Editing IPCC:AR6/SROCC/Chapter-4 (section)

=== 4.3.2 Dimensions of Exposure and Vulnerability to Sea Level Rise ===

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<span id="point-of-departure"></span>
==== 4.3.2.1 Point of Departure ====

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==== 4.3.2.2 Settlement Trends ====

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Major changes in coastal settlement patterns have occurred in the course of the 20th century, and are continuing to take place due to various complex interacting processes (Moser et al., 2012; Bennett et al., 2016) that together configure and concentrate exposure and vulnerability to climate change and SLR along the coast (Newton et al., 2012; Bennett et al., 2016) . These processes include population growth and demographic changes (Smith, 2011; Neumann et al., 2015) , urbanisation and a rural exodus, tourism development, and displacement or (re)settlement of some indigenous communities (Ford et al., 2015) . This has resulted in a growing number of people living in the Low Elevation Coastal Zone (LECZ, coastal areas below 10 m of elevation; around 11% of the world’s population in 2010; Neumann et al., 2015; Jones and O’Neill, 2016; Merkens et al., 2016) and in significant infrastructure and assets being located in risk-prone areas ( ''high confidence'' ). High density coastal urban development is commonplace in both developed and developing countries, as documented in recent case studies, for example in Canada (Fawcett et al., 2017) , China (Yin et al., 2015; Lilai et al., 2016; Yan et al., 2016) , Fiji (Hay, 2017) , France (Genovese and Przyluski, 2013; Chadenas et al., 2014; Magnan and Duvat, 2018) , Israel (Felsenstein and Lichter, 2014) , Kiribati (Storey and Hunter, 2010; Duvat et al., 2013) , New Zealand (Hart, 2011) and the USA (Heberger, 2012; Grifman et al., 2013; Liu et al., 2016b) . This has implications for levels of SLR risk at regional and local scales ( ''medium evidence, high agreement'' ). In Latin America and the Caribbean, for example, it is estimated that 6–8% of the population live in areas that are at high or very high risk of being affected by coastal hazards (Reguero et al., 2015; Calil et al., 2017; Villamizar et al., 2017) , with higher percentages in Caribbean islands (Mycoo, 2018) . In the Pacific, ~57% of Pacific Island countries’ built infrastructure are located in risk-prone coastal areas (Kumar and Taylor, 2015) . In Kiribati, due to the flow of outer, rural populations to limited, low-elevated capital islands, together with constraints inherent in the sociocultural land tenure system, the built area located &lt;20 m from the shoreline quadrupled between 1969 and 2007–2008 (Duvat et al., 2013) . Other examples of rural exodus are reported in the recent literature, for example in the Maldives (Speelman et al., 2017) .

Population densification also affects rural areas’ exposure and vulnerability, and interacts with other factors shaping settlement patterns, such as the fact that ‘indigenous peoples in multiple geographical contexts have been pushed into marginalised territories that are more sensitive to climate impacts, in turn limiting their access to food, cultural resources, traditional livelihoods and place-based knowledge (…) [and therefore undermining] aspects of social-cultural resilience’ (Ford et al., 2016b, p. 350) . In the Pacific, for example, ‘while traditional settlements on high islands (…) were often located inland, the move to coastal locations was encouraged by colonial and religious authorities and more recently through the development of tourism’ (Ballu et al., 2011; Nurse et al., 2014, p. 1623; Duvat et al., 2017) . Although these population movements are orders of magnitude smaller than the global trends described above, they play a critical role at the very local scale in explaining the emergence of, or changes in exposure and vulnerability. In atoll contexts, for example, the growing pressure on freshwater resources together with a loss in local knowledge (e.g., how to collect water from palm trees), result in increased exposure of communities to brackish, polluted groundwater, inducing water insecurity and health problems (Storey and Hunter, 2010; Lazrus, 2015) .

