Climate models have anticipated higher future extreme precipitations and streamflows for various regions. Urban stormwater facilities are vulnerable to these changes as the design assumes stationarity. However, recent climate change studies have argued about the existence of non-stationarity of the climate. Distribution method adopted on extreme precipitation varies spatially and may not always follow same distribution method. In this research, two different natural extremities were analyzed for two separate study areas. First, the future design storm depth based on the stationarity of climate and GEV distribution method was examined with non-stationarity and best fit distribution. Second, future design flood was analyzed and routed on a river to estimate the future flooding. Climate models from North American Regional Climate Change Assessment Program (NARCCAP) and Coupled Model Intercomparison Project phase 5 (CMIP5) were fitted to 27 different distribution using Chi-square and Kolmogorov Smirnov goodness of fit. The best fit distribution method was used to calculate design storm depth as well as design flood. Climate change scenarios were adopted as delta change factor, a downscaling approach to transfer historical design value to the climate adopted future design value. Most of the delta change factor calculated were higher than one, representing strong climate change impact on future. HEC-HMS and HEC-RAS models were used to simulate the stormwater infrastructures and river flow. The result shows an adverse effect on stormwater infrastructure in the future. The research highlights the importance of available climate information and suggests a possible approach for climate change adaptation on stormwater design practice.

An increase in the frequency and intensity of storm events is predicted by numerous climate researchers for the north of Europe. Not only this but also landuse change in form of clear-cutting can have an impact on the discharge of rivers and with that on road drainage structures. Extensive societal costs can be the consequences of blockage and underdimensioned structures. Hydrological models are powerful instruments that can be used to assess the future dimension requirements for road drainage structures especially in specifically vulnerable areas. In this thesis the hydrological model MIKE SHE was set up to study the discharge and water level at two pipe bridges and one culvert within the catchment of the river Hakerud in Västra Götaland, Sweden. Three scenarios were considered including a changing climate until 2050 and 2100 and a clear-cut scenario aiming to find out if the current design is sufficient for the future. This model can be used as an example model set-up for similar studies taking the recommendations of the experience gained in this thesis into consideration. For the Swedish Transport Administration further studies on this basis can contribute to decision making on the dimensioning of road drainage structures in the future to ensure a safe and robust infrastructural system.

