# Coastal flooding in Asian megadeltas: recent advances, persistent challenges, and call for actions amidst local and global changes

Melanie Becker, K. Seeger, A. Paszkowski, M. Marcos, F. Papa, R. Almar, P. Bates, C. France-Lanord, Md Hossain, Md Khan, et al.

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Melanie Becker, K. Seeger, A. Paszkowski, M. Marcos, F. Papa, et al., Coastal flooding in Asian megadeltas: recent advances, persistent challenges, and call for actions amidst local and global changes. *Reviews of Geophysics*, 2024, 62 (4), pp.e2024RG000846. 10.1029/2024RG000846. *hal-04842545*

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# Reviews of Geophysics®

## REVIEW ARTICLE

10.1029/2024RG000846

**Key Points:**

* Urgent action is needed to address data gaps in monitoring coastal flooding in Asian megadeltas

* Integrated approaches, including in situ data, remote sensing and advanced modeling, are needed to understand coastal flooding impacts

* Unprecedented changes in Asian megadeltas call for integrated studies; decisions made today shape deltaic sustainability for decades

**Supporting Information:**

Supporting Information may be found in the online version of this article.

**Correspondence to:**

M. Becker,
mbecker@univ-lr.fr

**Citation:**

Becker, M., Seeger, K., Paszkowski, A., Marcos, M., Papa, F., Almar, R., et al. (2024). Coastal flooding in Asian megadeltas: Recent advances, persistent challenges, and call for actions amidst local and global changes. *Reviews of Geophysics*, 62, e2024RG000846. https://doi.org/10.1029/2024RG000846

Received 18 APR 2024
Accepted 23 NOV 2024

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non-commercial and no modifications or
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## Coastal Flooding in Asian Megadeltas: Recent Advances, Persistent Challenges, and Call for Actions Amidst Local and Global Changes

M. Becker¹ ID, K. Seeger²,³ ID, A. Paszkowski⁴ ID, M. Marcos⁵ ID, F. Papa⁶ ID, R. Almar⁶ ID, P. Bates⁷ ID, C. France-Lanord⁸ ID, Md S. Hossain⁹ ID, Md J. U. Khan⁶ ID, M. A. Karegar¹⁰ ID, M. Karpytchev¹ ID, N. Long¹ ID, P. S. J. Minderhoud³ ID, J. Neal⁷ ID, R. J. Nicholls¹¹ ID, and J. Syvitski¹² ID

¹Littoral Environnement et Sociétés (LIENSs), UMR 7266 CNRS, La Rochelle Université, La Rochelle, France, ²Institute of Geography, University of Cologne, Cologne, Germany, ³Soil Geography and Landscape Group, Wageningen University & Research, Wageningen, The Netherlands, ⁴Environmental Change Institute, University of Oxford, Oxford, UK, ⁵IMEDEA (UIB-CSIC), Esporles, Spain, ⁶Laboratoire d'Etudes en Géophysique et Océanographie Spatiales (LEGOS), Université Toulouse III, CNRS, CNES, IRD, Toulouse, France, ⁷School of Geographical Sciences, University of Bristol, Bristol, UK, ⁸Centre de Recherches Pétrographiques et Géochimiques, Université de Lorraine, CNRS UMR 7358, Vandoeuvre-lès-Nancy, Vandoeuvre, France, ⁹School of Social & Environmental Sustainability, University of Glasgow, Dumfries, Scotland, ¹⁰Institute of Geodesy and Geoinformation, University of Bonn, Bonn, Germany, ¹¹Tyndall Centre for Climate Change Research, University of East Anglia (UEA), Norwich, UK, ¹²INSTAAR and CSDMS, University of Colorado, Boulder, CO, USA

**Abstract** Asian megadeltas, specifically the Ganges-Brahmaputra-Meghna, Irrawaddy, Chao Phraya, Mekong, and Red River deltas host half of the world's deltaic population and are vital for Asian countries' ecosystems and food production. These deltas are extremely vulnerable to global change. Accelerating relative sea-level rise, combined with rapid socio-economic development intensifies these vulnerabilities and calls for a comprehensive understanding of current and future coastal flood dynamics. Here we provide a state-of-the-art on the current knowledge and recent advances in quantifying and understanding the drivers of coastal flood-related hazards in these deltas. We discuss the environmental and physical drivers, including climate influence, hydrology, oceanography, geomorphology, and geophysical processes and how they interact from short to long-term changes, including during extreme events. We also jointly examine how human disturbances, with catchment interventions, land use changes and resource exploitations, contribute to coastal flooding in the deltas. Through a systems perspective, we characterize the current state of the deltaic systems and provide essential insights for shaping their sustainable future trajectories regarding the multifaceted challenges of coastal flooding.

**Plain Language Summary**
Asian megadeltas, including the Ganges-Brahmaputra-Meghna, Irrawaddy, Chao Phraya, Mekong, and Red River deltas, are home to half of the world's deltaic population and play a critical role in Asian food production. However, these deltas are at high risk to climatic changes, with the vast majority of the world's coastal flood exposure observed in these systems. Rising sea levels and rapid socio-economic development worsen these vulnerabilities, necessitating a comprehensive understanding of coastal flooding dynamics across the five deltas. This review provides up-to-date insights on the current understanding and quantification of the drivers of coastal flood-related hazards. It examines environmental and biophysical factors, such as climate, hydrology, oceanography, geomorphology, and geophysical processes, and how these may interact during extreme events. The review also explores how human activities, like catchment interventions, land use changes, and resource exploitation, contribute to coastal flooding. By offering a systematic perspective, this review characterizes the current state of knowledge on these deltaic systems and provides valuable insights for shaping sustainable future trajectories in the face of the challenges posed by coastal flooding.

## 1. Introduction

Deltas are sedimentary depositional features situated where rivers enter permanent bodies of water (Bhatta-charya, 2006; Goodbred & Saito, 2012; Moore & Asquith, 1971; Syvitski et al., 2022). Although their definitions
vary, they are primarily shaped by river discharge, fluvial sediment load, and the dynamic reworking and interplay with marine processes, such as waves, tides, and coastal currents (Goodbred & Saito, 2012), as well as the influence of climate, precipitation, ecology, and intensive human activities (Nicholls et al., 2020). The constant supply of freshwater and nutrient-rich sediments result in diverse ecosystem services, including fertile soils, fisheries and aquaculture. Deltas have historically been foci for human settlement and trade (Paszkowski et al., 2021), the populations of which have adapted to the danger of flooding, the consequences of storm surges, significant tidal events, and excessive rainfall and runoff by learning to live with these hazards.

Nevertheless, increasing human interventions, growing populations and demands for resources, together with pressures from climate change, such as enhanced cyclone activity and relative sea-level rise (SLR), are pushing delta communities beyond their capacity to adapt, leading inexorably to loss and damage. In parallel, irrigation has continued to expand, agriculture has intensified, reflecting the relevance of these lands for large-scale food security (Darby et al., 2015; Nicholls et al., 2018; Schneider & Asch, 2020). Urbanization has accelerated (Santos & Dekker, 2020), and megacities (defined here as having 10+ million inhabitants) have sprung up on coastal lowlands of many deltas, with Bangkok, Kolkata, Dhaka, Shanghai, and Jakarta as examples (Nicholls et al., 2020). Today, it is estimated that between 340 and 500 million people live within deltas globally (Edmonds et al., 2020; Ericson et al., 2006; Scown et al., 2023; Syvitski et al., 2022) and many more in nearby surrounding areas. Ecosystem services and the economic potential that deltas provide, their estimated monetary value ranging from hundreds of billions to trillions of dollars per year (Giosan et al., 2014; Loucks, 2019), are juxtaposed with deltas also being global hotspots of risk, particularly of coastal flooding (Becker et al., 2023; Tessler et al., 2015). Coastal flooding refers to the inundation of land by seawater, which often also coincides with fluvial or pluvial flooding in delta systems (Figure 1).

Examples of high-impact coastal flooding events include Cyclone Bhola in 1970, which hit the Ganges-Brahmaputra-Meghna delta (>300,000 deaths; Frank & Husain, 1971), generating storm surges in excess of 9 m. Cyclone Nargis in 2008 swept across the entire Irrawaddy delta with a storm surge of 5 m and storm waves of an additional 2 m, causing a death toll of 140,000 people (Fritz et al., 2009; Pelling & Dill, 2010; Webster, 2008). More recently in 2020, Cyclone Amphan struck the coasts in West Bengal, India, and Bangladesh, impacting more than 2.6 million people and causing damages of up to US$13 billion, although the longer-term costs are still being calculated. If realized, this would make Amphan the costliest cyclone ever recorded in the North Indian Ocean (ADB, 2022; Khan et al., 2021). Depending on the basin, cyclone activity can coincide with heavy monsoon events that result in inundation from storm surges, from river flooding and breaching of riverbanks, and from heavy rainfall. With the growing threats of climate change and anthropogenic interventions, processes such as SLR, land subsidence, shoreline retreat and erosion, sediment starvation, and rapidly growing populations with increasing demands (Scown et al., 2023) are likely to exacerbate the exposure of people and assets to flood risks in deltas (Fanchette, 2022; IPCC, 2019).

This situation is especially relevant in South and Southeast Asian deltas, where 45% of the global deltaic population lives (Edmonds et al., 2020; Tellman et al., 2021). Five deltas in particular, namely the Ganges-Brahmaputra-Meghna, the Irrawaddy, the Chao Phraya, the Mekong, and the Red river deltas, are the rice bowls for most Asian countries (Schneider & Asch, 2020) and have diverse socio-economic settings, provide a variety of ecosystem services, and have all been highlighted as some of the most flood-prone and climate-vulnerable areas in the world (Scown et al., 2023).

In these five Asian deltas, ambitious socio-economic development objectives are leading to rapid changes in areas that are vulnerable to natural hazards (Arto et al., 2019), and where a solid understanding of the complexity of coastal flood risk dynamics is still lacking. Our ability to understand these dynamics is hampered by: (a) a lack of in situ measurements and difficulties in instrumenting observational sites; (b) a lack of integrated approaches in assessing these systems; and (c) complex interactions over a wide range of spatial and temporal scales. While significant funding and scientific attention have been allocated to the five deltas, a suite of best practices for using available information to conduct impact assessments of coastal flood hazards remains to be developed.

Traditional approaches to study coastal flooding in deltas generally focus on specific physical processes and rarely on their interactions, while neglecting longer-term and cumulative effects, uncertainties, human behavior and management, and ecosystem services (Figure 1). Additionally, approaches are often sectoral and fragmented, lacking an integrated and interdisciplinary perspective. Such issues become important when
<figure><img src="image_5.png" /><figcaption>**Figure 1.** Conceptual framework of this review showing the interactions of geophysical drivers and human pressures in the context of flooding in deltaic systems. Numbers in italics refer to the relevant sections of this review.</figcaption></figure>

these estimates (SLR, subsidence, flooded areas, etc.) are used as a basis for guiding adaptation strategies to
coastal flood hazards. Thus, it is essential to get a better understanding of what processes, interactions, and
impacts have been quantified, how, and across which spatial and temporal scales. Data collection, pro-
cessing and analysis capabilities are improving at an impressive rate. The increasing quantity and quality of
in situ observations, alongside the accessibility of remote sensing information and ever-growing model
capabilities, is vast but also becoming ever more complex to apply. A clear understanding of what
information can be used to quantify distinct deltaic processes and their interactions is lacking. Our review aims to provide a state-of-the-art understanding and define data and modeling requirements for addressing urgent scientific and policy-relevant questions in the five selected Asian megadeltas. Although the focus of this review is on the Ganges-Brahmaputra-Meghna, Irrawaddy, Chao Phraya, Mekong, and Red River deltas, the quantification of deltaic processes and interactions discussed herein is relevant to other major deltas in Asia (e.g., Yangtze, Yellow, Pearl) and beyond.

## 2. The Five Asian Megadeltas

### 2.1. Ganges-Brahmaputra-Meghna Delta

The Ganges-Brahmaputra-Meghna Delta is the largest delta in the world, 92,000 km² in size (Figure 2), and is formed by the Ganges, Brahmaputra, and Meghna Rivers that flow into the Bay of Bengal. The drainage basin covers 1.7 million km² that includes India, Nepal, China, Bhutan, and Bangladesh (Allison, 1998; Brown & Nicholls, 2015). For the Ganges, modern sediment fluxes (~300–450 Mt/yr) are similar to long-term averages over the Holocene (~308–426 Mt/yr), while sediment load in the Brahmaputra shows much higher variability (~500–650 Mt/yr at present compared to ~377–1119 Mt/yr over the Holocene) and reflects the river's sensitivity to changes in monsoon intensity and related sediment availability (Raff et al., 2023 and references therein). Goodbred and Kuehl (1998) estimated that about a third of this overall sediment flux is sequestered on the delta plains and the remainder is deposited on the delta shelf or transported to the Bengal fan. Human activities in the form of river diversions and dams upstream, as well as significant interventions within the delta itself (e.g., poldering), have recently led to substantial changes in river and sediment flows and transport capacities, impacting the overall geomorphic stability of parts of the delta (Paszkowski et al., 2021 and references therein).

Currently, approximately 147 million people live on the deltaic floodplains, with highest concentrations found in the megacities of Dhaka and Kolkata. Besides urban areas, the land is primarily used for agricultural production of irrigated and rain-fed crops, accompanied by rural settlements (Brown & Nicholls, 2015; Hoque et al., 2019). The deltaic landscape was tremendously altered during the 1960s to the 1980s, when vast areas were embanked to protect agricultural lands from flooding (R. Rahman & Salehin, 2013).

### 2.2. Irrawaddy (Ayeyarwady) Delta

The Irrawaddy River is a north-south-oriented fluvial system, originating from tributaries from the Himalaya, Shan Highland, Rakhine Mountains, and the Central Dry Zone in Myanmar (Figure 2). The river drains more than 60% of Myanmar (i.e., >421,000 km²; Figure 2; Brakenridge et al., 2017) before branching into distributaries, forming a delta of approximately 33,000 km² in size, and debouching into the Andaman Sea. Sediment supply to the delta is estimated to be between 260 and 360 Mt/yr (Baronas et al., 2020; Milliman & Syvitski, 1992; Robinson et al., 2007). Detailed sediment discharges reveal that 20%–60% of the sediment is transported to the shelf during the monsoon season, where accumulation rates vary between 1 and 10 cm/yr (Glover et al., 2021; Kuehl et al., 2019).

The delta is home to approximately 13 million people, of which more than 5 million are based in Yangon City. Agricultural areas have expanded and intensified, by shifting from dry to irrigated crops, and together with increasing aquacultural production and urban growth, drove deforestation and mangrove loss, decreasing in surface area from 72% (1972) to 2% (Thi et al., 2012; Vogel et al., 2022). While deforestation in the catchment has raised fine sediment supply in the delta (Anthony et al., 2019), and the delta shoreline has been relatively stable for the last ~150 years (Hedley et al., 2010), upstream dam construction, sand mining, and dredging has reduced coarse sediment supply, exacerbating erosion and undermining the balance of the delta in the face of SLR (Anthony et al., 2019; D. Chen et al., 2020).

