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The compound occurrence of extreme weather and/or climate events can cause stronger negative impacts than the individual events, and its frequency is increasing in several regions of the world. In this work, the effect of antecedent drought conditions on hot extremes during the months of December-February in Australia was analysed for two periods (1979-2019 and 1950-2019). The standardized precipitation evapotranspiration index (SPEI) and the indices number of hot days (NHD) and number of hot nights (NHN) were used to assess drought and extreme temperature events. While the link between dry and heat events is more important in the north in February, in December and January it is strong in the east coast. When temporal lags of 1-3 months are considered, there is a strong correlation between SPEI and NHD/NHN for the concurrent month on most of the study area. For the previous 1-3 months, the area and the correlation values decreased, but consistent spatial patterns were obtained for each month, namely negative correlations on the southwest and southeast in December, and the east in February. Tropical areas showed large areas of correlation between SPEI and NHN, including for the previous 3 months, whereas temperate climates showed the smaller area of correlation with NHN, including at the concurrent month. Significant correlations obtained for lead times longer than 1 month, namely with night heat extremes, point to a predictive ability in several regions of Australia. Moreover, the correlation coefficients obtained using the more recent period (1979-2019) show similar spatial patterns, but with higher values than for the 1950-2019 period. The results highlight the prospect of an early prediction of hot summer extremes in regions affected by drought in spring., International Journal of Climatology, 42 (11), ISSN:0899-8418, ISSN:1097-0088

A 38-year hindcast water-level product is developed for the US Southeast Atlantic coastline from the entrance of Chesapeake Bay to the southeast tip of Florida. The water-level modeling framework utilized in this study combines a global-scale hydrodynamic model (Global Tide and Surge Model, GTSM-ERA5), a novel ensemble-based tide model, a parameterized wave setup model, and statistical corrections applied to improve modeled water-level components. Corrected water-level data are found to be skillful, with an RMSE of 13 cm, when compared to observed water-level measurement at tide gauge locations. The largest errors in the hindcast are location-based and typically found in the tidal component of the model. Extreme water levels across the region are driven by compound events, in this case referring to combined surge, tide, and wave forcing. However, the relative importance of water-level components varies spatially, such that tides are found to be more important in the center of the study region, non-tidal residual water levels to the north, and wave setup in the north and south. Hurricanes drive the most extreme water-level events within the study area, but non-hurricane events define the low to mid-level recurrence interval water-level events. This study presents a robust analysis of the complex oceanographic factors that drive coastal flood events. This dataset will support a variety of critical coastal research goals including research related to coastal hazards, landscape change, and community risk assessments.

The effectiveness of adaptive measures tackling the effects of climate change is dependent on robust climate projections. This becomes even more important in the face of intensifying extreme events. One example of these events is flooding, which embodies a major threat to highly vulnerable coastal urban areas. This includes eastern Asia, where multiple coastal megacities are located, e.g. Shanghai and Shenzhen. While the ability of general circulation models (GCMs) and regional climate models (RCMs) to project atmospheric changes associated with these events has improved, systematic errors (biases) remain. This study therefore assess capabilities of improving the quality of regional climate projections for eastern Asia. This is performed by evaluating an ensemble consisting of bias adjustment methods, GCM-RCM model runs and future emission scenarios based on representative concentration pathways (RCP) obtained from EAS-CORDEX. We show that bias adjustment significantly improves the quality of model output and best results are obtained by applying quantile delta mapping. Based on these results we evaluate potential future changes in crucial hydrometeorological predictors, univariate extreme events and compound extreme events, focusing on high wind speeds and extreme precipitation. Key findings include an increase in daily maximum temperature of 1.5 to nearly 4 °C, depending on the scenario, as well as increased levels of precipitation under RCP 8.5. Furthermore, a distinct intensification of extreme events including high temperatures and heavy precipitation is detected and this increase exceeds the increase of the overall mean of these predictors. The annual number of compound events including heavy precipitation and extreme wind speeds shows a significant increase of up to 50% for RCP 8.5 in the South China Sea as well as the adjacent coastal areas.

Extreme climatic events have significant impacts on society and the environment, especially when multiple hazards occur concurrently (e.g., drought and heat waves) or consecutively (wildfires and extreme precipitation). A large number of indicators have been developed to detect and study changes in extreme events across space and time. While the current climate extreme indicators provide useful information, most do not provide any information on compound/concurrent events. A compound event corresponds to a situation in which multiple (often interrelated) hazard drivers lead to an extreme outcome. Therefore, current univariate methods used for frequency analysis and risk assessment may underestimate the risk or occurrence probability of extreme events. After a comprehensive review of the existing methods, this study outlines frameworks for detecting, modeling, and analyzing inter-related events and processes including compound extremes.

