Increasing population size and economic dependence on the coastal zone, coupled with the growing need for residential, agricultural, industrial, commercial, and green space infrastructure, are key drivers of land reclamation. Until now, there has been no comprehensive assessment of the global distribution of land use on reclaimed space at the coast. Here, we analyse Landsat satellite imagery from 2000 to 2020 to quantify the spatial extent, scale, and land use of urban coastal reclamation for 135 cities with popultions in excess of one million.  Findings indicate that 78% (106/135) of these major coastal cities have resorted to reclamation as a source of new ground, contributing a total of 253,000 ha of additional land to the Earth’s surface in the 21st century, equivalent to an area the size of Luxembourg. Reclamation is especially prominent in East Asia, the Middle East, and Southeast Asia, followed by Western Europe and West Africa. The most common land uses on reclaimed spaces are port extension (>70 cities), followed by residential/commercial (30 cities) and industrial (19 cities). While increased global trade and rapid urbanization have driven these uses, we argue that a city’s prestigious place-making effort to gain global reputation is emerging as another major driver underlying recent reclamation projects to create tourist and green spaces Meanwhile, the study suggests that 70% of recent reclamation has occurred in areas identified as potentially exposed to extreme sea level rise (SLR) by 2100 and this presents a significant challenge to sustainable development at the coast.  

How to cite: Sengupta, D., Rae Choi, Y., Bo Tian, B., Brown, S., Meadows, M., R Hackney, C., Banerjee, A., Li, Y., Chen, R., and Zhou, Y.: Mapping 21st Century global coastal land reclamation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8345, https://doi.org/10.5194/egusphere-egu23-8345, 2023.

Coastal areas are recognized as the most at risk due to climate change. They exhibit low-lying elevation, very high urban density and valuable economical assets. Sea level rise, storm events and coastal floods that are increasingly frequent and more powerful will increase damage to fragile coasts. Human activities (especially the reduction in natural defences), sediment balance and natural phenomenon are disrupted and increase the coast’s vulnerability.

As part of the GDA-DR initiative, we aim to produce some new indicators derived from EO to better understand the coastal system. As part of these new indicators, improved flooding maps are being developed using corrected elevation data and additional layers to better represent water behaviour in coastal cities. 

• The Copernicus DEM GLO-30m is proving too crude to provide suitable flood modelling. It offers an accuracy of 4m, leading to an accuracy of earth features’ localization of less than 2.6 m. By using LIDAR measurements from the ISS-borne GEDI sensor and additional altimeter missions such as ATLAS on-board the ICESAT-2 satellite, we provide an improved and corrected DSM and DTM
• Additional layers produced for DEMs correction are used to improve floods modeling such as land cover maps to extract drag coefficients and 3D settlement layers to take into account channelling effects.

This new approach can provide improved flood maps to better support flood mitigation planning. We propose to present DSM and DTM, for Dili in Timor Leste, based on the most recent space-lidar data, GEDI (2019-2021) and of IceSAT-2/ATLAS (2019- on going) and hoe those new DEMs can impact flood risk assessment.

How to cite: Vallette, A., Beck, A.-L., Jones, M., Cook, H., and Taji, M. A.: Improved Terrain Modelling to aid flood mapping in coastal cities, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14451, https://doi.org/10.5194/egusphere-egu23-14451, 2023.

Future coastal risk will largely depend on where people build and settle, and not only on increases in extreme events or sea level rise as a result of global warming. In the past, hard engineering has been used to protect settlements in coastal lowlands. However, as this option becomes less viable and more expensive due to rapidly rising sea levels, coastal managers are increasingly turning to landuse planning interventions, such as setback zones or managed retreat. Although various studies show that one of the most effective approaches is to prevent urban expansion inside the coastal floodplain, limited research has been done to assess the potential of setback zones in minimizing future coastal exposure in Europe. This study enhances our understanding of the potential of coastal setback zones of different shapes in the EU by (1) assessing the avoided urban exposure resulting from the implementation of setback zones/retreat under different socioeconomic futures and (2) providing country-specific information on which type of setback zones is most beneficial in reducing urban exposure. For this purpose, we created spatially explicit projections of urban extent that consider different socio-economic futures and different types of setback zones to examine the effectiveness of these planning strategies in decreasing future urban exposure in Europe’s coastal lowlands. Our results show that by the year 2100, the majority of EU coastal countries can reduce the exposure of new urban land by at least 50% if coastal setback zones are established; and highlight that how we plan, build, and develop urban space in the EU coastal lowlands will be the defining factor on how exposed future urban areas are to sea-level rise.

