Subsidence and human influences in mega deltas: The case of the Ganges-Brahmaputra-Meghna

Mechanisms of cholera transmission via environment in India and Bangladesh: state of the science review

OBJECTIVES

Cholera has a long history in India and Bangladesh, the region where six out of the past seven global pandemics have been seeded. The changing climate and growing population have led to global cholera cases remaining high despite a consistent improvement in the access to clean water and sanitation. We aim to provide a holistic overview of variables influencing environmental cholera transmission within the context of India and Bangladesh, with a focus on the mechanisms by which they act.

CONTENT

We identified 56 relevant texts (Bangladesh n = 40, India n = 7, Other n = 5). The results of the review found that cholera transmission is associated with several socio-economic and environmental factors, each associated variable is suggested to have at least one mediating mechanism. Increases in ambient temperature and coastal sea surface temperature support cholera transmission via increases in plankton and a preference of Vibrio cholerae for warmer waters. Increased rainfall can potentially support or reduce transmission via several mechanisms.

SUMMARY AND OUTLOOK

Common issues in the literature are co-variance of seasonal factors, limited access to high quality cholera data, high research bias towards research in Dhaka and Matlab (Bangladesh). A specific and detailed understanding of the relationship between SST and cholera incidence remains unclear.

Can river flow prevent land subsidence in urban areas?

Land subsidence, a silent death, occurs due to various factors like significant reduction in groundwater (GW) levels. It is a widespread phenomenon with irreparable consequences on buildings, infrastructures, and, in severe cases, groundwater aquifers. This study aims to assess the impact of river flow on the acceleration and control of land subsidence in an arid and semi-arid region. To achieve this goal, we analyze the interconnection between GW and SW and investigate the role of the Zayandeh-Rud River's drying up on land subsidence in the Isfahan-Borkhar aquifer in Iran's central plateau. To facilitate this assessment, we utilize the Interferometric Synthetic Aperture Radar (InSAR) technique to estimate the vertical deformation velocity of the aquifer (average land subsidence rate). The results show that the Isfahan-Borkhar aquifer has experienced a significant annual decline of more than 25 m, with an alarming rate exceeding 0.8 m/year. Our analysis of 31 piezometric wells (P-Wells) from 2000 to 2022 reveals a downward monotonic (in 16 P-Wells) and nonmonotonic (in 12 P-Wells) trend in groundwater table changes. Moreover, the GW table in the P-Wells near the river depends entirely on river flow. Furthermore, our findings indicate that river regulation exerts a dominant role in the control of land subsidence. Consequently, when water flows in the Zayandeh-Rud River, the rate of land subsidence declines significantly, particularly in urban regions. Therefore, maintaining a constant flow of water in the river can prevent or reduce ongoing land subsidence in Isfahan.

Machine learning-based techniques for land subsidence simulation in an urban area

Understanding and mitigating land subsidence (LS) is critical for sustainable urban planning and infrastructure management. We introduce a comprehensive analysis of LS forecasting utilizing two advanced machine learning models: the eXtreme Gradient Boosting Regressor (XGBR) and Long Short-Term Memory (LSTM). Our findings highlight groundwater level (GWL) and building concentration (BC) as pivotal factors influencing LS. Through the use of Taylor diagram, we demonstrate a strong correlation between both XGBR and LSTM models and the subsidence data, affirming their predictive accuracy. Notably, we applied delta-rate (Δr) calculus to simulate a scenario with an 80% reduction in GWL and BC impact, revealing a potential substantial decrease in LS by 2040. This projection emphasizes the effectiveness of strategic urban and environmental policy interventions. The model performances, indicated by coefficients of determination R2 (0.90 for XGBR, 0.84 for LSTM), root-mean-squared error RMSE (0.37 for XGBR, 0.50 for LSTM), and mean-absolute-error MAE (0.34 for XGBR, 0.67 for LSTM), confirm their reliability. This research sets a precedent for incorporating dynamic environmental factors and adapting to real-time data in future studies. Our approach facilitates proactive LS management through data-driven strategies, offering valuable insights for policymakers and laying the foundation for sustainable urban development and resource management practices.

Identifying the interfaces between perceived multi-hazards and socio-ecological risks to strengthen local adaptations

Multi-hazards are a great concern in the present world. Likewise, the coastal part of Bangladesh is highly vulnerable to multi-hazards, including waterlogging, surface water salinity, land use change, prolonged dry seasons, and groundwater salinity. Multi-hazards and associated risks make local adaptations more difficult over time. Thus, the purpose of this study is to explore the connection between multi-hazards and their associated socio-ecological risks in the southwestern coastal part of Bangladesh. Mixed-methods approaches were used to collect all the data, and statistical analyses were performed to analyze the data. Results revealed that waterlogging significantly influenced local food access, poverty, child marriage, and divorce problems. Surface water salinity and land use change showed significant differences with the widening of salinity-affected areas. Waterlogging, land use change, and a prolonged dry season all showed significant differences in freshwater access. Prolonged dry seasons and groundwater salinity both have a significant impact on human health. Waterlogging and groundwater salinity significantly influence human migrations. These findings may strengthen local adaptation policies for salinity hazards, land use planning, household poverty, food access, livelihoods, water access, health effects, child marriage, and human migration. In addition, our findings indicate the potential to address the existing knowledge gaps pertaining to coastal hazards, risks, and adaptation issues.

