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The annual monomodal flood pulse of the Amazon River is a key driver for this globally important hydrological system. Understanding the behaviour and characteristics of this flood wave and its influence on the dynamics of river and floodplain interaction is important to many studies attempting to quantify processes dependent upon it, including estimates of carbon fluxes from the wetlands and sediment movement to the ocean. The main aim of the research presented in this thesis was to investigate the hydrodynamic behaviour of the Amazon River and its floodplain, in order to better understand its components and processes. The key scientific question underpinning this aim is: How do the floodplain storage and fluxes affect the passage of the Amazon flood wave and in turn how does the passage of the flood wave control the dynamics on the floodplain? Research was carried out using a synthesis of hydraulic characterisation, numerical modelling, spatial analysis of remote sensing data and field surveys. In combination with information from published floodplain studies, these results were then used to inform the development of a conceptual hydrodynamics framework for the Amazon floodplain. Hydraulic characterisation of the Amazon flood wave was undertaken showing that it is subcritical and diffusive in nature with dominating backwater effects. Experiments with the main channel using hydraulic models showed that main channel water levels were relatively insensitive (0.5 m error on a 12 m flood wave amplitude) to the geometric representation of the channel and that simplified bathymetry is adequate for calibrated predictions, provided the mean cross sectional area can be reasonably well approximated. Hydraulic model results compare well with observed data despite explicit exclusion of the floodplain, indicating that the storage volume of the floodplain has a minimal affect on the passage of the Amazon flood wave for this reach. The spatial analysis of Landsat TM images shows a total of 1,762 floodplain channels in the study area with a mean width of 47 m. Shuttle radar topography mission data has difficulty in resolving many of these floodplain channels due to 96% of the channels having a width less than the SRTM spatial resolution of 90 m. Comparing floodplain channel widths with their frequency reveals a power law relationship, showing patterns of structure that are self-similar over many orders of magnitude. Analysis of the floodplain channel network connectivity showed that the complex floodplain can be divided into floodplain hydrologic units (FHU) and that each unit type has different geomorphic characteristics resulting from a different mixture of water inputs, with each unit isolated from other units for much of the flood cycle by sediment barriers. A field survey of the floodplain channels was conducted in order to measure and characterise their morphology. The findings of this survey together with numerical connectivity experiments, demonstrate that floodplain channels could be playing a much more significant role in the floodplain hydrodynamics then previously acknowledged. Floodplain channels were grouped into three types by depth: (i) for channels carrying only river flood water, depth was strongly correlated with the flood wave’s mean annual range of 11.4 m; (ii) for channels which carried river flood water and local runoff, the mean depth was substantially deeper at 15.9 m; and, (iii) main river island channels were deeper again, with a mean depth of 17.7 m. Finally, a coherent, evidence based, conceptual floodplain hydrodynamics framework was developed to inform a broad range of future Amazon wetland research. The framework represents an extension to the macro level understanding available in the scientific literature and provides an important link between the macro and detailed level study scale.

Malaysia has a relatively limited capacity to deal with the effects of climate change while being one of the most vulnerable nations to its effects. As a developing country, the lack of a consistent temporal and spatial data source has always been an issue, and the region is also considered data-scarce. This study's primary goal is to evaluate the effects of climate change on Malaysia's water resources, particularly the Selangor River Basin (SRB). Instead of using a single source input dataset, cross-combined datasets from multiple sources were used in order to optimise the hydrological model. Five input variables, including precipitation, temperature, solar radiation, relative humidity, and wind speed, were used to define seven scenarios using single and cross- combined method. To improve the hydrological model multi-site calibration method is employed. Finally, climate change prediction data from several Global Climate Models (GCMs) is utilised to assess the effects of climate change on SRB water supplies. The CFSR and CMADS global reanalysis datasets show a highly significant relationship on precipitation, with an r-value of 0.81 for both datasets. However, for temperature data, CMADS surpasses CFSR on maximum and minimum temperatures, with 0.6 and 0.7, respectively. In the SWAT model, most of the scenarios achieved a 'good' performance range on the calibration and validation processes. However, SWAT model with CFSR as input data achieved an 'unsatisfactory' range with R2 of 0.35, NSE of 0.16, Pbias of 0.00, KGE of 0.50, and RSR of 0.92. For a cross-combined approach, the result shows the combination of the observed and CMADS datasets performed better than the combination of the observed and CFSR datasets. The sequential technique outperformed the simultaneous and basin-by-basin techniques by achieving 'satisfactory' range at all outlets. The SRB's assessment of climate change predicted an increase in precipitation and temperature from 2030 to 2050. Climate data from 'ensemble average' realisation predicted SRB would receive a huge amount of precipitation in November and April every year, and high temperatures from February to June. Additionally, a few sub-basins are expected to have water availability greater than 5 m3/s for three consecutive years.