How to cite: Wolff, C., Bonatz, H., and Vafeidis, A. T.: Coastal setback zones can lessen Europe's future exposure to sea level rise., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1441, https://doi.org/10.5194/egusphere-egu23-1441, 2023.

In this study we aim to improve our understanding of household adaptation and migration responses to coastal hazards induced by sea level rise, such as coastal flooding. We apply a global ABM (agent-based model; DYNAMO-M), which simulates all ~200 million individual people and households in coastal flood zones around the world. The model simulates in yearly timesteps flood events and changing flood risk and whether residents migrate or adapt (e.g. flood proof their house) to lower their risk. Agents’ migration and adaptation decisions are based on the Subjective Expected Utility Theory (SEUT). Here, agents maximize their utility based on subjective risk assessments, such as their subjective perception of flood risk. However, the current risk perception parameter in the SEUT equation in DYNAMO-M is based on a single empirical study in France. Additional data is needed to address the heterogeneity of risk perceptions across different global coastal households. In order to assess the differences in risk perceptions in different areas around the world, we combine different data sets: (1) we conducted unique surveys on perceptions of flood risk and their determinants as well as people’s intention to adapt or migrate under future SLR scenarios in 6 countries with varying socio-economic backgrounds (Argentina, France, Mozambique, the Netherlands, the US, and Vietnam). Using these survey data, we identify the generic decision rule for the determinants of risk perception parameters such as the perceived probability and damage of flooding through regression analysis. (2) Next, we use additional global datasets on individual characteristics such as the World Value Survey (demographic and residential information data) and Cloud2Street (flood experience data) and use these data as explanatory variables for transferring risk perception parameters to countries where no primary survey data is available. This analysis may aid the understanding of global patterns in risk perceptions of people/agents. We believe our study serves as a basis for research on individual behavior under risk, the role of risk perception, and the use of the data in global ABMs.

How to cite: Kim, M., Botzen, W., Haer, T., de Bruijn, J., and Aerts, J.: Assessing perceived probability and damage of flood risk across the globe, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13799, https://doi.org/10.5194/egusphere-egu23-13799, 2023.

Like many other coastal megacities, Ho Chi Minh City in Southern Vietnam is regularly affected by flooding from torrential rainfalls and storm surges. Global climate heating will exacerbate these natural hazards, but rapid urbanization has a share in the intensification of urban disaster risk as well. Proceeding surface sealing, uncontrolled land subsidence and the urban heat island effect are only a few of the factors increasing exposure to flooding, which often hits the most vulnerable parts of the population. To defend local residents and reduce flood-induced losses, classic protection measures like embankments, flood gates and large-scale pumping stations are implemented across Ho Chi Minh City. But as low-level inundations continue to cause frequent disruptions to the local economy, ecosystem-based solutions or so-called blue-green infrastructure are more and more seen as promising means to complement existing management strategies and ensure sustainable flood resilience.

In our study, we investigate whether the role of coastal ecosystems in mitigating floods in Ho Chi Minh City is already reflected in recent urban developments. Specifically, we use multi-spectral Sentinel-2 imagery to calculate the Normalized Difference Vegetation Index (NDVI), a dimensionless indicator to describe terrestrial vegetation. The obtained annual NDVI maps have a spatial resolution of ca. 1 arc second and cover the years 2016 to 2022; special attention was given to the composition of cloud-free images. On this basis, temporal trends were determined that specify the (qualitative) development of bio-activity across the urban and suburban districts of the province. These local trends are complemented by annual histograms, which describe the relative frequency of specific NDVI ranges and thus allow estimations of ecosystem density. Unsurprisingly, preliminary results suggest constant ecosystem density for the Can Gio biosphere reserve in the South of the HCMC province, except for slight changes along its boundaries. For the urban districts of HCMC, however, ongoing urbanization can be traced by decreasing ecosystem density according to our assessment of NDVI values. The employed method provides promising results, yet currently still lacks a decent validation through ground truth data.

