillation (ENSO). A weaker AMOC strength leads to warming of the south Atlantic Ocean and cooling of the north Atlantic Ocean, indicating a southward shift of the ITCZ. More El Niño like conditions lead to weakening of the mean zonal circulation along the equator and an eastward migration of the Walker Circulation in the Pacific. The occurrence of an increase in upward motion over the Pacific ITCZ and an increase in downward motion over Suriname also indicate an eastward migration of the Walker Circulation. With an eastward migrated Walker circulation an upward motion of moisture is strengthened and deep convection is increased over the Pacific. At the same time the opposite happens in Suriname where deep convection is decreased due to downward motion of air.
Models with relatively high future drying project the ITCZ at a more southward position, leading to low precipitation amounts in Suriname. Next to that, these models project a relatively stronger southward shift of the ITCZ compared to wet models, leading to an even stronger drying effect in Suriname. The climate models show a mean weakening of the AMOC strength, especially the dry models. More El Niño like conditions, with a decrease in deep convection in projections from dry models, are another reason for lower precipitation projections for dry models than for wet models.
After a bias-correction with Quantile Delta Mapping (QDM) the intermodel spread decreases significantly from 1-8 mm/day to 4-7.5 mm/day. Next to that, QDM correction has shifted the historical multimodel mean precipitation upwards by approximately 3 mm/day, almost doubling it. The doubling of the average precipitation indicates that climate models fail to accurately simulate the climate in Suriname. The 10% most extreme 1-day and 5-day cumulative precipitation values decrease according to climate projections. This is probably due to a decrease in the average projected precipitation in Suriname throughout the year. Extreme precipitation increases for the 0.1% most extreme 1-day and 5-day cumulative precipitation values. Despite the projected decrease in average precipitation, the models project more intense extreme precipitation events. The higher values for 0.1% extreme events can be explained by future warming, giving rise to a higher air capacity for water vapor.

This study investigates the dynamics of seasonal precipitation in Suriname with the help of moisture tracking and Sea Surface Temperature correlations. Prolonged drought periods, heavy precipitation events and accompanying flash floods and landslides are expected to increase in Suriname in the coming years. Reliable seasonal precipitation forecasts are therefore of great value to the agriculture, livestock and energy sectors in Suriname, but also for drought and flood mitigation. The objective of this study is to look into the sources of the predictive skill for seasonal precipitation forecasts for Suriname.

First, Spearman rank correlation analysis between sea surface temperature anomalies and Suriname precipitation anomalies was performed to establish the potential for statistical seasonal precipitation forecasts from SSTs. Second, moisture tracking with the model WAM2layers was applied to find the origin areas of evaporation for precipitation in Suriname. At last, these patterns of correlation and moisture tracking were combined with composite data to determine which potential drivers are influencing the precipitation anomalies in Suriname.

The results show that the ASON season displays the largest SST correlations (0.4 for Nino3 and Nino3.4 index with a lag of 3 months) and the longest lag time at which correlations are visible. This is due to the significant influence of ENSO on the ASON season precipitation. The DJFM season shows a clear link towards the Tropical Atlantic Variability and especially the AMM mode. Positive (negative) SST in the tropical north Atlantic cause a northward (southward) shift of the ITCZ over
Suriname. The AMJJ season reveals the smallest skill to predict seasonal precipitation anomalies. This could possibly be enhanced by performing a sub-seasonal analysis.

The processes of potential skill found in this thesis report can be of help to forecasters for the improvement of seasonal precipitation forecasts for Suriname

Hydrological and meteorological research are of great value to comprehend the complex Eastern Africa system, and more specifically the Kenyan Rift Valley, as impacts of climate change and indisputable changes in the hydrology of the lakes have become more apparent in recent decades. Lakes in the Kenyan Rift Valley region, spanning from the south to the north of Kenya, have been experiencing an increase in their water levels resulting in severe flooding in some cases. The flooding has been a relatively gradual but steady process throughout the 2010s. However, since 2020 the lakes have been rising more rapidly. This research aims to understand the main drivers behind the overflowing of Lake Nakuru as one of the lakes in the Kenyan Rift Valley and specifically examines the hypothesis that increased precipitation in the catchment area is the primary cause of this phenomenon. The study utilises various methodologies to analyse spatio-temporal variability in precipitation, investigate the dynamics of Lake Nakuru, and identify the origin and driving forces behind the changing precipitation patterns.