Floods are the most frequent and damaging natural hazard globally. To adequately prepare for floods it is essential to know where they will occur and what their impacts will be. This can be done by developing a flood model. Traditionally, these models have been local in scale, limited to areas with the necessary expertise and data to develop a flood model. However, the last decade has seen the proliferation of several global flood models, which use global datasets and automated approaches to map flood hazard globally. When combined with global datasets of exposure and vulnerability, they can be used to assess global flood risk. The development of these global flood risk datasets marks a potential paradigm shift in flood risk analysis from the traditional "ad hoc" approach to global datasets which can be used to assess flood risk anywhere in the world. Despite this, these global flood risk datasets still need significant evaluation to understand the limits of their effective application. This thesis furthers the evaluation and explores the potential applications of global flood risk datasets. The current state of global flood risk modelling is reviewed, highlighting the different models, their history, structure, and application. The models are then collectively validated for the first time against observed flood events, demonstrating the skill of some models and identifying model characteristics which influence performance. The impact of river size thresholds, a key difference identified between the models, are quantified by calculating flood exposure to different sized rivers globally. Both the chosen river size thresholds and the global population maps used to calculate exposure are found to have a significant impact on flood exposure estimates. The use of global flood risk data is then explored nationally as global datasets of hazard, exposure, and vulnerability are evaluated for flood risk management in five countries. While some global datasets are found to be of potential use, there is still significant uncertainty in their national flood risk estimates and potential issues are identified related to the capacity there is to use them nationally.

Flooding is the most prevalent disaster worldwide accounting for 43% of all recorded global disaster events in the past 20 years leading up to 2018 Choy (2018). While migration from rural settlements to urban areas often mirrors economic advancement, it also presents socioeconomic and environmental challenges. Rapid urban growth strains existing infrastructure and also discourages the preservation of natural habitat in favour of building more developments causing urban flooding. Climate change and urbanisation have been reported as the major contributors to the increasing damaging effects of flooding to lives and livelihoods worldwide (Aerts et al., 2014). There is lack of adequate research focused on the dual impacts of climate change and urbanisation on urban flooding and water quality in rapidly developing urban areas of the world - a gap that will result in an increase in fluvial and pluvial flood risk, and further reduction in water quality (Miller and Hutchins, 2017). This study highlights the importance of the use of free global datasets in the development of a city-scale 2D hydraulic model that assesses the impact of land use change and climate change on urban pluvial flooding in rapidly developing cities. This thesis presents three key results chapters assessing the ability of a simplified city-scale hydrological and hydraulic models to estimate urban pluvial flood inundation in a large catchment, before going on to establish the impacts of climate change and land use change on flood hazard. Topography has been identified as a key dataset of estimating flood extent (Horritt and Bates, 2001) and models of flood extent rely on DEMs in order to simulate paths of water flow, flood extent and depth. Errors in Digital Elevation Models (DEMs) can substantially affect the results of flood models (Stephens et al., 2012, Hawker et al., 2018). Therefore, in order to increase the accuracy of the outputs from the hydrological and hydraulic models used in this study, a methodology for the correction of building error in DEMs was developed in Chapter 4 for removing building elevation artefacts from six global DEMs namely: (i) NASADEM, (ii) SRTM, (iii) MERIT, (iv) ALOS, (v) TanDEM-X 12 m, and (vi) TanDEM-X 90 m. The findings show that the removal of building elevation artefacts/error from global DEMs resulted in the improvement of the vertical height accuracy of global DEMs. The findings