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==== 4.3.2.4 Other Human Dimensions ====

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The development of local scale case studies from a social science perspective, for example, in the Arctic (Ford et al., 2012 <sup>[[#fn:r1075|1075]]</sup> ; Ford et al., 2014 <sup>[[#fn:r1076|1076]]</sup> ) , small islands (Petzold, 2016 <sup>[[#fn:r1077|1077]]</sup> ; Duvat et al., 2017 <sup>[[#fn:r1078|1078]]</sup> ) and within cities (Rosenzweig and Solecki, 2014 <sup>[[#fn:r1079|1079]]</sup> ; Paterson et al., 2017 <sup>[[#fn:r1080|1080]]</sup> ; Texier-Teixeira and Edelblutte, 2017 <sup>[[#fn:r1081|1081]]</sup> ) or at the household level (Koerth et al., 2014 <sup>[[#fn:r1082|1082]]</sup> ) support a better understanding of the anthropogenic drivers of exposure and vulnerability. Four examples of drivers that were only emerging at the time of the AR5 are discussed below. Very importantly, another major emerging dimension that is not discussed here but rather in Section 4.4.4, relates to power asymmetries, politics, and the prevailing political economy, which are important drivers of exposure and vulnerability to SLR-related coastal hazards, and consequently adaptation prospects (Eriksen et al., 2015 <sup>[[#fn:r1083|1083]]</sup> ; Dolšak and Prakash, 2018 <sup>[[#fn:r1084|1084]]</sup> ) . Recent literature provides examples in coastal megacities like Jakarta, Indonesia (Shatkin, 2019 <sup>[[#fn:r1085|1085]]</sup> ) as well as in smaller cities, like Maputo, Mozambique (Broto et al., 2015 <sup>[[#fn:r1086|1086]]</sup> ) and Surat, India (Chu, 2016a; Chu, 2016b) , and many other coastal cities and settlements around the world ( ''high confidence'' ; Jones et al., 2015 <sup>[[#fn:r1087|1087]]</sup> ; Allen et al., 2018 <sup>[[#fn:r1088|1088]]</sup> ; Hughes et al., 2018 <sup>[[#fn:r1089|1089]]</sup> ; Sovacool, 2018 <sup>[[#fn:r1090|1090]]</sup> ) .

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<span id="gender-inequality"></span>
===== 4.3.2.4.1 Gender inequality =====

Gender inequality came to prominence only recently in climate change studies (~15 years ago; see Pearse, 2017 <sup>[[#fn:r1091|1091]]</sup> ) . In light of sea-related hazards and SLR specifically, the issue is still mainly investigated in the context of developing countries, although growing attention is paid to the issue in developed countries (e.g., Lee et al., 2015; Pearse, 2017) . Recent studies in southern coastal Bangladesh, for example, show that women get less access than men to climate- and disaster-related information (both emergency information and training programmes), decision making processes at the household and community levels, economic resources including financial means such as micro-credit, land ownership, and mobility within and outside the villages (Rahman, 2013 <sup>[[#fn:r1092|1092]]</sup> ; Alam and Rahman, 2014 <sup>[[#fn:r1093|1093]]</sup> ; Garai, 2016 <sup>[[#fn:r1094|1094]]</sup> ) . Gender inequity may be inherent in unfavourable background conditions (higher illiteracy rates, deficiencies in food and calories intake and poorer health conditions) as a result of, among other things, traditions, social norms and patriarchy. Together, these barriers disadvantage women more than men in developing effective responses to anticipate gradual environmental changes such as persistent coastal erosion, flooding and soil salinisation ( ''medium evidence, high agreement'' ). Such conclusions are in line with the literature on gender inequality and climate change at large (Alston, 2013 <sup>[[#fn:r1095|1095]]</sup> ; Pearse, 2017 <sup>[[#fn:r1096|1096]]</sup> ) , thus suggesting no major SLR-inherent specificities.