Exploratory modelling of the impacts of sea-level rise on the Sundarbans mangrove forest, West Bengal, India

In this paper we conduct exploratory simulations of the possible evolution of the Indian Sundarbans mangroves to 2100 under a range of future sea-level rise (SLR) scenarios, considering the effects of both inundation and shoreline erosion. The Sea Level Affecting Marshes Model (SLAMM) is used to simulate habitat transitions due to inundation and these outputs are combined with an empirical model of SLR-driven shoreline erosion. A set of plausible climate-induced SLR scenarios are considered, together with delta subsidence and constrained vertical sediment accretion. Significant mangrove decline is found in all cases: the greater the rise in sea level the greater the losses. By the end of the century, the Indian Sundarbans mangroves could lose between 42 % and 80 % of their current area if current management is continued. Managed realignment could offset these losses but at the expense of productive land and the migration of the human population.

An assessment of mangrove vegetation changes in reference to cyclone impacted climatic alterations at land-ocean interface of Indian Sundarbans with application of remote sensing-based analytical tools

Mangrove ecoregions of the Indian Sundarbans (IS) are highly productive ecosystems in the Bengal delta of the Indian subcontinent. These mangroves are crucial in reducing the negative consequences of extreme environmental events like excessive wave movements and periodic storm surges, in addition to serving as an important habitat for a variety of distinct flora and animals. The Bay of Bengal has been increasingly affected by climatic changes like increase in sea surface temperature (SST), salinization, and sediment loads, a decrease in freshwater intake, and sea level rise. In the last two decades (2000-2020), these climatic phenomena have increased the frequency of tropical cyclones. From 2000 to 2020, the loss of landmass has been attributed to exposure to these climate changes. According to open-source satellite imaging data, such losses in land area have also led to a decrease in the amount of mangrove vegetation. Thus, to monitor the health of mangrove vegetation, Landsat-based health indicators like normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and combined mangrove recognition index (CMRI) have been considered in this study. CMRI, as a mangrove-specific index, was measured on the basis of the difference of NDVI and normalized difference water index for remote sensing of vegetation liquid water from space (NDWI_Gao). Furthermore, datasets for abiotic variables have been extrapolated from remotely sensed data for the said period using specific formulae. Both long-term and short-term temporal trends have been considered to better envisage the impact of episodic cyclonic events on mangrove health (1990-2020). Our findings indicate that cyclones altered the habitat with respect to land area and salinization status which would possibly render the dominance of more halotolerant forms with loss of freshwater mangrove biodiversity. Even though plantation efforts have shown the recovery of mangroves in this area, sudden storm surges and concomitant salinization of habitat put the plantation efforts in vain. A combination of factors like salinization, rise in SST, rainfall reduction in pre- and post-monsoon periods and episodic cyclonic events would probably lead to further deterioration of mangrove health in this area. Since the IS is suffering the most from climatic change and intermittent cyclonic occurrences, it is crucial to consider this when making policy decisions. Appropriate actions must be taken along with stronger conservation techniques, to protect this vulnerable environment. Better conservation tactics and ongoing plantation efforts would stop the loss of mangrove vegetation and its habitat, even though the growing frequency of episodic storm occurrences cannot be stopped.

Aligning the Global Delta Risk Index with SDG and SFDRR global frameworks to assess risk to socio-ecological systems in river deltas

River deltas globally are highly exposed and vulnerable to natural hazards and are often over-exploited landforms. The Global Delta Risk Index (GDRI) was developed to assess multi-hazard risk in river deltas and support decision-making in risk reduction interventions in delta regions. Disasters have significant impacts on the progress towards the Sustainable Development Goals (SDGs). However, despite the strong interlinkage between disaster risk reduction and sustainable development, global frameworks are still developed in isolation and actions to address them are delegated to different institutions. Greater alignment between frameworks would both simplify monitoring progress towards disaster risk reduction and sustainable development and increase capacity to address data gaps in relation to indicator-based assessments for both processes. This research aims at aligning the GDRI indicators with the SDGs and the Sendai Framework for Disaster and Risk Reduction (SFDRR). While the GDRI has a modular indicator library, the most relevant indicators for this research were selected through a delta-specific impact chain designed in consultation with experts, communities and stakeholders in three delta regions: the Red River and Mekong deltas in Vietnam and the Ganges-Brahmaputra-Meghna (GBM) delta in Bangladesh and India. We analyse how effectively the 143 indicators for the GDRI match (or not) the SDG and SFDRR global frameworks. We demonstrate the interconnections of the different drivers of risk to better inform risk management and in turn support delta-level interventions towards improved sustainability and resilience of these Asian mega-deltas.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11625-023-01295-3.

A novel framework for addressing uncertainties in machine learning-based geospatial approaches for flood prediction

Globally, many studies on machine learning (ML)-based flood susceptibility modeling have been carried out in recent years. While majority of those models produce reasonably accurate flood predictions, the outcomes are subject to uncertainty since flood susceptibility models (FSMs) may produce varying spatial predictions. However, there have not been many attempts to address these uncertainties because identifying spatial agreement in flood projections is a complex process. This study presents a framework for reducing spatial disagreement among four standalone and hybridized ML-based FSMs: random forest (RF), k-nearest neighbor (KNN), multilayer perceptron (MLP), and hybridized genetic algorithm-gaussian radial basis function-support vector regression (GA-RBF-SVR). Besides, an optimized model was developed combining the outcomes of those four models. The southwest coastal region of Bangladesh was selected as the case area. A comparable percentage of flood potential area (approximately 60% of the total land areas) was produced by all ML-based models. Despite achieving high prediction accuracy, spatial discrepancy in the model outcomes was observed, with pixel-wise correlation coefficients across different models ranging from 0.62 to 0.91. The optimized model exhibited high prediction accuracy and improved spatial agreement by reducing the number of classification errors. The framework presented in this study might aid in the formulation of risk-based development plans and enhancement of current early warning systems.