### 2.3. Chao Phraya Delta

Fed by four tributaries from northern Thailand, the Chao Phraya River catchment is 164,000 km² and discharges into the Gulf of Thailand (Figure 2; Sayama et al., 2015; Sinsakul, 2000). The flux of suspended sediment to the delta is around 1.5 Mt/yr (Park et al., 2021). This is much lower than that of the other Asian deltas of this study
<figure><img src="image_6.png" /><figcaption>**Figure 2.** Setting of the five Asian deltas reviewed in this study. Elevation for river basins (a) and the deltas (b) are shown based on FABDEM relative to EGM2008 (Hawker et al., 2022). Population data for each delta by elevation (c) was derived from the WorldPop 2020 data set (WorldPop, 2018). The size of the circles reflects the overall population of the deltas. The delta polygons are based on delta extents provided by Tesslerer et al. (2015). The river network is derived from the HydroSHEDS data set (https://www.hydrosheds.org/).</figcaption></figure>
that are fed by high rates of mountain erosion. The sequestration of a large part of the river sediment in upstream floodplains also limits the sediment influx to the delta, making it even more fragile to changing dynamics taking place.

In contrast to other deltas considered in this review, the Chao Phraya Delta is highly urbanized, with more than 11 million people living in the megacity of Bangkok (World Population Review, 2023). Palaeoenvironmental reconstructions in Bangkok indicate first intense agricultural practices occurring around 1660 CE, which were accompanied by the expansion of urban, industrial, and aquacultural activities during the 20th century (Pun-wong et al., 2023), thereby replacing agricultural, shrub, and bare lands (Hara et al., 2005; Thi et al., 2012). Although these areas are relevant for flood retention, conversion into built-up/industrial areas has led to increased flood exposure and vulnerability (Carpenter, 2012; Likitswat & Sahavacharin, 2023; Thi et al., 2012; Van Oldenborgh et al., 2012). Dam construction on the tributaries of the Chao Phraya River reduced sediment load from 30 Mt/yr before 1965 to less than 5 Mt/yr by the 1990s (Winterwerp et al., 2005), which, when combined with excessive groundwater withdrawal, has resulted in widespread and significant subsidence in the delta (Aobpaet et al., 2013).

## 2.4. Mekong Delta

Originating as the Lancang River in the Tibetan Plateau of China, the Mekong River stretches over approximately 5,000 km until it enters the South China Sea in Vietnam, thereby covering an area of 810,000 km² (Mekong River Commission, 2019) across China, Myanmar, Laos, Thailand, Cambodia and Vietnam (Figure 2; Mallick, 2022). The 50,000 km² Mekong Delta (Tessler et al., 2015) forms the third largest delta plain in the world (Anthony et al., 2015), and is located in Cambodia and southern Vietnam (Figure 2). The delta receives around 37 Mt/yr of sediment, based on measurements from 2009 to 2016 (Thi Ha et al., 2018). This is exceptionally low compared to the other mountain-source basins and is linked to major damming at several points in the river basin. The pre-dam flux of suspended sediment to the delta was above 150 Mt/yr for the period 1986–1993, illustrating the profound impact that dams have had on sediment delivery to the delta by 2016 (Thi Ha et al., 2018; Walling, 2009).

The Mekong Delta is home to approximately 18 million people (up to 25 million when adjacent Ho Chi Minh city is considered; Figure 2) and both its landscape and morphodynamics have been significantly altered over time. Since Vietnam's transition to a market economy in 1986, the delta has undergone vast agricultural intensification, urbanization and industrialization. With initial focus on rice cultivation, other land uses like aquaculture, cash crops, urban expansion and industrial intensification have strongly increased in the 21st century (Minderhoud et al., 2018).

## 2.5. Red (Hong) River Delta

The 6,000 km² Red River Delta in northern Vietnam constitutes the smallest of the five Southeast Asian deltas studied herein, although its drainage basin is of similar size to the Chao Phraya Delta (i.e., around 164,000 km²; Figure 2; Ve et al., 2021), and covers parts of China, northern Vietnam and small areas of Laos (Figure 2). The Red River originates in the mountains of the Yunnan province in southern China and is mainly fed by the Da, Lo, and Thao rivers, sourced in the Chinese uplands (T. H. Dang et al., 2010).

Currently, the delta is home to more than 6 million people. The Red River Delta has become the most important region for economic activities in northern Vietnam (Mai et al., 2009) and records some of the highest land conversion rates in the country, mainly from agriculture to urban (Thien et al., 2023; Tuan, 2022). In addition, agriculture highly depends on hydraulic infrastructure (such as dikes, sluice gates, polders and irrigation systems, including pumping stations) to protect against saline intrusion, and fertilizers to maintain soil fertility (M. T. Nguyen et al., 2019 and references therein). Although water regulation due to dam construction along the Red River and its tributaries caused substantial changes in freshwater discharge and sediment supply (Gao et al., 2015), only one of five dams was responsible for reducing sediment flux by more than 60% (Ve et al., 2021). In contrast, deforestation and other land-use-land-cover changes increased sediment load in the river system and, together with land reclamation projects in the intertidal zone, contributed to a new coastal morphodynamic equilibrium (Ve et al., 2021).
# 3. Recent Advances in Understanding and Quantifying the Drivers of Coastal Flood Hazards in the Five Deltas

## 3.1. Main Geophysical Drivers

The complexity of interactions in the five Asian deltas merits unpicking the driving forces behind those interactions, and how they can be measured and quantified. This section delves into the predominant geophysical drivers shaping the Asian megadeltas, with key messages summarized in Table 1.

### 3.1.1. The Monsoon and River Discharge

The Asian megadelta region experiences a tropical monsoon climate, characterized by heavy rainfall, often in the form of intense downpours. This leads to seasonal increases in river discharge, can overwhelm the capacity of rivers and drainage channels, and can lead to waterlogging in low-lying deltaic plains. The Asian monsoon system comprises two distinct subsystems: the South Asian (or Indian) monsoon system and the East Asian monsoon system. The seasonal variation in river discharge (Figure 3a) of the Ganges-Brahmaputra-Meghna (annual average: ~40,000 m³/s, Papa et al., 2010) and Irrawaddy (~14,000 m³/s, Milliman & Haq, 1996) systems are subject to glacial and snowmelt from the Himalaya and the Tibetan Plateau in the fall, winter and spring, while the Indian monsoon precipitation is the major contributor to summer flow (Chowdhury & Ward, 2004; Jian et al., 2009; M. P. Rao et al., 2020; Shaman et al., 2005). The annual flood pulse resulting from a seasonal monsoon climate is also the main characteristic of river discharge in the Mekong (annual average ~13,500 m³/s, Räsänen et al., 2016), Chao Phraya (890 m³/s, G. S. Nguyen et al., 2022) and the Red River (~4,300 m³/s, Luu et al., 2010), with distinct low flows in December-May and high flows in June-November.

Studies on river discharge often rely on in situ observations and measurements (Fekete et al., 2015) obtained from gauging networks that have been installed for several decades. For the Ganges-Brahmaputra-Meghna system, hydrological observations across Bangladesh and the delta have been collected by the Bangladesh Water Development Board and the Bangladesh Inland Water Transport Authority since the early 1920s (www.bwdb.gov.bd/; https://biwta.gov.bd/), but public access to these records remains limited (Jian et al., 2009; Papa et al., 2010), which is also the case for India. Similarly, for the Irrawaddy (Furuichi et al., 2009), the Mekong (Binh et al., 2020), Red River (Vinh et al., 2014) and Chao Phraya (Park et al., 2021), several institutional sources provide in situ water level and discharge information but the time series can be irregular, incomplete, not up-to-date, and/or not freely accessible, despite being published in the scientific literature.

Satellite altimetry-derived discharge time series are available over the Ganges and Brahmaputra rivers, as well as their combined discharge before entering the delta for the period 1993–2022 (monthly basis) and for 2008–2022 (10-day basis) (Papa et al., 2010, 2012). A similar approach was applied in the Irrawaddy River system (Frappart et al., 2015), for which an altimetry-derived discharge time series from 1993 provides complementary information to the traditional gauge data available on the Global Runoff Data Center (GRDC) website, as well as the Mekong River (Birkinshaw et al., 2010), for which a long-term and continuous record (1993–2022) is available (Biancamaria et al., 2017). Over the Mekong, river discharges from satellites are tested at various locations across the basin (Kim et al., 2019). For the Red River and Chao Phraya, no such satellite-derived methodology has yet been applied to estimate discharge, despite the availability of new estimates covering rivers worldwide (Belloni et al., 2021).

Other satellite techniques, such as imagery observations in the visible or infra-red (Landsat, Moderate Resolution Imaging Spectroradiometer (MODIS), Sentinel-2), or from passive microwave imagery (Kettner et al., 2021, https://floodobservatory.colorado.edu/technical.html), are also currently being used to estimate discharge, based on rating curves paired with river width variations and in situ or modeled discharge data (Riggs et al., 2023; Tarpanelli et al., 2013, 2019). Elmi et al. (2024) combined satellite imagery- and altimetry-derived river width and water height observations to extend the monthly discharge time series for inactive GRDC stations globally.

The launch of the Surface Water Ocean Topography (SWOT, Fu et al., 2024) satellite mission in December 2022 promises to add substantially to our ability to understand water level and discharge variations in megadeltas. SWOT is an imaging altimeter that produces near-complete spatial data on water level, river slope, and inundation using Ka-band radar interferometry at high resolution (Biancamaria et al., 2016). The mission science requirements specify a water height accuracy of <10 cm and slope accuracy of 1.7 cm/km when averaging over water areas greater than 1 km², and the nominal orbit will result in repeat measurements every 10.5 days
<table><caption>**Table 1**

Geophysical Drivers Overview: What We Know, What Gaps Remain, and the Key Challenges in Coastal Flood Assessments in the Asian Megadeltas</caption><thead><tr><th>What we know</th><th>Gaps in terms of observations and/or models</th><th>Key challenges to be addressed in the context of Asian megadeltas</th></tr></thead><tbody><tr><td><strong>3.1.1 Monsoon and river discharge</strong><br>Coastal flooding poses significant challenges as it often coincides with pluvial and fluvial flooding due to seasonal increases in discharge, which can exceed the capacity of rivers and drainage channels and cause waterlogging</td><td><ul><li><strong>Limited long-term records:</strong> The limited and incomplete record of long-term discharge observations make it challenging to observe past discharge trends</li><li><strong>Lack of recent and up-to-date data:</strong> The availability of recent and up-to-date time series data on river discharge is limited, making it challenging to assess current conditions and trends accurately</li><li><strong>Limited availability of simultaneous in-situ and altimetry data:</strong> In the Red River and Irrawaddy River, the application of satellite altimetry data to in situ observations has not yet been done, which limits the spatial and temporal understanding of discharge trends in these deltas</li><li><strong>Incomplete gauge coverage within deltas:</strong> Within deltas, there is incomplete gauge coverage of contributory channels, which are important for accurately estimating river discharge. This can result in uncertainties when assessing the overall river discharge and when setting up hydrodynamic models for deltas</li><li><strong>Difficulty in observing channel bathymetry:</strong> Current satellite systems face difficulties in directly observing channel bathymetry, which is necessary for understanding river dynamics and accurately estimating river discharge</li></ul></td><td><ul><li><strong>Identify and address the primary challenges in obtaining comprehensive discharge data for coastal flood analysis</strong> by conducting research to improve methods, data sharing protocols, and monitoring systems</li><li><strong>Investigate how the lack of data from rivers affects coastal flood modelling and prediction accuracy.</strong> Develop strategies to mitigate these impacts, such as incorporating alternative data sources or developing statistical models to estimate missing data</li><li><strong>Apply existing approaches of combining in situ and satellite altimetry data to estimate discharge in the Red River and Irrawaddy River systems.</strong> Incorporate lessons from other deltas</li><li><strong>Overcome altimetry limited spatial coverage and temporal sampling challenges</strong>, particularly in the context of the SWOT mission. More resources need to be allocated to increase in situ data collection and monitoring systems in order to ground-truth global data sets</li><li><strong>Enhance our understanding and modelling of the complex interactions between backwater effects, river flows, tides, and storm surges</strong> that contribute to coastal flooding in megadeltas, developing integrated modeling approaches</li></ul></td></tr><tr><td><strong>3.1.2 Tides</strong><br>Tides are a major driver of coastal flooding in Asian deltas, interacting with factors such as river discharge, topographic and bathymetric changes, SLR, and storm surges. This interaction greatly influences the region's coastal processes and flood dynamics</td><td><ul><li><strong>Limited observational data:</strong> The scarcity of coastal tide gauges and the challenges associated with obtaining accurate tide measurements from coastal altimetry data impede our understanding of tidal patterns and their contribution to coastal flooding</li><li><strong>Difficulty in observing nearshore bathymetry:</strong> Due to observational challenges, the nearshore bathymetry is often not well monitored. Global bathymetric data used in modeling often exhibit biases in shallow deltaic coastlines, which significantly impacts models' ability to capture tidal fluctuations, and thus coastal flooding</li><li><strong>Gap in current flood hazard assessment approaches:</strong> Neglecting the interactions between tides and other factors in modeling and analyses can lead to an over- or underestimation of extreme coastal flood impacts</li></ul></td><td><ul><li><strong>Assess whether the impact of tides on coastal flooding in the Asian megadeltas has been over- or underestimated</strong> by studying the tide-surge interactions</li><li><strong>Investigate how climate change-induced SLR amplifies the influence of tides</strong>, leading to increased risks of coastal flooding in Asian megadeltas</li><li><strong>Allocate resources to deploy more tide gauges</strong>, in order to ground-truth against advancing altimetry technologies (SWOT) and reprocessing data with improved algorithms</li><li><strong>Explore the application of remote sensing and numerical modelling to improve the observation of bathymetry and topography</strong> over nearshore areas of the Asian megadeltas</li><li><strong>Bridge the gap between the coastal ocean, estuaries and tidal channels through cross-scale numerical modelling to account for all relevant interactions with tides.</strong> This requires improved model accuracy, validated results with field observations, and the incorporation of local knowledge</li></ul></td></tr><tr><td><strong>3.1.3 Tropical cyclones, storm surges and wind-waves</strong><br>The Asian megadeltas are exposed to various intensities of tropical cyclones, ranging from frequent and intense storms in the Red River and Ganges-Brahmaputra-Meghna deltas, to fewer but more intense cyclones in the Irrawaddy delta,</td><td><ul><li><strong>Limited in-situ data availability:</strong> Similar to the limitations faced in tide and discharge observations, challenges exist for observations of storm surges and waves. Similarly, the lack of nearshore topographic-bathymetric observation impedes storm surge</li></ul></td><td><ul><li><strong>Collect and validate high-quality data on cyclones, surges and waves in the Asian megadeltas to improve the accuracy and reliability of hydrodynamic models</strong>, deploying additional</li></ul></td></tr></tbody></table>
<table><caption>**Table 1**
*Continued*</caption><tr><td>as well as tropical storms and depressions in the Mekong and Chao Phraya deltas, respectively</td><td>modeling and a comprehensive understanding of coastal flood extents and magnitudes<br/>• Satellite altimetry as a tool to observe storm surges: Although satellite altimetry has shown success in observing storm surges in the Bay of Bengal, its use is not common due to sparse spatial and temporal sampling<br/>• Coupled tide-surge-wave model: Coupled tide-surge-wave models are necessary for proper simulation of storm surges in shallow areas, but often not used in application. Modeling of compound flooding, coming from storm surges and associated extreme rainfall, is also missing<br/>• Effective approach to monitor extreme water-levels: Utilizing an altimetry multi-mission approach has proven effective in monitoring water level extremes in the Mekong Delta</td><td>monitoring systems and utilizing advanced remote sensing technologies<br/>• Identify and incorporate the key parameters and interactions that are crucial for accurately simulating cyclone and wave behaviour in the Asian megadeltas into hydrodynamic models (such as tidal dynamics, sediment transport, coastal morphology, and local bathymetry)<br/>• Conduct comprehensive studies to evaluate the potential impacts of climate change on cyclone frequency, intensity, and resulting coastal flooding in Asian megadeltas<br/>• Explore the potential of satellite missions such as CFOSAT, SWOT, Sentinel-1/2/3/6, and PACE platforms to enhance coastal flooding monitoring capabilities</td></tr><tr><td>3.1.4 Sea-level change<br/>Over 1993–2023, altimetry data shows absolute SLR ranging between 3.5 and 5 mm/yr along most coastal areas of the five deltas</td><td>• Limited in-situ data availability: Limited data poses challenges in observing sea-level changes, which affects our understanding of the baseline water level prior to extreme events</td><td>• Establish a comprehensive network of sea and water level monitoring stations in the Asian megadeltas<br/>• Foster collaboration among countries and institutions in the region to share sea/water level data and collaborate on data collection and analysis<br/>• Integrate relative SLR into coastal modelling systems to improve the accuracy of coastal flood prediction<br/>• Improve advancements in data processing algorithms and calibration/validation techniques to enhance the accuracy and resolution of coastal altimetry measurements</td></tr><tr><td>3.1.5 Vertical land motion and land elevation dynamics<br/>Land subsidence in the Asian megadeltas can exceed global or regional sea-level rise rates VLM in the Asian megadeltas is a complex response to various processes occurring at different temporal and spatial scales Vertical errors and artifacts in global DEMs significantly undermine the reliability and precision of SLR and coastal flood exposure assessments in the Asian megadeltas</td><td>• Sparse or no coverage of GNSS sites and RSET-MH set-up, limiting the ability to measure subsidence and/or land aggradation. GNSS has the potential to provide valuable insights, as is the case for the Mekong Delta and the Chao Phraya River Delta, which have good GNSS coverage but inaccessible data<br/>• Limited availability of extensive modelling studies beyond 1D calculations in the Asian megadeltas due to the requirement of detailed local subsurface data and a complex setup<br/>• Improvement of global DEMs: Studies have utilized machine learning algorithms and satellite LiDAR data to enhance older published DEMs, yet the vertical reference of elevation data to local sea-level remains underrepresented and needs further attention<br/>• Artiefacts and discrepancies in global DEMs: Global DEMs may differ substantially from local elevation data, resulting in height residuals caused by limited training data. The coarse spatial resolution of available high-accuracy data in the Asian megadeltas often challenges its use for flood risk assessments</td><td>• Increase the availability of GNSS data in flood-prone areas of Asian megadeltas to better understand VLM's impact on coastal flooding<br/>• Facilitate the sharing of VLM data among researchers, institutions, and government agencies through open-access platforms and collaborative agreements<br/>• Develop standardisedstandardized guidelines for documenting and reporting VLM rates to ensure consistent and reliable data collection across studies and localities<br/>• Encourage researchers to thoroughly document and justify the selection of VLM rates, providing clear explanations of data sources, methodologies, and assumptions made<br/>• Encourage researchers to collaborate and share insights to enhance modelling studies beyond 1D calculations, integrating detailed local subsurface data<br/>• Address the dynamic nature of topographic elevations in deltas by regularly updating DEMs to ensure the improvement of elevation-informed coastal flood hazard assessments</td></tr></table>
<caption>**Table 1**
*Continued*</caption>