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<span id="loss-of-indigenous-knowledge-and-local-knowledge"></span>
===== 4.3.2.4.2 Loss of indigenous knowledge and local knowledge =====

Despite the identification of this issue in AR4, its treatment in AR5 remained limited. Recent literature partly focussing on SLR reaffirms that indigenous knowledge and local knowledge (IK and LK; Cross-Chapter Box 4 in Chapter 1 and Glossary) are key to determining how people recognise and respond to environmental risk (Bridges and McClatchey, 2009 <sup>[[#fn:r1097|1097]]</sup> ; Lefale, 2010 <sup>[[#fn:r1098|1098]]</sup> ; Leonard et al., 2013 <sup>[[#fn:r1099|1099]]</sup> ; Lazrus, 2015 <sup>[[#fn:r1100|1100]]</sup> ), and therefore to increasing adaptive capacity and reducing long-term vulnerability (Ignatowski and Rosales, 2013 <sup>[[#fn:r1101|1101]]</sup> ; McMillen et al., 2014 <sup>[[#fn:r1102|1102]]</sup> ; Hesed and Paolisso, 2015 <sup>[[#fn:r1103|1103]]</sup> ; Janif et al., 2016 <sup>[[#fn:r1104|1104]]</sup> ; Morrison, 2017 <sup>[[#fn:r1105|1105]]</sup> ).

IK and LK contribute both as a foundation for and an outcome of customary resource management systems aimed at regulating resource use and securing critical ecosystem protection (examples in Indonesia; Hiwasaki et al., 2015 <sup>[[#fn:r1106|1106]]</sup> ), structuring the relationship between people and authorities, and framing and maintaining a strong sense of place in the community (examples in Timor Leste; Hiwasaki et al., 2015 <sup>[[#fn:r1107|1107]]</sup> ). In turn, this allows local communities to predict and prepare for both sudden shock events that have historical precedent and, when IK and LK are embedded in day-to-day rituals and decision making processes, to also anticipate the consequences of gradual changes, as in sea level (examples in Indonesia; Hiwasaki et al., 2015 <sup>[[#fn:r1108|1108]]</sup> ). Customary resource management systems based on IK and elders’ leadership—for instance, Rahui in French Polynesia (Gharasian, 2016 <sup>[[#fn:r1109|1109]]</sup> ), or Mo in the Marshall Islands (Bridges and McClatchey, 2009)—also allow communities to diversify access to marine and terrestrial resources using seasonal calendars, to ensure collective food and water security, and to maintain ecological integrity (McMillen et al., 2014 <sup>[[#fn:r1111|1111]]</sup> ). In rural Pacific atolls, traditional food preservation and storage (e.g., storing germinated coconuts or drying fish) still play a role in anticipating disruptions in natural resource availability (Campbell, 2015 <sup>[[#fn:r1112|1112]]</sup> ; Lazrus, 2015 <sup>[[#fn:r1113|1113]]</sup> ). Such practices have enabled the survival of isolated communities from the Arctic to tropical islands in constraining sea environments for centuries to millennia (McMillen et al., 2014 <sup>[[#fn:r1114|1114]]</sup> ; Nunn et al., 2017a <sup>[[#fn:r1115|1115]]</sup> ). Morrison (2017) argues that IK and LK can also play a role in supporting internal migration in response to SLR, by avoiding social and cultural uprooting (Cross-Chapter Box 4 in Chapter 1).

In some specific contexts, climate change will also imply no-analogue changes, such as rapid ice-melt and changing conditions in the Arctic that have no precedent in the modern era, and could thus limit the relevance of IK and LK in efforts to address significantly different circumstances. Except in these specific situations, the literature suggests that the loss of IK and LK, and related social norms and mechanisms, will increase populations’ exposure and vulnerability to SLR impacts (Nakashima et al., 2012 <sup>[[#fn:r1117|1117]]</sup> ). The literature notably points out that modern, externally-driven socioeconomic dynamics, such as the introduction of imported food (noodles, rice, canned meat and fish, etc.), diminish the cultural importance of IK-based practices and diets locally, together with introducing dependency on monetisation and external markets (Hay, 2013; Campbell, 2015 <sup>[[#fn:r1118|1118]]</sup> ).

As a result, the loss of IK and LK may increase long-term vulnerability to SLR ( ''medium evidence, high agreement'' ). Given that IK and LK are largely based on observing and ‘making sense’ of the surrounding environment (moon, waves, winds, animal behaviours, topography, etc.), such a loss reflects a more general concern about the weakening of environmental connectedness in contemporary societies, which is not limited to remote, rural and developing communities ( ''medium confidence'' ). In developed contexts too, the loss of LK has played a critical role in recent coastal disasters (e.g., Katrina in 2005 in the USA, Kates et al., 2006) and increasing vulnerability to SLR (e.g., Newton and Weichselgartner, 2014; Wong et al., 2014 <sup>[[#fn:r1119|1119]]</sup> ).

