Explorative coastal oceanographic visual analytics : oceans of data

The widely acknowledged challenge to data analysis and understanding, resulting from the exponential increase in volumes of data generated by increasingly complex modelling and sampling systems, is a problem experienced by many researchers, including ocean scientists. The thesis explores a visualization and visual analytics solution for predictive studies of coastal shelf and estuarine modelled, hydrodynamics undertaken to understand sea level rise, as a contribution to wider climate change studies, and to underpin coastal zone planning, flood prevention and extreme event management. But these studies are complex and require numerous simulations of estuarine hydrodynamics, generating extremely large datasets of multi-field data. This type\ud of data is acknowledged as difficult to visualize and analyse, as its numerous attributes present significant computational challenges, and ideally require a wide range of approaches to provide the necessary insight. These challenges are not easily overcome with the current visualization and analysis methodologies employed by coastal shelf hydrodynamic researchers, who use several software systems to generate graphs, each taking considerable time to operate, thus it is difficult to explore different scenarios and explore the data interactively and visually. The thesis, therefore, develops novel visualization and visual analytics techniques to help researchers overcome the limitations of existing methods (for example in understanding key tidal components); analyse data in a timely manner and explore different scenarios. There were a number of challenges to this: the size of the data, resulting in lengthy computing time, also many data values becoming plotted on one pixel (overplotting). The thesis presents: (1) a new visualization framework (VINCA) using caching and hierarchical aggregation techniques to make the data more interactive, plus explorative, coordinated multiple views, to enable the scientists to explore the data. (2) A novel estuarine transect profiler and flux tool, which provides instantaneous flux calculations across an estuary. Measures of flux are of great significance in oceanographic studies, yet are notoriously difficult and time consuming to calculate with the commonly used tools. This derived data is added back into the database for further investigation and analysis. (3) New views, including a novel, dynamic, spatially aggregated Parallel Coordinate Plots (Sa-PCP), are developed to provide different perspectives of the spatial, time dependent data, also methodologies for developing high-quality (journal ready) output from the visualization tool. Finally, (4) the dissertation explored the use of hierarchical data-structures and caching techniques to enable fast analysis on a desktop computer and to overcome the overplotting challenge for this data

Coastal management and adaptation: an integrated data-driven approach

Coastal regions are some of the most exposed to environmental hazards, yet the coast is the preferred settlement site for a high percentage of the global population, and most major global cities are located on or near the coast. This research adopts a predominantly anthropocentric approach to the analysis of coastal risk and resilience. This centres on the pervasive hazards of coastal flooding and erosion. Coastal management decision-making practices are shown to be reliant on access to current and accurate information. However, constraints have been imposed on information flows between scientists, policy makers and practitioners, due to a lack of awareness and utilisation of available data sources. This research seeks to tackle this issue in evaluating how innovations in the use of data and analytics can be applied to further the application of science within decision-making processes related to coastal risk adaptation. In achieving this aim a range of research methodologies have been employed and the progression of topics covered mark a shift from themes of risk to resilience. The work focuses on a case study region of East Anglia, UK, benefiting from the input of a partner organisation, responsible for the region’s coasts: Coastal Partnership East. An initial review revealed how data can be utilised effectively within coastal decision-making practices, highlighting scope for application of advanced Big Data techniques to the analysis of coastal datasets. The process of risk evaluation has been examined in detail, and the range of possibilities afforded by open source coastal datasets were revealed. Subsequently, open source coastal terrain and bathymetric, point cloud datasets were identified for 14 sites within the case study area. These were then utilised within a practical application of a geomorphological change detection (GCD) method. This revealed how analysis of high spatial and temporal resolution point cloud data can accurately reveal and quantify physical coastal impacts. Additionally, the research reveals how data innovations can facilitate adaptation through insurance; more specifically how the use of empirical evidence in pricing of coastal flood insurance can result in both communication and distribution of risk. The various strands of knowledge generated throughout this study reveal how an extensive range of data types, sources, and advanced forms of analysis, can together allow coastal resilience assessments to be founded on empirical evidence. This research serves to demonstrate how the application of advanced data-driven analytical processes can reduce levels of uncertainty and subjectivity inherent within current coastal environmental management practices. Adoption of methods presented within this research could further the possibilities for sustainable and resilient management of the incredibly valuable environmental resource which is the coast