**3.1.6 Sediment, shoreline and deltaic landform changes**

Sediment fluxes vary significantly among Asian megadeltas. Ganges, Brahmaputra, and Irrawaddy rivers carry substantial sediment loads, while the Mekong, Red River, and Chao-Praya have reduced fluxes due to human activities

The Mekong Delta coastline experiences the most significant erosion rates, while the Irrawaddy Delta also undergoes erosion in substantial portions. Coastal erosion rates in Chao Phraya and the Red River deltas are comparatively lower. In the Ganges-Brahmaputra-Meghna Delta, shoreline progradation continues to dominate

* **Limited in-situ data availability:** Sediment fluxes to and from deltas remain approximate due to challenges in maintaining long-term surveys, political restrictions on data display, and technical difficulties, particularly in tidal zones of the deltas

* **Erosion dynamics using satellite imagery:** Utilizing satellite imagery to analyse erosion dynamics can be laborious and often requires manual delineation to detect changes over time, posing challenges for comprehensive analysis

* **Limited ground-truthing potential:** Due to the limited in situ data on sediment dynamics and erosion and accretion processes, ground-truthing of satellite-derived change detection is a challenge

* **Allocate resources to deploy more sediment gauges across the five Asian megadeltas** to better understand the changing patterns of sediment flux to the deltas

* **Facilitate the sharing of sediment data among researchers, institutions, and government agencies** through open-access platforms and collaborative agreements, particularly for trans-boundary basins

(Fu et al., 2024). A non-sun synchronous orbit with a steep inclination (78°) was chosen for the SWOT mission, as such an orbit minimizes tidal aliasing and ensures that over the mission lifetime of at least 3 years, the full spectrum of tidal states at a given site can be sampled. The accuracy, spatial resolution, coverage and sampling frequency of the measurements will allow river discharge to be inferred in the absence of ground gauge data for rivers wider than 100 m (Durand et al., 2023).

**3.1.2. Tides**

The Asian megadeltas are exposed to a range of tidal fluctuations with diverse tidal characteristics (Figure 3b). The tides in the Ganges-Brahmaputra-Meghna and Irrawaddy deltas are semidiurnal (Krien et al., 2016; Sindhu & Unnikrishnan, 2013). The Mekong Delta experiences a mixed semidiurnal/mixed diurnal regime. Along the Chao Phraya and Red River deltas, the tide is more distorted with a diurnal regime. In terms of tidal amplitude, the Irrawaddy coastline experiences the largest tidal range among the Asian megadeltas, where amplitudes can exceed 5 m (P. S. Rao et al., 2005). A similar, albeit a bit smaller, tidal range is observed along the Ganges-Brahmaputra-Meghna delta coastline. The mean tidal range along the Chao Phraya and Red River deltas is relatively lower, at around 2 m (Figure 3b). The vast seasonal river discharges of the Mekong, Ganges, and Brahmaputra rivers (Section 3.1.1, Figure 3a) can lead to compounding impacts. As the tide propagates along the estuary inside the delta, it interacts with the narrowing river coastline and the discharge of the rivers (Khan et al., 2020; Rodrigues do Amaral et al., 2023). In the Ganges-Brahmaputra-Meghna and the Irrawaddy deltas, tides are observed to propagate approximately 200–300 km inland (Figure S1 in Supporting Information S1; Hedley et al., 2010; Khan et al., 2020) and in the Mekong Delta, tides could reach distances of 200–300 km upstream (T. C. Nguyen et al., 2023). During the wet season, the tide can substantially contribute to the estuarine and riverine water levels and contribute to coastal flood hazards.

Seasonal sea-level variation causes measurable changes in tides. For example, the M2 tidal constituent at the mouth of the Ganges-Brahmaputra-Meghna increases by about 7 cm during the monsoon season (Tazkia et al., 2017) and it is also significantly modulated in response to sea-level changes at interannual and longer time scales (Rose et al., 2022). Consequently, fluvial flooding during the wet season experiences a stronger backwater effect, potentially exacerbating fluvio-tidal coastal flooding (Mirza, 2003). Conversely, during the dry season, reduced river flows allow the tide to push the salt wedge further inland, which leads to saltwater intrusion into deltaic coastal areas (Akter et al., 2020; Sherin et al., 2020). This problem is exacerbated due to tidal amplification along the river/estuary (Eslami et al., 2019; Khan et al., 2020). Such amplification is supercharged by man-made control structures (such as dikes and polders), which force the tide to propagate further upstream with higher amplitudes (van Maren et al., 2023).

In addition, in macro-tidal regions with large continental shelves, like the Ganges-Brahmaputra-Meghna and Irrawaddy deltas, a significant contribution from tide-surge interaction is expected (Horsburgh & Wilson, 2007; Idier et al., 2019). Such interactions are often neglected in modeling and analyses (e.g., Wood et al., 2023), which
<figure><img src="image_7.png" /><figcaption>b) Mean tidal range along the coast (metres)</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>c) Tropical cyclone categories over 1980-2023</figcaption></figure>

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /><figcaption>d) Tropical cyclone monthly frequency over 1980-2023</figcaption></figure>

<figure><img src="image_11.png" /></figure>

**Figure 3.** Summary of geophysical drivers of coastal flood risk in the Asian megadeltas: (a) monthly river discharge in the major rivers ±1 standard deviation (shaded areas), based on altimetry (Papa et al., 2010, 2012); (b) Mean tidal range (meters) from the open ocean to the coast of the deltas, excluding the river channels, as modeled in the FES2014 tidal atlas (Lyard et al., 2021); (c) spatial distribution of tropical cyclones passing through a 100 km radius of each of the deltas, based on IBTrACS data from 1980 to 2023 (Knapp et al., 2010, 2018); (d) monthly tropical cyclone frequency. Note: the discharge plots in (a) exclude the Red River discharge as this information is not publicly available. In addition, the Ganges and Brahmaputra rivers have been separated for legibility and identification of seasonal variation within each river. The legend items in (c) represent the tropical cyclone category based on the Saffir-Simpson scale. The river network is derived from the HydroSHEDS data set (https://www.hydrosheds.org/).
can lead to overestimations of extreme flood impacts (Arns et al., 2020). Climate-change induced rising mean sea-levels further exacerbate the impact of tides in these deltas. It is also shown that in a resonant estuary, like the Ganges-Brahmaputra-Meghna, tidal range amplifies in par with SLR; a 1 m SLR may change mean high water by twice as much in the estuary (Khan et al., 2020; Rose & Bhaskaran, 2022).

Tidal fluctuations and trends are typically observed via tide gauges, recording tidal patterns at high frequency (typically hourly or less). Similar to discharge data (Section 3.1.1), only a few coastal tide gauges, in many cases only one or two stations per delta, provide publicly available information across the Asian megadeltas (Haigh et al., 2023). In order to advance our understanding of past and future tidal trends, coastal ocean circulation models have become the de facto tools (Krien et al., 2016; Sindhu & Unnikrishnan, 2013), also measuring salinity propagation (Bricheno & Wolf, 2018), storm surges (Khan et al., 2021; Krien et al., 2017; Tasnim et al., 2015; Wood et al., 2023; Section 3.1.3), and coastal flooding (Khan et al., 2021, 2022). The main challenge that remains is obtaining a truthful representation of the bathymetry, particularly at the nearshore shallower regions (Krien et al., 2016). For example, the development of dedicated regional bathymetry over the northern Bay of Bengal revealed a strong shallow bias (2 and 3 m) in the global bathymetric data, and led to a 3–5 fold improvement compared to the global tidal atlas (Krien et al., 2016, 2019).

The utilization of satellite-based high-resolution optical imagery with wave-based techniques enables the estimation of coastal bathymetry in regions characterized by fluctuating water turbidity and suspended sediments, as these techniques rely on shallow-water propagation over a flat bottom (Almar et al., 2021; Cesbron et al., 2021; Daly et al., 2022). This approach offers promising opportunities for studying the Asian megadeltas. Emerging new techniques, such as satellite-derived bathymetry leveraging deep learning algorithms, as demonstrated by Sagawa et al. (2019) and Al Najar et al. (2023), hold significant potential in deriving a better bathymetry. In addition, future space missions, like the Constellation Optique 3D mission (https://co3d.cnes.fr/, to be launched in 2025) are expected to provide global coverage of stereoscopic data with 50 cm resolution.

### 3.1.3. Tropical Cyclones, Storm Surges, and Wind-Waves

Tropical cyclones are termed as cyclones in the Northern Indian Ocean, including the Bay of Bengal, and as typhoons in the West Pacific, including in the South China Sea. Figure 3c illustrates the historical tropical cyclone tracks passing within 100 km of each Asian megadelta and their along-track intensity as reported in the International Best Track Archive for Climate Stewardship data set over the period 1980–2023 (Knapp et al., 2010, 2018). The largest number of tropical cyclones are recorded over the Red River Delta (67 cyclones, 1.56 cyclones/year) and Ganges-Brahmaputra-Meghna Delta (42 cyclones, 0.98 cyclones/year). However, the Ganges-Brahmaputra-Meghna Delta shows much more propensity to be hit by a tropical cyclone (Category-1 or higher on the Saffir Simpson Hurricane Wind Scale, wind speeds >118 kmph, https://www.nhc.noaa.gov/aboutsshws.php). In the Irrawaddy Delta, only a few cyclones have reported to make landfall (9 cyclones in 43 years), but half of the landfalling cyclones are of Category-1 or higher. The cyclones that have passed the Mekong Delta are of tropical storm strength (maximum wind speeds between 63 and 118 kmph), and all the cyclones through the Chao Phraya Delta are of tropical depression strength (maximum wind speeds not exceeding 62 kmph).

Waves also play a significant role in coastal flooding in the Asian megadeltas, contributing to the severity and extent of inundation events (Laignel et al., 2023). Waves can lead to increased overtopping of coastal defenses, such as seawalls and embankments, allowing floodwaters to penetrate further inland during storm surge events. Waves can also erode coastal landforms and structures, weakening natural and man-made defenses against flooding (Anthony, 2015; Woodroffe et al., 2006). Given the complex interplay between waves, tides, storm surges, and river discharge in the Asian megadeltas, understanding the role of waves in coastal flooding is crucial for effective disaster management and adaptation strategies.

Observations of storm surges and waves are limited by in situ data scarcity and public accessibility. As is the case for river discharge (Section 3.1.1) and tides (Section 3.1.2), satellite altimetry has emerged as a valuable tool for observing water level extremes and storm surges in coastal regions, although with the same limitations as discussed above. Boergens et al. (2019) highlight the benefits of utilizing long-repeat orbit missions in a multi-mission approach to monitor water level extremes in the Mekong River Basin, showcasing the effectiveness of satellite altimetry for hydrological research. Similarly, Antony et al. (2014) demonstrate the utility of satellite altimetry in observing storm surges in the Bay of Bengal, providing valuable insights for disaster management
and coastal protection strategies. Khan et al. (2021) present a case study focusing on Supercyclone Amphan in the Bay of Bengal, emphasizing the role of satellite observations, including altimetry, in enhancing storm surge and inundation forecasting systems in the Ganges-Brahmaputra-Meghna Delta. Looking ahead, satellite missions, such as the China France Oceanography Satellite (CFOSAT, Hauser et al., 2021), SWOT, or Sentinel-1 radar and optical Sentinel-2, and the Plankton, Aerosol, Cloud, Ocean Ecosystem mission (https://pace.gsfc.nasa.gov/), offer further potential for advancing our understanding of coastal dynamics and enhancing our ability to monitor and manage coastal hazards effectively.

### 3.1.4. Sea-Level Change

Along coastlines, relative SLR (i.e., sea level rise with respect to the Earth's surface) is most critical. Relative SLR results from the combination of geocentric (or absolute) sea level changes and vertical land motion, which is driven by both natural processes and human pressures (see Section 3.1.5). In deltaic areas, anthropogenic subsidence amplifies climate-induced mean SLR and often becomes the dominant mechanism of increasing relative sea level (Fox-Kemper, 2021). This increase in relative sea level exacerbates coastal flood risks, as the potential for inundation during high tides, storm surges, and extreme weather events is heightened.