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<span id="social-capital"></span>
===== 4.3.2.4.3 Social capital =====

Coastal communities draw on social structures and capabilities that can reduce risk and increase adaptive capacity in the face of coastal hazards (Aldrich, 2017 <sup>[[#fn:r1120|1120]]</sup> ; Petzold, 2018 <sup>[[#fn:r1121|1121]]</sup> ) . Although the term is subject to debate (Meyer, 2018 <sup>[[#fn:r1122|1122]]</sup> ) , social capital—that is, the level of cohesion between individuals, between groups of individuals, and between people and institutions, within and between communities—is considered to be a key enabler for collective action to reduce risk and build adaptive capacity (Adger, 2010 <sup>[[#fn:r1123|1123]]</sup> ; Aldrich and Meyer, 2015 <sup>[[#fn:r1124|1124]]</sup> ; Petzold and Ratter, 2015 <sup>[[#fn:r1125|1125]]</sup> ) . Levels of social capital can be influenced by underlying social processes, such as socioeconomic (in)equalities, gender issues, health, social networks and social media. It applies to both developing and developed countries, for example in densely populated deltas (Jordan, 2015 <sup>[[#fn:r1126|1126]]</sup> ) , European coasts (Jones and Clark, 2014 <sup>[[#fn:r1127|1127]]</sup> ; Petzold, 2016 <sup>[[#fn:r1128|1128]]</sup> ) , Asian urban or semi-urban coastal areas (Lo et al., 2015 <sup>[[#fn:r1129|1129]]</sup> ; Triyanti et al., 2017 <sup>[[#fn:r1130|1130]]</sup> ) and Pacific islands (Neef et al., 2018 <sup>[[#fn:r1131|1131]]</sup> ) . Social capital framed as an enabler for reducing vulnerability has been studied in the context of extreme events (risk prevention mechanisms, emergency responses and post-crisis actions) and collective environmental management (e.g., replanting mangroves, beach cleaning, etc.). Social capital also enables adaptation prospects. For example, its role has been explored in public acceptability of long-term coastal adaptation policies in the UK (Jones and Clark, 2014 <sup>[[#fn:r1132|1132]]</sup> ; Jones et al., 2015 <sup>[[#fn:r1133|1133]]</sup> ) . The role of social capital in building resilience to climate stress in coastal Bangladesh was explored by Jordan (2015) , who found complex and even contradictory interactions between social capital and resilience to climate stress. Among others, Jordan (2015) also advises caution about uncritical importation of such Westernised concepts in seeking to understand and address coastal vulnerability in developing countries.

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<span id="risk-perception"></span>
===== 4.3.2.4.4 Risk perception =====

Risk perception, which is context-specific and varies from one individual to another, may influence communities’ exposure and vulnerability as it shapes authorities’ and people’s attitudes towards sudden and slow onset hazards, as shown by Terpstra (2011) <sup>[[#fn:r1134|1134]]</sup> , Lazrus (2015), Elrick-Barr et al. (2017) <sup>[[#fn:r1135|1135]]</sup> and O’Neill et al. (2016) in the Netherlands, Tuvalu, Australia and Ireland, respectively. The progressive discounting of coastal hazard risks and subsequent loss of risk memory also played a role in coastal disasters such as Hurricane Katrina in 2005 in the USA (Burby, 2006 <sup>[[#fn:r1136|1136]]</sup> ; Kates et al., 2006 <sup>[[#fn:r1137|1137]]</sup> ) and Storm Xynthia in 2010 in France (Vinet et al., 2012 <sup>[[#fn:r1138|1138]]</sup> ; Genovese and Przyluski, 2013 <sup>[[#fn:r1139|1139]]</sup> ; Chadenas et al., 2014 <sup>[[#fn:r1140|1140]]</sup> ).