Sea-level rise (SLR) threatens coastal communities by increasing flood exposure for people, homes, businesses, and critical infrastructure. In order to protect residents and the infrastructure systems on which they rely, coastal communities will need to plan for and adapt to higher sea levels and increased flood hazards. This dissertation contributes to the understanding of SLR impacts on human, engineered, and natural systems. More specifically, the text addresses three important aspects of SLR adaptation: the need for data-driven methods to inform regional SLR adaptation networks, the impacts of SLR on critical infrastructure and service disruptions, and the influence of coupled SLR, precipitation, and storm surge on coastal groundwater aquifers and flooding.Many collaborative networks aimed at promoting SLR adaptation at the global, national, or regional scale are primarily based on geographic proximity or voluntary participation and are not grounded in any quantitative assessment of shared vulnerability. The first part of this dissertation presents a method to group communities based on similar SLR exposure variables using statistical cluster analysis. The method is applied to cities and counties in the San Francisco Bay Area to demonstrate the usefulness for developing regional SLR adaptation networks.In coastal communities, critical infrastructure assets, such as wastewater treatment plants, are susceptible to flooding due to SLR. However, the extent and progression of this exposure and the impacts on service disruptions are poorly understood. The second part of this work presents a geospatial analysis of wastewater exposure to SLR-induced marine flooding at the national scale and a similar analysis of SLR-induced groundwater flooding at the regional scale in the San Francisco Bay Area. Overall, hundreds of wastewater treatment plants could face flood disruptions due to SLR, which could lead to a loss of wastewater services for millions of residents. In some cases, groundwater is more of a threat than marine flooding.To understand the potential impacts of SLR on coastal groundwater aquifers and flooding, most previous studies have used steady-state methods and have focused on SLR and other climate factors, such as precipitation, separately. However, the strong coupling between precipitation and other weather variables that influence coastal water levels suggests a need for multivariate approaches. The final part of this dissertation describes the use of a copula approach to correlate precipitation with wind speed and atmospheric pressure. These variables are then used to determine changes in water levels due to wind setup and pressure-driven surge under current and future climate scenarios. Forcing a groundwater model with this data provides insight into the potential for a narrower unsaturated zone and groundwater emergence.Together, these chapters explore the local disruptions and regional impacts of SLR, as well as the coupled processes that lead to flooding.

Coastal deltas are prone to flooding due to their low elevation and dense population. Flooding in these areas can occur from several drivers, including pluvial (rainfall excess), fluvial (high river discharge), and coastal (storm surge and waves). However, floods can also occur or be exacerbated from interactions between these drivers, which by itself do not have to be extreme. If these drivers are statistically dependent, their joint likelihood might be misrepresented if dependence is not accounted for. At the global scale, however, flood risk models still analyze each flood driver in isolation. At the local scale, state-of-the-art hydrodynamic models have been developed for compound flooding, but these are not globally applicable. The overall aim of this thesis is therefore, to improve our understanding and modeling capabilities of compound flood risk anywhere globally. Specifically, the objectives are: 1) to identify deltas globally that are prone to compound flooding; and 2) to develop and apply a globally-applicable computational framework for compound flood risk modeling. To improve our understanding of compound flooding at the global scale, I coupled a global riverine flood model with a global tide and surge model to obtain continuously simulated water levels in 3,433 deltas globally. In 19.7% of the deltas, compound flooding is the dominant driver of extreme water levels. These deltas generally have larger surge extremes and are located in basins with faster discharge response to rainfall, and/or flat topography. If compound surge-discharge events are ignored, flood depths are significantly underestimated for 30.7 million out of a total of 332.0 million (9.3%) of the annual expected population exposed to flooding in deltas. To enable high-resolution local compound flood modeling anywhere in the world, I developed two tools: the Iterative Hydrography Upscaling (IHU) algorithm to upscale gridded flow direction data; and the Hydro Model Tools (HydroMT) package to enable transparent and reproducible model setups anywhere in the world. Based on the IHU algorithm, rivers in a coarse-resolution hydrological model are seamlessly connected with rivers in a high-resolution hydrodynamic compound flood model. With HydroMT, a ready-to-use model can be built from (global) datasets, such as topography, landuse and meteorological data, with a single configuration file that describes a sequence of workflows and the datasets used in each workflow. Using these tools, I developed a globally-applicable framework for compound flood hazard modeling. The framework is validated against two historical events in the Sofala province of Mozambique based on satellite-derived flood extents. Compare to a global flood model, the globally-applicable model generally has better skill. Furthermore, it results in more realistic flood depth maps due to better floodplain connectivity and provides a more holistic representation of flood processes as it includes direct coastal and pluvial flooding. However, the resolution of the globally-applicable model is not yet sufficient to capture fine grained channels, which play an important role in the drainage of precipitation, thereby, overestimating pluvial flooding. Finally, I extended the framework with an event-based approach for compound flood risk assessments from pluvial, fluvial, and coastal flood drivers and applied it to the same case-study. Here, rare events cause more impact if accounting for observed dependence between flood drivers compared to independence, e.g. 12% at the 100-year return period. The overall difference in risk of 0.55% in expected annual damage is however small as the physical interaction of drivers for frequent events is small. This thesis contributes to ongoing efforts to better understand and mitigate flood risks at the global scale. The coupled global models can be used to identify deltas prone to compound flooding, whereas the globally-applicable model framework can be used for comprehensive flood risk assessments in deltas.