The global average mean SLR has been estimated at 1.7 mm/year since 1900 (Fox-Kemper, 2021) and has accelerated since the 1970s (Dangendorf et al., 2019), with ongoing rates exceeding 3.6 mm/year during the last 30 years, as observed by satellite altimetry (http://www.aviso.altimetry.fr/). There is an unequivocal human fingerprint in global SLR (e.g., Becker et al., 2014; Marcos et al., 2017). Rates of sea level change vary regionally, mostly because of uneven thermal expansion and heat redistribution within the ocean (e.g., Hamlington et al., 2020). In the wide ocean region of the Asian megadeltas, sea level has risen by 15 cm since the early 20th century (i.e., average rate of 1.1 mm/yr), according to the mean sea level reconstruction by Dangendorf et al. (2019). After 1970, the rate of mean sea level rise in the region has accelerated by up to 2 mm/yr. Over the altimetric period (since 1993), linear mean sea level trends calculated from satellite altimetry observations display spatial patterns linked to regional processes, with rates ranging between 3.5 and 5 mm/yr along most of the coastal areas of the five deltas (Figure S2 in Supporting Information S1) (Cazenave & Moreira, 2022). The rates of SLR in the open ocean contrast with those derived from coastal tide gauges (Becker et al., 2019). For example, in the Ganges-Brahmaputra-Meghna delta, rates of relative SLR from in situ measurements exceed 3 mm/yr since 1970 (Becker et al., 2020). Similarly, Pham et al. (2024) found significantly higher relative SLR in the Mekong Delta calculated from tide gauge records along the Vietnamese coastline than in the open ocean, for the altimetry period. These differences highlight the major role of land subsidence in deltas, as well as the differences between the coastal region and the open ocean.

### 3.1.5. Vertical Land Motion and Land Elevation Dynamics

Vertical land motion (VLM) is the upward or downward motion of a land surface horizon relative to a fixed datum. Coastal subsidence, which is downward VLM (Shirzaei et al., 2021; Törnqvist & Blum, 2024) instigates elevation loss, which, if not compensated by new sediments (organically and/or inorganically derived), leads to increased flood exposure in coastal lowlands. The contribution of land subsidence to local and regional relative SLR and coastal flooding in delta plains is often considerable, and its magnitude can be equal to or exceed the present and projected global or regional sea-level changes (Becker et al., 2020; Gehrels et al., 2011; Nicholls et al., 2021; Piecuch et al., 2018; Syvitski, 2008). VLM in deltas is a complex response to a broad array of processes occurring over a wide range of spatial and temporal scales (Figure 4). Natural processes that cause VLM include tectonics, Glacial Isostatic Adjustment (GIA), Sediment Isostatic Adjustment (SIA), natural sediment compaction, and peat oxidation. Long-term VLM, such as GIA, SIA, and natural sediment compaction at larger depths, are often linear on centennial time scales and govern the local and regional short-term and seasonal to decadal natural VLM dynamics in the order of mm/yr. However, human-caused or accelerated VLM processes, often related to land-use change (Section 3.2.2) or resource extraction (e.g., groundwater withdrawal, Section 3.2.2), can create non-linear VLM, with annual rates in the range of multiple cms-dm/yr (e.g., Galloway & Burbey, 2011; Karegar et al., 2015; Oelsmann et al., 2024).

Shallow subsidence originating from sediment compaction and dewatering of Holocene deposits in the upper 20–30 m can be measured in situ using Rod Surface Elevation Tables with Marker Horizons (RSET-MH) (Jankowski et al., 2017; Webb et al., 2013) and/or Global Navigation Satellite System (GNSS) interferometric reflectometry
<figure><img src="image_12.png" /><figcaption>**Figure 4.** Spatial and temporal scales of processes causing coastal VLM.</figcaption></figure>

(Karegar et al., 2020, 2023). In the Ganges-Brahmaputra-Meghna Delta, RSET-MH measurements, although very scarce, yield average shallow subsidence rates ranging between 5 mm/yr in the polder areas and 8.7 mm/yr in the natural Sundarbans of the delta plain (Akter et al., 2024). In 2019, a larger network of 22 RSET-MH sites (Figure 5) was established in the tidal plain of the Ganges-Brahmaputra-Meghna delta (Wilson et al., 2021), but longer time series are needed to obtain robust shallow compaction rates. The Mekong Delta also contains a growing RSET-MHs network (first one installed in 2010), but results so far are only published for three sites in coastal mangrove forests indicating shallow compaction rates of up to a staggering 42 mm/yr at one site, but these rates are based on short data record (3 years) and include large uncertainty (Lovelock et al., 2015).

Deeper subsidence, driven, for example, by GIA, SIA, deep sediment compaction, tectonic and groundwater over-extraction, can be measured using monitoring techniques like GNSS reference stations (Karegar et al., 2015, 2016, 2020). Available GNSS sites in the Ganges-Brahmaputra-Meghna delta show subsidence rates of 4–8 mm/yr (Steckler et al., 2022). Unfortunately, the availability of GNSS data in the Ganges-Brahmaputra-Meghna, Irrawaddy and Red River deltas is very limited, with sparse or no coverage (Figure 5). Although Vietnam and Thailand have relatively dense GNSS reference networks, data accessibility for the Mekong and Chao Phraya deltas remains an issue that hinders accurate measurements of VLM (Figure 5).

InSAR is a powerful remote sensing technique for measuring VLM with high spatial resolution (Candela & Koster, 2022). InSAR can be applied across the Asian megadeltas (Erban et al., 2014; Higgins, 2016), but it faces challenges due to the decorrelation caused by dense vegetation cover (Umarhadi et al., 2021). The capabilities of InSAR are illustrated in Figure 5 for the Asian megadeltas (Wu et al., 2022). As evident from Figure 5, the city of Yangon in the Irrawaddy Delta is experiencing subsidence of up to 90 mm/yr (2017–2023) and up to 20 mm/yr of subsidence occurs in Bangkok (2015–2021) and Dhaka (2014–2020), likely caused by localized groundwater over-extraction (see Section 3.2.2). A recent study showed that combining GNSS rates with InSAR rates improves the spatio-temporal resolution of VLM for studies on relative SLR and related flood hazards (Sherpa et al., 2023).

Viscoelastic Earth models provide estimates of the present VLM contribution from long-term processes such as GIA and SIA caused by ice, water, and sediment loading. The Asian megadeltas are all situated “far-field” from the Pleistocene ice sheets, thus the impact should be minor. However, modeled meltwater loading of the ocean basin could generate mantle currents that act to lift the adjacent continental margins up (Tamisiea & Mitrovica, 2011) and thus, all five Asian megadeltas could have experienced an uplift from GIA. Using Peltier’s model (ICE-6G; Peltier et al., 2015), uplift rates of about 0.3–0.4 mm/yr for the Ganges-Brahmaputra-Meghna, Irrawaddy and Chao Phraya deltas, and 0.2–0.3 mm/yr for the Mekong and Red River deltas were estimated. Faster
<figure><img src="image_13.png" /><figcaption>**Figure 5.** VLM observations available across the five Asian megadeltas. The middle panel shows locations of GNSS continuously operating reference stations (red triangles) and RSET-MH sites (yellow squares). Most of the GNSS station data is not publicly available. GNSS stations are from Steckler et al. (2016, 2022); Steckler and Scott (2020), for the Ganges-Brahmaputra-Meghna Delta; from VNGEONET (http://vngeonet.vn) for the Mekong and Red River deltas, from the National Thai CORS data center (https://www.cdg.co.th/website/en/industries/cors-data-center/) for the Chao Phraya Delta, and from Myanmar GNSS CORS network established in 2011 (handled by Myanmar Earthquake Committee) for the Irrawaday Delta. The raw GNSS data for the Irrawaday Delta from 2011 to 2017 are publicly available through the Earth Observatory of Singapore's scientific data set (https://earthobservatory.sg/). RSET-MH sites for the Mekong Delta are from Lovelock et al. (2015) and from Wilson et al. (2021) for the Ganges-Brahmaputra-Meghna Delta. The Indo-Burma subduction zone is shown by a black line with triangles in the Ganges-Brahmaputra-Meghna Delta. The Sagaing Fault is a major right-lateral strike-slip fault striking north-south to the eastern edge of the Irrawaddy Delta. Major fault lines in the Red River are shown with dashed lines. The upper and lower panels illustrate VLM derived in Line-of-Sight from Sentinel-1 A/B satellites for major cities shown with unfilled squares. VLM rates in Dhaka (2014–2020), Ho Chi Minh City (2017–2021), Hanoi (2015–2021), Khulna (2014–2021) and Bangkok (2015–2021) are from Wu et al. (2022). VLM rates in Yangon City (2017–2023) are from Seeger et al. (2023). VLM rates in the Vietnamese Mekong Delta (2014–2019) are from Copernicus Emergency Management Service—Risk and Recovery Mapping activation 062. The delta polygons are based on delta extents provided by Tessler et al. (2015).</figcaption></figure>

uplift rates due to GIA are predicted by Lambeck's model (Lambeck et al., 2014, 2017; see Text S1 in Supporting Information S1) in the Ganges-Brahmaputra-Meghna Delta (0.6–1 mm/yr) and Mekong and Red River deltas (0.7–0.8 mm/yr), whereas slower uplift rates are estimated in the Irrawaddy Delta (0–0.4 mm/yr) and negligible rates in the Chao Phraya Delta. The modeled GIA rates depend on the chosen type of viscoelasticity, mantle layering, viscosity values, lithospheric thickness, etc (see Text S1 in Supporting Information S1 for details). Nevertheless, it is worth highlighting agreement between the models that the GIA induces uplift rates of an order of magnitude smaller than VLM generated by other natural and anthropogenic drivers, such as shallow sediment compaction or gas/water extraction.

Holocene sedimentation can lead to SIA, contributing to a proportion of present-day long-term subsidence in the Asian megadeltas (Ferrier et al., 2015; Ivins et al., 2007; Karpytchev et al., 2018). The present contribution of SIA in the Ganges-Brahmaputra-Meghna Delta, obtained from models, induces subsidence in the range of 1 and
2 mm/yr, which is further amplified in the east by tectonic faults in the delta (Karpytchev et al., 2018; Krien et al., 2019). However, it is generally rather difficult to isolate the contribution of SIA from VLM observations in deltas, as it is often dominated by shallow sediment compaction and other effects (Grall et al., 2018; Teatini et al., 2011; Törnqvist et al., 2008).

Most of the Asian megadeltas do not have extensive VLM modeling studies available beyond 1D calculations (e.g., Erban et al., 2014; Karlsrud et al., 2017), as they require detailed local (subsurface) data and are demanding to set up. The exception is the Mekong delta, for which in recent years several models were built. The contemporary contribution of natural compaction was modeled for a 2D transect of the southern part of the Mekong delta (Zoccarato et al., 2018), revealing that observed shallow compaction rates (up to >4 cm/yr in the top 20 m; Lovelock et al., 2015) may well be caused by natural autocompaction (hydrologically delayed consolidation and creep) resulting from extremely rapid delta progradation over past centuries.

Land elevation and topography are either measured directly by land leveling and GNSS, or are measured remotely via aircrafts or satellites (optical, radar, LiDAR) and interpolated to a digital elevation model (DEM). High-quality elevation data at high horizontal and vertical accuracy is not available for the majority of the world's coastlines. This results in the use of global satellite-derived DEMs, such as the Shuttle Radar Topography Mission (SRTM, Rabus et al., 2003) and ALOS World 3D (AW 3D, Tadono et al., 2016) for assessing coastal flooding in lowlands, such as in the Irrawaddy Delta (ICHAM, 2016). However, these global DEMs suffer from large vertical errors and artifacts that impact the quality of coastal flood exposure assessments (Minderhoud et al., 2019; Seeger et al., 2023). Recent studies aimed to improve older DEMs by using machine learning algorithms, as shown with advances such as the CoastalDEM v2.1 (Kulp & Strauss, 2021; source DEM: SRTM; data acquired in 2000) and FABDEM (Hawker et al., 2022; source DEM: Copernicus DEM; data acquired in 2010–2015), or by new DEM generations using recently available satellite LiDAR data (Pronk et al., 2024; Vernimmen & Hooijer, 2023). All of these aim to enhance the much needed vertical accuracy (e.g., Gesch, 2018; Schumann & Bates, 2018).

Though equally important, the vertical reference of elevation data to local sea-level is still underrepresented, but recent publications reveal that there is increasing awareness and advancements (Hauser et al., 2023; Minderhoud et al., 2019; Seeger et al., 2023; Vernimmen et al., 2020). For example, with their local TopoDEM, which is based on directly measured data from topographic maps, Minderhoud et al. (2019) showed that the Mekong delta is only approximately 0.8 m above local mean sea-level, while the global SRTM and MERIT DEMs significantly overestimate the delta elevation by more than 2 m. Even larger discrepancies have been documented for the Irrawaddy Delta, where an in-depth comparison of currently available DEMs found uncertainties ranging between 0.1% and 50% of the deltaic area, which could result in between 6,200 and 3.8 million more people being exposed to inundation due to a 1 m SLR by 2100 or sooner, depending on the DEM used (Figure 6 and Figure S3 in Supporting Information S1; Seeger et al., 2023).

Finally, the topography of deltas is not constant; it dynamically responds to changes in sediment accumulation or subsidence-induced elevation loss and morphological changes. Consequently, the actuality of DEMs is critical to represent recent elevation and morphology (such as channel networks). This issue is particularly relevant for outdated DEMs, such as the SRTM or its post-processed versions (e.g., CoastalDEM) with source data from 2000, that no longer adequately reflect the present-day deltaic landscape. Besides the need for actual elevation data sets, DEMs require regular updates, integrating information on sedimentation dynamics and VLM, which then also need to be incorporated into existing elevation-informed coastal flood hazard assessments.

### 3.1.6. Sediment, Shoreline, and Deltaic Landform Changes

Despite their importance, estimates of sediment fluxes to and from deltas remain relatively approximative due to difficulties in obtaining long-term surveys, political restrictions to display data, or technical difficulties in assessing reliable sediment fluxes, particularly in tidal zones of deltas. The most complete surveys of sediment delivery are typically undertaken alongside constructions of important infrastructure, such as bridges and dams. A significant portion of such assessments is not available to the public and remains as gray literature. In addition, sediment gauging is not always part of a longer hydrological monitoring survey. For instance, for the Ganges-Brahmaputra-Meghna Delta, there is no public data set on the Indian side of the delta, and Bangladesh has not released sediment concentration data since the mid-nineties (M. Rahman et al., 2018). This situation hampers the capacity to measure long-term trends in sediment transport and deposition and how these have been responding to
<figure><img src="image_14.png" /><figcaption>**Figure 6.** Projected area below sea-level following relative SLR scenarios of 1, 2, and 5 m for the (a) Irrawaddy, and (b) Mekong Delta, as indicated by different local, radar satellite, and satellite LiDAR DEMs referenced to local continuous mean sea-level (Hawker et al., 2022; Kulp & Strauss, 2021; Minderhoud et al., 2019; Seeger et al., 2023; Vernimmen & Hooijer, 2023). Areas in the AD-DEM containing interpolation artifacts are marked with hatching (see also Seeger et al., 2023).</figcaption></figure>

anthropogenic activities and climate variability, factors that are critical for effective decision making going forward. Alternative approaches have been adopted based on river discharge data coupled to models of sediment transport, calibrated with in situ velocity profiling and water column suspended load sampling (Baronas et al., 2020; Lupker et al., 2011). Sediment transport has also recently been more integrated into semi-distributed physically based hydrological models, such as the INCA (INtegrated CAtchment) model, allowing predictive assessments on the future of sediment transport (e.g., Bussi et al., 2021; Dunn et al., 2019).

