Spatial Modeling of Coastal Flood Vulnerability Driven by Land Subsidence and Sea Level Rise Based on Altimetry and Geospatial Data

Global climate change-induced sea level rise has emerged as a critical environmental challenge for coastal cities in the 21st century. Shanghai, China's economic, financial, and shipping hub, faces significantly amplified inundation risks in its coastal areas due to the compounding effects of sea level rise, land subsidence, and storm surges. This study constructs a multi-case simulation framework using the sixth assessment report of intergovernmental panel on climate change sea level projection data, land subsidence monitoring records, and historical storm surge data to evaluate the impacts of three cases on future inundation risks: sea level rise alone (case1), sea level rise combined with land subsidence (case 2), and sea level rise coupled with land subsidence and storm surges (case 3). Leveraging Autoregressive Integrated Moving Average model time-series modeling, Geographic Information System spatial analysis, and numerical simulations, the study predicts relative sea level rise and inundation extents for 2050, 2070, and 2100. Results indicate that Shanghai's relative sea level rise rate far exceeds the global average, with land subsidence and storm surges synergistically amplifying disaster risks in low-lying coastal zones. Under case 1, the projected inundation area reaches 361.32 km2 by 2100. Case 2 increases this area to 460.97 km2, while case 3 shows a dramatic escalation to 1,331.91 km2 by 2100-a surge of 870.94 km2 compared to case 2-highlighting the dominant role of storm surges in extreme weather events. Spatial analysis identifies Chongming District, Pudong New Area, and Fengxian District as high-risk zones, with Chongming Island being the most severely affected (54.5% inundation by 2100). This study elucidates the compound impact mechanisms of sea level rise, land subsidence, and storm surges in Shanghai, providing a scientific foundation for coastal disaster mitigation and adaptive urban management. Recommendations include enhancing coastal flood defenses, optimizing land-use planning, improving extreme weather early-warning systems, and fostering international collaboration and technological innovation to bolster urban resilience against climate risks.

Global climate change-induced sea level rise has emerged as a critical environmental challenge for coastal cities in the 21st century. Shanghai, China’s economic, financial, and shipping hub, faces significantly amplified inundation risks in its coastal areas due to the compounding effects of sea level rise, land subsidence, and storm surges. This study constructs a multi-case simulation framework using the sixth assessment report of intergovernmental panel on climate change sea level projection data, land subsidence monitoring records, and historical storm surge data to evaluate the impacts of three cases on future inundation risks: sea level rise alone (case1), sea level rise combined with land subsidence (case 2), and sea level rise coupled with land subsidence and storm surges (case 3). Leveraging Autoregressive Integrated Moving Average model time-series modeling, Geographic Information System spatial analysis, and numerical simulations, the study predicts relative sea level rise and inundation extents for 2050, 2070, and 2100. Results indicate that Shanghai’s relative sea level rise rate far exceeds the global average, with land subsidence and storm surges synergistically amplifying disaster risks in low-lying coastal zones. Under case 1, the projected inundation area reaches 361.32 km2 by 2100. Case 2 increases this area to 460.97 km2, while case 3 shows a dramatic escalation to 1,331.91 km2 by 2100—a surge of 870.94 km2 compared to case 2—highlighting the dominant role of storm surges in extreme weather events. Spatial analysis identifies Chongming District, Pudong New Area, and Fengxian District as high-risk zones, with Chongming Island being the most severely affected (54.5% inundation by 2100). This study elucidates the compound impact mechanisms of sea level rise, land subsidence, and storm surges in Shanghai, providing a scientific foundation for coastal disaster mitigation and adaptive urban management. Recommendations include enhancing coastal flood defenses, optimizing land-use planning, improving extreme weather early-warning systems, and fostering international collaboration and technological innovation to bolster urban resilience against climate risks.