Coastal deltas are prone to flooding due to their low elevation and dense population. Flooding in these areas can occur from several drivers, including pluvial (rainfall excess), fluvial (high river discharge), and coastal (storm surge and waves). However, floods can also occur or be exacerbated from interactions between these drivers, which by itself do not have to be extreme. If these drivers are statistically dependent, their joint likelihood might be misrepresented if dependence is not accounted for. At the global scale, however, flood risk models still analyze each flood driver in isolation. At the local scale, state-of-the-art hydrodynamic models have been developed for compound flooding, but these are not globally applicable. The overall aim of this thesis is therefore, to improve our understanding and modeling capabilities of compound flood risk anywhere globally. Specifically, the objectives are: 1) to identify deltas globally that are prone to compound flooding; and 2) to develop and apply a globally-applicable computational framework for compound flood risk modeling. To improve our understanding of compound flooding at the global scale, I coupled a global riverine flood model with a global tide and surge model to obtain continuously simulated water levels in 3,433 deltas globally. In 19.7% of the deltas, compound flooding is the dominant driver of extreme water levels. These deltas generally have larger surge extremes and are located in basins with faster discharge response to rainfall, and/or flat topography. If compound surge-discharge events are ignored, flood depths are significantly underestimated for 30.7 million out of a total of 332.0 million (9.3%) of the annual expected population exposed to flooding in deltas. To enable high-resolution local compound flood modeling anywhere in the world, I developed two tools: the Iterative Hydrography Upscaling (IHU) algorithm to upscale gridded flow direction data; and the Hydro Model Tools (HydroMT) package to enable transparent and reproducible model setups anywhere in the world. Based on the IHU algorithm, rivers in a coarse-resolution hydrological model are seamlessly connected with rivers in a high-resolution hydrodynamic compound flood model. With HydroMT, a ready-to-use model can be built from (global) datasets, such as topography, landuse and meteorological data, with a single configuration file that describes a sequence of workflows and the datasets used in each workflow. Using these tools, I developed a globally-applicable framework for compound flood hazard modeling. The framework is validated against two historical events in the Sofala province of Mozambique based on satellite-derived flood extents. Compare to a global flood model, the globally-applicable model generally has better skill. Furthermore, it results in more realistic flood depth maps due to better floodplain connectivity and provides a more holistic representation of flood processes as it includes direct coastal and pluvial flooding. However, the resolution of the globally-applicable model is not yet sufficient to capture fine grained channels, which play an important role in the drainage of precipitation, thereby, overestimating pluvial flooding. Finally, I extended the framework with an event-based approach for compound flood risk assessments from pluvial, fluvial, and coastal flood drivers and applied it to the same case-study. Here, rare events cause more impact if accounting for observed dependence between flood drivers compared to independence, e.g. 12% at the 100-year return period. The overall difference in risk of 0.55% in expected annual damage is however small as the physical interaction of drivers for frequent events is small. This thesis contributes to ongoing efforts to better understand and mitigate flood risks at the global scale. The coupled global models can be used to identify deltas prone to compound flooding, whereas the globally-applicable model framework can be used for comprehensive flood risk assessments in deltas.