Satellite imagery (e.g., Landsat) has been extensively used to assess landform changes of large river systems, as well as their deltas and coastlines, with some key examples in the Asian megadeltas including Kummu et al. (2008), Lam-Dao et al. (2011), Li et al. (2017), and Marchesiello et al. (2019) for the Mekong River and its coastal deltaic areas, and Baki and Gan (2012), Hossain et al. (2013), and Dewan et al. (2017) for the large river systems in the Ganges-Brahmaputra-Meghna Delta. These studies found that in the Mekong delta, erosion is a profound and widespread hazard. At present, 50%-66% of the entire deltaic coastline experiences erosion, with rates reaching up to 50 m per year in some areas (Anthony, 2015; Li et al., 2017; Marchesiello et al., 2019). Contrastingly, along the coastline of the Ganges-Brahmaputra-Meghna Delta, land creation continues to exceed land erosion, but this is highly spatially heterogeneous, with erosion more prominent further away from the river mouth (Paszkowski et al., 2021 and references therein). In the Irrawaddy delta, Anthony et al. (2019) and D. Chen et al. (2020) use Landsat imagery to delineate areas of eroding channels and coastlines, and find that from 1974 to 2019, between 42% and 49% of the delta's shoreline underwent erosion, with the most significant erosion observed in the western areas, where maximum rates reached 19.3 m per year (D. Chen et al., 2020). Similarly, in the Chao Phraya and Red River deltas, erosion hazards seem to be widespread but less severe, with mean annual
coastal erosion rates of approximately 10 m along the most severe areas in both deltas (Bidorn et al., 2021; Saito et al., 2007).

Despite the unique opportunities provided by remotely sensed data, using satellite imagery for analysing erosion dynamics can be laborious, requiring the manual delineation of images to detect changes over time. Therefore, automating this process is of great value. Passalacqua et al. (2013) initially developed a statistical framework to automatically extract deltaic channel networks from satellite imagery in order to determine key attributes, such as channel width and island geometries, and analyse their statistical behavior. Isikdogan et al. (2015, 2017) developed this approach further by establishing a model called RivaMap, that automatically extracts complex deltaic distributaries and braided rivers from satellite imagery at multiple scales. Jarriel et al. (2020) further built on the RivaMap model to create the DeepWaterMap model, which enables more detailed quantification of deltaic changes and why these may be occurring and what their impacts are. They applied the DeepWaterMap model to the Ganges-Brahmaputra-Meghna deltaic channel network and found that anthropogenically modified embanked parts of the delta have higher rates of geomorphic instability than the adjacent natural Sundarban mangrove forest (see Jarriel et al., 2020 for further detail on the approach). This modeling approach has now been applied to 48 deltas across the world to assess the patterns and rates of channel changes within river deltas (Jarriel et al., 2021). Finally, Boothroyd et al. (2021) illustrate the power of Google Earth Engine in transforming the spatiotemporal quantification of fluvial geomorphological dynamics, emphasizing that it enables research on the wider riverscape (i.e., water, sediment, and vegetation) to take place at higher spatial resolutions, over greater spatial extents, and at finer temporal resolutions than ever before. Thus, via the integration of satellite imagery, Google Earth Engine, data-driven modeling, and GIS analyses, landform changes in the typically inaccessible and relatively data-scarce Asian megadeltas can be characterized and quantified continuously, as more remotely sensed data becomes available (Boothroyd et al., 2021; Piégay et al., 2020; Shamsuzzoha & Ahamed, 2023).

To conclude Section 3.1, it is important to highlight the critical value of local knowledge in the assessment and quantification of the aforementioned geophysical drivers. Leveraging technological advances and combining these with local knowledge is the only way to obtain a nuanced understanding of how these processes drive coastal flooding on the ground. Typically passed down through generations, this wisdom offers invaluable insights into flood hazard patterns and traditional adaptation strategies, as well as their evolution, in response to such hazards. By entwining this deep-rooted understanding with advanced methods of data collection and assessment, more targeted and context-specific resilience measures can be developed.

## 3.2. Main Human Pressures

Alongside the complex interactions of the key natural drivers of change in the five Asian deltas discussed in Section 3.1, anthropogenic activities have played increasingly dominant roles in shaping deltaic dynamics. These range from extensive land use change, such as urbanization, to resource exploitation, and large-scale infrastructure, such as dams or diversions, constructed within the river catchments of these important five deltas. This section delves into the predominant anthropogenic pressures in the Asian megadeltas, with the key messages summarized in Table 2.

### 3.2.1. Population, Economy, and Megacities

Population growth has emerged as a significant driver of coastal flood risk in the low elevation coastal zone, as it expanded human settlement into highly exposed coastal zones (Figure 2c; Reimann et al., 2023). The significant population growth and economic development in Asian megadeltas drive changes in land use patterns and encourage the construction of flood control infrastructure, coastal development projects, and water management practices. Such alterations can inadvertently disrupt natural water and sediment flow dynamics, disturb ecosystems, and increase the risk of coastal regions to flooding.

The most dramatic population growth has occurred in the Chao Phraya Delta, nearly doubling in size (+81%) over the past 20 years, totaling almost 26 million inhabitants in 2020 (Table S1 in Supporting Information S1). This accounts for ~37% of Thailand's total population, concentrated on less than 5% of the national land area, and reflects migration to the delta, especially to Bangkok. The population density in the delta has also doubled over two decades, reaching 1,100 inhabitants/km² in 2020 (Figure 2c). In contrast, the Red River Delta has seen little population change (1%) over the same period, reflecting that the large growing city of Hanoi is outside the delta limits, while the other four deltas all contain at least one major growing city (Figure 2c).
<table><caption>**Table 2**

Overview of the Main Human Pressures: What We Know, the Remaining Gaps, and Key Challenges Associated With Coastal Flood Assessments in the Asian Megadeltas</caption><thead><tr><th>What we know</th><th>Gaps in terms of observations and/or models</th><th>Key challenges to be addressed in the context of Asian megadeltas</th></tr></thead><tbody><tr><td><strong>3.2.1 Population, economy and megacities</strong><ul><li>Major cities in Asian megadeltas have experienced a threefold or more increase in urban population since 1950</li><li>By 2030 (compared to 2010), the population in Yangon and Bangkok will likely increase by ~50%, while Dhaka and Ho Chi Minh City are estimated to experience a population growth of over 80%</li><li>Population growth, combined with economic growth, contributes to coastal flooding via urbanization, land use changes, groundwater extraction, infrastructure construction, flood protection structures, and sand mining</li></ul></td><td><ul><li><strong>Knowledge gaps:</strong> The intricate relationship between surface water and urbanization in Asian megadeltas remains largely unexplored, leaving significant knowledge gaps</li><li><strong>Lack of comprehensive analysis:</strong> Integrating LULC data sets, derived from remote sensing products (such as population density and presence of built-up areas), and socioeconomic indicators in the Asian megadeltas, hinders a comprehensive understanding of the influence of urbanization on coastal flood dynamics</li><li><strong>Assessing the cumulative effects of agricultural practices, aquaculture activities, and mangrove retreat on coastal flooding, involves understanding their combined impacts and the underlying mechanisms through which they contribute to the dynamics of coastal flooding</li><li>It remains a challenge to attribute patterns in coastal flood dynamics to specific changes in land use. Given the complexity of interacting factors, understanding is still limited on which LULC changes lead to the changes observed in flood dynamics</li><li><strong>Limited available information:</strong> The Chao Phraya and Mekong deltas are the most well-documented, while the Red River and Ganges-Brahmaputra-Meghna deltas are partially documented. Groundwater monitoring is almost entirely missing in the Irrawaddy Delta. There are a lack of reliable estimates for groundwater abstraction in rural deltaic areas, and the extent of groundwater use for agricultural purposes remains unknown</li><li><strong>Nonlinear groundwater influence:</strong> The link between subsidence and groundwater extraction is complex, influenced by various non-linear factors associated with human activities, which are still not fully understood</li><li><strong>Impacts of mitigation measures:</strong> could introduce unintended negative side effects due to limited understanding and estimation of their impacts</li></ul></td><td><ul><li><strong>Evaluate and understand the effects of urbanization on coastal flooding in the Asian megadeltas.</strong> The assessment should consider factors such as variations in peak discharge, runoff volume, changes in impervious surface areas, and the extent of coastal flooding</li><li><strong>Determine how quickly coastal flood dynamics respond to increased urbanization to account for the fact that the combined effects of urbanization and climate change are accelerating in Asian megadeltas</strong></li><li><strong>Improve hydrodynamic models that incorporate factors on land cover changes, water management practices, sediment dynamics, and tidal influences to accurately assess coastal flooding in Asian megadeltas and understand the interactions between agriculture, aquaculture, mangrove retreat and coastal flooding</strong></li><li><strong>Understand the role of mangroves in mitigating coastal flood extents.</strong> This includes quantifying the ability of mangroves to attenuate wave energy, trap sediment, reduce erosion, and provide natural coastal flood protection</li><li><strong>Improve monitoring networks in undocumented and data-scarce areas, and strengthen regulations of groundwater abstraction</strong> rates across all five Asian megadeltas</li><li><strong>Investigate the highly non-linear nature of groundwater extraction-induced land subsidence and its relationship with coastal flooding</strong></li><li><strong>Install an in-situ subsidence monitoring network</strong> to accurately track and understand the extent of land subsidence, to unravel urban differential subsidence into individual drivers and processes at different depths and determine the groundwater extraction-induced contribution to subsidence in Asian megadeltas</li><li><strong>Make projections of extraction-induced subsidence, based on different scenarios of future groundwater use,</strong> as has been developed for the Mekong Delta</li><li><strong>Adopt a systems perspective,</strong> including the relevant subsurface processes and feedback loops when designing mitigation strategies</li></ul></td></tr><tr><td><strong>3.2.2 Deltaic land use/land cover changes</strong><p><strong>Urbanization</strong><ul><li>Transitioning from vegetated to urban areas reduces water absorption, impacting runoff and coastal flood hazards</li><li>Since 1974, urbanization increased by ~1,500% in the Irrawaddy Delta, specifically in Yangon, and by ~300% in urban areas <5 m above mean sea level</li><li>Since 1990, urban areas that lie below 5 m above mean sea level have increased by ~72% in the Ganges-Brahmaputra-Meghna Delta, by 194% in the Chao Phraya Delta, and by 70%-80% in the two Vietnamese deltas</li></ul><p><strong>Agriculture, aquaculture and mangrove losses</strong><ul><li>Since the 1970s, in the mangrove regions of Asian megadeltas, significant land use change has been taking place, particularly to brackish water aquaculture</li><li>Conversion of mangroves to aquaculture areas leads to a loss of ecologically important areas, as well as a series of interconnected consequences, including subsidence and erosion of coastlines, which directly contribute to coastal flooding</li></ul><p><strong>Groundwater extraction</strong><ul><li>Groundwater depletion has been observed in all five Asian megadeltas</li><li>Currently, cities and rural areas in the Asian megadeltas experience high land subsidence rates exceeding 1 cm/year due to groundwater over-extraction</li><li>Extraction-induced land subsidence dominates the total subsidence signal in Asian megadeltas, particularly in their cities</li></ul></td><td></td><td></td></tr></tbody></table>
<caption>**Table 2**
*Continued*</caption>

**Sand mining**

Rapid socio-economic development in the region has created a substantial demand for river sand for construction, resulting in escalated rates of sand extraction

Sand mining contributes to the deepening of river and estuary channels, causing erosion along riverbanks and coastal areas. It also alters geomorphology, deepens riverbeds, and impacts tidal dynamics within the delta

* **Incomplete monitoring:** Sand mining monitoring in Asian megadeltas is often insufficient, leading to gaps in tracking extraction rates, volumes, and locations. It is suspected that the reported volumes, obtained through legal licenses, do not capture the full extent of sand mining, including unreported or illegal extraction, which is believed to be much larger

* **Uncertain coastal impacts:** Consequences of sand mining in Asian megadeltas, such as the long-term effects on river morphology, sediment transport, and coastal stability, are not yet fully understood. This knowledge gap limits the ability to accurately predict and mitigate potential impacts on coastal flooding

* **Develop modelling approaches in the Asian megadeltas to simulate the complex interactions of sand mining, sediment dynamics, and geophysical drivers** to assess these cumulative effects on elevation maintenance of deltas and coastal flooding

* **Use remote sensing and deep learning techniques which aid in monitoring and assessment efforts** as they hold potential for quantifying sand mining activities and sediment budgets in Asian megadeltas that are not possible with the limited in situ data available

* **Support Governments in strengthening regulations on sand mining activities** via more comprehensive monitoring and assessment systems

**3.2.3 Upstream interventions (dams)**

Dam impacts on coastal flooding can be indirect but profound, regulating river flows and trapping sediments, which can influence and contribute to coastal flood dynamics

Sediment dynamics in the Mekong and Red River deltas have already substantially reduced. In the Ganges-Brahmaputra-Meghna and the Irrawaddy deltas, the sustainability of future sediment loads is of concern, as the river basins face many planned dams. The Chao Phraya Delta seems not to be impacted by dams constructed upstream

* **Neglected role of dams:** Despite their potential significance, the inclusion of dams in coastal flood assessments in Asian megadeltas is often neglected

* **Lack of comprehensive data** on dams, coupled with the difficulties in accurately estimating reservoir outflows and integrating dam characteristics into hydrodynamical models, contribute to the existing gaps in understanding the impacts of dams on coastal flooding in the deltas

* **Remote sensing observations** have increasingly been used to assess water and sediment fluctuations as a result of upstream interventions, particularly within transboundary river systems where data sharing is particularly concerning. However, their use to monitor the impacts of dams is still limited

* **Conduct research to better understand the complexities of sediment transport in dam-affected river systems.** Develop methodologies to predict the magnitude and timing of sediment reduction caused by dams, considering both short-term and long-term impacts on coastal flooding

* **Improve hydrodynamic models that incorporate dams and accurately simulate reservoir operations, water releases, and sediment dynamics.** This will enable a more comprehensive understanding of the interaction between dams and coastal flooding

* **Utilizing remote sensing data in the Asian megadeltas** can provide a comprehensive understanding of the complex interactions between dam construction and coastal flooding, offering valuable insights into dam design, water levels and extent over time, and operational dynamics

When focusing on urban population changes in the Asian megadeltas, however, all large cities have grown threefold or more between 1950 and 2010 (Table S2 in Supporting Information S1). Gross Domestic Product has significantly increased since 1990 in all five Asian megadeltas (Table S1 in Supporting Information S1). Such changes have an impact on the damages that occur during floods: while fatalities due to coastal flooding have decreased over time as protection of deltaic regions improve, economic damages due to coastal flood events are on the rise (e.g., in Bangladesh, Alam & Dominey-Howes, 2015; Lumbroso et al., 2017). The economies of the deltas are growing rapidly, with service and manufacturing sectors gaining traction over more traditional agriculture and fishery activities (Arto et al., 2019).