Estimate earth fissure hazard based on machine learning in the Qa' Jahran Basin, Yemen

Earth fissures are potential hazards that often cause severe damage and affect infrastructure, the environment, and socio-economic development. Owing to the complexity of the causes of earth fissures, the prediction of earth fissures remains a challenging task. In this study, we assess earth fissure hazard susceptibility mapping through four advanced machine learning algorithms, namely random forest (RF), extreme gradient boosting (XGBoost), Naïve Bayes (NB), and K-nearest neighbor (KNN). Using Qa' Jahran Basin in Yemen as a case study area, 152 fissure locations were recorded via a field survey for the creation of an earth fissure inventory and 11 earth fissure conditioning factors, comprising of topographical, hydrological, geological, and environmental factors, were obtained from various data sources. The outputs of the models were compared and analyzed using statistical indices such as the confusion matrix, overall accuracy, and area under the receiver operating characteristics (AUROC) curve. The obtained results revealed that the RF algorithm, with an overall accuracy of 95.65% and AUROC, 0.99 showed excellent performance for generating hazard maps, followed by XGBoost, with an overall accuracy of 92.39% and AUROC of 0.98, the NB model, with overall accuracy, 88.43% and AUROC, 0.96, and KNN model with general accuracy, 80.43% and AUROC, 0.88), respectively. Such findings can assist land management planners, local authorities, and decision-makers in managing the present and future earth fissures to protect society and the ecosystem and implement suitable protection measures.

Sustainability of the coastal zone of the Ganges-Brahmaputra-Meghna delta under climatic and anthropogenic stresses

The Ganges-Brahmaputra-Meghna (GBM) delta is one of the world's largest deltas. It is currently experiencing high rates of relative sea-level rise of about 5 mm/year, reflecting anthropogenic climate change and land subsidence. This is expected to accelerate further through the 21st Century, so there are concerns that the GBM delta will be progressively submerged. In this context, a core question is: can sedimentation on the delta surface maintain its elevation relative to sea level? This research seeks to answer this question by applying a two-dimensional flow and morphological model which is capable of handling dynamic interactions between the river and floodplain systems and simulating floodplain sedimentation under different flow-sediment regimes and anthropogenic interventions. We find that across a range of flood frequencies and adaptation scenarios (including the natural polder-free state), the retained volume of sediment varies between 22% and 50% of the corresponding sediment input. This translates to average rates of sedimentation on the delta surface of 5.5 mm/yr to 7.5 mm/yr. Hence, under present conditions, sedimentation associated with quasi-natural conditions can exceed current rates of relative sea-level rise and potentially create new land mass. These findings highlight that encouraging quasi-natural conditions through the widespread application of active sediment management measures has the potential to promote more sustainable outcomes for the GBM delta. Practical measures to promote include tidal river management, and appropriate combinations of cross-dams, bandal-like structures, and dredging.

Dynamics and Causes of Sea Level Rise in the Coastal Region of Southwest Bangladesh at Global, Regional, and Local Levels

Global greenhouse gas emissions have caused sea level rise (SLR) at a global and local level since the industrial revolution, mainly through thermal expansion and ice melting. Projections indicate that the acceleration of SLR will increase in the near future. This will affect coastal and deltaic populations worldwide, such as in Bangladesh, where almost half of the population resides in regions lower than 5 m above sea level. This study analyzed three coastal tidal gauges and five deltaic gauge stations, which showed increases in SLR at greater rates than the regional and global averages. This research also used satellite altimetry data to analyze regional and global SLR averages in the recent past and the 21st century. There is a trend towards increasing sea level based on results from three tide gauge stations: Char Changa with 7.6 mm/yr, Hiron Point at 3.1 mm/yr from 1993 to 2019, and 14.5 mm/yr at Cox’s Bazar from 1993 to 2011. Based on the linear trend from these time frames, it is projected that SLR in Char Changa will increase by 228 mm cm from 2020 to 2050, and by 608 mm by 2100, at Hiron Point by 93mm in 2050 and 248 mm by 2100, and at Cox’s Bazar by almost 435.7 mm by 2050, and more than 1162 mm by 2100. Based on an average from satellite altimeters, assuming a linear increase in SLR, the Bay of Bengal shows an increase of 0.4 mm compared to the global trend. Other river delta stations in the study area also show increasing SLR, specifically, at Kalaroa, Benarpota, Kaikhali, Tala Magura, and Elarchari. Kalaroa and Benarpota show the highest, with SLR of >40 mm/yr. It is also observed that increasing SLR trends are far higher than coastal tide gauges, indicating that physical processes in the delta region are affecting SLR, further contributing to either an increase in water volume/SLR or activating land subsidence. This is partly due to the subsidence of the delta as a result of natural and anthropomorphic effects, as well as an increase in Himalayan glacier melting due to global warming. This indicates that Bangladesh coastal areas will soon experience a far greater SLR than the rest of the Bay of Bengal or other global coastal areas.