Testing storm impact modelling at São Pedro de Moel beach

Numerical models are very powerful tools to predict the effects of the extreme conditions associated with coastal storms. The main objective of this work was to simulate the effects of coastal storms in São Pedro de Moel beach, in terms of overtopping and morphological evolution associated, by using XBeach. To simulate the coastal storms using XBeach, it was necessary to have the data regarding the nearshore sea state. This was obtained by propagating offshore wave data conditions to nearshore using the SWAN model. The XBeach model was divided into two setups to analyse two different situations, overtopping events (non-hydrostatic setup) and coastal evolution (surf beat setup). Sensibility tests were performed for both setups testing different model parameters. The model was also, calibrated and validated using information from past storms. The non-hydrostatic setup demonstrated sensibility to the bathymetric resolution and for the intrinsic model parameters related to the bed friction and the non-hydrostatic correction. The results from the XBeach simulation of the overtopping event were compared against results from an empirical formula (Mase et al., 2013), which simulates the overtopping events associated with a seawall. The comparison of results showed lower values obtained with the empirical formula. The surf beat setup demonstrated sensibility to the bathymetric resolution, and the intrinsic model parameters related to wave dissipation, sediment transport and morphology. The results from the calibration and past storm simulation of the coastal evolution setup point out to the necessity of having better field data before and after storms to improve the model settings and accuracy.Os modelos numéricos constituem uma ferramenta muito útil para prever os efeitos associados à ocorrência de tempestades na zona costeira. O objetivo principal deste trabalho foi simular os efeitos de tempestades costeiras na praia de São Pedro de Moel, em termos do galgamento e da evolução morfodinâmica, através do uso do modelo XBeach. Ao usar o modelo numérico XBeach para simular as tempestades costeiras, é necessário ter dados relativos às condições do mar na zona costeira. Esses dados foram obtidos através da propagação do clima de ondas ao largo para a zona costeira, utilizando o modelo numérico SWAN. Neste trabalho, utilizaram-se duas configurações do modelo XBeach para simular duas situações diferentes: ocorrência de galgamento (configuração não hidrostática) e evolução do perfil de praia (configuração surf beat). As duas configurações foram submetidas a testes de sensibilidade para diferentes parâmetros e de seguida o modelo foi calibrado e validado, usando informação de tempestades já passadas. A configuração não-hidrostática demonstrou maior sensibilidade associada à resolução batimétrica e aos parâmetros relacionados com a fricção de fundo e correção nãohidrostática. Os resultados obtidos através do XBeach relativos aos eventos de galgamento foram comparados com os resultados da fórmula empírica desenvolvida por Mase et al. (2013). Esta fórmula empírica simula os galgamentos numa praia com estrutura de proteção. A comparação de resultados demonstrou que os valores obtidos pela fórmula empírica eram inferiores aos obtidos pelo XBeach. A configuração surf beat demonstrou maior sensibilidade associada à resolução batimétrica e aos parâmetros relacionados com dissipação de ondas, transporte de sedimentos e morfologia. Os resultados da calibração e da simulação de tempestade anterior desta configuração realçaram a necessidade de se obter dados com melhor qualidade pré e pós tempestade para melhorar a configuração e precisão do modelo.This thesis was supported by the National Laboratory of Civil Engineering (LNEC). A thank you to Paula Freire for the data provided from the Mosaic.pt project. The author acknowledges the following projects: Mosaic.pt, Ref. PTDC/CTAAMB/ 28909/2017, To-SEAlert, Ref. PTDC/EAM-OCE/31207/2017 and EWCoast ALGLISBOA- 01-145-FEDER-028657

Towards an end-to-end analysis and prediction system for weather, climate, and marine applications in the Red Sea