**3.2.2. Deltaic Land Use/Land Cover Changes**

**3.2.2.1. Urbanization**

Increasing urbanization is leading to an increase in artificial and impervious surfaces, such as roads and buildings. In the Asian megadeltas, urban expansion is still ongoing. Figure 7 displays satellite-derived night light brightness maps, obtained from the National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) Day/Night Band (Small & CIESIN, 2020), serving as a proxy for urban growth since 1992 (Small et al., 2018). In the Ganges-Brahmaputra-Meghna Delta (Figure 7a), a significant increase in brightness is observed in two densely populated megacities, Kolkata and Dhaka, and in their surrounding areas. Additionally, a significant developmental shift post-2002 is evident in the western part of the delta in India, in contrast to the remainder of the delta. The Chao Phraya Delta (Figure 7c), with Bangkok brightly lit in the center, shows a significant increase in
<figure><img src="image_15.png" /><figcaption>**Figure 7.** Spatial distribution and temporal evolution of nighttime lights between 1992 and 2013 from the National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) Day/Night Band (Small & CIESIN, 2020) for (a) the Ganges-Brahmaputra-Meghna Delta, (b) the Irrawaddy Delta, (c) the Chao Phraya Delta, (d) the Mekong Delta, and (e) the Red River Delta. The delta polygons are based on delta extents provided by Tessler et al. (2015).</figcaption></figure>

brightness after 2002 across the entire delta. In the Mekong and Red River deltas (Figures 7c and 7d), we observe a significant expansion of brightness across the deltas since 2002, emphasizing the interconnected spatial network of development. Analysing satellite images from 1974 to 2021, Vogel et al. (2022) revealed a 1,500% increase in urban areas in the Irrawaddy Delta, expanding from under 100 km² to about 1,640 km² by 2021 (~5% of the delta area), primarily attributed to the development of Yangon City (Figure 7b). They also noted that urban areas located below 5 m in the delta have experienced a growth of ~300%, making them more susceptible to flooding and the impacts of SLR. McGranahan et al. (2023), using a combination of the MERIT DEM and the degree of urbanization data set based on population density, built-up density, and total population, have shown that between 1990 and 2015, the urban area in the Bangladesh part of the Ganges-Brahmaputra-Meghna Delta has increased by 158% (~72% < 5 m above sea level) and by 106% for the Chao Phraya Delta (~194% < 5 m above sea level). They also estimated the changes in extents of built-up areas in land below 5 m in the two Vietnamese deltas, the Mekong and the Red River deltas, and observed a 70%–80% increase.

The conversion of vegetation into areas used for housing and industry diminishes the land's water absorption capacity, resulting in increased runoff, amplified peak flow discharges and their frequencies, and heightened risk of coastal flooding (Morita, 2016). The Chao Phraya Delta provides a prime example of this phenomenon. The completion of the Chao Phraya Dam in 1957 triggered rapid urbanization, transforming the coastal landscape and
converting agricultural land, floodplains, and waterways into developed areas (World Bank, 2009). Historical floods in 1983 and 1995 in the Chao Phraya Delta highlighted the impact of urbanization on drainage capabilities and flood protection. The reduced drainage capacity, combined with tidal and meteorological factors, caused extensive flooding in Bangkok, leading to significant damages (Tang et al., 1992; Terry et al., 2021). Urban expansion also impacts the local hydrogeological conditions, as the phreatic water table is actively lowered through drainage to create favorable conditions for construction, resulting in increased shallow compaction (Minderhoud et al., 2018) and driving urban differential subsidence (de Wit et al., 2021), and increased groundwater extraction to meet the growing water demand from deeper layers (see section below).

### 3.2.2.2. Agriculture, Aquaculture, and Mangrove Losses

A range of land use/land cover (LULC) changes have been occurring in the agricultural sector, including the retreat of certain crops like rice, in favor of the development of aquaculture farms due to increased salinization, market orientation, and waterlogging. Vu et al. (2022), for example, show that in the Mekong delta, rice-growing areas increased from 2001 to 2015 and then declined until 2020, while aquaculture areas increased between 2001 and 2020. For the coastal part of this delta, M. H. Phan and Stive (2022) confirm that in 1973 aquaculture was virtually non-existent, and by 2020, it occupied 32% of the territory, to the detriment of vegetation zones (crops and mangroves). The mangrove area in the Mekong Delta has experienced a significant decline of 45% between 1973 and 2020, primarily attributed to factors such as aquaculture encroachment and coastal erosion (M. H. Phan & Stive, 2022). In the Chao Phraya Delta, 30%-40% of mangroves have been lost to shrimp farms (Hamilton, 2013), a trend that is also prevalent in the Red River Delta where 49% of mangroves have been covered to aquacultural land uses (Long et al., 2021). The high commercial value of shrimp cultivation in the Ganges-Brahmaputra-Meghna, the Chao Phraya and the Mekong deltas has also resulted in significant growth of this sector, now contributing ~6% to Bangladesh's and ~1% to Thailand's and Vietnam's Gross Domestic Product, respectively (Johnson et al., 2016; Lan, 2013; Szuster, 2003; Thu Trang & Loc, 2021). In the Irrawaddy Delta, mangrove coverage drastically declined by 72% between 1974 and 2021 (Vogel et al., 2022). This significant loss can similarly be attributed to the expansion of aquaculture, brine ponds, and agricultural areas.

The conversion of mangroves into aquaculture and agricultural areas across all Asian megadeltas leads to exacerbated erosion of coastlines and the loss of ecologically diverse and important areas, reduced retention of sediments and organic matter by mangroves, leading to increased subsidence, changes in flow and sediment dynamics, and significant reduction in the natural absorption of coastal wave energy during extreme events, which all directly contribute to coastal flood impacts.

### 3.2.2.3. Groundwater Extraction

Groundwater extraction is significant in all five Asian megadeltas (Erkens et al., 2015), observed using both monitoring wells (Jasechko et al., 2024) or satellite-based estimates (Rodell et al., 2018), especially from the Gravity Recovery and Climate Experiment (GRACE) twin satellites (Rodell & Reager, 2023). GRACE makes it possible to map total water storage variations at the global and regional scales at 10-day to a monthly timescale since 2002, from which groundwater storage trends can be derived (J. Chen et al., 2016).

Widespread groundwater exploitation in the Chao Phraya Delta started in Bangkok in the 1950s for municipal and industrial purposes, which resulted in substantial land subsidence, with peak rates exceeding 10 cm/yr during the 1980s (Lorphensri et al., 2011; Phien-wej et al., 2006). By imposing strict groundwater policies and a gradual increase in groundwater taxation, the city managed to stop the overexploitation and the current groundwater extraction-induced subsidence in the city center has reduced to less than 1 cm/yr, with some parts seeming to experience a mild surface rebound due to groundwater recovery (e.g., Intui et al., 2022; Ishitsuka et al., 2014). However, past extraction-induced subsidence has caused the city to irreversibly lose more than 1 m in elevation, which has greatly increased the city's exposure to coastal floods (Phienwej & Nutalaya, 2005). While Bangkok has stabilized, large parts of the surrounding more rural areas of the Chao Phraya Delta still experience extraction-induced subsidence rates of up to 2 and 3 cm/yr (Pumpuang & Aobpaet, 2020).

Groundwater pumping in the Mekong Delta greatly increased following Vietnam's transition to a market economy in 1986, which instigated rapid land-use change, agricultural intensification and urbanization, all increasing groundwater demands and acceleration of land subsidence rates (Minderhoud et al., 2018). GRACE observations, along with both in situ measurements and hydrological models, recently revealed widespread groundwater
depletion in the Mekong Delta Aquifer of the Lower Mekong region (Upadhyay et al., 2024). Over 2003–2016, groundwater storage has been declining at a rate of ~0.68 cm/year, resulting in a total volume loss of 18.28 km³ over the 14-year period, with depletion rates higher in the coastal regions of the delta (Upadhyay et al., 2024). InSAR-based estimates revealed subsidence rates in parts of the delta of 1–4 cm/yr during 2006–2010 (Erban et al., 2014) and delta-wide hydrogeomechanical modeling of groundwater extraction since 1991 revealed an acceleration in extraction-induced subsidence, with a delta-wide average of 1.1 cm/yr in 2015 and in some places exceeding 2.5 cm/yr (Minderhoud et al., 2017, best-estimate scenario). While policies to protect groundwater have recently been put in place, groundwater depletion still persists in many parts of the delta (Duy et al., 2021) and recent InSAR-based estimates (2014–2019) show peak rates of up to 5 and 6 cm/yr (Minderhoud, Hlavacova, et al., 2020), suggesting a continuation of the previous accelerating trend as projected for the business-as-usual extraction-induced subsidence scenarios (Minderhoud, Middelkoop, et al., 2020).

In the Ganges-Brahmaputra-Meghna Delta, declining groundwater levels (more than 1 m/yr during 1985–2005) of shallow aquifers in Bangladesh were reported in urban and peri-urban areas around Dhaka and in the north- central, northwestern, and southwestern parts the country due to intensive abstraction of groundwater during the dry-season for rice cultivation (Shamsudduha et al., 2009). These observations were later confirmed with declining trends in groundwater storage estimated from GRACE (Shamsudduha & Panda, 2019), which sug- gestively drives land subsidence, for example, in East Dhaka and Khulna (Higgins et al., 2014; Steckler et al., 2022).

Deciphering the impacts of groundwater extraction on land subsidence in the Irrawaddy Delta remains chal- lenging, as groundwater monitoring only exists for a few artesian irrigation projects and in urban areas (Vio- ssanges et al., 2017). Among the Southeast Asian cities, some of the highest abstraction rates are observed in Yangon City (Hashimoto et al., 2022), contributing 4–10 cm of subsidence per year (Seeger et al., 2023; van der Horst et al., 2018). For the more rural deltaic areas, reliable abstraction estimates are missing and groundwater use for cultivation (horticulture, aquaculture, etc.) is unknown (Viossanges et al., 2017).

3.2.2.4. Sand Mining

The rapid urbanization of the Asian megadeltas, as well as development nationally and regionally, has created a substantial demand for river sand for construction, resulting in escalated rates of sand extraction that often surpass the natural replenishment capacity of rivers (Kumar et al., 2023). In many of the river channels of the Asian megadeltas, particularly in the Mekong, Ganges-Brahmaputra-Meghna, and Irrawaddy deltas, sand is being extracted (D. Chen et al., 2020; Gruel et al., 2022; Hackney et al., 2020; Saito et al., 2007), partly under legal licenses with reported volumes, but presumably the real volumes including the unreported or illegal extractions are much larger (Bari & Haque, 2022; Yuen et al., 2024).

Sand mining leads to the deepening of river and estuary channels, can result in erosion of riverbanks and coastal areas, as well as modifications to the geomorphology, and affects the tidal dynamics in the delta (Park, 2024; Section 3.1.2). A data-driven, process-based modeling study in the Hau River in the Mekong Delta revealed the increase of tidal amplitude, and tidal propagation length, increasing the backwater effect upstream as a result of extensive sand mining (Eslami et al., 2019, 2021). Sediment mining is prevalent throughout the Irrawaddy Basin, with a particular emphasis on extracting coarse sand and gravel, which are valuable materials for construction purposes. According to D. Chen et al. (2020), the documented annual volume of sediment mining reached 20 million tonnes, ~10% of the estimated total sediment load. However, the actual amount of unreported or illegal sand mining could be significantly higher, posing a substantial threat to the stability of the delta (WWF, 2018).

Gruel et al. (2022) used Sentinel-1 (10 m spatial resolution) and Landsat-8 imagery (30 m spatial resolution) over the Mekong Delta to detect vessels for sand extraction, and correlated these with locally measured bathymetric survey data. They estimated that sand mining in the Vietnam Mekong Delta increased by 25% from 2015 to 2020, reaching ~47 Mm³/year. Hackney et al. (2020) employed PlanetScope imagery (5 m spatial resolution) to es- timate sand extraction volumes, tracking sand barges, in a specific section of the Upper Mekong Delta. They showed that extraction rates in 2020 (~59 Mm³) were nearly twice as high as previous estimates for sand extraction in this region and exceed the current best estimates for the entire Mekong Basin (~50 Mm³). Kumar et al. (2023) developed a cost-effective remote sensing-based deep learning framework for quantifying sand mining activities and budgets in the Mekong Delta. Their study reveals that sand mining increased by ~52% between 2015 and 2022. Notably, the Lower Mekong Delta, where intensive mining activities are concentrated,
accounts for nearly 90% of the total sand mining volume extracted within the entire delta. This automated remote sensing-based deep learning framework exhibits great potential for application in other Asian megadeltas.

### 3.2.3. Upstream Interventions

The impacts of dams on coastal flooding in the Asian megadeltas is typically indirect but profound. When dams regulate river flows and trap sediment, they modify natural flow patterns, disrupt hydrological regimes, and alter sediment transport processes, critical for coastal equilibrium. Improper dam operations, such as abrupt releases or excessive water storage, can result in sudden flood events in downstream reaches. When these releases coincide with coastal storms, flooding in delta systems can be widespread and severe. Nevertheless, the consideration of dams in coastal flood assessments is frequently overlooked due to limited data availability, as well as the complexities associated with estimating reservoir outflows and the challenges of incorporating dam features and operational decisions into hydrodynamical models (Boulange et al., 2021).

The most widely studied impacts of dams on sediment dynamics has been in the Mekong basin, given the extensive development of hydropower projects across the catchment. More than 50% of the annual sediment supply from the Mekong River basin is already trapped behind upstream dams (Kondolf et al., 2022). This sediment reduction, combined with approximately one-third of the pre-dam sediment influx currently being mined from riverbeds in Vietnam and Cambodia (Kondolf et al., 2022), is resulting in widespread and profound sediment starvation and accelerated subsidence in the lower Mekong delta, exacerbating coastal flooding and erosion. In the Red River basin, the construction of the Hoa Binh Dam in northern Vietnam in 1988 had a significant impact on the distribution of suspended sediment in the Red River delta. According to Vinh et al. (2014), despite a modest 9% decrease in water discharge, the annual suspended sediment flux at Son Tay, near Hanoi, experienced an average reduction of 61%.

Although the Ganges-Brahmaputra-Meghna delta is still currently classified as a stable delta (Brammer, 2014), if the planned 414 dams within the catchment are constructed, the sediment flux to the delta could reduce by 88% by the end of the century (Best, 2019; Dunn et al., 2019; Higgins et al., 2018; M. Rahman et al., 2018). This significant reduction in sediment supply will impact the delta's ability to maintain its elevation above sea-level and will increase its risk to coastal flooding in the future (Higgins et al., 2018; Paszkowski et al., 2021 and references therein; M. M. Rahman et al., 2022). The Chao Phraya and Irrawaddy deltas have also been observed to remain stable, with limited impacts felt by upstream dam construction. In the Chao Phraya River system, for instance, two large storage dams, one on the Ping River (Bhumibol Dam, 1964) and one on the Nan River (Sirikit Dam, 1972) have been found to not have had an impact on sediment delivery to the delta (Bidorn et al., 2021). In the Irrawaddy Basin, there are few dams, and the limited availability of sediment data makes it difficult to assess the impact of these dams on sediment fluxes to the delta. While Latrubesse et al. (2021) document the river's sediment delivery not being dramatically impacted so far, other studies report induced reductions of sediment load by up to 30% and substantial geomorphological changes (D. Chen et al., 2020; Lazarus et al., 2019; Syvitski et al., 2009). As the rate of dam construction increases alongside population growth and energy demand (mainly supplied by hydropower), planned projects could reduce sediment loads by an additional 19% (Tessler et al., 2018).

It is difficult to attribute changes taking place in delta systems to interventions made upstream. Recently, space observations have increasingly been used to assess water and sediment fluctuations as a result of upstream interventions (e.g., Foteh et al., 2018; Moragoda et al., 2023), particularly within transboundary river systems where data sharing is particularly challenging. The use of remote sensing technologies to monitor the impacts of dams is limited in the five Asian deltas and only a few studies exist. For instance, Zhang et al. (2014) combined a suite of satellite observations, namely water surface area estimations from MODIS and surface elevation measurements derived from the Geoscience Laser Altimeter System on board the Ice, Cloud, and land Elevation Satellite, in order to construct an area-elevation relationship and monitor changes in reservoir storage in South Asia, including some dams and associated reservoirs that feed into the Ganges-Brahmaputra-Meghna delta. Beveridge et al. (2020) use visible and Near Infra-Red reflectance from the Landsat satellite series to detect changes in suspended sediment concentrations as a result of recent dam constructions in a sub-basin of the Lower Mekong River system. In addition to analysing suspended sediment concentrations, the study also uses remotely sensed nighttime light data and land cover to better explain the potential impacts of settlements and land-use changes on the amount of sediment being trapped behind dams. T. D. Dang et al. (2018) used similar approaches of analysing suspended sediment concentrations for the rivers and channel networks of the Mekong delta.
4. Coastal Flood Mapping and Assessing Impacts in the Five Asian Deltas

All of the five Asian megadeltas are densely populated, with densities ranging from ~400 people/km² in the Irrawaddy delta to ~1,600 people/km² in the Ganges-Brahmaputra-Meghna delta (Figure 2c). These dense populations exposed to coastal flooding can result in profound impacts on lives and livelihoods of millions of people annually. Across the five Asian megadeltas, there are currently 113 million people living in the low elevation zones (≤5 m above sea level). Population numbers in these areas are expected to increase in the next decades, as Asia is projected to have the highest absolute growth of exposed populations by 2050 (Merkens et al., 2018). This makes assessments of coastal flood vulnerability and mitigation efforts more topical than ever.