On the Exploitation of Remote Sensing Technologies for the Monitoring of Coastal and River Delta Regions

Remote sensing technologies are extensively applied to prevent, monitor, and forecast hazardous risk conditions in the present-day global climate change era. This paper presents an overview of the current stage of remote sensing approaches employed to study coastal and delta river regions. The advantages and limitations of Earth Observation technology in characterizing the effects of climate variations on coastal environments are also presented. The role of the constellations of satellite sensors for Earth Observation, collecting helpful information on the Earth’s system and its temporal changes, is emphasized. For some key technologies, the principal characteristics of the processing chains adopted to obtain from the collected raw data added-value products are summarized. Emphasis is put on studying various disaster risks that affect coastal and megacity areas, where heterogeneous and interlinked hazard conditions can severely affect the population.

Long and Short-Term Coastal Changes Assessment Using Earth Observation Data and GIS Analysis: The Case of Sperchios River Delta

The present study provides information about the evolution of the Sperchios River deltaic area over the last 6500 years. Coastal changes, due to natural phenomena and anthropogenic activities, were analyzed utilizing a variety of geospatial data such as historic records, topographic maps, aerial photos, and satellite images, covering a period from 4500 BC to 2020. A qualitative approach for the period, from 4500 BC to 1852, and a quantitative analysis, from 1852 to the present day, were employed. Considering their scale and overall quality, the data were processed and georeferenced in detail based on the very high-resolution orthophoto datasets of the area. Then, the multitemporal shorelines were delineated in a geographical information system platform. Two different methods were utilized for the estimation of the shoreline changes and trends, namely the coastal change area method and the cross-section analysis, by implementing the digital shoreline analysis system with two statistical approaches, the end point rate and the linear regression rate. Significant river flow and coastline changes were observed with the overall increase in the delta area throughout the study period reaching 135 km2 (mean annual growth of 0.02 km2/yr) and the higher accretion rates to be detected during the periods 1805–1852, 1908–1945 and 1960–1986, especially at the central and north part of the gulf. During the last three decades, the coastline has remained relatively stable with a decreasing tendency, which, along with the expected sea-level rise due to climate change, can infer significant threats for the coastal zone in the near future.

Assessment and Attribution of Mangrove Forest Changes in the Indian Sundarbans from 2000 to 2020

The Indian Sundarbans, together with Bangladesh, comprise the largest mangrove forest in the world. Reclamation of the mangroves in this region ceased in the 1930s. However, they are still subject to adverse environmental influences, such as sediment starvation due to migration of the main river channels in the Ganges–Brahmaputra delta over the last few centuries, cyclone landfall, wave action from the Bay of Bengal—changing hydrology due to upstream water diversion—and the pervasive effects of relative sea-level rise. This study builds on earlier work to assess changes from 2000 to 2020 in mangrove extent, genus composition, and mangrove ‘health’ indicators, using various vegetation indices derived from Landsat and MODIS satellite imagery by performing maximum likelihood supervised classification. We show that about 110 km2 of mangroves disappeared within the reserve forest due to erosion, and 81 km2 were gained within the inhabited part of Sundarbans Biosphere Reserve (SBR) through plantation and regeneration. The gains are all outside the contiguous mangroves. However, they partially compensate for the losses of the contiguous mangroves in terms of carbon. Genus composition, analyzed by amalgamating data from published literature and ground-truthing surveys, shows change towards more salt-tolerant genus accompanied by a reduction in the prevalence of freshwater-loving Heiritiera, Nypa, and Sonneratia assemblages. Health indicators, such as the enhanced vegetation index (EVI) and normalized differential vegetation index (NDVI), show a monotonic trend of deterioration over the last two decades, which is more pronounced in the sea-facing parts of the mangrove forests. An increase in salinity, a temperature rise, and rainfall reduction in the pre-monsoon and the post-monsoon periods appear to have led to such degradation. Collectively, these results show a decline in mangrove area and health, which poses an existential threat to the Indian Sundarbans in the long term, especially under scenarios of climate change and sea-level rise. Given its unique values, the policy process should acknowledge and address these threats.

A global analysis of extreme coastal water levels with implications for potential coastal overtopping

Climate change and anthropogenic pressures are widely expected to exacerbate coastal hazards such as episodic coastal flooding. This study presents global-scale potential coastal overtopping estimates, which account for not only the effects of sea level rise and storm surge, but also for wave runup at exposed open coasts. Here we find that the globally aggregated annual overtopping hours have increased by almost 50% over the last two decades. A first-pass future assessment indicates that globally aggregated annual overtopping hours will accelerate faster than the global mean sea-level rise itself, with a clearly discernible increase occurring around mid-century regardless of climate scenario. Under RCP 8.5, the globally aggregated annual overtopping hours by the end of the 21^st-century is projected to be up to 50 times larger compared to present-day. As sea level continues to rise, more regions around the world are projected to become exposed to coastal overtopping.

As sea levels rise, coasts are being increasingly  threatened by overtopping caused by the combination of sea level rise, storm surge and wave runup. Here the authors find that global coastal overtopping has increased by over 50% in the last two decades, and under a RCP 8.5 scenario this could increase up to 50 times by 2100 compared to today.