How to cite: Scheiber, L. and Schlurmann, T.: Tracing the development of coastal ecosystems through satellite imagery: a case study from Ho Chi Minh City, Vietnam, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5546, https://doi.org/10.5194/egusphere-egu23-5546, 2023.

Flood events have been generating great risks and intensifying the challenges of water management in coastal megacities. Instead mitigating the impact of climate change, improving the resilience has an expanding scope of application in environmental science, covering climate change, risk and disaster management [1]. In the past decades, metrological and hydrological causes have been the main drivers of disasters [2], which makes vulnerability assessment methods such as Flood vulnerability index (FVI) clear development pathways [3].

This study is built on the household survey data in HCMC (Ho Chi Minh City, Vietnam) in the framework of the DECIDER project (DECisions for Adaptive Pathway Design and the Integrative Development, Evaluation and Governance of Flood Risk Reduction Measures in Transforming Urban-Rural-Systems). It aimed at creating a framework to interpret social-economic attributes of flood vulnerability with physical features of household. As an essential part of the influencing factor for social vulnerability, the data is regionally intrinsic and mostly accessible only by field survey. Proxy variables were obtained to conduct contextual analysis based on remote sensing images, environmental risk estimates, as well as elevation data, in which study concludes that this method can contribute to identifying the key indicators and optimize the social vulnerability assessment to be more efficient [4]. In this study, 17 socioeconomic indicators only accessible from survey data were weighted using the Principal Component Analysis (PCA), and further aggregated into the FVI. Then 11 physical proxy indicators were collected from field inspection, remote sensing data and environmental flood risk estimates, and trained machine learning models to predict FVI. The AdaBoost model identified the most important physical indicators and the model was able to predict the test data with a MAE of 0.089 but small R2. Another decision tree model, however, was overfitted and yielded a moderate accuracy (~0.4) and further machine learning classification models were also applied on both eleven indicators and selected indicators for each case but no obvious difference showed among these models. Therefore, the socioeconomic FVI could be predicted with physical proxy variables with AdaBoost accurately, but more featured data should be acquired and model rendering can be done in the future for a better prediction model, especially for regional prediction with the scale of households and community.

References

[1] O’Brien, K. Global environmental change II. Progress in Human Geography 2012, 36, 667–676, doi:10.1177/0309132511425767.

[2] Birkmann, J.; Teichman, K. von. Integrating disaster risk reduction and climate change adaptation: key challenges—scales, knowledge, and norms. Sustain Sci 2010, 5, 171–184, doi:10.1007/s11625-010-0108-y.

[3] Balica, S.F.; Wright, N.G.; van der Meulen, F. A flood vulnerability index for coastal cities and its use in assessing climate change impacts. Nat Hazards 2012, 64, 73–105, doi:10.1007/s11069-012-0234-1.

[4] Ebert, A.; Kerle, N.; Stein, A. Urban social vulnerability assessment with physical proxies and spatial metrics derived from air- and spaceborne imagery and GIS data. Nat Hazards 2009, 48, 275–294, doi:10.1007/s11069-008-9264-0.

How to cite: Song, Z., Yang, E., and Tuo, Y.: Piloting a physical-metric-based Index bench-marked by a social-economical Index measuring Flood Resilience in Ho Chi Minh City, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9192, https://doi.org/10.5194/egusphere-egu23-9192, 2023.

Transforming rural-urban-systems such as Ho Chi Minh City, Vietnam, are facing exacerbating flood risk due to climatic and socio-economic changes, necessitating effective adaptation solutions. Risk-based adaptation planning requires plausible and accurate flood loss estimation. However, state-of-the-art flood loss models for the region that take into account the multi-causality of flood damage and convey information about predictive uncertainty are lacking.