Compound events for a long time did not get enough scientific attention, due to the complexity of these occurrences. Yet, taking synchronisation of various natural process into account can significantly improve the reliability and accuracy of modelling, design and prediction of natural disasters. In this thesis the topic of compound events, especially spatial and temporal clustering of precipitation is addressed. The analysis of the rainfall clustering is performed for the Sava River catchment in Slovenia. For the investigation hourly precipitation values obtained from ARSO (Slovenian Environment Agency) were used. Afterwards, hydrological modelling of the Sava River catchment up to the Čatež gauging station was performed using GR4H model with included snow module. Analysis of temporal clustering shown, that 3 hours period before the extreme precipitation event is the most crucial in terms of contributing into antecedent rainfall amount. Spatial clustering analysis performed on both hourly and daily data shown a distinctive clustering of rainfall in space: very strong clustering pattern in the western are of the catchment with rapid drop in clustering radius towards the eastern part. The results from both clustering analysis and hydrological modelling were used in order to obtain a design rainfall event that could be used as an input for hydrological modelling in ungauged catchments. The design rainfall event was obtained by the use of Huff and intensity-duration-frequency (IDF) curves. Final results show that even on such a small area (i.e., around 10,000 km2), very distinctive precipitation clustering patterns, both in space and time, can be noticed and as such they should be included in the future design of the rainfall events in the flood studies of this area. Overall, it is also highly recommended to keep in mind and pay attention to including possible synchronisation of natural events more frequently in scientific research. Zaradi kompleksnosti pojava sestavljeni dogodki dolgo časa niso bili deležni dovolj znanstvene pozornosti, vendar pa lahko upoštevanje sočasnosti pojava različnih naravnih procesov bistveno izboljša zanesljivost in natančnost modeliranja, načrtovanja in napovedovanja naravnih nesreč. V magistrskem delu je obravnavana tematika sestavljenih dogodkov, predvsem prostorskega in časovnega nastopa padavin. Analiza sočasnosti nastopa padavin je bila izvedena za porečje reke Save v Sloveniji. Za raziskavo so bile uporabljene urne vrednosti padavin, pridobljene od Agencije RS za okolje (ARSO). Dodatno je bil izdelan hidrološki model porečja reke Save do vodomerne postaje Čatež z uporabo modela GR4H z vključenim snežnim modulom. Analiza sočasnosti nastopa padavin je pokazala, da je obdobje treh ur pred ekstremnim padavinskim dogodkom ključno pri upoštevanju predhodne količine padavin. Analiza sočasnega prostorskega nastopa, izvedena na urnih in dnevnih podatkih, je pokazala značilno lastnost padavin na območju Slovenije: relativno izrazit vzorec sočastnosti nastopa v zahodnem delu porečja in nekoliko manj izrazit vzorec v vzhodnemu delu. Rezultati analize sočasnosti nastopa padavin in rezultati hidrološkega modeliranja so bili uporabljeni za določitev projektnega padavinskega dogodka, ki se ga lahko uporabi kot vhodni podatek za hidrološki model v primeru nemerjenih porečij. Skupna količina padavin in porazdelitev med dogodkom je bila določena z uporabo Huffove krivulje in krivulje intenzivnosttrajanje-povratna doba (ITP). Končni rezultati kažejo, da je že na relativno majhnem območju (tj. okoli 10.000 km2) mogoče opaziti zelo izrazite vzorce sočasnosti nastopa padavin, tako v prostoru kot v času, in jih je kot take treba vključiti v določanje projektnih padavinskih dogodkov pri izdelavi hidroloških študij. Na splošno je tudi zelo priporočljivo upoštevati in biti pozoren na pogostejše vključevanje morebitne sočasnosti naravnih procesov v znanstvene raziskave.

Coastal compound flooding events occur when extreme events of rainfall, river discharge and sea level coincide and collectively increase water surface elevation, exacerbating flooding. The meteorological conditions that generate these events are usually low-pressure systems that generate high winds and intense rainfall. In this study, we identify the types of synoptic atmospheric conditions that are typically associated with coastal compound events using a weathertype approach, for the North Atlantic coastlines (encompassing northwest Europe and the east coast of the United States). Compound events are identified along the estuaries of the study region from 1980 to 2014 based on an impact function defined by water surface elevation that resulted from the combination of river discharge and sea level. We find that compound events are more frequent along European as opposed to U.S. coastlines. In both cases, they are associated with a few dominant weather patterns. European hotspots of compound events are concentrated in the west coast of United Kingdom, the northwest coast of the Iberian Peninsula and around the Strait of Gibraltar. These areas share the same weather patterns which represent the main pathways of storms that cross the North Atlantic Ocean. In the case of U.S. locations, the areas with highest number of compound events are located mainly in the Gulf of Mexico and along Mexico and along the mid-eastern U.S. coastlines. In these areas, compound events are produced by transitional weather patterns, which describe storms that travel northward parallel to the coastline. Splitting the occurrence of compound events in the corresponding weather types discriminates the interannual variability based on the relationship with dominant climate indices in the North Atlantic Ocean. This research forms part of the CHANCE project, which is supported by awards from the UK Natural Environment Research Council (NE/S010262/1) and US National Science Foundation (1929382). We would like to thank Dirk Eilander for providing support about the use of dataset of simulated water levels and discharge at river mouth locations globally which is available on Zenodo(doi: 10.5281/zenodo.3665734).