The Development of a Framework for the Integrated Assessment of SDG Trade-Offs in the Sundarban Biosphere Reserve

The United Nations Sustainable Development Goals (SDGs) and their corresponding targets are significantly interconnected, with many interactions, synergies, and trade-offs between individual goals across multiple temporal and spatial scales. This paper proposes a framework for the Integrated Assessment Modelling (IAM) of a complex deltaic socio-ecological system in order to analyze such SDG interactions. We focused on the Sundarban Biosphere Reserve (SBR), India, within the Ganges-Brahmaputra-Meghna Delta. It is densely populated with 4.4 million people (2011), high levels of poverty, and a strong dependence on rural livelihoods. It is adjacent to the growing megacity of Kolkata. The area also includes the Indian portion of the world’s largest mangrove forest––the Sundarbans––hosting the iconic Bengal Tiger. Like all deltaic systems, this area is subject to multiple drivers of environmental change operating across scales. The IAM framework is designed to investigate socio-environmental change under a range of explorative and/or normative scenarios and explore associated policy impacts, considering a broad range of subthematic SDG indicators. The following elements were explicitly considered: (1) agriculture; (2) aquaculture; (3) mangroves; (4) fisheries; and (5) multidimensional poverty. Key questions that can be addressed include the implications of changing monsoon patterns, trade-offs between agriculture and aquaculture, or the future of the Sundarbans’ mangroves under sea-level rise and different management strategies. The novel, high-resolution analysis of SDG interactions allowed by the IAM will provide stakeholders and policy makers the opportunity to prioritize and explore the SDG targets that are most relevant to the SBR and provide a foundation for further integrated analysis.

A review of estimating population exposure to sea-level rise and the relevance for migration

This review analyses global or near-global estimates of population exposure to sea-level rise (SLR) and related hazards, followed by critically examining subsequent estimates of population migration due to this exposure. Our review identified 33 publications that provide global or near-global estimates of population exposure to SLR and associated hazards. They fall into three main categories of exposure, based on definitions in the publications: (i) the population impacted by specified levels of SLR; (ii) the number of people living in floodplains that are subject to coastal flood events with a specific return period; and (iii) the population living in low-elevation coastal zones. Twenty of these 33 publications discuss connections between population migration and SLR. In our analysis of the exposure and migration data, we consider datasets, analytical methods, and the challenges of estimating exposure to SLR followed by potential human migration. We underscore the complex connections among SLR, exposure to its impacts, and migration. Human mobility to and from coastal areas is shaped by diverse socioeconomic, demographic, institutional, and political factors; there may be 'trapped' populations as well as those who prefer not to move for social, cultural, and political reasons; and migration can be delayed or forestalled through other adaptive measures. While global estimates of exposed and potentially migrating populations highlight the significant threats of SLR for populations living in low-lying areas at or near coastlines, further research is needed to understand the interactions among localised SLR and related hazards, social and political contexts, adaptation possibilities, and potential migration and (im)mobility decision-making.

Land subsidence and its relation with groundwater aquifers in Beijing Plain of China

Beijing is a major metropolis with significant land subsidence because of long-term overexploitation of groundwater. While the South-to-North Water Diversion Project (SWDP) has provided new water sources Beijing; it has changed the pattern of land subsidence evolution in Beijing since 2015. Here we address how land subsidence evolution before and after SWDP, and we quantify also the impact of groundwater level changes in different aquifers on land subsidence at spatial scale. Subsidence evolution before and after SWDP were compared by adopting Persistent Scatterer Inteferomotry (PSI) with Radarsat-2 and Sentinel-1 data. Spatial correlation between Interferometric Synthetic Aperture Radar (InSAR) derived subsidence and groundwater levels in four aquifers was investigated using the Random Forest (RF) machine learning algorithm and Geographical Detectors (GD) technique. Extensometer deformation data and corresponding variation in groundwater level observations at three monitoring stations were used for validations. The study reveals that: firstly, both InSAR-derived subsidence area and maximum annual deformation rate decreased from 79.2% and 141 mm/yr before SWDP, to 60.1% and 135 mm/yr after SWDP. A reduction of time series deformation at four subsidence centers started about two years after the commence of SWDP in 2015. Secondly, the variation of groundwater level in the second confined aquifer has the strongest spatial correlation with subsidence in all the aquifers, but its impact on this aquifer has decreased after SWDP. These findings have an important scientific significance for the rational allocation of water resources and management strategy for mitigating hazards associated with subsidence against the background of SWDP.

Understanding of Contemporary Regional Sea-Level Change and the Implications for the Future

Global sea level provides an important indicator of the state of the warming climate, but changes in regional sea level are most relevant for coastal communities around the world. With improvements to the sea-level observing system, the knowledge of regional sea-level change has advanced dramatically in recent years. Satellite measurements coupled with in situ observations have allowed for comprehensive study and improved understanding of the diverse set of drivers that lead to variations in sea level in space and time. Despite the advances, gaps in the understanding of contemporary sea-level change remain and inhibit the ability to predict how the relevant processes may lead to future change. These gaps arise in part due to the complexity of the linkages between the drivers of sea-level change. Here we review the individual processes which lead to sea-level change and then describe how they combine and vary regionally. The intent of the paper is to provide an overview of the current state of understanding of the processes that cause regional sea-level change and to identify and discuss limitations and uncertainty in our understanding of these processes. Areas where the lack of understanding or gaps in knowledge inhibit the ability to provide the needed information for comprehensive planning efforts are of particular focus. Finally, a goal of this paper is to highlight the role of the expanded sea-level observation network-particularly as related to satellite observations-in the improved scientific understanding of the contributors to regional sea-level change.