A change point analysis is performed for weather station data throughout the catchment including lake precipitation and catchment precipitation. A water balance model is built to analyse the impact of precipitation on the lake through precipitation bin analysis and different catchment interactions such as catchment runoff, lake evaporation, and sub-surface catchment storage. Multiple linear regression has been applied to identify key catchment characteristics for the variability of the lake volume. Through moisture tracking as part of this research with WAM2layers it was possible to identify changes in moisture sources and climate drivers of precipitation in the catchment.

The findings of the research reveal significant changes in precipitation patterns, with a notable increase in precipitation since 2010. Change point analysis indicates that this increase coincides with the rise in lake volume, suggesting a strong correlation between precipitation and the overflowing of the lake. The water balance model employed in the study further emphasises the impact of precipitation on the lake, highlighting the complex interactions between precipitation and the catchment. Moisture tracking and climate driver analysis provide insights into the origins of the precipitation variability. The study identifies the western Indian Ocean region, particularly east of Madagascar, as an increasingly important moisture source for the catchment area. Moreover, the study highlights a negative correlation between El Niño and September precipitation, indicating the influence of climate oscillations on the changing precipitation patterns. These findings suggest that future climate changes, including increasing sea surface temperatures and intensifying La Niña and El Niño events, may further contribute to the likelihood of flooding in the area.

Overall, the research strongly supports the hypothesis that the overflowing of Lake Nakuru is primarily caused by changes in precipitation. The study emphasises the need for further research and monitoring to better understand the complex interactions between climate, precipitation, and the lake system.

BaCla is a new stochastic parametric precipitation model to estimate rainfall associated with Tropical cyclones (TCs) in a computationally efficient way. It is validated for a number of calibration cases along with the current benchmark IPET deterministic method. The results of these models are compared with the observed StageIV-based rainfall. Two predictive parameters, the pressure deficit [hPa] (Δ𝑃) and maximum sustained wind speed [m/s] (vmax) are tested for the BaCla model. Frank copula is used by the BaCla model to predict the peak amount of rainfall. It employs the adapted Holland wind profile to create a 1D rain profile. Asymmetry may be added to make a 2D rain profile. The calibration study challenges the assumption that the radius of maximum wind speed (Rvmax) is equal to the radius of maximum precipitation (Rpmax) in the BaCla model. Additionally, it is observed that the radial fit overestimates rainfall in scenarios where the peak amount of rainfall is less than 2.8mm/hr. These observations lead to modifications in the relationship between Rvmax and Rpmax, the radial fit threshold, and the fitting coefficients of the adapted Holland wind profile for rainfall distribution in the improved BaClHa model. The new BaClHa model is verified with new and calibrated sets of TCs. The BaClHa model shows potential in achieving good accuracy along with global applicability at a limited computational expense.

Managing water resources for the future is challenging, given the wide range of climatic and hydrological uncertainty. To support decision makers in formulating robust adaptation plans and finding their way through the broad range of available climate data and models, decision scaling was introduced: an approach for bottom-up climate vulnerability assessments, informed by Global Climate Models (GCMs). This study aims to improve decision scaling as developed by Brown et al. (2012) by introducing three recent advances in climate adaptation and uncertainty science.

First, the concept of environmental flows (eflows) was adopted to represent the local ecology and variable hydrology with a broad range of indicators for evaluating the impact of climate change. Second, the GCM weighting strategy of Knutti et al. (2017) was applied to account for model performance and interdependency when estimating the plausibility of future climate conditions. Lastly, climate stress testing was not only done for annual average climate changes, but also for a prolonged dry season to represent the interannually variable character of climate change. The potential application of the novel decision scaling approach was illustrated through a case study of the Mokolo River. This river is situated in the South African Waterberg Biosphere Reserve, which faces competing water demands from tourism, industry, agriculture, and ecology under a changing climate.

It was found that the additions contribute to decision scaling, as eflows indicators introduced the climate impact on multiple flow components, which provides extra information on the climate vulnerability of the river during different flow conditions. In Waterberg, low and average flow conditions were found similarly sensitive to climate change. Moreover, GCM weighting increased the range of temperature uncertainty and showed high weights for both wet and dry GCM projections, which emphasizes the need for robust climate adaptation in Waterberg. Next, the additional stress test showed that prolonging the dry season by one month influences flows throughout the following year, especially in the posterior months. In this way, understanding the impact of plausible characteristics of future climate was improved.

Finally, this study revealed that local activities, such as groundwater extractions and land use changes, and available knowledge challenges the application of decision scaling to a real case study as it requires models and quantification of indicators. Therefore, carefully matching models, performance indicators, local concerns and knowledge are required for formulating climate adaptation strategies with decision scaling.