Coastal cliff erosion is a widespread problem that threatens property and infrastructure along many of the world’s coastlines. The management of this risk calls for robust quantification of cliff erosion rates, which are often difficult to obtain along rocky coasts. Quantification of sea-cliff rates of retreat on annual to decadal time scales has typically been limited to rapidly eroding soft rock coastlines. Rates of erosion used for shoreline management in the UK are generally based on analysis of historic maps and aerial photographs which, in rocky coast environments, does not wholly capture the detail and timing at which the processes operate and the failures occur across the cliff face. The first stage of this study uses airborne LiDAR (Light Detection and Ranging) data at nine sites around a rocky coastline (Cornwall, UK) to gain a quantitative understanding of cliff erosion where average recession rates are relatively low (c. 0.1 m yr-1). It was found that three-dimensional volumetric changes on the cliff face and linear rates of retreat can be reliably calculated from consecutive digital elevation models (DEMs) several years apart. Rates of erosion ranged between 0.03–0.3 m yr-1. The spatial variability in recession rates was considered in terms of the relationship with the varying boundary conditions (rock mass characteristics, cliff geometries, beach morphology) and forcing parameters (wave climate and wave exposure). Recession rates were statistically correlated with significant wave height (Hs), rock mass characteristics (GSI) and the ratio between the two (GSI/Hs). Although the rates derived using airborne LiDAR are comparable to the longer term rates of retreat, the detail of erosion to the cliff-face provides additional insight into the processes occurring in slowly eroding environments, which are vital for understanding the failure of harder rock coastlines. In addition to this, the importance of the wave climate and rainfall needs further attention on a more localised scale. Monthly cliff face volume changes, at two particularly vulnerable sites (Porthleven and Godrevy, Cornwall, UK), were detected using a Terrestrial Laser Scanner (TLS). Using these volumes alongside information on beach profile, beach- cliff junction elevation changes and nearshore hydrodynamics have allowed an insight into how the cliffs respond to seasonal fluctuations in wave climate and beach morphology. Monthly variability in beach morphology between the two sites over a one-year survey period i  indicated the influence that beach slope and the elevation of the beach-cliff junction have on the frequency of inundation and the power of wave-cliff impacts. Failure mechanisms between the two sites ranged from rotational sliding of superficial material to quarrying and block removal over the entire cliff elevation, according to the extent of wave-cliff interaction. This particular survey period highlighted the sensitivity of cliff erosion to the variability in wave climate and beach morphology at two different locations in the south-west of the UK, where the vast majority (over 85% of the annual value) of cliff face erosion occurs during the winter when extreme storm waves prevail. Coastal cliff erosion from storm waves is observed worldwide but the processes are notoriously difficult to measure during extreme storm wave conditions when most erosion normally occurs, limiting our understanding of cliff processes. Over January-March 2014, during the largest Atlantic storms in at least 60 years with deep water significant wave heights of 6 – 8 m, cliff-top ground motions of a rocky cliff in the south-west of the UK (Porthleven, Cornwall) showed vertical ground displacements in excess of 50–100 μm; an order of magnitude larger than observations made previously. Repeat terrestrial laser scanner surveys, over a 2-week period encompassing the extreme storms, gave a cliff face volume loss 2 orders of magnitude larger than the long-term erosion rate. Cliff-top ground motions and erosion volumes were compared at two different locations, one a reflective beach with steeply shelving bathymetry (Porthleven, Cornwall) and the other an intermediate, low tide bar-rip beach with a wide coastal slope (Godrevy, Cornwall). Under similar wave conditions (6–8 m Hs and 15–20 s. Tp) the vertical ground motions were an order of magnitude greater at the cliffs fronted by steeply shelving bathymetry, where the breaking waves plunge right at the shoreline, with little prior dissipation, leading to large energetic runup impacting the cliff. These storm results imply that erosion of coastal cliffs exposed to extreme storm waves is highly episodic and that long-term rates of cliff erosion will depend on the frequency and severity of extreme storm wave impacts as well as the wave dissipation that occurs as a function of the nearshore bathymetry. Having recorded microseismic cliff-top motion on this scale for the first time and determined an effective method of monitoring the energetic wave impacts, this study emphasises how investigations of cliff behaviour during storms is not only obtainable, but paramount to understanding coastal evolution under extreme conditions.

Extreme rainfall statistics are important for the design and management of the water resource infrastructure. The standard approach for extreme rainfall event severity assessment is the Intensity-Duration-Frequency (IDF) method. However, this approach does not consider the spatial context of rainfall and consequently does not properly describe rainfall storm severity, nor rarity. This study provides a critical account of the current standard practice and presents an approach that takes into consideration both the spatial context of rainfall storms, and indirectly incorporates runoff to produce a representative approach to assessing urban rainfall storm severity in terms of flood potential. A stepwise regression analysis was performed on a dataset of individual rainfall storm characteristics to best represent documented basement floodings in the City of Edmonton. Finally, the urban rainfall storm flood severity index was shown to be most representative of the documented basement floodings' severity when compared to that of the IDF method.

Extreme rainfall statistics are important for the design and management of the water resource infrastructure. The standard approach for extreme rainfall event severity assessment is the Intensity-Duration-Frequency (IDF) method. However, this approach does not consider the spatial context of rainfall and consequently does not properly describe rainfall storm severity, nor rarity. This study provides a critical account of the current standard practice and presents an approach that takes into consideration both the spatial context of rainfall storms, and indirectly incorporates runoff to produce a representative approach to assessing urban rainfall storm severity in terms of flood potential. A stepwise regression analysis was performed on a dataset of individual rainfall storm characteristics to best represent documented basement floodings in the City of Edmonton. Finally, the urban rainfall storm flood severity index was shown to be most representative of the documented basement floodings' severity when compared to that of the IDF method.