Stochastic weather generators have been increasingly used as downscaling tools for climate change impact assessments. In spite of their widespread use, their potential to simulate climate extremes – especially multivariate extremes – is largely unexplored. The aim of this study is to assess the ability of the Richardson type six-variate weather generator SiSi to simulate the frequency of various univariate as well as multivariate extremes with focus on extremes related to the non-normally distributed weather variables relative humidity and wind speed. A total of 83 sites with different elevation and proximity to each other – thereby defining a European, a country (Austria) and a local (catchment) scale – and diverse climates across Europe are selected. Results show that SiSi is able to simulate univariate and multivariate extremes generally and equally well in all climate zones. The results depend on the nature of the individual variables involved in the extreme events. Among all the extreme events, the weather generator has a tendency to underestimate the extremes related to minimum temperature. The first-order auto-regressive (AR(1)) model used for modeling non-precipitation variables assumes the distribution of variables to be Gaussian. This assumption has been enforced in this study by transforming each non-precipitation variable to a normal distribution, but nevertheless the weather generator consistently underestimates the cold extremes. This is due to the multimodal nature of the distribution of temperature. The AR(1) model is not able to reproduce the multimodality of the distributions. The performance of SiSi does not depend on the climate type of a region or the proximity of sites to one another, rather it depends on the characteristics of a variable at an individual location.

Compound weather extreme events, such as tornadoes and flash floods, can significantly impact societies and infrastructure systems. Disaster response agencies provide instructions to the exposed communities to retreat to safety specific to the natural hazard. However, the instructions can become confusing if natural hazards demand conflicting responses. This study characterizes the compound tornado and flash flood (TORFF) events to assess and predict the simultaneous occurrence probability of such hazards across Canada in the long term. We quantify dependencies between the tornadoes and flash floods using ground-based and reanalysis datasets. Tornado data are available based on the recorded Fujita rating for each event, and the corresponding wind speed values are determined through a resampling approach. The TORFF events are clustered and the bivariate probability distributions of the resampled windspeed and precipitation are characterized based on Copula. The corresponding individual and joint return levels are investigated under different scenarios (AND, OR, and conditional) across Canada. Results show positive dependencies between resampled windspeed and associated precipitation in Saskatchewan, followed by Alberta, Manitoba, Ontario, and Quebec (least dependency) regions. Higher dependencies between tornadoes and flash floods over regions such as Saskatchewan suggest that analyzing these events in isolation can underestimate the associated risks. Higher precipitation is also expected during extreme wind speed, as observed in the conditional assessment of precipitation given windspeed. This study provides insight for more realistic recurrence interval estimation for tornadoes and flash floods to aid in the evacuation decision-making process.

The societal and economic losses due to compound extremes, i.e., extreme events often result from one extreme state or amplification due to many other contributing mild or extreme states, are manifold higher in magnitude compared to the impacts of those from individual extremes alone. To understand the future impact of compound flood events at different spatio-temporal aggregation levels and climate zones, i.e., mountains, flood plains, and coasts, it is inevitable to examine the mechanisms of compound floods. To this end, this thesis highlights the importance of an integrated process-based physical modelling approach (atmospheric, hydrological and hydrodynamic) for the proper investigation of future flood risk. The research described in this thesis cover several relevant topics related to a complete flood risk chain (from mountains to coasts) and their impact on water resources in regions around the world (i.e., Europe and Asia). First part of this thesis focuses on empirical, univariate and bivariate approaches to understand historical climate trends and associated hazards in data scarce High Mountain Asia (HMA). This study provides a concise overiew of climate related changes across different regions which were difficult to align and compare either due to use of different data or spatio-temporal scales. Analysis reveals while the temperature is consistently increasing at a higher rate than the global warming rate, precipitation changes are not uniform, with substantial temporal and spatial variation in particular for the higher altitudes of HMA. Changes in climatic variables give rise to nonlinear, non-stationary, and non-uniform responses of cryospheric and hydrological variables such as snow, glaciers, soil moisture, and groundwater. Therefore, second part of this thesis investigates the systemic effect of the compound occurrence of extreme precipitation and temperature, at a wide range of spatio-temporal scales, and their impacts on the seasonality and trends in total water availability for HMA using a high-resolution cryospheric-hydrological model. This study shows a shift in the magnitude and peak of water availability, at seasonal time scale, to earlier in the year due to earlier onset of melting. Analysis reveals that the contrasting responses of HMA's rivers are primarily dictated by their hydrological regimes. To understand the risk due to compound floods in coastal regions, the third study of this thesis investigates the possibility of finding near-simultaneous storm surge and extreme river discharge along the Dutch coast using an extended dataset derived from a storm surge model and two hydrological river-discharge models forced with conditions from a high-resolution regional climate model in ensemble mode. This study conceptually shows that a strong storm whose winds set up a storm surge will need time to reach the Rhine headwaters where heavy rainfall will find its way to the river mouth after multiple days of travel time. The fourth and final study of this thesis investigates and quantifies the role of memory components in hydrological and meteorological systems which are responsible to generate extreme discharge, i.e. soil moisture, snow accumulation and the antecedent hydro-climatic conditions. Results show that the meteorological autocorrelation (manifesting itself by the occurrence of clustered precipitation events) has a strong impact on the magnitude of peak discharge. Finally, the study illustrates how hydrological memory from snow accumulation and soil moisture complements the generation of extreme discharges. A new valuable insight on the mechanisms of compound floods and its potential impact on water resources (both for historical and future climate) has been gained with novel modelling approaches. The outcomes generated from this thesis might contribute to support climate change adaptation policy planning and outreach programs in these climate change hotspots, i.e., the mountains and deltas.