Egypt is facing several hazardous environmental phenomena, particularly Sea Level Rise (SLR) and land subsidence. It has been considered to be one of the countries most likely to be affected by SLR because of the low elevation of the Nile Delta region’s northern coastal zone, like all world Deltas. For that, a GIS inundation model has been generated using an original high accuracy local digital elevation model for the Nile Delta region, Egypt (LDEM) and the SLR data measured by the tide-gauges. This model has been used to determine the vulnerable low laying areas to inundation from future SLR in 2050 and to determine land-use types and percentages that are most likely to be affected in the northern Mediterranean coast of Nile Delta region. Finally, a total Hazard Index Map (HIM) has been produced from combining SLR HIM, and the subsidence HIM for the study area which will present the full danger of the two phenomena on the coastal region. The results have shown that the inundated area calculated from the SLR in 2050 model is about 50 km2. From the land-use maps, the areas flooded by the sea represent almost 38.40 km2, 3.80 km2, 5.20 km2, and 2.60 km2 for the urban, agricultural lands, fishing farms, and bare areas, respectively. The authorities and decision makers should pay more attention to the hazardous effects due to the impacts of subsidence and SLR in the Nile Delta region, especially the northern coastal zone. More protection construction such as seawalls and breakwaters should be built at the northern coastal zone of the Nile Delta region vulnerable to inundation of SLR, to prevent the occurrence of the predicted inundation scenarios that could be occurred to this region in the future.

In recent years, the upward trend of sea level rise caused by climate change continues to increase. This condition threatens urbanization that occurs in urban areas, especially in coastal area. Coastal is a strategic location for various activities such as ports, recreation, fisheries and agriculture, but it is vulnerable to changes caused by community and natural activities. Semarang is one of the coastal city located in the north of Central Java. Dense and slum settlements are dominating the coastal area of Semarang, one of them is fisherman’s settlement in Tambak Lorok. This area has been undergoing land subsidence for a long time and is now threatened by sea level rise due to climate change. The purpose of this study is to formulate adaptive urban design principles on land subsidence and sea level rise in Tambak Lorok, Semarang. This study is carried out with qualitative method using the approach of research and development in building a formula that applies to the conditions formed by land subsidence and sea level rise. The design principles formulated from this study are general principles that serve as guidelines for designing coastal areas that adaptive to land subsidence and sea level rise. We believe, the adaptation of urban space can reduce the impact of the disaster and create a resilient coastal area to reduce the risk of natural-induced disasters.

Public health threats of diminished treatment of onsite sewage

Projecting the sea level rise (SLR), storm surges, and related inundation in the Pacific Islands due to climate change is important for assessing the impact of climate change on coastal regions as well as the adaptation of the coastal regions. The compounding effects of storm surges and SLR are one of the major causes of flooding and extreme events; however, a quantitative impact assessment that considers the topographical features of the island has not been properly conducted.Therefore, this study projects the impact of storm surge and SLR due to climate change on Viti Levu, which is the biggest and most populous island in Fiji. The impact of SLR on the inundation in coastal areas was simulated using a dynamic model based on the IPCC SROCC scenarios and the 1/100 years return period storm surge implemented based on the RCP8.5 equivalent scenario. The affected inundation area and population due to storm surges and SLRs are discussed based on the compound effects of SLR and storm surge.Although the contribution of SLR to the inundation area was quite significant, the 1/100 year storm surge increased by 10 to 50% of the inundation area. In addition, a narrow and shallow bay with a flat land area had the largest impact of storm surge inundation. Furthermore, the western wind direction had the most severe storm surge inundation and related population exposure due to the topographic and bathymetric characteristics of Viti Levu Island.

The relationship between sea surface height (SSH) and seawater density anomalies, which affects the pressure on the seafloor (PSF) anomalies off the southeastern coast of Hokkaido, Japan, was analyzed using the eddy-resolving spatial resolution ocean assimilation data of the JCOPE2M for the period 2001–2018. On an interannual (i.e., year-to-year) timescale, positive SSH anomalies of nearly 0.1 m appeared off the southeastern coast of Hokkaido, Japan, in 2007, associated with a warm-core ring (WCR), while stronger SSH anomalies (∼0.2 m) related to a stronger WCR occurred in 2016. The results show that the effects of such positive SSH anomalies on the PSF are almost canceled out by the effects of negative seawater density anomalies from the seafloor to the sea surface (SEP; steric effect on PSF) due to oceanic baroclinic structures related to the WCRs, especially in offshore regions with bottom depths greater than 1000 m. This means that oceanic isostasy is well established in deep offshore regions, compared with shallow coastal regions. To further verify the strength of the oceanic isostasy, oceanic isostasy anomalies (OIAs), which represent the barotropic component of SSH anomalies, are introduced and analyzed in this study. OIAs are defined as the sum of the SSH anomalies and SEP anomalies. Our results indicate that the effect of oceanic fluid changes due to SSH and seawater density anomalies (i.e., OIAs) on PSF changes cannot be neglected on an interannual timescale, although the amplitudes of the OIAs are nearly 10% of those of the SSH anomalies in the offshore regions. Therefore, to better estimate the interannual-scale PSF anomalies due to crustal deformation related to slow earthquakes including afterslips, long-term slow slip events, or plate convergence, the OIAs should be removed from the PSF anomalies.