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.

Hydrological models are designed to represent the interactions between the
physical process and the water storage in short-term or long-term forecasting. The hydrological models in climate models (also know as Land Surface Models) aim to represent hydrological processes and interactions at a global scale. In this research, one Land Surface Model, HTESSEL is introduced. Previous studies have shown that HTESSEL is not reproducing hydrological fluxes well at a catchment scale.
In this study, the problems in representing discharge in HTESSEL can be summarized in three aspects: mismatch in peaks, slower recession,and the monthly delay. Thus, the aim of this research is to investigate the reasons of the poor performance in HTESSEL, and provide possible suggestions for a better fit. Therefore, the hydrological models HBV and GR4J (that operate on catchment scales) are introduced to identify the problems in HTESSEL by model comparison.
HTESSEL, HBV and GR4J models are used to simulate river discharge in 15 catchments are compared in terms of structure and parameterization. HTESSEL use tabulated parameter values, while HBV and GR4J are calibrated to match observations firstly. In order to investigate the influence of different model processes and parameters, a second calibration of the HBV and GR4J parameters is applied. Here, the model parameters are calibrated to the HTESSEL model output. Comparing the two calibration results, the parameter differences can be identified.
The results show that the soil column in HTESSEL is a key factor that influences the surface and subsurface runoff. On the one hand, HBV and GR4J can reproduce the slower falling limb in humid region by increasing their slow reservoirs. On the other hand, the top 50 cm of soil column is the effective depth that influences maximum infiltration rate. Thus, the changing of effective depth and the parameterization of orography variable b, which influences the fast runoff in HTESSEL, are necessary in temperate and mediterranean catchments.
According to this study, to solve the problem of mismatch in peaks this 50cm should be a spatial variable firstly. The increase of effective depth could overcome the overestimation in some places and the decrease of effective depth could overcome the underestimation of peaks in other places. In addition to effective depth, optimizing parameter b is also necessary, because it influences the fast runoff. Moreover, for the problem of slower recession and monthly delay, decreasing the size of soil column in HTESSEL is one way to get a better fit. Thus, in future, more study could focus on the interplay of the soil infiltration capacity and the fast runoff parameters. It might be helpful to improve the simulation.

It is well understood that precipitation extremes will increase in a warming climate. On (sub-)hourly timescales, the apparent scaling of precipitation extremes is stronger than expected from the Clausius-Clapeyron relation. Here, we focus on a common hypothesis which states that feedbacks from the local dynamics of clouds are responsible.

The effects of climate change on intensification of extreme precipitation in the Netherlands are modeled using the Dutch Atmospheric Large Eddy Simulation (Lochbihler et al. 2019). The objective is to understand more about extreme precipitation events in a warming climate in the Netherlands. A feedback loop is proposed in which cold-pools increase precipitation intensification with super-CC scaling.

Atmospheric conditions from a composite of days with extreme precipitation are perturbed.The entire atmospheric temperature column is warmed and cooled by 4 degrees Kelvin under constant relative humidity. The convective precipitation is described with a 10 minute timescale. A period of organized convection in the simulations is selected for comparison between the simulations. In this timeframe of extreme precipitation, the yield increases 8.5\% per degree Kelvin.

Convection grows deeper and updrafts in the clouds become stronger with warming. The updrafts that are the most representative of precipitation yield increase in velocity by 4.7\% per degree Kelvin. At 7 km height, updraft speeds increase up to 20.7 \% per degree Kelvin.

Moisture transport occurs predominantly low in the clouds, near cloud base.
With warming, more moisture is transported higher up in the clouds. Moisture transport near the surface increases along organized gustfront lines. On average the increase in transport by updrafts in the sub-cloud layer is dominated by increased moisture rather than due to strong updrafts becoming stronger. The moisture that is concentrated into convective cores coincides with intenser precipitation cores. With warming the subsiding motions accelerate and a stronger drying effect is present around precipitation cores. Cold-pools become stronger, forming bigger gustfront structures.