Risk perceptions stem from intertwined predictors such as ‘gender, political party identification, cause-knowledge, impact-knowledge, response-knowledge, holistic affect, personal experience with extreme weather events, [social norms] and biospheric value orientations’ (Kellens et al., 2011 <sup>[[#fn:r1141|1141]]</sup> ; Carlton and Jacobson, 2013 <sup>[[#fn:r1142|1142]]</sup> ; Lujala et al., 2015 <sup>[[#fn:r1143|1143]]</sup> ; van der Linden, 2015, p. 112; Weber, 2016 <sup>[[#fn:r1144|1144]]</sup> ; Elrick-Barr et al., 2017 <sup>[[#fn:r1145|1145]]</sup> ; Goeldner-Gianella et al., 2019 <sup>[[#fn:r1146|1146]]</sup> ). In general, there is a lack of education, training and thus knowledge and literacy on recent and projected trends in sea level, which compromises ownership of science facts and projections at all levels, from individuals and institutions to society at large.

While some studies have begun to highlight the influence of the distance from the sea on risk perceptions (Milfont et al., 2014 <sup>[[#fn:r1147|1147]]</sup> ; Lujala et al., 2015 <sup>[[#fn:r1148|1148]]</sup> ; O’Neill et al., 2016), there is still little knowledge about how risk perceptions vary across different geographical and social contexts, and how this influences exposure and vulnerability to coastal hazards (e.g., Terpstra, 2011; van der Linden, 2015 <sup>[[#fn:r1149|1149]]</sup> ). There is a critical lack of studies specifically addressing SLR. Some recent works conducted in coastal Australia suggest that while people are confident about their ability to cope with an already experienced event, when it comes to SLR, the dominant narrative is articulated around the barriers related to the ‘uncertainty in the nature and scale of the impacts as well as the response options available’ (Elrick-Barr et al., 2017, p. 1147). Similar conclusions have been highlighted in the Caribbean islands of St. Vincent (Smith, 2018 <sup>[[#fn:r1150|1150]]</sup> ) and the Bahamas (Thomas and Benjamin, 2018 <sup>[[#fn:r1151|1151]]</sup> ). SLR is rarely addressed separately from sea-related extreme events, which masks a crucial difference between already-observed and delayed impacts. Climate change is considered a “distant psychological risk” (Spence et al., 2012 <sup>[[#fn:r1152|1152]]</sup> ), making it and SLR per se ‘markedly different from the way that our ancestors have traditionally perceived threats in their local environment’ (Milfont et al., 2014 <sup>[[#fn:r1153|1153]]</sup> ; Lujala et al., 2015 <sup>[[#fn:r1154|1154]]</sup> ; van der Linden, 2015, p. 112; O’Neill et al., 2016).

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<span id="towards-a-synthetic-understanding-of-the-drivers-of-exposure-and-vulnerability"></span>
==== 4.3.2.5 Towards a Synthetic Understanding of the Drivers of Exposure and Vulnerability ====

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Recent literature confirms that anthropogenic drivers played an important role, over the last century, in increasing exposure and vulnerability worldwide, and indicates that they will continue to do so in the absence of adaptation (medium evidence, high agreement). Some scholars argue that ‘even with pervasive and extensive environmental change associated with ~2oC warming, it is non-climatic factors that primarily determine impacts, response options and barriers to adapting’ (Ford et al., 2015, p. 1046). Although it is the interaction of climate and non-climate factors that eventually determine the level of impacts, acknowledging the role of a range of purely anthropogenic drivers has important implications for action. It suggests that major action can be taken now to enhance long-term adaptation prospects, notwithstanding uncertainty about local RSL rise and resultant impacts in the distant future (medium evidence, high agreement; Magnan et al., 2016 <sup>[[#fn:r1155|1155]]</sup> ). Acting on the human-driven drivers and root causes of vulnerability could yield co-benefits, for example by improving the state and condition of coastal ecosystems – and hence the capacity to cope with or adapt to SLR impacts – or, in deltaic regions, lowering the rates of anthropogenic subsidence and, in turn, minimising changes in sea level.