The impact of sea-level rise on the coast of Tianjin-Hebei, China

This paper presents a scenario-based study that investigates the interaction between sea-level rise and land subsidence on the storm tides induced fluvial flooding in the Huangpu river floodplain. Two projections of relative sea level rise (RSLR) were presented (2030 and 2050). Water level projections at the gauging stations for different return periods were generated using a simplified algebraic summation of the eustatic sea-level rise, land subsidence and storm tide level. Frequency analysis with relative sea level rise taken into account shows that land subsidence contributes to the majority of the RSLR (between 60 % and 70 %). Furthermore, a 1D/2D coupled flood inundation model (FloodMap) was used to predict the river flow and flood inundation, after calibration using the August 1997 flood event. Numerical simulation with projected RSLR suggests that, the combined impact of eustatic sea-level rise and land subsidence would be a significantly reduced flood return period for a given water level, thus effective degradation of the current flood defences. In the absence of adaptation measures, storm flooding will cause up to 40 % more inundation, particularly in the upstream of the river.

Policy planning and practical implementations of coastal adaptation strategies to reduce the impact of sea level rise in the coastal region of Kanyakumari District, Tamil Nadu, India

Sea Level Rise (SLR) above the Mean Sea Level (MSL) may pose a substantial risk to coastal regions. This research investigates the possible impact of climate change and sea level rise in coastal areas. It locally analyzes the impact of sea level rise on Richmond, British Columbia, Canada. A model of Potentially Inundated Areas, based on a digital elevation model (DEM) was created, manipulated and processed in ArcGIS. Through this model, the impact of sea level rise was assessed on the surface area, residential areas, and a number of buildings, the number of dwellings, road network, and population. After the susceptible areas were delineated, it was estimated that at worst case scenario of 4 m sea level rise will impact Richmond by losing 46 percent of its total surface area, 462 km of road network will be under water, 637 buildings will be affected, 15 Sq. km of residential areas will be under water, and 30,000 houses will be affected. As a result, 89,000 people in the city will be displaced.

Global warming has intensified the rise in sea levels and has caused severe ecological disasters in shallow coastal waters such as the Northeastern China's Bohai Sea. The prediction of the sea surface height anomaly (SSHA) has great significance in the context of monitoring changes in sea levels. However, the non-linearity of SSHA due to the occurrence of dynamic physical phenomena poses a challenge to current methods(e.g., ROMS, MITgcm) that aim to provide accurate predictions of SSHA. In this study, we have developed an optimized Simple Recurrent Unit (SRU) deep network for the short- to medium-term prediction of the SSHA using Archiving Validation and International of Satellites Oceanographic (AVISO) data. Thanks to the parallel structure of the SRU, the computational complexity of the deep network can be reduced to a considerable extent and this makes the short- to medium-term prediction more efficient. To avoid over-fitting and a vanishing gradient, a skip-connection strategy has been utilized for model optimization, and this improves significantly the accuracy of prediction. Detailed experiments were carried out in the Bohai Sea to evaluate the proposed model and it was demonstrated that the proposed framework (i) outperformed significantly the current deep learning methods such as the BP (Backpropagation), the RNN (Recurrent Neural Network), the LSTM (Long Short-term Memory), and the GRU (Gated Recurrent Unit) algorithms for 1, 5, 20, and 300-day prediction; (ii) can predict the short-term trend in the SSHA (for the next day or 2 days) in real time; and (iii) achieves medium-term prediction in seconds for the next 5–20 days and shows great potential for applications requiring medium- to long-term predictions. To the best of our knowledge, this is the first paper that investigates the effectiveness of the SRU deep learning model for short- to medium-term SSHA predictions.