The societal and economic losses due to compound extremes, i.e., extreme events often result from one extreme state or amplification due to many other contributing mild or extreme states, are manifold higher in magnitude compared to the impacts of those from individual extremes alone. To understand the future impact of compound flood events at different spatio-temporal aggregation levels and climate zones, i.e., mountains, flood plains, and coasts, it is inevitable to examine the mechanisms of compound floods. To this end, this thesis highlights the importance of an integrated process-based physical modelling approach (atmospheric, hydrological and hydrodynamic) for the proper investigation of future flood risk. The research described in this thesis cover several relevant topics related to a complete flood risk chain (from mountains to coasts) and their impact on water resources in regions around the world (i.e., Europe and Asia). First part of this thesis focuses on empirical, univariate and bivariate approaches to understand historical climate trends and associated hazards in data scarce High Mountain Asia (HMA). This study provides a concise overiew of climate related changes across different regions which were difficult to align and compare either due to use of different data or spatio-temporal scales. Analysis reveals while the temperature is consistently increasing at a higher rate than the global warming rate, precipitation changes are not uniform, with substantial temporal and spatial variation in particular for the higher altitudes of HMA. Changes in climatic variables give rise to nonlinear, non-stationary, and non-uniform responses of cryospheric and hydrological variables such as snow, glaciers, soil moisture, and groundwater. Therefore, second part of this thesis investigates the systemic effect of the compound occurrence of extreme precipitation and temperature, at a wide range of spatio-temporal scales, and their impacts on the seasonality and trends in total water availability for HMA using a high-resolution cryospheric-hydrological model. This study shows a shift in the magnitude and peak of water availability, at seasonal time scale, to earlier in the year due to earlier onset of melting. Analysis reveals that the contrasting responses of HMA's rivers are primarily dictated by their hydrological regimes. To understand the risk due to compound floods in coastal regions, the third study of this thesis investigates the possibility of finding near-simultaneous storm surge and extreme river discharge along the Dutch coast using an extended dataset derived from a storm surge model and two hydrological river-discharge models forced with conditions from a high-resolution regional climate model in ensemble mode. This study conceptually shows that a strong storm whose winds set up a storm surge will need time to reach the Rhine headwaters where heavy rainfall will find its way to the river mouth after multiple days of travel time. The fourth and final study of this thesis investigates and quantifies the role of memory components in hydrological and meteorological systems which are responsible to generate extreme discharge, i.e. soil moisture, snow accumulation and the antecedent hydro-climatic conditions. Results show that the meteorological autocorrelation (manifesting itself by the occurrence of clustered precipitation events) has a strong impact on the magnitude of peak discharge. Finally, the study illustrates how hydrological memory from snow accumulation and soil moisture complements the generation of extreme discharges. A new valuable insight on the mechanisms of compound floods and its potential impact on water resources (both for historical and future climate) has been gained with novel modelling approaches. The outcomes generated from this thesis might contribute to support climate change adaptation policy planning and outreach programs in these climate change hotspots, i.e., the mountains and deltas.

