Global Average Sea Level Research Articles

Seasonal Patterns, Inter‐Annual Variability, and Long‐Term Trends of Mean Sea Level Along the Western Iberian Coast and the North Atlantic Islands

AbstractSea level rise is challenging for coastal communities and land management decision makers. Understanding the patterns of regional variations at different temporal and spatial scales is key to implement adaptation plans that mitigate the local impacts of sea level rise. In this study, in situ observations from 14 tide gauges were complemented with satellite altimetry data to assess seasonality, multidecadal variability and long‐term trends in mean sea level around the Western Iberian Coast (WIC) and the Portuguese archipelagos (Azores and Madeira). Results show varying spatial seasonal patterns between regions, with minimum (maximum) sea level observed in April (September) at the islands and minimum (maximum) observed in July (November) at the WIC. The influence of coastal upwelling on the seasonal mean sea level variations was detected over mainland. Although the influence of atmospheric patterns was observed on sea level inter‐annual variability, the Atlantic Multidecadal Oscillation (AMO) showed a greater correlation with the sea level inter‐decadal patterns. Finally, the trend analysis confirmed widespread sea level rise along the mainland and around the islands, which has intensified in recent decades. The regions of La Coruña and Cascais showed trends that were similar to the global average sea level rise since 1993, but the mainland regional average pointed to lower rates of rise (2.00 ± 0.06 mm/year). This work reinforces the need for long‐term monitoring networks of sea level, ensuring the vertical stability of instruments and platforms. The implementation of regional adaptation plans to sea level rise is deeply dependent on high quality information.

The Rising Concern for Sea Level Rise: Altimeter Record and Geo-Engineering Debate

The Oceans from Space V Symposium, held in Venice, Italy, on 24–27 October 2022, devoted special sessions to sea level rise, as described by a series of satellite altimeters, and to remediations of consequent calamities in vulnerable mediterranean seas. It emerged that various aspects of climate change can be modelled in time as a Single Exponential Event (SEE), with a similar trend (a 54–year e–folding time) for CO2 concentration in the Earth’s atmosphere, global average sea surface temperature, and global average sea level. The sea level rise record, combining tide gauges data starting in 1850, as well as more recent altimeter data, for the last 30 years, is already 25 cm above historical values. If the curve continues to follow the exponential growth of the simple SEE model, it will reach about 40 cm by the year 2050, 1 m by 2100, and 2.5 m by 2150. As a result, dramatic impacts would be expected for most coastal areas in the next century. Decisive remediations, based on geo-engineering at the basin scale, are possible for semi-enclosed seas, such as the Mediterranean and Black Seas. Damming the Strait of Gibraltar would provide an alternative to the conclusion that coastal sites such as the City of Venice are inevitably doomed.

Time Series Modeling of Global Average Absolute Sea Level Change

This study aimed to demonstrate the effectiveness of time series models in modeling long-term records of global average absolute sea level changes from 1880 to 2014. Following the Box–Jenkins methodology, the ARIMA(0,1,2) model with drift was identified as the best-fit model for the time series due to its lowest AIC value. Using the LM algorithm, the results revealed that the NARNN model with 7 neurons in the hidden layer and 7 time delays exhibited the best performance among the nonlinear autoregressive neural network models, as indicated by its lower MSE. While ARIMA models excel in modeling linear problems within time series data, NARNN models are better suited for nonlinear patterns. However, a HYBRID model was explored, which combines the strengths of both ARIMA and NARNN models, offering the capability to address both linear and nonlinear aspects of time series data. The comparative analysis of this study demonstrated that the HYBRID model, with 6 neurons in the hidden layer and 7 time delays, outperformed the NARNN model with 7 neurons in the hidden layer and 7 time delays, as well as the ARIMA(0,1,2) model, with the lowest MSE in this study. These findings represent a significant step in time series forecasting by leveraging the strengths of both statistical and machine learning methods.