In addition, coastal ecosystem degradation is acknowledged as another major non-climatic driver of exposure and vulnerability (high confidence). The ability of coastal ecosystems to serve as a buffer zone between the sea and human assets (settlements and infrastructure), and to provide regulating services with respect to SLR-related coastal hazards (including inundation and salinisation), is progressively being lost due to coastal squeeze, pollution, and habitat and land degradation mainly due to land-use conversion.

We now better understand the diversity and interactions of the climate and non-climate drivers of exposure and vulnerability, as well as their dynamics over time (Bennett et al., 2016 <sup>[[#fn:r1156|1156]]</sup> ; Duvat et al., 2017 <sup>[[#fn:r1157|1157]]</sup> ). As a result, it is now realised how many context-specificities interact (including geography, economic development, social inequity, power and politics, and risk perceptions) and play a critical role in shaping the direction and influence of individual drivers and of their possible combinations on the ground (medium evidence, high agreement; Eriksen et al., 2015 <sup>[[#fn:r1158|1158]]</sup> ; Hesed and Paolisso, 2015 <sup>[[#fn:r1159|1159]]</sup> ; McCubbin et al., 2015 <sup>[[#fn:r1160|1160]]</sup> ). This also provides a stronger foundation to identify the range of possible responses (Sections 1.6.1, 1.6.2 and 4.4.3) to observed impacts and projected risks, as well as critical areas of action to enhance adaptation pathways (Section 4.4.4).

Recent studies (e.g., cited in Sections 4.3.2.1.1, 4.3.2.2, 4.3.2.4.2 and 4.3.2.4.4) also confirm AR5 conclusions that both developing and developed countries are exposed and vulnerable to SLR (high confidence).

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<span id="terrestrial-processes-shaping-coastal-exposure-and-vulnerability"></span>
==== 4.3.2.3 Terrestrial Processes Shaping Coastal Exposure and Vulnerability ====

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Coastal areas, including deltas, are highly dynamic as they are affected by natural and/or human-induced processes locally or originating from both the land and the sea. Changes within the catchment can therefore have severe consequences for coastal areas in terms of sediment supply, pollution, and/or land subsidence. Sediment supply reaching the coast is a critical factor for delta sustainability (Tessler et al., 2018 <sup>[[#fn:r1037|1037]]</sup> ) and has declined drastically in the last few decades due to dam construction, land use changes and sand mining (Ouillon, 2018 <sup>[[#fn:r1038|1038]]</sup> ; ''high confidence'' ). For instance, Anthony et al. (2015) reported large-scale erosion affecting over 50% of the delta shoreline in the Mekong delta between 2003 and 2012, which was attributed in part to a reduction in surface-suspended sediments in the Mekong river potentially linked to dam construction within the river basin, sand mining in the river channels, and land subsidence linked to groundwater over-abstraction locally. Schmitt et al. (2017) <sup>[[#fn:r1039|1039]]</sup> demonstrated that these and other drivers in sediment budget changes can have severe effects on the very physical existence of the Mekong delta by the end of this century, with the most important single driver leading to inundation of large portions of the delta being ground-water pumping induced land subsidence. Thi Ha et al. (2018) estimated the decline in sediment supply to the Mekong delta to be around 75% between the 1970s and the period 2009–2016. In the Red River, the construction of the Hoa Binh Dam in the 1980s led to a 65% drop in sediment supply to the sea (Vinh et al., 2014 <sup>[[#fn:r1040|1040]]</sup> ). Based on projections of historical and 21st century sediment delivery to the Ganges-Brahmaputra-Meghna, Mahanadi and Volta deltas, Dunn et al. (2018) showed that these deltas fall short in sediment and may not be able to maintain their current elevation relative to sea level, suggesting increasing salinisation, erosion, flood hazards and adaptation demands.