Gaining or losing ground? Tracking Asia's hunger for 'new' coastal land in the era of sea level rise

Many coastal cities are short of land for development and, coupled with the need to mitigate the impact of extreme events against a background of ongoing sea-level rise, coastal land reclamation (CLR) has emerged as a frequently applied solution, most especially in Asia. However, the sustainability of these newly reclaimed lands under the combined onslaught of increasing population pressure, SRL, greater frequency of extreme events, and land subsidence is largely unknown. In order to assess the spatial extent and temporal trends in recent CLR projects, we mapped and tabulated the annual magnitude of change in coastal land gain from 1988 to 2018 for eight major Asian coastal cities. Across these cities, both the spatial extent and rate of CLR is remarkable; some 700 km² has been reclaimed in just three decades. >35% of this new coastal land has been constructed in Shanghai alone (562 km²), while Singapore and Incheon have also experienced substantial land gains. These three cities alone account for almost 10% of all the land gained globally over the last three decades. An analysis of the spatio-temporal patterns reveals that, since recently reclaimed areas are predominantly characterized by construction, including ports, airports, commercial and residential uses, economic development is the most prominent driver. Shanghai, however, represents a significant departure from this trend, whereby >50% of the new coastal land gained during the recent past has not been devoted to construction projects and is vegetated, suggesting a different policy context. Commercial or otherwise, subsidence is widely reported as a major characteristic of recently reclaimed coastal land and is a major environmental challenge. Mapping recent rates of land subsidence over these newly reclaimed lands reveal that most are subject to significant levels of deformation, in the case of the international airport at Incheon, Republic of Korea, exceeding 25 cm annually.

The potential of Tidal River Management for flood alleviation in South Western Bangladesh

Reduced sediment deposition, land subsidence, channel siltation, and salinity intrusion has been an unintended consequence of the construction of polders in the south western delta of Bangladesh in the 1960s. Tidal River Management (TRM) is a process that is intended to temporarily reverse these processes and restore sediment deposition and land elevation at the low-lying sites, known as 'beels', where TRM is carried out. However, there is limited evidence to prioritise sites for TRM on the basis of its potential effectiveness at alleviating flooding. In this study, the south western delta of Bangladesh was classified according to different flood susceptible zones. In south western Bangladesh, the major portion of agricultural and aquaculture land is located within flood susceptible zones (65% and 81%, respectively). 44.5% of the total population in embanked regions live in areas classified as being flood susceptible. This study identified 106 'beels' suitable for TRM. Modelling of potential sediment deposition predicted that the consequent increase in land elevation could be up to 1.4 m in five years, which would alleviate land subsidence and modify several geomorphological factors such as aspect, slope, curvature, and Stream Power Index (SPI). Implementation of TRM at these sites could potentially reduce the probability of annual flooding from 0.86 (on average) to 0.57 (on average). Therefore, TRM could lower the flood susceptible area by 35% in suitable 'beels'. Whilst during the implementation of TRM agriculture has to cease for a few years, a systematic programme of TRM could result in a long-term increase in agricultural production by reducing flood susceptibility of agricultural lands in delta regions.

A novel ensemble computational intelligence approach for the spatial prediction of land subsidence susceptibility

Land subsidence (LS) is a significant problem that can cause loss of life, damage property, and disrupt local economies. The Semnan Plain is an important part of Iran, where LS is a major problem for sustainable development and management. The plain represents the changes occurring in 40% of the country. We introduce a novel-ensemble intelligence approach (called ANN-bagging) that uses bagging as a meta- or ensemble-classifier of an artificial neural network (ANN) to predict LS spatially on the Semnan Plain in Semnan Province, Iran. The ensemble model's goodness-of-fit (to training data) and prediction accuracy (of the validation data) are compared to benchmarks set by ANN-bagging. A total of 96 locations of LS and 12 LS conditioning factors (LSCFs) were collected. Each feature in the LS inventory map (LSIM) was randomly assigned to one of four groups or folds, each comprising 25% of cases. The novel ensemble model was trained using 75% (3 folds) and validated with the remaining 25% (1 fold) in a four-fold cross-validation (CV) system, which is used to control for the effects of the random selection of the training and validation datasets. LSCFs for LS prediction were selected using the information-gain ratio and multi-collinearity test methods. Factor significance was evaluated using a random forest (RF) model. Groundwater drawdown, land use and land cover, elevation, and lithology were the most important LSCFs. Using the k-fold CV approaches, twelve LS susceptibility maps (LSSMs) were prepared as each fold employed all three models (ANN-bagging, ANN, and bagging). The LS susceptibility mapping showed that between 5.7% and 12.6% of the plain had very high LS susceptibility. All three models produced LS susceptibility maps with acceptable prediction accuracies and goodness-of-fits, but the best maps were produced by the ANN-bagging ensemble method. Overall, LS risk was highest in agricultural areas with high groundwater drawdown in the flat lowlands on quaternary sediments (Qcf). Groundwater extraction rates should be monitored and potentially limited in regions of severe or high LS susceptibility. This investigation details a novel methodology that can help environmental planners and policy makers to mitigate LS to help achieve sustainability.