show that building density has an influence on vertical accuracy of global digital elevation models (DEMs). This finding was a key step prior to research undertaken in subsequent chapters. In chapter 5, a city-scale hydrological and hydraulic model of the Nairobi catchment is built. The purpose of creating the models is to use the raw DEM and corrected global DEM derived in Chapter 4 to estimate the impact of land use change and climate change on urban pluvial flood hazard at city-scale level using global datasets. The HEC-RAS software is used to create five categories of models for the extreme rainfall event of 1st to 13th of March 2018; a baseline model (S1-Baseline), S2-2000LU model, S3-CP4uplift model, S4-P25uplift model, and the S5-RawDEM model. The five sets of models are created in 2D and make use of the diffusive wave equation for simplification. The results showing a lot of promises by providing evidence for the hypothesis that urban flood models built at city-scale level using free global datasets have a good level of skill and are proficient enough to accurately estimate urban flood inundation depth and extent in rapidly developing cities characterised by sparsity of data. Chapter 6 discusses the results of the flood inundations and flood hazard vulnerability maps from the HEC-RAS 2D hydraulic model under 5 different scenarios. It is found that topography plays an important role in flood inundation maps and that the accuracy of flood inundation maps can be improved simply by using urban corrected DEMs over raw DEMs as key input data when conducting both hydrological and hydraulic modelling. The findings also show that urban pluvial flooding is affected by both change in climate change and land use change, however, climate change is found to contribute significantly to surface water runoff and exacerbate the problems of urban flooding. The results also show that the baseline model using the urban corrected DEM as input data produced flood inundation and flood hazard vulnerability maps with better accuracy in comparison to a similar baseline model using the raw DEM as key input data. Chapter 7 further explored the influence of climate change and land use change that is due to rapid urbanization on urban pluvial flood hazard. Chapter 7 focuses on the synthesis of the results obtained from the results of the 5 set of 2D hydraulic models discussed in chapter 6. Results demonstrated that climate change had more influence on urban pluvial flood hazard than land use change. It is found that climate change, rather than land use change is a bigger threat to urban area in terms of flood risk. Specifically, the effects induced by climate change under the CP4 and P25 climate rainfall models are much higher than the effects induced by land use change due to urbanisation in Nairobi from 2000 to 2020. The findings show that the changes caused by current and future changes in rainfall intensities and frequencies are most likely to render most large urban areas vulnerable to extreme rainfall and pluvial flooding due to lack of resilience in existing drainage infrastructure and flood mitigation systems. Assessment of land use changes alone cannot fully account for hydrological and hydraulic alterations in the urban context and it is important for policy makers and people with responsibilities for managing urban flood risks to consider adaptation and mitigation strategies that considers increasing threat of urban flooding emanating from increased runoff from climate change rainfall. This thesis has subsequently enhanced our understanding of the value of free global hydrological and hydraulic models developed at city-scale to model the impacts of climate change and land use change on urban pluvial flood hazard in data-sparse context of rapidly developing cities where availability of high-quality data for urban flood studies are a rarity. Finally, one of the key findings of this study is that in the context of conducting urban flood modelling in data sparse regions in rapidly developing cities across the world, it is possible to leverage the opportunities provided by the growing availability of free, global datasets to develop urban flood models. Traditional urban flood models rely on the use of high-quality datasets as key input data and require computers with high computational efficiency to run detailed flood inundation models. Most importantly, the study has demonstrated that it is possible to achieve a trade-off between complexity and resolution by the use of simplified 2D hydraulic flood models that use global dataset as key input data.