Another rarely considered factor is the shift in TC climatology which also plays a critical role in explaining changes in fluvial suspended sediment loads to deltas as demonstrated by Darby et al. (2016) <sup>[[#fn:r1041|1041]]</sup> , again for the Mekong delta. More generally, most conventional engineering strategies that are commonly employed to reduce flood risk (including levees, sea walls, and dams) disrupt a delta’s natural mechanisms for building land. These approaches are rather short-term solutions which overall reduce the long-term resilience of deltas (Tessler et al., 2015 <sup>[[#fn:r1042|1042]]</sup> ; Welch et al., 2017 <sup>[[#fn:r1043|1043]]</sup> ). Systems particularly prone to flood risk due to anthropogenic activities include North America’s Mississippi River delta, Europe’s Rhine River delta, and deltas in East Asia (Renaud et al., 2013 <sup>[[#fn:r1044|1044]]</sup> ; Day et al., 2016 <sup>[[#fn:r1045|1045]]</sup> ). In regions where suspended sediments are still available in relatively large quantities, rates of sedimentation can vary depending on multiple factors, including the type of infrastructure present locally, as was shown by Rogers and Overeem (2017) <sup>[[#fn:r1046|1046]]</sup> for the Ganges-Brahmaputra-Meghna (Bengal) delta in Bangladesh as well as seasonal differences in sediment supply and place of deposition. For example, in meso-tidal and macro-tidal estuaries, during floods most of the sediments are depositing in the coastal zones and a large part of these sediments are brought back to the estuary during the low flow season by tidal pumping. This can lead to significantly higher deposition rates in the dry season as shown by Lefebvre et al. (2012) in the lower Red River estuary and by Gugliotta et al. (2018) <sup>[[#fn:r1048|1048]]</sup> in the Mekong delta. Enhanced sedimentation further upstream in estuaries and a silting-up of estuarine navigation channels can have high economic consequences for cities with a large estuarine harbour. In Haiphong city, in North Vietnam, the authorities decided to build a new harbour further downstream, for a cost estimated at 2 billion USD (Duy Vinh et al., 2018).

Overall, reduced freshwater and sediment inputs from the river basins are critical factors determining delta sustainability (Renaud et al., 2013 <sup>[[#fn:r1049|1049]]</sup> ; Day et al., 2016 <sup>[[#fn:r1050|1050]]</sup> ). In some contexts, this can be addressed through basin-scale management which allow more natural flows of water and sediments through the system, including methods for long-term flood mitigation such as improved river-floodplain connectivity, the controlled redirection of a river (i.e., avulsions) during times of elevated sediment loads, the removal of levees, and the redirection of future development to lands less prone to extreme flooding (Renaud et al., 2013 <sup>[[#fn:r1051|1051]]</sup> ; Day et al., 2016 <sup>[[#fn:r1052|1052]]</sup> ; Brakenridge et al., 2017 <sup>[[#fn:r1053|1053]]</sup> ). These actions could potentially increase the persistence of coastal landforms in the context of SLR. Next to decreasing sediment inputs to the coast, river bed and beach sand mining has been shown to contribute to shoreline erosion, for example, for shorelines of Crete (Foteinis and Synolakis, 2015 <sup>[[#fn:r1054|1054]]</sup> ), and several sub-Saharan countries such Kenya, Madagascar, Mozambique, South Africa and Tanzania (UNEP, 2015 <sup>[[#fn:r1055|1055]]</sup> ). At the global scale, 24% of the world’s sandy beaches are eroding at rates exceeding 0.5 m yr <sup>–1</sup> , while 28% are accreting for the period 1984–2016. The largest and longest eroding sandy coastal stretches are in North America (Texas; Luijendijk et al., 2018 <sup>[[#fn:r1056|1056]]</sup> ).

