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The Physical Oceanography Unit was established in the early 90's under the Malta Council for Science and Technology. It now constitutes the research arm of the IOI-Malta Operational Centre at the University of Malta. The PO-Unit undertakes fundamental research in coastal meteorology, hydrography and physical oceanography with a main emphasis on the experimental study of the hydrodynamics of the sea in the vicinity of the Maltese Islands. It offers facilities for the gathering, processing, analysis and management of high quality physical oceanographic observations both for long term and baseline studies as well as for general applications in marine environmental research and assessments. The Unit endeavours to enhance its activity on an operational scale by the installation and maintenance of permanent monitoring systems which provide data for ocean forecasting, and by applying numerical modelling techniques in the study of physical marine systems. It operates in collaboration with international organisations with which it has expanded its activities through a number of funded research projects. The Unit provides services and technical support to local entities including government departments and private agencies. It organises conferences, seminars, workshops and specialised training programs in order to promote oceanography in Malta and in the Mediterranean. ; N/A

Construction on low elevation coastal zones is risky for both residents and taxpayers who bail them out, especially when sea levels are rising. We study this construction using spatially disaggregated data on the US Atlantic and Gulf coasts. We document nine stylized facts, including a sizeable rise in the share of coastal housing built on flood-prone land from 1990-2010, which concentrated particularly in densely populated areas. To explain our findings, we develop a model of a monocentric coastal city, which we then use to explore the consequences of sea level rise and government policies.

Construction on low elevation coastal zones is risky for both residents and taxpayers who bail them out, especially when sea levels are rising. We study this construction using spatially disaggregated data on the US Atlantic and Gulf coasts. We document nine stylized facts, including a sizeable rise in the share of coastal housing built on flood-prone land from 1990-2010, which concentrated particularly in densely populated areas. To explain our findings, we develop a model of a monocentric coastal city, which we then use to explore the consequences of sea level rise and government policies.

Understanding Sea-Level Rise and Variability identifies the major impacts of sea-level rise, presents up-to-date assessments of past sea-level change, thoroughly explores all of the factors contributing to sea-level rise, and explores how sea-level extreme events might change. It identifies what is known in each area and what research and observations are required to reduce the uncertainties in our understanding of sea-level rise so that more reliable future projections can be made. A synthesis of findings provides a concise summary of past, present and future sea-level rise and its imp.

Semi-empirical sea-level models (SEMs) exploit physically motivated empirical relationships between global sea level and certain drivers, in the following global mean temperature. This model class evolved as a supplement to process-based models (Rahmstorf (2007)) which were unable to fully represent all relevant processes. They thus failed to capture past sea-level change (Rahmstorf et al. (2012)) and were thought likely to underestimate future sea-level rise. Semi-empirical models were found to be a fast and useful tool for exploring the uncertainties in future sea-level rise, consistently giving significantly higher projections than process-based models. In the following different aspects of semi-empirical sea-level modelling have been studied. Models were first validated using various data sets of global sea level and temperature. SEMs were then used on the glacier contribution to sea level, and to infer past global temperature from sea-level data via inverse modelling. Periods studied encompass the instrumental period, covered by…

Semi-empirical sea-level models (SEMs) exploit physically motivated empirical relationships between global sea level and certain drivers, in the following global mean temperature. This model class evolved as a supplement to process-based models (Rahmstorf (2007)) which were unable to fully represent all relevant processes. They thus failed to capture past sea-level change (Rahmstorf et al. (2012)) and were thought likely to underestimate future sea-level rise. Semi-empirical models were found to be a fast and useful tool for exploring the uncertainties in future sea-level rise, consistently giving significantly higher projections than process-based models. In the following different aspects of semi-empirical sea-level modelling have been studied. Models were first validated using various data sets of global sea level and temperature. SEMs were then used on the glacier contribution to sea level, and to infer past global temperature from sea-level data via inverse modelling. Periods studied encompass the instrumental period, covered by…

"This book will cover all aspects of modern sea-level studies, with a focus on the most robust scientific approaches and techniques"--

Abstract. Increased coastal flooding caused by extreme sea levels (ESLs) is one of the major hazards related to sea level rise. Estimates of return levels
obtained under the framework provided by extreme-event theory might be biased under climatic non-stationarity. Additional uncertainty is related to the choice of the model. In this work, we fit several extreme-value models to two long-term sea level records from Venice (96 years) and Marseille (65 years): a generalized extreme-value (GEV) distribution, a generalized Pareto distribution (GPD), a point process (PP), the joint probability method (JPM), and the revised joint probability method (RJPM) under different detrending strategies. We model non-stationarity with a linear dependence of the model's parameters on the mean sea level. Our results show that non-stationary GEV and PP models fit the data better than
stationary models. The non-stationary PP model is also able to reproduce the rate of extremes occurrence fairly well. Estimates of the return levels
for non-stationary and detrended models are consistently more conservative than estimates from stationary, non-detrended models. Different models
were selected as being more conservative or having lower uncertainties for the two datasets. Even though the best model is case-specific, we show
that non-stationary extremes analyses can provide more robust estimates of return levels to be used in coastal protection planning.