The societal and economic losses due to compound extremes, i.e., extreme events often result from one extreme state or amplification due to many other contributing mild or extreme states, are manifold higher in magnitude compared to the impacts of those from individual extremes alone. To understand the future impact of compound flood events at different spatio-temporal aggregation levels and climate zones, i.e., mountains, flood plains, and coasts, it is inevitable to examine the mechanisms of compound floods. To this end, this thesis highlights the importance of an integrated process-based physical modelling approach (atmospheric, hydrological and hydrodynamic) for the proper investigation of future flood risk. The research described in this thesis cover several relevant topics related to a complete flood risk chain (from mountains to coasts) and their impact on water resources in regions around the world (i.e., Europe and Asia). First part of this thesis focuses on empirical, univariate and bivariate approaches to understand historical climate trends and associated hazards in data scarce High Mountain Asia (HMA). This study provides a concise overiew of climate related changes across different regions which were difficult to align and compare either due to use of different data or spatio-temporal scales. Analysis reveals while the temperature is consistently increasing at a higher rate than the global warming rate, precipitation changes are not uniform, with substantial temporal and spatial variation in particular for the higher altitudes of HMA. Changes in climatic variables give rise to nonlinear, non-stationary, and non-uniform responses of cryospheric and hydrological variables such as snow, glaciers, soil moisture, and groundwater. Therefore, second part of this thesis investigates the systemic effect of the compound occurrence of extreme precipitation and temperature, at a wide range of spatio-temporal scales, and their impacts on the seasonality and trends in total water availability for HMA using a high-resolution cryospheric-hydrological model. This study shows a shift in the magnitude and peak of water availability, at seasonal time scale, to earlier in the year due to earlier onset of melting. Analysis reveals that the contrasting responses of HMA's rivers are primarily dictated by their hydrological regimes. To understand the risk due to compound floods in coastal regions, the third study of this thesis investigates the possibility of finding near-simultaneous storm surge and extreme river discharge along the Dutch coast using an extended dataset derived from a storm surge model and two hydrological river-discharge models forced with conditions from a high-resolution regional climate model in ensemble mode. This study conceptually shows that a strong storm whose winds set up a storm surge will need time to reach the Rhine headwaters where heavy rainfall will find its way to the river mouth after multiple days of travel time. The fourth and final study of this thesis investigates and quantifies the role of memory components in hydrological and meteorological systems which are responsible to generate extreme discharge, i.e. soil moisture, snow accumulation and the antecedent hydro-climatic conditions. Results show that the meteorological autocorrelation (manifesting itself by the occurrence of clustered precipitation events) has a strong impact on the magnitude of peak discharge. Finally, the study illustrates how hydrological memory from snow accumulation and soil moisture complements the generation of extreme discharges. A new valuable insight on the mechanisms of compound floods and its potential impact on water resources (both for historical and future climate) has been gained with novel modelling approaches. The outcomes generated from this thesis might contribute to support climate change adaptation policy planning and outreach programs in these climate change hotspots, i.e., the mountains and deltas.

The societal and economic losses due to compound extremes, i.e., extreme events often result from one extreme state or amplification due to many other contributing mild or extreme states, are manifold higher in magnitude compared to the impacts of those from individual extremes alone. To understand the future impact of compound flood events at different spatio-temporal aggregation levels and climate zones, i.e., mountains, flood plains, and coasts, it is inevitable to examine the mechanisms of compound floods. To this end, this thesis highlights the importance of an integrated process-based physical modelling approach (atmospheric, hydrological and hydrodynamic) for the proper investigation of future flood risk. The research described in this thesis cover several relevant topics related to a complete flood risk chain (from mountains to coasts) and their impact on water resources in regions around the world (i.e., Europe and Asia). First part of this thesis focuses on empirical, univariate and bivariate approaches to understand historical climate trends and associated hazards in data scarce High Mountain Asia (HMA). This study provides a concise overiew of climate related changes across different regions which were difficult to align and compare either due to use of different data or spatio-temporal scales. Analysis reveals while the temperature is consistently increasing at a higher rate than the global warming rate, precipitation changes are not uniform, with substantial temporal and spatial variation in particular for the higher altitudes of HMA. Changes in climatic variables give rise to nonlinear, non-stationary, and non-uniform responses of cryospheric and hydrological variables such as snow, glaciers, soil moisture, and groundwater. Therefore, second part of this thesis investigates the systemic effect of the compound occurrence of extreme precipitation and temperature, at a wide range of spatio-temporal scales, and their impacts on the seasonality and trends in total water availability for HMA using a high-resolution cryospheric-hydrological model. This study shows a shift in the magnitude and peak of water availability, at seasonal time scale, to earlier in the year due to earlier onset of melting. Analysis reveals that the contrasting responses of HMA's rivers are primarily dictated by their hydrological regimes. To understand the risk due to compound floods in coastal regions, the third study of this thesis investigates the possibility of finding near-simultaneous storm surge and extreme river discharge along the Dutch coast using an extended dataset derived from a storm surge model and two hydrological river-discharge models forced with conditions from a high-resolution regional climate model in ensemble mode. This study conceptually shows that a strong storm whose winds set up a storm surge will need time to reach the Rhine headwaters where heavy rainfall will find its way to the river mouth after multiple days of travel time. The fourth and final study of this thesis investigates and quantifies the role of memory components in hydrological and meteorological systems which are responsible to generate extreme discharge, i.e. soil moisture, snow accumulation and the antecedent hydro-climatic conditions. Results show that the meteorological autocorrelation (manifesting itself by the occurrence of clustered precipitation events) has a strong impact on the magnitude of peak discharge. Finally, the study illustrates how hydrological memory from snow accumulation and soil moisture complements the generation of extreme discharges. A new valuable insight on the mechanisms of compound floods and its potential impact on water resources (both for historical and future climate) has been gained with novel modelling approaches. The outcomes generated from this thesis might contribute to support climate change adaptation policy planning and outreach programs in these climate change hotspots, i.e., the mountains and deltas.