On Modelling Sea State Bias of Jason-2 Altimeter Data Based on Significant Wave Heights and Wind Speeds

Altimeter data processing is very important to improve the quality of sea surface height (SSH) measurements. Sea state bias (SSB) correction is a relatively uncertain error correction due to the lack of a clear theoretical model. At present, the commonly used methods for SSB correction are polynomial models (parametric models) and non-parametric models. The non-parametric model usually was constructed by collinear data. However, the amount of collinear data was enormous, and it contained redundant information. In this study, the non-parametric regression estimation model was optimized by using the parameter replacement method of ascending and descending tracks based on the crossover data. In this method, significant wave heights from the Jason-2 altimeter data during cycles 200–301 and wind speed from the ERA5 reanalysis data were used. The non-parametric regression estimation model of Jason-2 was constructed by combining it with local linear regression, Epanechnikov kernel function and local window width. At the same time, based on the significant wave height and wind speed at the crossover points, the SSB polynomial model containing six parameters was constructed by using the Taylor series expansion, and the model was optimized. By comparing polynomial model construction with different parameters, the optimized model was obtained. The SSH of the crossover points and the tide gauge records were used to validate these results derived from two models and GDR. Compared with the crossover discrepancies of SSH corrected by the polynomial model, the RMS of the crossover discrepancies of SSH corrected by the non-parametric regression estimation model was reduced by 7.9%. Compared with the crossover discrepancies of SSH corrected by the conventional non-parametric model from GDR, the RMS of the crossover discrepancies of SSH corrected by the non-parametric regression estimation model was reduced by 4.1%. This shows that the precision of the SSHs derived by after the SSB correction, as calculated by the non-parametric regression estimation model, was better than that of the polynomial model and the SSB correction from GDR. Using the Jason-2 altimeter data, the along-track geoid gradient and the sea level change rate of the global ocean were determined by using two models to correct the SSB. By comparing the results of the two models, the accuracy of the geoid gradient along the orbit that was obtained by the non-parametric regression estimation model was better than that of the polynomial model and GDR. The global average sea level change rate after the non-parametric regression estimation model correction was 3.47 ± 0.09 mm/y, which was the closest to the average sea level change rate that has been published in the international literature within this field.

Analysis of floating house by using ANSYS: platform material comparison of expanded polystyrene and polyvinyl chloride pipe

Even if living against the water is challenging, living with the rising water level is doable. The global average sea level is rising as a result of global warming. The land surface is being occupied by rising water levels. Several places of the globe are in danger of disappearing from the map, vanishing under water, due to rising sea levels. Society will have to adjust and possibly live in floating dwellings one day. Some countries are further along in their adaptation to the effects of global warming, particularly the rising sea level, than others, depending on their geographical location. In locations where the land is mostly covered by water, the sole option to survive the rising waters is floating house. A 96.6m. sq. long floating house with dimensions of 11.5mx8.4m is considered. This paper aims to construct a floating house and use Ansys software to analyse the structure.

The effect of lateral variations in Earth structure on Last Interglacial sea level

SUMMARYIt is generally agreed that the Last Interglacial (LIG; ∼130–115 ka) was a time when global average temperatures and global mean sea level were higher than they are today. However, the exact timing, magnitude and spatial pattern of ice melt is much debated. One difficulty in extracting past global mean sea level from local observations is that their elevations need to be corrected for glacial isostatic adjustment (GIA), which requires knowledge of Earth’s internal viscoelastic structure. While this structure is generally assumed to be radially symmetric, evidence from seismology, geodynamics and mineral physics indicates that large lateral variations in viscosity exist within the mantle. In this study, we construct a new model of Earth’s internal structure by converting shear wave speed into viscosity using parametrizations from mineral physics experiments and geodynamic constraints on Earth’s thermal structure. We use this 3-D Earth structure, which includes both variations in lithospheric thickness and lateral variations in viscosity, to calculate the first 3-D GIA prediction for LIG sea level. We find that the difference between predictions with and without lateral Earth structure can be metres to 10s of metres in the near field of former ice sheets, and up to a few metres in their far field. We demonstrate how forebulge dynamics and continental levering are affected by laterally varying Earth structure, with a particular focus on those sites with prominent LIG sea level records. Results from four 3-D GIA calculations show that accounting for lateral structure can act to increase local sea level by up to ∼1.5 m at the Seychelles and minimally decrease it in Western Australia. We acknowledge that this result is only based on a few simulations, but if robust, this shift brings estimates of global mean sea level from these two sites into closer agreement with each other. We further demonstrate that simulations with a suitable radial viscosity profile can be used to locally approximate the 3-D GIA result, but that these radial profiles cannot be found by simply averaging viscosity below the sea level indicator site.