Current climate change characterized by increasing temperature has led to an increase in the intensity and frequency of extreme droughts that have more prolonged and profound ecohydrological and social impacts. By paying attention to the hydrological change before and after extreme drought and the patterns of drought recovery of ecohydrological system, it is possible to better understand the consequences of extreme drought on ecohydrological system. Both climate change and landscape change have an influence on catchment hydrological condition. The drought-related hydrological change is, therefore, the combination of changes induced by these two drivers. To further explore extreme drought impacts and the root causes of hydrological change under extreme drought events, it is necessary to separate the impact of drought-related climate change from the impact of landscape change. This study aims to characterize the variations in hydroclimatic conditions before and after extreme drought by studying the hydroclimatic movements in Budyko space, explore post-drought ecohydrological system recovery, and further separate and investigate the effects of climate and landscape change on catchment hydrological conditions. Monthly SPEI at a 12-month timescale was used to characterize and define the extreme drought events. The Budyko framework was applied to study the hydroclimatic changes of 63 basins in the United States induced by extreme drought events from 1990 to 2013 by quantifying the hydroclimatic movements in Budyko space. The climate effect on precipitation partitioning was distinguished from the landscape effect that is mainly related to vegetation response to extreme drought events. The contributions of precipitation and potential evaporation were quantified to further understand the effect of climate change which is caused by alterations of these climatic variables. To understand the effect of drought-related vegetation change on catchment precipitation partitioning, NDVI was applied to examine the response of vegetation to drought in terms of alteration in vegetation greenness and patterns of vegetation recovery. There were significant hydroclimatic changes in the basins before and after extreme drought. In post-drought period, more precipitation tended to be partitioned into evaporation in most basins. Change in streamflow was larger than the change in evaporation. 63.5% of all the basins experienced wetter conditions and more precipitation after drought. All basins gradually recovered in post-drought period, but not fully restored to their pre-drought states. The hydrological change under extreme drought was not explained by climate change alone in these basins, suggesting the existence of landscape drivers. The climate and landscape effects on precipitation partitioning could either enhance or counteract each other. The landscape drivers contributed more to change in catchment precipitation partitioning. In terms of the climatic effect that associated with the change in aridity index, climate change affects catchment precipitation partitioning by changing the precipitation and potential evaporation, among which precipitation is a more crucial climatic driver. From a vegetation-related landscape perspective, vegetation greenness reverted to pre-drought level within three years in most basins. The rapid or slow recovery, regrowth and even degradation of vegetation in post-drought period cause landscape-driven changes in catchment precipitation partitioning through directly changing vegetation transpiration and streamflow.

The root zone storage capacity is a critical determinant in hydrology, playing a major role in the partitioning of precipitation into evaporation and runoff. Besides, it is an important parameter in climatological and hydrological models. Understanding of the root zone storage capacity and its major determining processes is therefore fundamental in environmental sciences. Several studies have investigated root zone storage capacity magnitude and its descriptor variables, but mainly in snow absent regions. Computation and analysis of root zone storage capacities in snow dominant regions is therefore underexposed. As such, additional understanding of the major descriptor variables of the root zone storage capacity in boreal regions and in particular the influence of snow on the root zone storage capacity is desired. The aim of this study is therefore to quantify catchment average root zone storage capacities, identify its main descriptor variables and their regional variability and determine the influence of snow on root zone storage capacities in Canada. Root zone storage capacities were computed for 230 Canadian catchments using a simple water balance approach with additional snow module and were found to be normally distributed with mean magnitude of 183 mm and a standard deviation of 70 mm. Individual correlation of climate, discharge and landscape variables showed most relevant relationship between root zone storage capacities and yearly potential evaporation, runoff coefficient and seasonality index, although with considerable variance. Subsequent investigation on the mutual effect of several variables showed that the aridity index, runoff coefficient and seasonality timing index are major descriptor variables of the root zone storage capacity, by how they indicate the allocation of water for transpiration in a catchment and describe the degree of synchronisation between liquid input and atmospheric water demand. Application of a multiple linear regression model using the aridity index, runoff coefficient and seasonality timing index showed these variables can be used to predict root zone storage capacities in Canada with an R2 of 0.72. Subsequent tests of the predictive capability of this model Finland resulted in an R2 of 0.62. The influence of snow on the root zone storage capacity in Canada was identified by comparing its magnitude computed with and without a snow module. Whenever significantly present, snow effects showed a decrease in root zone storage capacity magnitude, caused by increased overlap between liquid input and transpiration output in a catchment. These effects are encapsulated by the seasonality timing index. To determine the regional variability of root zone storage capacity descriptors in Canada, catchments were clustered based on similar functioning. The results indicated that different variables have an effect on the root zone storage capacity in different functionally comparable regions and that a large part of the functional behaviour of the clusters can be explained by the geographical location of their catchments. The influence of these regionally dependent variables on the root zone storage capacity is encapsulated in the earlier defined main descriptor variables aridity index, runoff coefficient and seasonality timing index.