Household water demand has dramatically increased in Gulf Cooperation Council (GCC) countries in the last few decades, due to rapid population growth and changing lifestyles. Growing demand has been met by increasing supply capacity, largely via new desalination plants and over-abstracting groundwater. The continuous investment in water supply to avoid a supply-demand gap has been the default strategy in the GCC, yet reliance on a supply-side approach is unsustainable and associated with declining groundwater, and reliance on desalination that results in major carbon emission and environmental impact whilst also taking a large and growing share of oil revenues. This study examines the main issues associated with water scarcity in the GCC region, then determines the scale of conservation measures that will be needed to reduce water demand to a more sustainable level. In addition, to explore the wider economic and environmental benefits resulting from adopting the conservation measures, as well as, to determine the management feasibility of doing so. The state of Kuwait has been selected as a case study as it shares the hydrological characteristics of GCC countries but is the most challenging case, experiencing extreme water stress, due to its very high per capita consumption (PCC), but lowest per capita freshwater availability in the world. The research focuses on the household sector which represents 60% of total water demand in Kuwait. Based on the initial 'Business As Usual' demand forecast of 664 MCM in 2050, annual demand must reduce to 456 Million Cubic Metre (MCM) under a no new water target 'Light scenario' (1% saving per annum; 32% in total), to 345 MCM under a moderate target 'Intermediate scenario' (48% saving), and to 239 MCM (64% saving) to meet the most aggressive target 'Optimistic scenario' for 2050. To identify how to meet these targets, the study has used the backcasting approach whereby water conservation measures have been examined for their ability to reduce demand to the 2050 targets. Three backcast conservation measures have been utilised; a) technology (adoption of innovative appliances); b) economic (water pricing) and; c) education (public awareness) interventions. Backcast scenarios have been modelled using a Micro-Component (MC) demand model that has enabled addressing of specific household uses (e.g., showering, toilet flushing) linked to conservation interventions. These interventions have been manipulated in the backcast MC model in which two backcast packages have been developed for each scenario. The output of the backcast scenarios has shown that; (i) technology intervention has superior performance relative to other interventions; (ii) the Optimistic scenario delivered a very efficient economic and environmental performance but is hard to implement; and (iii) the Light scenario delivered a fair economic and environmental performance but is more feasible to implement.

Hydrodynamic processes that occur along the Congo Middle Reach are a key determinant of risks pertaining to biogeochemical cycling, ecology, public health, transportation, and flood risk. Knowledge of channel hydraulics is paramount to understanding and modelling these hydrodynamic processes, yet such knowledge is severely lacking here. The aims of the research presented in this thesis were twofold. The first aim was to assess the water surface and in-channel hydraulic conditions along the Congo Middle Reach, and the capacity of satellite observations to determine these conditions. The second aim was to evaluate methods of channel geometric representation in hydrodynamic models of the multichannel Congo mainstem. Fieldwork was central to achieving these aims; the field data having been used to characterise hydraulics, assess satellite altimetry datasets, model bathymetry, and model fluvial hydraulics and hydrodynamics. A key finding of the hydraulic characterisation was a complete absence of river flow constrictions that cause backwater effects, which partly explains the relatively subtle nature of inundation here. Assessment of existing satellite profiling altimetry datasets showed their spatial coverage adequately captures the water surface profile along more than 1,200 kilometres of the middle reach. However, coverage was insufficient through the Chenal entrance, where a downstream increase in bed-slope generates a significant drawdown effect. Satellite altimetry deviated from field measurements by two metres here, which is half the annual flood wave amplitude. The findings show that these satellite profiling altimeters cannot be relied on to capture significant water surface slope variability resulting from gradually varied flow conditions, even on the world’s largest rivers. Modelling work showed that the Congo’s multi-threaded channel geometry can be simplified to an effective single channel in a hydrodynamic model, without introducing significant error. The resultant root mean square error in water surface elevation was estimated to be less than 0.25 metres, providing channel friction and shape parameters are calibrated to observations obtained across the entire flow range. This finding may apply to other large multi-threaded channel reaches, which are commonly found on the world’s largest rivers.