Time-frequency dependency of temperature and sea level: a global perspective.

The importance of environmental sustainability to all human aspects has been spiking as the world keeps evolving. Economies around the world are on the move to ensure sustainable economic development and a clean atmosphere through the use of renewable energy sources. Since the end of the nineteenth century, it is recognized that a global mean sea level rise speed about 1.7 ± 0.2 mm/year, but the rate has increased to 3.2 ± 0.4 mm/year. In this study, we investigated the dynamic linkage between average temperature and sea level within the global framework covering 1881 to 2013. The paper employs wavelet analysis to investigate the short-term and long-run causal links between global average temperature anomalies and global sea level. In this respect, our findings indicate that (i) a significant vulnerability in global average temperature and sea level is observed over the selected study period; (ii) global average temperature has considerable power for predicting sea level, particularly in the long-term. The causality test revealed a bi-directional causal relationship between global average temperature and sea level and unidirectional flow from global average temperature to sea level. And the study confirms the "conservation hypothesis," and it has long-run implications for environmental quality. Thus, minimizing global average temperature anomalies is a decisive ingredient for minimizing sea level rise. Based on these outcomes, both developed and developing countries' policymakers should support the Paris agreement (COP21) agreement to control CO2 emissions growth. Policymakers should encourage the usage of environmentally friendly energy sources that will enhance environmental quality and hold the increase in global average temperature below 1.5 °C.

Unmixing deep-sea paleoclimate records: A study on bioturbation effects through convolution and deconvolution

Marine sediments are typically subject to bioturbation from benthic macrofauna, which disrupt sediments and can have a substantial impact on the amplitude and timing of climate proxy signals recorded in marine sediment cores. This study explores a computational approach to quantify and remove these post-depositional effects from marine climate proxy records. The bioturbation process is modeled as a linear time-invariant filter building upon prior work (Guinasso and Schinck, 1975). Using forward modeling we find that, given modeled mixing layer depths ≥ 5 cm, simulated centennial scale climate variability is generally not preserved even when the sedimentation rate in our model is above 15 cm/kyr. On the scale of ice ages (104-105 yr), the observed effects of bioturbation have the smallest impact, considering the event scale and typical sedimentation rates and mixed layer depth in the deep sea. For millennial scale events, the signal attenuation strongly depends on the event scale and specific bioturbation and sedimentation parameters. To account for the bioturbation effect on a given climate proxy series, a deconvolution method is proposed. We apply the approach to three individual benthic foraminifera oxygen isotope records to reconstruct δ18O across the last interglacial (Marine Isotope Stage 5e; MIS 5e), and to assess the magnitude of the deglacial shift between MIS 6 and MIS 5e. In the highest resolution records (Site MD952042, Site 1012), the deconvolution results reveal an increase in the amplitude of the recovered deglacial δ18O shift by approximately 0.1‰, with implications for reconstructed global average sea level variations and temperature estimates from benthic foraminifera δ18O. The recovered signal at Site 677 suggests high amplitude δ18O variability during MIS 5e, but exhibits larger uncertainty due to a lower sampling frequency, lower sedimentation rate, and an inferred higher noise level. While uncertainties on the deconvolution results can be large, the results provide a new view of the potential impact of bioturbation on paleoclimate reconstruction. Future work can reduce the uncertainty in the deconvolution estimates, through quantitative integration of additional constraints, provided by our knowledge of the climate and depositional systems.