Water level changes, subsidence, and sea level rise in the Ganges-Brahmaputra-Meghna delta

Being one of the most vulnerable regions in the world, the Ganges-Brahmaputra-Meghna delta presents a major challenge for climate change adaptation of nearly 200 million inhabitants. It is often considered as a delta mostly exposed to sea-level rise and exacerbated by land subsidence, even if the local vertical land movement rates remain uncertain. Here, we reconstruct the water-level (WL) changes over 1968 to 2012, using an unprecedented set of 101 water-level gauges across the delta. Over the last 45 y, WL in the delta increased slightly faster (∼3 mm/y), than global mean sea level (∼2 mm/y). However, from 2005 onward, we observe an acceleration in the WL rise in the west of the delta. The interannual WL fluctuations are strongly modulated by El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) variability, with WL lower than average by 30 to 60 cm during co-occurrent El Niño and positive IOD events and higher-than-average WL, by 16 to 35 cm, during La Niña years. Using satellite altimetry and WL reconstructions, we estimate that the maximum expected rates of delta subsidence during 1993 to 2012 range from 1 to 7 mm/y. By 2100, even under a greenhouse gas emission mitigation scenario (Representative Concentration Pathway [RCP] 4.5), the subsidence could double the projected sea-level rise, making it reach 85 to 140 cm across the delta. This study provides a robust regional estimate of contemporary relative WL changes in the delta induced by continental freshwater dynamics, vertical land motion, and sea-level rise, giving a basis for developing climate mitigation strategies.

Provenance of Bengal Shelf Sediments: 2. Petrology and Geochemistry of Sand

The Bangladesh lowlands are traversed by the largest sediment flux on the planet. Detritus generated mostly in Himalayan highlands and conveyed through the Ganga–Brahmaputra rivers and Meghna estuary reaches the Bay of Bengal, where it forms a composite deltaic system. This study integrates the vast existing database on Ganga–Brahmaputra sediments of all grain sizes from clay to sand with new petrographic, mineralogical, and geochemical data on estuarine and shallow-marine sands. A large spectrum of compositional signatures was used to: (i) assess the relative supply of the Ganga and Brahmaputra rivers to estuarine and shelfal sediments; (ii) define the compositional variability of estuarine sediments and the impact exerted by hydraulic sorting and climate-related chemical weathering on provenance signals; (iii) define the compositional variability of shelf sediments and the potential hydrodynamic segregation of fast-settling heavy minerals in coastal environments and of slow-settling platy micas on low-energy outer-shelf floors; (iv) consider the potential additional mud supply from the western subaerial part of the delta formerly built by the Ganga River; and (v) draw a preliminary mineralogical comparison between fluvio-deltaic sediments and turbidites of the Bengal–Nicobar deep-sea fan, thus tracing sediment dispersal across the huge sedimentary system extending from Tibet to the equatorial Indian Ocean. All investigated mineralogical and geochemical parameters, as well as Sr and Nd isotope ratios and clay–mineral assemblages, showed a clear prevalence in sediment supply from the Brahmaputra (60–70%) over the Ganga (30–40%). Heavy-mineral suites and Sr and Nd isotope fingerprints of Bengal shelf sediments are nearly identical to those of the Brahmaputra River and Meghna estuary, also because the Brahmaputra carries almost twice as many Ca-plagioclase grains and heavy minerals including epidote than the Ganga, and these minerals control the large majority of the Sr and Nd budgets. The experience gained in modern settings can be directly extrapolated only to the recent past, because sediments older than the late Pleistocene and buried more than a few hundred meters begin to lose less durable ferromagnesian minerals by selective chemical dissolution, which makes quantitative estimates progressively less robust in more deeply buried older strata.

Have coastal embankments reduced flooding in Bangladesh?

From the 1960s, embankments have been constructed in south western coastal region of Bangladesh to provide protection against flooding, but the success of the polder programme is disputed. We present analysis of floods during the years 1988-2012, diagnosing whether the floods were attributable to monsoonal precipitation (pluvial flooding), high upstream river discharge into the tidal delta (fluvio-tidal flooding), or cyclone-induced storm surges. We find that pluvial flooding was the most frequent, but typically resulted in less flooded area (11.44% of the region on average) compared with the other forms of flooding. The greatest area of inundation (48% of total area) occurring in 2001 as a consequence of fluvio-tidal and surge flooding, whilst cyclone Sidr in 2007 flooded 35% of the area. We modelled these different forms of inundation to estimate what flooding might have been had the polders not been constructed. For the 'no embankment' counter-factual scenario, our model demonstrated that because of a combination of subsidence and inadequate drainage, construction of the polders has increased the pluvial flooded area by 6.5% on average (334 km²). However, during the 1998 fluvio-tidal flood, the embankments protected an estimated 54% of the area from flooding. During the cyclone Sidr storm surge event, embankment failure in several polders and pluvial inundation resulted in 35% area inundation, otherwise, the total inundation would have been 18% area. We conclude that whilst polders have provided protection against storm surges and fluvio-tidal events of moderate severity, they have exacerbated more frequent pluvial flooding and promoted potential flooding impacts during the most extreme storm surges.