Shoreline erosion leads to coastal squeeze if the eroding coastline approaches fixed and hard built or natural structures as noted in AR5 (Pontee, 2013 <sup>[[#fn:r1057|1057]]</sup> ; Wong et al., 2014 <sup>[[#fn:r1058|1058]]</sup> ), a process to which SLR also contributes (Doody, 2013 <sup>[[#fn:r1059|1059]]</sup> ; Pontee, 2013 <sup>[[#fn:r1060|1060]]</sup> ). The AR5 further noted that coastal squeeze is expected to accelerate due to rising sea levels (Wong et al., 2014 <sup>[[#fn:r1061|1061]]</sup> ). Doody (2013) characterised coastal squeeze as coastal habitats being pushed landward through the effects of SLR and other coastal processes on the one hand and, on the other hand, the presence of static natural or artificial barriers effectively blocking this migration, thereby squeezing habitats into an ever narrowing space. Distinctions are made between coastal squeeze being limited to (1) the consequences of SLR vs. other environmental changes on the coastline and (2) the presence of only coastal defence structures vs. natural sloping land or other artificial infrastructure (Pontee, 2013 <sup>[[#fn:r1062|1062]]</sup> ). Recent publications have emphasised coastal squeeze related to SLR, although inland infrastructure blocking habitat migration is not necessarily limited to defence structures (Torio and Chmura, 2015 <sup>[[#fn:r1063|1063]]</sup> ; McDougall, 2017 <sup>[[#fn:r1064|1064]]</sup> ). Coastal ecosystem degradation by human activities leading to coastal erosion is also an important consideration (McDougall, 2017 <sup>[[#fn:r1065|1065]]</sup> ). Taking into consideration the current challenges to attribute coastal impacts to SLR (Section 4.3.3.1), it can be hypothesised here that as long as SLR impacts remain moderate, the dominant driving factor of coastal squeeze will be anthropogenic land-based development (e.g., Section 4.3.2.2). With higher SLR scenarios and in the case of no further development at the coast, SLR may become the dominant driver before the end of this century.

Preserved coastal habitats can play important roles in reducing risks related to some coastal hazards and initiatives are being put in place to reduce coastal squeeze, such as managed realignment (Sections 4.1, 4.4.3.1) which includes removing inland barriers (Doody, 2013 <sup>[[#fn:r1066|1066]]</sup> ). Coastal squeeze can lead to degradation of coastal ecosystems and species (Martínez et al., 2014 <sup>[[#fn:r1067|1067]]</sup> ), but if inland migration is unencumbered, observation data and modelling have shown that the net area of coastal ecosystems could increase under various scenarios of SLR, depending on the ecosystems considered (Torio and Chmura, 2015 <sup>[[#fn:r1068|1068]]</sup> ; Kirwan et al., 2016 <sup>[[#fn:r1069|1069]]</sup> ; Mills et al., 2016 <sup>[[#fn:r1070|1070]]</sup> ). However, recent modelling research has shown that rapid SLR in a context of coastal squeeze could be detrimental to the areal extent and functionality of coastal ecosystems (Mills et al., 2016 <sup>[[#fn:r1071|1071]]</sup> ) and, for marshes, could lead to a reduction of habitat complexity and loss of connectivity, thus affecting both aquatic and terrestrial organisms (Torio and Chmura, 2015 <sup>[[#fn:r1072|1072]]</sup> ). Contraction of marsh extent is also identified by Kirwan et al. (2016) <sup>[[#fn:r1073|1073]]</sup> when artificial barriers to landward migration are in place. Adaptation to SLR therefore needs to account for both development and conservation objectives so that trade-offs between protection and realignment that satisfy both objectives can be identified (Mills et al., 2016 <sup>[[#fn:r1074|1074]]</sup> ).

In summary, catchment-scale changes have very direct impacts on the coastline, particularly in terms of water and sediment budgets ( ''high confidence'' ). The changes can be rapid and modify coastlines over short periods of time, outpacing the effects of SLR and leading to increased exposure and vulnerability of social-ecological systems ( ''high confidence'' ). Without losing sight of this fact, management of catchment-level processes contribute to limiting rapid increases in exposure and vulnerability. Further to hinterland influences, coastal squeeze increases coastal exposure as well as vulnerability by the loss of a buffer zone between the sea and infrastructure behind the habitat undergoing coastal squeeze. The clear implication is that coastal ecosystems progressively lose their ability to provide regulating services with respect to coastal hazards, including as a defence against SLR driven inundation and salinisation ( ''high confidence'' ). Vulnerability is also increased if freshwater resources become salinised, particularly if these resources are already scarce. The exposure and vulnerability of human communities is exacerbated by the loss of other provisioning, supporting and cultural services generated by coastal ecosystems, which is especially problematic for coast-dependent communities ( ''high confidence'' ).

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