The Role of Plant Breeding Under Changing Climate

Climate change is now unequivocal, particularly in terms of increasing temperature, increasing CO2 concentration, widespread melting of snow and ice and rising global average sea level, while the increase in the frequency of drought is very probable but not as certain. Even though climate change is one of the major current global concerns, it is not new. Several climate Changes have occurred before, with dramatic consequences in history. Plant breeding is the activity of developing diverse plant varieties that can contribute usefully to cropping and production systems. These breeding efforts are directed at plant improvement. But ‘improvement’ is a subjective and relative goal and it is useful to regularly break up plant breeding objectives and procedures into clearly defined and manageable units. Owing to the imperatives of food security, plant breeding must combine the objective of ecological intensification with that of adaptation to overall societal and global changes. Keywords: Climate change, Green revolution, Genetic gain DOI: 10.7176/JBAH/11-4-04 Publication date: February 28 th 2021

Estimation of Sea Level Variability in the China Sea and Its Vicinity Using the SARIMA and LSTM Models

With a gradually rising global average sea level, it is of great significance to predict changes in the sea level. However, sea level variations often exhibit both linear and nonlinear characteristics, complicating the prediction of sea level changes with a single model. The seasonal autoregressive integrated moving average (SARIMA) model can fully consider the linear characteristics of time series, but its nonlinear prediction ability is poor; the long short-term memory (LSTM) model can compensate for this shortcoming. To predict complex sea level changes, we propose a strategy to combine the SARIMA and LSTM models to increase the sea level prediction accuracy. In our method, sea level anomaly (SLA) time series are decomposed into the trend and seasonal term and random term; then, the SARIMA model is used to predict the trend and seasonal term of sea level variations, whereas the random term is predicted by LSTM. Sea surface height data from 1993 to 2018 are used in an experiment. Compared with other models, the performance of the SARIMA+LSTM model is superior in predicting sea level changes with a minimum root mean square error of 1.155 cm and a maximum determination coefficient ( R 2) of 0.89 during the testing period. Furthermore, the predicted results are in close agreement with the SLA data, which indicates that the SARIMA+LSTM model could be successfully used for the estimation of sea level variability.

Bias in Estimates of Global Mean Sea Level Change Inferred from Satellite Altimetry

Estimates of regional and global average sea level change remain a focus of climate change research. One complication in obtaining coherent estimates is that geodetic datasets measure different aspects of the sea level field. Satellite altimetry constrains changes in the sea surface height (SSH; or absolute sea level), whereas tide gauge data provide a measure of changes in SSH relative to the crust (i.e., relative sea level). The latter is a direct measure of changes in ocean volume (and the combined impacts of ice sheet melt and steric effects), but the former is not since it does not account for crustal deformation. Nevertheless, the literature commonly conflates the two estimates by directly comparing them. We demonstrate that using satellite altimetry records to estimate global ocean volume changes can lead to biases that can exceed 15%. The level of bias will depend on the relative contributions to sea level changes from the Antarctic and Greenland Ice Sheets. The bias is also more sensitive to the detailed geometry of mass flux from the Antarctic Ice Sheet than the Greenland Ice Sheet due to rotational effects on sea level. Finally, in a regional sense, altimetry estimates should not be compared to relative sea level changes because radial crustal motions driven by polar ice mass flux are nonnegligible globally.

Sea Level Rise and Impacts on Maritime Zones and Limits

As the oceans warm and ice melts, the Intergovernmental Panel on Climate Change (ipcc) in its Fifth Assessment Report (AR5) now predicts a global average sea level rise of up to one meter by 2100. AR5 also emphasizes that sea level rise will have “a strong regional pattern, with some places experiencing significant deviations of local and regional sea level change from the global mean change.” These predictions pose serious and possibly existential threats to the inhabitants of low-lying islands and coastal areas, and pose challenges for the international legal system to respond in an orderly and humane way to these novel situations. In 2012, the International Law Association (ila) established a new Committee to look specifically at these issues. This article looks at the work undertaken by the Committee to date regarding the law of the sea aspects of its mandate and identifies some considerations for its future work.