This study presents a Bayesian network for flood loss estimation for the residential sector in Ho Chi Minh City. We developed the graphical probabilistic model based on new object-level survey data with flood-affected households (n=1530), which cover the topics of flood intensity, household characteristics, warning and emergency, private precaution, and damages. An analysis of the survey data concerning the explanatory power for flood damage revealed a subset of relevant variables, which we used for model elicitation. Using a systematic learning procedure, we identified a robust Bayesian network structure that reflects the local circumstances of flood damage processes at the study site. That is, the resulting model takes into account flood intensity variables such as water depth but also vulnerability variables such as households’ flood experience or adaptive behavior. We confirmed the identified damage influencing variables by comparisons to other established statistical and machine learning methods (i.e., random forest and grid search cross-validation with multivariable regression). A prediction exercise with repeated cross-validation demonstrated that the developed Bayesians network model is capable of estimating building loss accurately. However, similar to previous studies in the field, we observed considerable predictive errors for severe loss cases for which data records are scarce. In addition, we show that the predictive skill of the Bayesian network is competitive to non-parametric modeling alternatives such as random forest.

Our validated Bayesian network loss model exhibits high practical value for applications at the city-scale since it enables loss estimation even when information about the predictor variables is only partially available. Moreover, the inclusion of vulnerability variables as predictors in the loss model facilitates the consideration of adaptive behavior in loss and risk assessment. Ultimately, the fully probabilistic model design inherently quantifies predictive uncertainty, which fosters the uncertainty propagation to subsequent elements of flood risk assessment and well-informed decision-making.

How to cite: Rafiezadeh Shahi, K., Sairam, N., Schoppa, L., Sang, L. T., Tan, D. L. H., and Kreibich, H.: A Probabilistic Flood Loss Model for Adaptation Planning in Ho Chi Minh City, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2370, https://doi.org/10.5194/egusphere-egu23-2370, 2023.

There is typically limited knowledge on the key factors causing critical flood situations in the context of climate change in many regions, including in large cities like Ho Chi Minh City (HCMC), Vietnam. It is important to improve our understanding of the causes of flood and how to incorporate flood adaptation measures, especially the integration of technical measures into local climate change adaptation plans. This presentation describes the results and experiences of applying a hydraulic model (MIKE 11, 21 and Flood) for simulating flood situations under different climate change scenarios and integration into the master plan for flood prevention (2008) in HCMC. It was found that the water level in Sai Gon River will not increase under the current climate following completion of Phase 1 of constructing flood prevention infrastructure according to the master plan of 2008. However, under the same flood infrastructure prevention condition, the water level at Phu An station will increase from 1.74 m to 2.28 m under the most extreme scenario of Dau Tieng discharge and climate change without land subsidence. Flood prevention infrastructure was identified as a key factor reducing flooding in HCMC. Further studies are recommended to simulate alternative flood situations by applying the same hydraulic model under the new master plan for flood prevention (2021) in HCMC to support flood adaptation measures and strategies.

Key words: Flooding, hydraulic model, flood prevention, climate change   

How to cite: Vu Thanh Noi, L., Phu Quynh, N., and Dac Hai, D.: Assessing flood situations and technical adaptation measures in the context of climate change in Ho Chi Minh City, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13148, https://doi.org/10.5194/egusphere-egu23-13148, 2023.

Socio-hydrological approach is a new science that is aimed at understanding the dynamics and co-evolution of coupled human-water systems. We use this concept to analyze how flood risk related to social resilience in Can Tho city, one of the biggest urban area located in the Vietnamese Mekong delta. The study employed both secondary and primary data collected in two inner districts of Ninh Kieu and Cai Rang. Key informant interview with related stakeholders and focus group discussion with local community were conducted in the reserach sites. The results showed that urban flood tends to rise up year by year because of various drivers such as increase of rainfall in a short time combined with the blockage of sewers due to garbage or/and lack of green areas. In term of hydrological aspect, rainfall, river water level and discharge are key factors. In addition, the social drivers like ineffective urban planning and inappropriate human behaviour also play an important role causing serious inundation. We also found that flood risk contributes to reduce social resilience by different ways including infrastructure damages, transportation disruption, livelihood decline, social network discontinuance, landscape degradation, environmental poluttion, human health and fatality. Therefore, it is necessary to take into account both social and hydrological drivers to mitigate the flood risk on one hand and enhance social resilience on the other. Green urban development which has the greatest potential for improving the quality of ecosystem services and providing opportunities for urban dwellers to reconnect with nature should be a good strategy for disaster risk reduction in this situation.

How to cite: Nguyen, T. B. and Tien, L. V. T.: Studying flood risk and social resilience at city level by socio-hydrological approach in Can Tho City of Vietnam, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17568, https://doi.org/10.5194/egusphere-egu23-17568, 2023.