V okviru raziskave smo analizirali dejavnike, ki vplivajo na nastanek poplav zaradi taljenja snežne odeje in spadajo pod t.i. sestavljene dogodke. Do sestavljenih dogodkov pride ob sočasnem nastopu različnih naravnih pojavov ali nesreč, ko se le-ti pojavijo časovno ali prostorsko sočasno. Posledično je vpliv takih dogodkov večji kot zaradi običajnih dogodkov. V okviru naloge smo analizirali različne vplivne dejavnike kot so temperatura zraka, padavine, debelina snežne odeje, hitrost vetra, vlažnost tal, itd.. Izbrali smo 107 porečij v Evropi in Severni Ameriki, kjer so bili na voljo merjeni podatki o pretokih za obdobje 1979-2019. Največje letne poplave smo klasificirali glede na definirano tipologijo z upoštevanjem naslednjih tipov poplav: poplave zaradi kombinacije padavin in taljenja snežne odeje, poplave zaradi taljenja snežne odeje, poplave zaradi dolgotrajnih padavin in poplave zaradi kratkotrajnih padavin. Omenjene tipe poplav smo nadalje razdelili še glede na mokre in sušne začetne razmere na porečju. Rezultati so pokazali, da se na izbranih porečjih najpogosteje pojavijo poplave zaradi taljenja snežne odeje. Na porečjih, ki so locirana na višjih nadmorskih višinah, se najpogosteje pojavljajo poplave zaradi kratkotrajnih ali dolgotrajnih padavinskih dogodkov. To je mogoče presenetljiv rezultat, saj so na višjih nadmorskih višinah temperature običajno nižje in bi pričakovali večji vpliv snega, vendar so bile poplave v primeru teh porečjih najpogosteje posledica večjih poletnih neviht. Na porečjih, ki so locirana na nižjih nadmorskih višinah, pa so poplave najpogosteje posledica taljenja snežne odeje. Nadalje smo ugotovili, da je predhodna vlažnost (mokri predhodni pogoji) eden izmed pomembnih dejavnikov nastanka poplav. Vlažnost tal je imela tudi največji relativni vpliv na merjene podatke o pretokih. Izvedene so bile tudi analize sezonskosti. Zaznali smo območja, kjer je sezonskost zelo izrazita, kar pomeni, da se poplave skoraj vedno pojavijo v istem obdobju leta, kot je Severna Amerika ter osrednja Norveška in Švedska. To lahko pripelje do sočasnega pojava poplav na večjih območjih, kar še povečuje njihov vpliv in z njimi povezano gmotno škodo. This study assessed the drivers of snowmelt floods in relation to compound events. Compound events are when multiple drivers/hazards occur in the same geographic region/time scale, thus amplifying their impacts. The climate drivers considered in this study are temperature, precipitation, snow thickness, snow liquid water equivalent, wind speed, vapour pressure and soil moisture content. 107 different catchments across North America and Europe were investigated, from the years 1979-2019. Each annual maximum flood was sorted into a different flood typology. These typologies were rain-on-snow floods, snowmelt floods, long precipitation floods, and short precipitation floods. These four flood typologies are all split into another two categories, with a wet initial condition and a dry initial condition. The results indicate that the considered catchments have snowmelt floods being the dominant flood type. The high elevation catchments had the dominant typologies being short precipitation floods and long precipitation floods. This initially surprised us, as higher elevations have colder weather and thus are expected to be more influenced by snowfall. However, these mountainous regions often experience large summer rainstorms, thus creating the maximum annual flood. Lower elevation catchments had snowmelt driven floods as the dominant typology. The wet initial conditions were also much more prevalent than the dry initial conditions, proving the importance of the soil moisture condition. This was confirmed through an investigation of the relative influence of the climate factors on the determination of the river discharge. Here, soil moisture had the largest relative influence. Results were determined for the seasonality of the floods as the average day of the year the floods occur on. Based on the results of this, there are geographic regions, such as northeastern North America and central Sweden and Norway, which have a strong seasonality and the floods occur at roughly the same time every year. This can lead to spatially compounding events, where many hazards occur in one geographic region at the same time, thus amplifying their impacts.