Author Posting. © American Meteorological Society, 2021. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Bulletin of the American Meteorological Society 102(1), (2021): E99-E122, https://doi.org/10.1175/BAMS-D-19-0005.1.The Red Sea, home to the second-longest coral reef system in the world, is a vital resource for the Kingdom of Saudi Arabia. The Red Sea provides 90% of the Kingdom’s potable water by desalinization, supporting tourism, shipping, aquaculture, and fishing industries, which together contribute about 10%–20% of the country’s GDP. All these activities, and those elsewhere in the Red Sea region, critically depend on oceanic and atmospheric conditions. At a time of mega-development projects along the Red Sea coast, and global warming, authorities are working on optimizing the harnessing of environmental resources, including renewable energy and rainwater harvesting. All these require high-resolution weather and climate information. Toward this end, we have undertaken a multipronged research and development activity in which we are developing an integrated data-driven regional coupled modeling system. The telescopically nested components include 5-km- to 600-m-resolution atmospheric models to address weather and climate challenges, 4-km- to 50-m-resolution ocean models with regional and coastal configurations to simulate and predict the general and mesoscale circulation, 4-km- to 100-m-resolution ecosystem models to simulate the biogeochemistry, and 1-km- to 50-m-resolution wave models. In addition, a complementary probabilistic transport modeling system predicts dispersion of contaminant plumes, oil spill, and marine ecosystem connectivity. Advanced ensemble data assimilation capabilities have also been implemented for accurate forecasting. Resulting achievements include significant advancement in our understanding of the regional circulation and its connection to the global climate, development, and validation of long-term Red Sea regional atmospheric–oceanic–wave reanalyses and forecasting capacities. These products are being extensively used by academia, government, and industry in various weather and marine studies and operations, environmental policies, renewable energy applications, impact assessment, flood forecasting, and more.The development of the Red Sea modeling system is being supported by the Virtual Red Sea Initiative and the Competitive Research Grants (CRG) program from the Office of Sponsored Research at KAUST, Saudi Aramco Company through the Saudi ARAMCO Marine Environmental Center at KAUST, and by funds from KAEC, NEOM, and RSP through Beacon Development Company at KAUST

Towards an end-to-end analysis and prediction system for weather, climate, and Marine applications in the Red Sea

AbstractThe Red Sea, home to the second-longest coral reef system in the world, is a vital resource for the Kingdom of Saudi Arabia. The Red Sea provides 90% of the Kingdom’s potable water by desalinization, supporting tourism, shipping, aquaculture, and fishing industries, which together contribute about 10%–20% of the country’s GDP. All these activities, and those elsewhere in the Red Sea region, critically depend on oceanic and atmospheric conditions. At a time of mega-development projects along the Red Sea coast, and global warming, authorities are working on optimizing the harnessing of environmental resources, including renewable energy and rainwater harvesting. All these require high-resolution weather and climate information. Toward this end, we have undertaken a multipronged research and development activity in which we are developing an integrated data-driven regional coupled modeling system. The telescopically nested components include 5-km- to 600-m-resolution atmospheric models to address weather and climate challenges, 4-km- to 50-m-resolution ocean models with regional and coastal configurations to simulate and predict the general and mesoscale circulation, 4-km- to 100-m-resolution ecosystem models to simulate the biogeochemistry, and 1-km- to 50-m-resolution wave models. In addition, a complementary probabilistic transport modeling system predicts dispersion of contaminant plumes, oil spill, and marine ecosystem connectivity. Advanced ensemble data assimilation capabilities have also been implemented for accurate forecasting. Resulting achievements include significant advancement in our understanding of the regional circulation and its connection to the global climate, development, and validation of long-term Red Sea regional atmospheric–oceanic–wave reanalyses and forecasting capacities. These products are being extensively used by academia, government, and industry in various weather and marine studies and operations, environmental policies, renewable energy applications, impact assessment, flood forecasting, and more.</jats:p

Flood Early Warning and Risk Modelling

Extreme hydrological phenomena are one of the most common causes of human life loss and material damage as a result of the manifestation of natural hazards around human communities. Climatic changes have directly impacted the temporal distribution of previously known flood events, inducing significantly increased frequency rates as well as manifestation intensities. Understanding the occurrence and manifestation behavior of flood risk as well as identifying the most common time intervals during which there is a greater probability of flood occurrence should be a subject of social priority, given the potential casualties and damage involved. However, considering the numerous flood analysis models that have been currently developed, this phenomenon has not yet been fully comprehended due to the numerous technical challenges that have arisen. These challenges can range from lack of measured field data to difficulties in integrating spatial layers of different scales as well as other potential digital restrictions.The aim of the current book is to promote publications that address flood analysis and apply some of the most novel inundation prediction models, as well as various hydrological risk simulations related to floods, that will enhance the current state of knowledge in the field as well as lead toward a better understanding of flood risk modeling. Furthermore, in the current book, the temporal aspect of flood propagation, including alert times, warning systems, flood time distribution cartographic material, and the numerous parameters involved in flood risk modeling, are discussed