Sea level rise caused by global climate change has significant impacts on coastal zone. The mangrove ecosystems occur at the intertidal zone in tropical and subtropical coasts and are particularly sensitive to sea level rise. To study the responses of mangrove ecosystems to sea level rise,assess the impacts of sea level rise on mangrove ecosystem and formulate the feasible and practical mitigation strategies are the important prerequisites for securing the coastal ecosystems. In this research,taking the mangrove ecosystems in the coastal zone of Qinzhou Bay,Guangxi province as a case study,the main impacts of sea level rise on the mangrove ecosystems were analyzed by adopting the SPRC( Source- Pathway-ReceptorConsequence) model. An indicator system for vulnerability assessment on coastal mangrove ecosystems under sea level rise was worked out,according to the IPCC definition of vulnerability,i.e. the aspects of exposure,sensitivity and adaptation.The rate of sea level rise,subsidence /uplift rate,habitat elevation,daily mean inundation duration,intertidal slope and sedimentation rate were selected as the key indicators,taking into account of the characteristics of quantification,data accessibility,spatial and temporal heterogeneity. A quantitatively spatial assessment method based on the GIS platform wasestablished by quantifying each indicator,calculating the vulnerability index and grading the vulnerability. The vulnerability assessment based on the sea-level rise rates of the present trend( the rate of sea level rise in the past 40 years),the B1 and A1FI scenarios in IPCC SRES were performed for three sets of projections of short-term( 2030s),mid-term( 2050s) and long-term( 2100s). The results showed the mangrove ecosystems in the coastal zone of Qinzhou Bay was within the grade of no vulnerability at the present sea level rise rate of 0.29 cm /a and the B1 scenario of 0.38 cm /a for the projections of2030s,2050s and 2100s,respectively. As the sedimentation and land uplift could offset the rate of sea level rise and the impact of sea level rise on habitats /species of mangrove ecosystems was negligible. While in the A1FI scenario at sea level rise rate of 0.59 cm /a,the percentage of mangrove ecosystems within the grade of low vulnerability could reach 41.3% in2050,and increased to 69.8% in 2100. The spatiotemporal occurrences of low vulnerability were mainly distributed in the northern coast of Maoweihai. The SPRC model and the methodology for vulnerability assessment developed from this study can objectively and quantitatively assess the vulnerability of coastal mangrove ecosystems in Qinzhou Bay under the impact of sea level rise caused by climate change. Based on the results from this study,some mitigation measures should be considered in the future for securing the coastal mangrove ecosystems,which include management of sedimentation,rehabilitating and recreating mangrove habitat,and controlling reclamation. The results from this study could provide a scientific basis on formulating feasible and practical mitigation strategies for coastal mangrove ecosystems under the impact of sea level rise,which is an important prerequisite for securing the coastal zone ecosystems.

Deformation measurements from satellite-borne synthetic aperture radar interferometry (InSAR) are usually measured relative to an arbitrary reference point (RP) of assumed stability over time. For InSAR rates to be reliably interpreted as uplift or subsidence, they must be connected to a defined Earth-centred terrestrial reference frame (TRF), usually made through GNSS continuously operating reference stations (CORS). We adapt and compare three methods of TRF connection proposed by different studies which we term the single CORS RP (SCRP), plane-fit multiple CORS (PFMC), and the multiple CORS RP (MCRP). We generalise equations for these methods, and importantly, develop equations to propagate InSAR and GNSS uncertainties through the transformation process. This is significant, because it is important to not only estimate the InSAR uncertainties, but also to account for the uncertainties that are introduced when connecting to the CORS so as to better inform our interpretation of the deformation field and the limitation of the measurements. We then test these methods using Sentinel-1 data in the Latrobe Valley, Australia. These results indicate that differences among the three TRF connection methods may be greater than their estimated uncertainties. MCRP appears the most reliable method, although it may be limited in large study areas with sparse CORS due to long wavelength InSAR errors and that gaps and/or steps may appear at the spatial limit from the CORS. SCRP relies on the quality of the single CORS connection, but can be validated by unconnected CORS in the study area. The PFMC method is suited to larger areas undergoing slow, constant deformation covering large spatial extents where there are evenly distributed CORS across the study area. Selecting an optimal method of TRF connection is dependent on local site conditions, CORS network geometry and the characteristics of the deformation field. Hence, the choice of TRF connection method should be carefully considered, because different methods may result in significantly different transformed deformation rates. We confirm slow subsidence across the Latrobe Valley relative to the vertical component of the ITRF2014, with localised high subsidence rates near open cut mining activities. Subsidence of ~ -6 mm/year is observed in the adjacent coastal region which may exacerbate relative sea level rise along the coastline, increasing future risks of coastal inundation.

Modelling the combined impact of sea level rise, land subsidence, and tropical cyclones in compound flooding of coastal cities