At the same time, flooding is also fundamental to the very existence of these deltas, providing floodplain lands with nutrient-rich sediments, washing away pollutants and saline water, contributing to vertical land creation, and rejuvenating breeding grounds for aquatic plants and fish (Hui et al., 2022; Son et al., 2013). In the Mekong Delta, annual floods provide the region with an average estimated benefit of around US$8–10 million each year (Mekong River Commission, 2010; Son et al., 2013). However, as a result of the interplay between the drivers set out in Section 3, as well as the increasing climatic changes, flooding in the five Asian megadeltas has become much less predictable, putting current management measures and adaptive approaches to the test (Tellman et al., 2021). In response to these increasing challenges, more research has been centered around improving predictions of future coastal flood dynamics by developing new models, methods, and applications (Bates, 2022; Mosavi et al., 2018).

Studying and understanding coastal flooding in these deltas is paramount due to their critical role in enhancing
climate resilience (Chan et al., 2024). Shaped by sediment deposition that keeps pace with relative sea-level rise,
and with coastal vegetation forming a vital natural shield against flooding, deltaic landscapes protect nearby
communities (S. Dasgupta et al., 2019). A comprehensive understanding of the dynamics of coastal flooding
within these ecosystems allows for a deeper appreciation of their contributions to community safety, biodiversity
preservation (Nicholls et al., 2018), and carbon sequestration (Lagomasino et al., 2019). This knowledge is
essential for developing effective management strategies that enhance the resilience of delta regions in the face of
increasing climate-related threats.

Section 3 illustrated how the Asian megadeltas studied here are prime global hotspots of coastal flooding, and provided insights into the dynamic interactions between riverine, coastal, and monsoonal processes with highly modified and densely populated human landscapes that shape flood hazards (Figure 8). These multifaceted driving forces can generate profound and widespread impacts on the deltaic environments and their inhabitants. This section summarizes the predominant impacts felt across the five Asian megadeltas and how these can be monitored and quantified.

**4.1. Impacts of Coastal Flooding in the Five Asian Deltas**

Given the significance of the five Asian megadeltas for agricultural production in the region (mainly rice), floods can substantially impact agricultural crops (either directly by damaging or indirectly by impairing growth conditions), local livelihoods, local and regional food security, and can result in significant economic losses (Figure 8; Brakenridge et al., 2017; N. Phan et al., 2019). For instance, the Mekong and Red River deltas are Vietnam's most important food baskets (N. Phan et al., 2019; Van Kien et al., 2020). In the Red River Delta, in 2018 during the Son-Tinh storm, ~50% of the total rice area was inundated and damaged (N. Phan et al., 2019). Though less exposed to storm surges than the Red River Delta, the Irrawaddy Delta is also severely affected by coastal storm surges (Section 3.1.1). Tropical cyclone Nargis (2008), for instance, incurred a cost exceeding US$4 billion, equivalent to 21% of Myanmar's Gross Domestic Product in 2008 (World Bank, 2015). The agricultural sector suffered severe consequences as 4 million hectares of rice fields, constituting 57% of the country's total production, were damaged (Post-Nargis Joint Assessment, 2008). Analysis of MODIS-derived vegetation indices by Omori et al. (2021) revealed a 19% reduction in paddy fields compared to the previous year, primarily due to the extensive destruction of personal properties, including homes, crops, livestock, and critical infrastructure. Rice production recovery was estimated to take around 3 years (Omori et al., 2021). In 2015, exceptionally intense monsoon precipitation combined with tropical storm Komen caused countrywide flooding in Myanmar, with an unprecedented extent. The floods affected more than 1.6 million people (causing 132 fatalities) and ~530,000 ha of land used for agriculture and aquaculture (Brakenridge et al., 2017; Government of the Union of Myanmar, 2015; IFRC, 2017).
<figure><img src="image_16.png" /><figcaption>**Figure 8.** Direct and indirect societal, economic, technical, socio-technical, physical and ecological impacts as well as beneficial effects of flooding in the five Asian deltas. All these impacts show various interlinkages in the form of consequential effects and/or feedbacks, which are shown by arrows. Examples of these interlinkages are highlighted in the text.</figcaption></figure>

As evident, coastal flooding in these deltas can significantly affect local livelihoods, particularly for rural resource-dependent populations. In these areas, homes are often temporary or semi-permanent structures, which can result in the complete loss of assets and homesteads during extreme flood events. The damage and disruptions from coastal flooding, as well as the increasing threats of saline intrusion, can therefore result in the loss of homes, the destruction of cultural heritage, rural unemployment, food insecurity, social unrest, conflicts, and forced migration (Figure 8; Adnan et al., 2019; Bernard et al., 2022; Johnson et al., 2016; Paszkowski et al., 2024; Singha et al., 2020). Coastal flooding events can also damage infrastructure assets and networks, such as transport networks, electricity, drinking water, wastewater treatment facilities, and communication services (Adshead et al., 2024). Such disruptions can lead to manufacturing, industry, and workforce disruptions, and inaccessibility to healthcare facilities or safe shelters, discontinuation of education, spread of infectious diseases in contaminated water, and profound psychological stress to victims (R. Dasgupta & Basu, 2023). Thus, coastal flooding can often generate significant setbacks in socio-economic development and can exacerbate poverty in these densely populated and highly resource-dependent Asian deltas (Figure 8; e.g., Adshead et al., 2024).

## 4.2. Coastal Flood Mapping and Modeling in the Five Asian Deltas

### 4.2.1. Remote Sensing for Flood Mapping and Impact Assessments

Flooded deltaic plains are exceptionally difficult to access during or immediately after flood events, which has historically resulted in errors and uncertainties being introduced in flood extent estimations and quantifications, and their impacts. Remote sensing technologies can help to address these challenges by providing large-scale and timely information on flood inundation without needing to access the flood localities, which can inform
immediate relief support and underpin longer-term planning for flood management (Chawla et al., 2020; DeVries et al., 2020; Munawar et al., 2022; N. Phan et al., 2019; Singha et al., 2020; Tellman et al., 2021). As opposed to flood models, satellite-based remote sensing enables the direct observation of inundation, accounting for changes in land use, infrastructure, settlement, and climate (Kuenzer et al., 2015), which may not be reflected in flood extents generated by models (Coltin et al., 2016; Tellman et al., 2021). The Global Surface Water database (GSW, Pekel et al., 2016) provides a Landsat-based data set of open inland water worldwide, at 30 m resolution, and their characteristics (occurrences, water occurrence change intensity and changes) over more than 30 years (1984–2021, https://global-surface-water.appspot.com/map). Tellman et al. (2021) compiled 15 years of global flood data based on MODIS satellite imagery to inform improved flood risk management (database accessible here: https://global-flood-database.cloudtostreet.ai/).

Both the GSW (Pekel et al., 2016) and the global flood database (Tellman et al., 2021) are based on optical sensors that rely on daylight and weather conditions to acquire images and record flood inundation. Consequently, mapping of precipitation-induced or associated floods, such as storm surge or monsoon floodings, is challenging and can often only be conducted post-event, without monitoring inundation at time steps during the event (e.g., Lin et al., 2016). In contrast, radar imagery obtained by an active sensor (such as SAR) overcomes these challenges by sending pulses at longer wavelengths than optical sensors (i.e., in the range of microwaves). Being independent of sunlight and cloud cover, flood inundation can be fully captured by the radar satellite instrument over a large spatial extent and in a timely manner (e.g., Kuenzer et al., 2013). Particularly, the launch of the Sentinel-1 mission in 2014 has led to an increasing application of radar imagery in flood mapping (McCormack et al., 2022 and references therein; Pham-Duc & Nguyen, 2022). It provides high temporal (6–12 days) and horizontal resolution (10 m) imagery globally and has thus been used for emergency mapping, such as for the Copernicus Emergency Management Service (CEMS). The Sentinel data therefore provides valuable information for flood hazard assessments, especially when these and local data are difficult to access (Nghia et al., 2022; Pham-Duc et al., 2017; Seeger et al., 2024; Uddin et al., 2019). The satellite's frequent revisit time also allows for regular updates of flood information, as has been shown for the exceptional monsoon floods in Myanmar in 2015 (Myanmar Information Management Unit, 2015). For documenting floods at higher spatial resolutions, the SAR system of the TerraSAR-X mission is favorable and produces data at up to 1–3 m horizontal resolution (DLR, 2013), but—in contrast to Sentinel—requires activation to start operation.

The use of satellite imagery in assessments of flood damages thus enables the direct quantification of flood impacts via geospatial assessments. For instance, overlaying satellite-derived land use maps with satellite images of a flood event can estimate the destruction of flooding to agricultural or aquacultural production (Kotera et al., 2016; Liew et al., 2016; N. Phan et al., 2019). However, in deltaic systems the differentiation between intended (e.g., flooded rice fields or aquaculture) versus unintended flooding remains challenging and requires detailed local and contextual knowledge (e.g., Seeger et al., 2024; Torbick et al., 2017). In the absence of this information, the GSW database (Pekel et al., 2016) can be used to exclude permanent and nearly permanent water bodies covered by water >10 months per year.

In some cases, different spaceborne data sets can also be combined to obtain a higher resolution understanding of flood impacts. In the Chao Phraya Delta, for instance, Liew et al. (2016) used a combination of high-resolution images from SPOT 4, WorldView-2, and GeoEye-1 to spatially depict the spread of floodwater through Bangkok, identifying physical infrastructure and features that controlled the spread of the shallow flood, and analysed the sediment content within flood waters. Such assessments can also be combined with other data sets and models to analyse further implications of flood hazards, such as with a DEM to obtain information on flood depth, a parameter that is critical for flood risk assessments (Maranzoni et al., 2023; Van et al., 2024). Lu et al. (2016) used satellite imagery in combination with 6 million de-identified mobile phone users in Bangladesh to assess both immediate and longer-term migration patterns as a result of Cyclone Mahasen in 2013. Similarly, Ceola et al. (2014) and Mård et al. (2018) used nighttime satellite data over a 20-year period (1992–2013) to understand how flood impacts have affected the proximity of populations to river systems over time, as well as how structural flood protection infrastructure affects these dynamics.

### 4.2.2. Coastal Flood Modeling

Despite the many opportunities that remote sensing provides for flood observations and measurements, they are limited to retrospectively assessing floods and their associated impacts (i.e., measuring past flood impacts). In
order to understand how flooding may behave in the future, models are required. These range from the more
simple bathtub models to complex physical hydrodynamic models that can capture many different interacting
processes.

The bathtub method is a commonly utilized GIS-based approach that uses the region below a specified elevation to identify large-scale flooded areas. This method relies on a high-resolution and accurate DEM as its primary input (Section 3.1.5). However, it has significant limitations as it may inaccurately indicate flooding in low-lying areas that are disconnected from the coast (such as polders). Moreover, bathtub models tend to overestimate coastal exposure to extreme flood levels as they do not account for the attenuation of water height over vegetated regions or over land with increasing distance from the ocean (Vafeidis et al., 2019; Vousdoukas et al., 2016). Despite these limitations and gross errors, the simplicity and ease of implementation of the bathtub method have led to its widespread use in numerous global-scale studies to assess coastal flooding due to future sea-level rise (Dullaart et al., 2021; Kirezci et al., 2020; Kulp & Strauss, 2019; Muis et al., 2016; Tay et al., 2022 among others), including research focused on the Asian megadeltas (e.g., Kanan et al., 2023; Mitra et al., 2023; Vernimmen & Hooijer, 2023; Vousdoukas et al., 2016). Although recent improvements have been made to the bathtub model, its projections have limited utility in deltas due to the flat, human-modified (e.g., poldering), and unconfined coastal floodplains and the failure of the bathtub method to account for erosion and sedimentation, which are particularly significant factors in deltas (Nienhuis & van de Wal, 2021).

In order to address some of these complexities, hydrodynamic models can be applied, which solve equations based on principles of mass and momentum conservation using numerical methods to simulate water movement over complex topographies and bathymetries. Unlike bathtub models, which conserve neither mass nor momentum, hydrodynamic approaches can properly represent flow connectivity and flood wave attenuation across vegetated surfaces and with distance from the sea and therefore solve the challenge of flood inundation prediction much better than static models, albeit at the expense of increased computational costs and reliance on up-to-date bathymetric data (see Bates, 2022 or Jafarzadegan et al., 2023 for a detailed discussion).

Numerous instances exist of the application of such models to estuaries and deltas. Indeed, over 50 years ago, one of the very first environmental applications of hydrodynamic modeling at a large scale was to the Mekong Delta (Zanobetti et al., 1970). Since then, there have been hundreds of numerical modeling studies using hydrodynamic approaches addressing a wide range of delta science problems, including understanding tide/surge/river flow interactions (e.g., Dube et al., 2009; Ramirez et al., 2016), flood hazard mapping (e.g., Yamazaki et al., 2014); erosion, water quality and salinity intrusion modeling (e.g., Attema & Hendriks, 2014; Cortese et al., 2024; Tran Anh et al., 2018), climate change impacts (e.g., Mitchell et al., 2022) and the development of near real-time warning systems (e.g., Khan et al., 2021). Nevertheless, while such models can provide accurate results when the bathymetry, terrain, boundary conditions and friction coefficients are known, in real life, all of these data inputs are uncertain or unavailable (see Section 3.1). While water level can be measured to within a few cm at gauges and to a few 10s of cm from satellite altimetry, measurements of surface water flux within channels and rainfall are only accurate to 10%–20%, even under ideal conditions (e.g., McMillan et al., 2012). Similarly, although modern DEMs are reasonably accurate for mapping bare-earth floodplain terrain (e.g., Hawker et al., 2022), channel bathymetry within deltas is scarce and may need to be inferred using additional algorithms and (sometimes strong) assumptions (e.g., Neal et al., 2021, see also Section 3.1.5). Lastly, the data used to calibrate and validate inundation models are not error free, with satellite-based estimates of flood inundation having a likely upper accuracy limit of 70%–80% (Horritt et al., 2001). As a result, there are fundamental limits to what inundation models can achieve, and caution is required to develop sound scientific inferences from their outputs (Bates, 2023).

**5. Paving the Way for Future Research in the Five Asian Megadeltas**

This review has provided a synthesis of the key natural and anthropogenic drivers of coastal flooding in the five
Asian megadeltas, as well as how these have historically and more recently been quantified. We also reviewed
how coastal flooding can be mapped and modeled and how the key impacts of flooding can be estimated. In this
section, these different drivers are brought together to illustrate how all these processes are interconnected,
emphasizing that a systems perspective, equalizing human and environmental compartments, is required for
effective future delta management. Subsequently, we provide a summary of the state of knowledge in the five Asian megadeltas, highlighting the key gaps that remain in our current understanding of coastal flood dynamics. In this final section, we use the findings from the review to develop trajectories of future research ventures, focusing on how approaches used in some deltas can be applied in other deltas to answer critical questions regarding flood risk in these sensitive coastal systems.