A probabilistic approach to 21st century regional sea-level projections using RCP and High-end scenarios

Sea-level change is an integrated climate system response due to changes in radiative forcing, anthropogenic land-water use and land-motion. Projecting sea-level at a global and regional scale requires a subset of projections - one for each sea-level component given a particular climate-change scenario. We construct relative sea-level projections through the 21st century for RCP 4.5, RCP 8.5 and High-end (RCP 8.5 with increased ice-sheet contribution) scenarios by aggregating spatial projections of individual sea-level components in a probabilistic manner. Most of the global oceans adhere to the projected global average sea level change within 5cm throughout the century for all scenarios; however coastal regions experience localised effects due to the non-uniform spatial patterns of individual components. This can result in local projections that are 10′s of centimetres different from the global average by 2100. Early in the century, RSL projections are consistent across all scenarios, however from the middle of the century the patterns of RSL for RCP scenarios deviate from the High-end where the contribution from Antarctica dominates. Similarly, the uncertainty in projected sea-level is dominated by an uncertain Antarctic fate. We also explore the effect upon projections of, treating CMIP5 model ensembles as normally distributed when they might not be, correcting CMIP5 model output for internal variability using different polynomials and using different unloading patterns of ice for the Greenland and Antarctic ice sheets.

Miocene relative sea level on the New Jersey shallow continental shelf and coastal plain derived from one-dimensional backstripping: A case for both eustasy and epeirogeny

Onshore drilling by Ocean Drilling Program (ODP) Legs 150X and 174AX and offshore drilling by Integrated Ocean Drilling Program (IODP) Expedition 313 provides continuous cores and logs of seismically imaged Lower to ­Middle Miocene sequences. We input ages and paleodepths of these sequences into one-dimensional backstripping equations, progressively accounting for the effects of compaction, Airy loading, and thermal subsidence. The resulting difference between observed subsidence and theoretical thermal subsidence provide relative sea-level curves that reflect both global average sea level and non-thermal subsidence. In contrast with expectations, backstripping suggests that the relative sea-level maxima in proximal onshore sites were lower than correlative maxima on the shelf. This requires that the onshore New Jersey coastal plain has subsided relative to the shelf, which is consistent with models of relative epeirogeny due to subduction of the Farallon plate. These models predict subsidence of the coastal plain relative to the shelf. Although onshore and offshore sea-level estimates are offset by epeirogeny, the ampli­tude of million-year–scale Early to Middle Miocene sea-level changes seen at the New Jersey margin is generally 5–20 m and occasionally as great as 50 m. These events are interpreted to represent eustatic variations, because they occur on a shorter time frame than epeirogenic influences. Correction for epeiro­genic effects largely reconciles differences between onshore and offshore rela­tive sea-level estimates and suggests that backstripping provides a testable eustatic model for the Early to Middle Miocene.

Economic Valuation of Sea Level Rise Impacts on Agricultural Sector in Northern Governorates of the Nile Delta

It is widely believed, according to the IPCC (Intergovernmental Panel on Climate Change) projections, that there will be an increase in the global average sea level of 0.18 cm to 0.59 cm through the twenty-first century. The coastal area of the Nile delta is considered to be one of the most vulnerable areas to Sea Level Rise (SLR) in the world. Where, the Nile Delta consists mostly of lowland areas which accommodate a significant proportion of the Egyptian agricultural and economic activities. SLR is expected to have a profound impact on the agricultural areas of the Nile Delta, through either inundation or higher levels and salinity of groundwater. Due to the prevailed vulnerability of the Nile delta and as guidance for decision and policy making, it is necessary to provide estimates of potential economic damage that can result from such SLR. The paper in hand intends to estimate the economic value of the agricultural areas, in the coastal governorates of the Nile Delta, susceptible to inundation due to SLR according to most recent SLR scenarios and to estimate the economic value of potential impacts of rising groundwater table, associated with SLR, on agricultural productivity by the year 2100. The results indicate that about 7.5%, 36.3%, and 44.0% of the total cultivated area of the coastal governorates of the Nile Delta (with a market value of 51.7, 196.6 and 232.6 billion EGP (Egyptian Pound)) will be susceptible to inundation under the different scenarios of SLR. Moreover, it was found that the future accumulative crop yield loss due to increasing groundwater level was estimated, using segmented linear regression, to be as much as 32.3 billion EGP. It is worth mentioning that these estimates do not include indirect impacts of higher levels of groundwater table, which may include loss of jobs and/or earnings, impacts on food supply and food security in the area.

Impact of Fossil Fuel Energy Consumption on CO2 Emissions: Evidence from Pakistan (1980-2010)

Global environmental problems are getting more attention especially the increase in earth temperatures and change in climate. Increase in world average air and ocean temperatures, widespread melting of snow and ice, and rising global average sea level are some evidences of global warming. A CO2 emission, which is a global pollutant is the main greenhouse gas that causes 58.8 percent of global warming and climate change [The World Bank (2007a)]. The intergovernmental panel on climate change (IPCC) reported a 1.1 to 6.4 °C rise in the world temperatures and an increase in the sea level of about 16.5 to 53.8 cm at the end of 21st century [IPCC (2007)]. Combined global land and ocean surface temperature for January 2010 on the average was 0.60°C (1.08°F) above the 20th century average of 12.0°C (53.6°F) and the average global temperature for January 2010 at the surface air was recorded 0.83°C (1.49°F) above the 20th century average of 2.8°C (37.0°F). Global warming is partly resulting from higher night temperature and partly due to rapid urbanisation. Other factors adding to global warming are the continuously changing irrigation systems, desertification and variations in the use of local lands. The developing countries need more energy consumption for economic growth that’s why these economies face more environmental issues.

Climate Change/Global Warming and Its Impacts on Parasitology/ Entomology

Climate change and global warming are important phenomena and do not mean the same thing as is wrongly conceived by some individuals. However, the link between the two is strong and one, global warming is strictly an average increase in the temperature of the atmosphere near the earth’s surface and in the troposphere, while the other, climate change is more diverse and refers to any significant change in measures of climate such as temperature, precipitation, or wind lasting for a long period of time usually several years. Climate change could thus be an increase or decrease in temperature. The most important of the two terms which is under spotlight is global warming, an increase in temperature which has been blamed largely to greenhouse effect. There can no longer be any doubt that the earth’s climate is changing. It is now obvious that even the most hardened sceptics are starting to waiver in their convictions. Climate has been thrown completely out of kilter and each day brings fresh proof such as frequent and more violent cyclones in the Caribean, floods in Africa, the Philippines, the gradual sinking of Islands in the Pacific, heat waves in Europe and the melting of glaciers. There is increase in global average air and ocean temperatures, widespread melting of snow and rising global average sea level. Impacts of global warming include the emergence and re-emergence of some parasitic infections and diseases.

Southern Ocean forcing of the North Atlantic at multi-centennial time scales in the Kiel Climate Model