**5.1. A Deltaic Systems Perspective**

The natural and anthropogenic drivers discussed in Section 3 all play critical roles in shaping unique flood dynamics in every locality of each delta system. However, interpreting and assessing these as individual drivers that act in isolation can be problematic, as they each only represent part of the wider interconnected system and key interactions that can exacerbate flood and other hazards are missed. For example, the construction of upstream dams can lead to reduced water and sediment flows to the delta (e.g., Binh et al., 2020), which results in a change in how much sediment is distributed and deposited on the delta's floodplains, affecting rates of elevation loss due to subsidence (e.g., Higgins et al., 2014; Minderhoud et al., 2017, 2018). Accelerated subsidence could result in saline water moving further inland, particularly during the dry season (e.g., Eslami et al., 2019, 2021; Park, 2024), which is further exacerbated by the upstream dams also reducing freshwater flows through the system (e.g., Eslami et al., 2021). The increased salinization of water sources in the delta typically lead to more groundwater pumping to meet freshwater demands (e.g., M. M. Rahman et al., 2019), leading to further localized subsidence (e.g., Erban et al., 2014). Such accelerated subsidence can then further exacerbate flood and erosion risk, disrupting lives and livelihoods, causing displacement and deepening poverty (e.g., J. Chen et al., 2022). These continuous feedback loops can lead to local populations getting trapped in cycles of risk, where the continuous setbacks caused by coastal flooding exceed communities' capacity to recover and adapt (Renaud et al., 2013).

In order to escape such risk cycles and develope more effective management approaches, decision-makers require targeted research that assesses these interacting dynamics in systematic ways by viewing deltas as dynamic socio-ecological systems (Brondizio et al., 2016; Nicholls et al., 2016; Sebesvari et al., 2016). By embracing a systems perspective, a more holistic and complete understanding of the intricate dynamics shaping these megadeltas can be achieved, including how these system dynamics may change in the future. For example, in order to effectively manage the increasing challenges of groundwater over-extraction in the Mekong Delta, decision makers need to know what would happen in the delta for a range of different future extraction scenarios. In response to this, (Minderhoud et al., 2017; Minderhoud, Middelkoop, et al., 2020) assessed the consequences of groundwater extraction, using a pre-established database of hydrogeological, groundwater, and VLM observations, and projected how subsidence may change in the future for a range of groundwater extraction scenarios in the Mekong Delta. In the lesser studied deltas, such as the Irrawaddy and the Red River deltas, such challenges are similarly present, but the required analyses are currently missing.

All five Asian deltas studied herein are undergoing changes due to multiple and incompletely understood drivers at unprecedented rates. Decisions made today will define deltaic sustainability for decades to come. It is therefore of paramount importance to advance integrated, interdisciplinary, and holistic studies, alongside continued disciplinary research, to support targeted and informed decision-making (Lázár et al., 2020; Paszkowski et al., 2021; Renaud et al., 2013). Currently, coastal flood management decisions in the five Asian deltas predominantly focus on recovering from and better preparing for future flood impacts. However, by allocating more resources and generating more knowledge on the root causes of coastal flooding, such as by quantifying the interactions discussed in this review, decision-makers can begin to target the multiple drivers of flood risk (amongst others, managing groundwater extraction, sand mining, damming, and land use, as well as climate change and SLR), rather than the impacts, which would fundamentally enhance their resilience to flood hazards in the long term.

**5.2. Status of Coastal Flood Assessments in the Five Asian Megadeltas**

When comparing the five Asian megadeltas, key observations emerge (Figure 9). The Ganges-Brahmaputra-Meghna and the Irrawaddy deltas exhibit high vulnerability to threats from extreme events, such as storm surges, cyclones, and salinization (see also Nicholls et al., 2020; Vogel et al., 2024). The future of these two deltas
<figure><img src="image_17.png" /><figcaption>**Figure 9.** A systemic perspective on the status of knowledge of the main natural drivers and human pressures contributing to coastal flooding across the five Asian megadeltas. (a.s.l = above sea level).</figcaption></figure>

is predominantly threatened by planned upstream dam construction, sand mining, groundwater extraction, relative SLR, and by the consequences of intense poldering and agricultural and aquacultural intensification. It is also important to note the challenges the Irrawaddy Delta faces due to the military takeover and outbreaks of violence since 2021, which disrupted aid and support initiatives and made efforts in vulnerability reduction and disaster risk management from regional to international levels more difficult, leading to huge uncertainties in present and future disaster risk management and its effectiveness (see also ACAPS, 2024; Vogel et al., 2024). This highlights the relevance of governance in coping with flooding, and the importance of its stability in preparing for managing future flood risk in deltas.

The Chao Phraya, Red River, and Mekong deltas are characterized by rapid urbanization, dam construction, sand mining, groundwater extraction and land-use changes (Figure 9). These deltas are primarily threatened by ongoing human development in areas exposed to coastal flooding, as well as in upstream reaches of the river systems. Across all five deltas, it is evident that human activities play a more critical role in shaping coastal
flood dynamics in the immediate term, with climate change playing a more important role toward the end of the century. It is important to note that there are processes for which the contribution to coastal flooding is hardly understood in any of the five deltas; thus, their impacts may be underestimated so far. These include the impacts of sand mining on modifying sediment and flow dynamics, therewith forcing shoreline change, as well as the various driving forces of land subsidence, where the quantification is challenged by data scarcity, amongst others.

It is evident from this review that all processes are interconnected, but that a sustainable influx of sediment to the five deltas is critical to maintain above mean sea level. Dunn et al. (2019) modeled the future change in sediment flux to 49 deltas to the end of the century, including the five deltas studied herein. Despite differences in how the processes interact across the different deltas, all of the five deltas will face profound challenges in the future if all planned upstream interventions are implemented; in the Mekong, a 77% reduction in sediment influx to the delta could be expected by the end of the century, whilst for the Irrawaddy, Ganges-Brahmaputra-Meghna, Chao Phraya, and Red River systems, reductions of 56%, 48%, 14%, and 14% are expected, respectively (Dunn et al., 2019). If realized, these sediment reductions will likely exacerbate subsidence and coastal flooding in each delta and will put even the most effective management strategies to the test.

From this review, it has also become apparent that the Ganges-Brahmaputra-Meghna and the Mekong deltas are the most widely studied deltas in South and Southeast Asia (see confidence scale of Figure 9). This is likely due to these two deltas being the biggest, most populous, and regionally most important trans-boundary (crossing 5 and 6 countries each) river systems. The Irrawaddy and Red River deltas, on the other hand, are the least studied deltas of the five assessed in this review. A variety of gaps and uncertainties persist for the Irrawaddy and Red River deltas, where baseline information (groundwater, subsidence, and sediment transport, amongst others) is entirely missing or inaccessible. This leads to challenges in attributing causes and impacts between the various interacting processes, resulting in significant assumptions needing to be made in hydrodynamic models and spatial assessments of risk. There is therefore an urgent need to establish these baseline parameters, by advancing targeted activities (see next Section 5.3), in order to conduct well-informed projections about the future of these deltas.

### 5.3. Future Research Directions in the Five Asian Megadeltas

By focusing not only on what drivers impact coastal flooding in the five Asian megadeltas, but also on how these have been quantified, this review provides a unique opportunity to highlight the key requirements for advancing quantifications of processes, as well as to shed light on the transferability of methodologies and approaches from some deltas to others. In order to advance our understanding of the interconnected processes in the five Asian deltas, more resources need to be allocated toward comprehensive data collection and monitoring. Although the Mekong and Ganges-Brahmaputra-Meghna deltas are the most widely studied megadeltas in South and Southeast Asia, improved data collection and monitoring systems apply to all five deltas, and beyond. The key data requirements include, but are not limited to: (a) a more complete network of discharge and sediment gauges in deltaic river channels; (b) a comprehensive network of sea and water level monitoring stations, as well as tide gauges; (c) regular bathymetry measurements, particularly in the near-shore areas and deltaic channels; (d) a subsidence monitoring network to enable a detailed understanding of the different drivers of elevation loss and the contribution of groundwater abstraction; and (e) regular updating of DEMs to improve elevation information for hazard assessments. To ensure this data collection is meaningful and relevant, better dissemination and data sharing is critical.

While some approaches discussed in this review are solely based on open data from satellite observations, and thus can be directly transferred to other deltas, others involve more complex data inputs or local in situ information, challenging their direct transfer to more data-sparse and inaccessible deltas. Table 3 below provides the priorities for direct and indirect reapplications of available methodologies, tools, and data sets proven in at least one delta to be transferred and advance our understanding of particular processes in the less studied deltas. Given the readily accessible information that the direct reapplication suggestions entail, these recommendations are the low hanging fruit for future research in this field across the five Asian deltas, and beyond.
<table><caption>**Table 3**
Priorities for Transferable Methodologies Across the Five Asian Megadeltas</caption><thead><tr><th>Process</th><th>Description of applicable methodology</th><th>Learning from which delta(s)</th><th>Apply to which delta(s)</th></tr></thead><tbody><tr><td colspan="4"><strong>Direct reapplication potential</strong></td></tr><tr><td>Elevation data</td><td>DEM uncertainty and suitability assessment. Obtaining the most accurate DEM and using water levels in the same vertical datum as the DEM is critical in assessments of flood risk</td><td><ul><li>Mekong (Minderhoud et al., 2019)</li><li>Irrawaddy (Seeger et al., 2023)</li></ul></td><td><ul><li>Ganges-Brahmaputra-Meghna</li><li>Chao Phraya</li><li>Red River</li></ul></td></tr><tr><td>Upstream catchment changes</td><td>Use near infra-red reflectance from Landsat satellite imagery as well as nighttime imagery to detect changes in suspended sediment concentrations as a consequence of upstream dams, changes in land use and settlement density</td><td><ul><li>Mekong (Beveridge et al., 2020; Dang et al., 2018)</li></ul></td><td><ul><li>Red River</li><li>Irrawaddy</li><li>Ganges-Brahmaputra-Meghna</li></ul></td></tr><tr><td>Sand mining</td><td>Apply an automated remote sensing-based deep learning framework for quantifying sand mining activities and sediment budgets</td><td><ul><li>Mekong (Gruel et al., 2022; Hackney et al., 2020; Kumar et al., 2023)</li></ul></td><td><ul><li>Ganges-Brahmaputra-Meghna</li><li>Irrawaddy</li><li>Red River</li></ul></td></tr><tr><td>Flood exposure assessment</td><td>Combine different (openly accessible) data sets of satellite imagery, (global) precipitation estimates and river discharge measurements with available information on the physical setting (e.g., land elevation, geomorphology) and society (e.g., population, land use) to create a first-order assessment of single and multiple flood hazard types</td><td><ul><li>Irrawaddy (Seeger et al., 2024)</li></ul></td><td><ul><li>Chao Phraya</li><li>Ganges-Brahmaputra-Meghna</li><li>Mekong</li><li>Red River</li></ul></td></tr><tr><td>Detailed flood mapping</td><td>Combine different spaceborne data sets (e.g., SPOT 4, WorldView-2, and GeoEye-1) to depict the spread of floodwater through urban environments at a very high resolution</td><td><ul><li>Chao Phraya (Liew et al., 2016)</li></ul></td><td>Urban centers of:<ul><li>Ganges-Brahmaputra-Meghna</li><li>Red River</li><li>Irrawaddy</li><li>Mekong</li></ul></td></tr><tr><td colspan="4"><strong>Indirect reapplication potential</strong></td></tr><tr><td>Discharge</td><td>Satellite altimetry-derived discharge time series, ideally at multiple locations in the river basin. Requires in situ measurements to be available as well</td><td><ul><li>Mekong (Biancamaria et al., 2017; Birkinshaw et al., 2010; Kim et al., 2019)</li><li>Irrawaddy (Frappart et al., 2015)</li><li>Ganges-Brahmaputra-Meghna (Papa et al., 2010, 2012)</li></ul></td><td><ul><li>Red River</li><li>Chao Phraya</li></ul></td></tr><tr><td>Vertical Land Motion (VLM)</td><td>Create 2D VLM models that go beyond 1D calculations to project scenarios of VLM. This requires detailed local subsurface data to be collected and can be technically demanding to set up</td><td><ul><li>Mekong (Lovelock et al., 2015; Minderhoud, Middelkoop, et al., 2020; Zoccarato et al., 2018)</li></ul></td><td><ul><li>Ganges-Brahmaputra-Meghna</li><li>Red River</li><li>Chao Phraya</li><li>Irrawaddy</li></ul></td></tr></tbody></table>

In addition to the recommendations in Table 3, we are also at an exciting moment where more satellite missions have recently been, or will soon be, launched that will offer openly available information on water and sediment dynamics at unprecedented spatial and temporal resolutions. These will be critical in monitoring changing coastal flood patterns and answering some of the most pressing questions across the five Asian megadeltas. It is imperative that these advances in remote sensing are also reflected in continued and enhanced efforts on data collection, as in situ data is absolutely vital to ground truth remote sensing observations and modeling studies. Lastly, research should be aware of delta management activities and priorities in each case. In
some cases this may provide the funds and resources for these activities, and the science will be coproduced with relevant stakeholders.

## 6. Conclusion

This review has provided an in-depth presentation of the state-of-the-art understanding of coastal flood drivers, and how these can be assessed, for five critical Asian megadeltas, although the findings are relevant for deltas globally. In order to advance research in this region and build flood resilience in the face of future uncertainty, it is critical that efforts focus on filling data gaps and leverage remote sensing, including improvements in the accessibility to long-term, data sets, also in combination with reliable in situ observations. Satellite observations and modeling efforts have contributed to better characterizing coastal flooding in megadeltas, but challenges remain due to uncertainties and limitations in spatial and temporal resolutions. However, remote sensing, including new and upcoming missions, holds great potential, especially through knowledge transfer between deltas. Therefore, we recommend making existing in situ archives (such as for river discharge, sediment dynamics, or GNSS observations) openly available as data, while space agencies should continue to ensure free access to satellite observations.

This review has underscored the critical role that human activities play in shaping near-term coastal flood dynamics in deltas, while emphasizing the growing significance of climate change impacts in the long term. By integrating the discussed drivers and pressures across temporal and spatial scales, targeted and effective strategies can be developed to tackle coastal flooding in the Asian megadeltas and beyond, enhancing resilience and sustainability in the face of changing conditions. This will require embracing a systems perspective that includes both on-the-ground information and remote sensing advances to effectively address the intricate dynamics of coastal flooding in these systems, which is essential for breaking cycles of risk and enhancing the long-term resilience of deltaic communities.

## Data Availability Statement

No new data was created or used for this research.

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## Acknowledgments

This research was supported by the International Space Science Institute (ISSI) in Bern, through ISSI International Team project #548: "Past, Present and Future of the Asian Mega-Deltas: The Role of Space Observations." This work also benefits from the support of the French CNES TOSCA program through the DeltAsia project. M.B., N.L., J.K., M.K., and F.P. were supported by the French Research Agency (ANR) under the DELTA project (ANR-17-CE03-0001). P. S.J.M. is supported by the Dutch Science Foundation (NWO) under the Drowning Deltas project (Veni-TTW-20231). K.S. is supported by the German Research Foundation (DFG) under the Rising Sea And Sinking Land project (project number 411257639). M.A.K was supported by DFG under SFB 1502/1-2022 (project number 450058266). The authors express their gratitude to K. Lambeck for providing the computations of his models for this review. The authors are grateful to A. Cazenave for providing valuable insights and helpful comments on the latest version of the manuscript.
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