Internal multi-centennial variability of open ocean deep convection in the Atlantic sector of the Southern Ocean impacts the strength of the Atlantic Meridional Overturning Circulation (AMOC) in the Kiel Climate Model. The northward extent of Antarctic Bottom Water (AABW) strongly depends on the state of Weddell Sea deep convection. The retreat of AABW results in an enhanced meridional density gradient that drives an increase in the strength and vertical extent of the North Atlantic Deep Water (NADW) cell. This shows, for instance, as a peak in AMOC strength at 30°N about a century after Weddell Sea deep convection has ceased. The stronger southward flow of NADW is compensated by an expansion of the North Atlantic subpolar gyre and an acceleration of the North Atlantic Current, indicating greater deep water formation. Contractions of the North Atlantic subpolar gyre enable warm water anomalies, which evolved in response to deep convection events in the Southern Ocean, to penetrate farther to the north, eventually weakening the AMOC and closing a quasi-centennial cycle.Gyre contractions are accompanied by increases in sea level of up to 20cm/century in some areas of the North Atlantic. In the Southern Ocean itself, the heat loss during the convective regime results in a sea surface height decrease on the order of 10cm/century, with a maximum of 30cm/century in the Weddell Sea. Hence, the impact of the Southern Ocean Centennial Variability (SOCV) on regional as well as North Atlantic sea level is of the same order of magnitude as the rise of global average sea level during the 20th century, which amounts to about 15–20cm. This suggests that internal variability on a centennial time scale cannot be neglected a priori in assessments of 20th and 21st century AMOC and regional sea level change.

GIS-based risk assessment for the Nile Delta coastal zone under different sea level rise scenarios case study: Kafr EL Sheikh Governorate, Egypt

Sea level changes are caused by several natural phenomena, including mainly ocean thermal expansion, glacial melt from Greenland and Antarctica. It was estimated, in this respect, that global average sea level rose, during the 20th Century, by at least 10 cm. This trend is expected to continue and most likely accelerated during the 21st Century due to human-induced global warming. Global average sea level is expected to rise, by the year 2100, due to global warming between 0.18 and 0.59 cm. Such a rise in sea-level will significantly impact coastal areas due to the high concentration of natural and socioeconomic activities and assets located along the coast. The northern coastal zone of the Nile Delta is generally low land, and is consequently vulnerable to direct and indirect impacts of sea level rise (SLR) due to climate changes, particularly inundation. Despite the uncertainty associated with developed scenarios for climate change and expected SLR, there is a need, according to precautionary approach, to assess and analyze the impacts of SLR. Such an assessment, on one hand, can assist in formulating effective adaptation options to specific, sometimes localized, impacts of SLR. On the other hand, such an analysis can contribute significantly to the development of integrated approach to deal with the impacts of SLR. The objective of this paper is to assess and spatially analyze the risks of expected sea level rise (SLR), in particular inundation, and its implications up to the year 2100 in Kafr El Sheikh Governorate, Egypt, using GIS techniques. For that purpose, a GIS was developed for the study area and then utilized to identify the spatial extent of those areas that would be vulnerable to inundation by SLR. Moreover, various land uses/land covers susceptible to such inundation were identified. Results indicate that more than 22.59 % and 24.50 % of the total area of Kafr El Sheikh Governorate would be vulnerable to inundation under B1 and A1FI (IPCC most optimistic and pessimistic scenarios), respectively. No significant difference was noticed between the two scenarios in terms of spatial extent of SLR impacts. It was also found that a significant proportion of these areas were found to be currently either undeveloped or wetlands. Moreover, it was found that about 90.13 % of the vulnerable areas are actually less exposed to the risks of SLR due to the existence of a number of man-made features, not intended as protection measures, e.g. International Coastal Highway, that can be used to limit the areas vulnerable to inundations by SLR.

Land subsidence at aquaculture facilities in the Yellow River delta, China

Differential Interferometric Synthetic Aperture Radar is applied to the coast of the Yellow River delta (YRD) in China. Like many deltas, the coastline of the YRD is dominated by aquaculture. Advanced Land Observation Satellite Phased Array L‐Band Synthetic Aperture Radar (SAR) and Envisat Advanced SAR data acquired between 2007 and 2011 show that subsidence rates are as high as 250 mm/y at aquaculture facilities, likely due to groundwater pumping. These rates exceed local and global average sea level rise by nearly 2 orders of magnitude and suggest that subsidence and associated relative sea level rise may present a significant hazard for Asian megadeltas.