Land subsidence modelling using tree-based machine learning algorithms

Land subsidence (LS) is among the most critical environmental problems, affecting both agricultural sustainability and urban infrastructure. Existing methods often use either simple regression models or complex hydraulic models to explain and predict LS. There are few studies that identify the risk factors and predict the risk of LS using machine learning models. This study compares four tree-based machine learning models for land subsidence hazard modelling at a study area in Hamadan plain (Iran). The study also analyzes the importance of six risk factors including topography (elevation, slope), geomorphology (distance from stream, drainage density), hydrology (groundwater drawdown) and lithology on LS. Thematic layers of each variable related to the LS phenomenon are prepared and utilized as the inputs to the four tree-based machine learning models, including the Rule-Based Decision Tree (RBDT), Boosted Regression Trees (BRT), Classification And Regression Tree (CART), and the Random Forest (RF) algorithms to produce a consolidated LS hazard map. The accuracy of the generated maps is then evaluated using the area under the receiver operating characteristic curve (AUC) and the True Skill Statistics (TSS). The RF approach had the lowest predictive error for mapping the LS hazard (i.e., AUC 96.7% for training, AUC 93.8% for validation, TSS 0.912 for training, TSS 0.904 for validation) followed by BRT. Groundwater drawdown was seen to be the most influential factor that contributed to land subsidence in the present study area, followed by lithology and distance from the stream network.

Spatio-temporal Patterns of Land Use/Land Cover Change in the Heterogeneous Coastal Region of Bangladesh between 1990 and 2017

Although a detailed analysis of land use and land cover (LULC) change is essential in providing a greater understanding of increased human-environment interactions across the coastal region of Bangladesh, substantial challenges still exist for accurately classifying coastal LULC. This is due to the existence of high-level landscape heterogeneity and unavailability of good quality remotely sensed data. This study, the first of a kind, implemented a unique methodological approach to this challenge. Using freely available Landsat imagery, eXtreme Gradient Boosting (XGBoost)-based informative feature selection and Random Forest classification is used to elucidate spatio-temporal patterns of LULC across coastal areas over a 28-year period (1990-2017). We show that the XGBoost feature selection approach effectively addresses the issue of high landscape heterogeneity and spectral complexities in the image data, successfully augmenting the RF model performance (providing a mean user’s accuracy > 0.82). Multi-temporal LULC maps reveal that Bangladesh’s coastal areas experienced a net increase in agricultural land (5.44%), built-up (4.91%) and river (4.52%) areas over the past 28 years. While vegetation cover experienced a net decrease (8.26%), an increasing vegetation trend was observed in the years since 2000, primarily due to the Bangladesh government’s afforestation initiatives across the southern coastal belts. These findings provide a comprehensive picture of coastal LULC patterns, which will be useful for policy makers and resource managers to incorporate into coastal land use and environmental management practices. This work also provides useful methodological insights for future research to effectively address the spatial and spectral complexities of remotely sensed data used in classifying the LULC of a heterogeneous landscape.

Coupling land use evolution and subsidence in the Po Delta, Italy: Revising the past occurrence and prospecting the future management challenges

The Po River delta is characterized by a large system of shallow lagoons, wetlands, and reclaimed lands. Like many other deltaic regions, the Po Delta has largely subsided due to natural processes and anthropogenic activities, with a strong impact on its geomorphological evolution and significant socio- economic consequences. This paper aimed at highlighting the evolution of the study area over the last 120 years, analysing the land-uses changes in relation to the land subsidence, and using these findings to discuss the main management issues of this fragile low lying area in the decades to come. The analysis of the land use evolution from 1892 to 2015 and the information derived from the metrics measurement suggest a simplification of the land use with a decrease of the saltmarsh extent and an enlargement of the agricultural cover. The analysis of land subsidence, mainly due to gas-bearing groundwater withdrawal, confirms a strong impact on the delta territory, and in particular on its geomorphological asset. In the early 1900s the elevation of the delta plain ranged between -1.0 and +1.6 m above msl, while in 2007 it reduced between -2.8 to +0.3 m above msl. The land reclamation projects have largely increased the farmland usable for crop production but the concurrent loss of land elevation has resulted in a territory extremely difficult to be managed. Because of its elevation, the delta is particularly vulnerable to weather and marine events. As a consequence, the present management of the area is mainly aimed at maintaining the agricultural productivity. Unfortunately, if the human impacts will continue with the actual trend, the stability of the natural ecosystems, such as the wetlands, which represent important socioeconomic realities, will be further undermined. Surely, the conservation of these natural environments should be a priority in the future land use management.

Mercury sequestration and transformation in chemically enhanced treatment wetlands

Mercury (Hg) pollution is a concern to human and wildlife health worldwide, and management strategies that reduce Hg inputs to aquatic systems are of broad interest. Using a replicated field-scale study in California's Sacramento-San Joaquin Delta, we tested the effectiveness of chemically enhanced treatment wetlands (CETWs) under two coagulation treatments, polyaluminum chloride (Al treatment) and ferric sulfate (Fe treatment), in their initial removal and longer-term sequestration of Hg compared to untreated control wetlands. The primary mechanism for Hg removal by CETWs was the transfer of Hg from filtered forms to insoluble particulate forms and enhanced settling of particles. CETWs resulted in total Hg annual load removals of 63 ng m^-2 yr^-1 (71%) and 54 ng m^-2 yr^-1 (54%) for the Al and Fe treatments, respectively. Control wetlands removed significantly less at 13 ng m^-2 yr^-1 (14%). Load removals indicate that Fe treatment wetlands more effectively reduced filtered and total methylmercury (MeHg) exports, while Al treatment wetlands more effectively reduced particulate MeHg and total Hg exports. These differences in Hg species load reductions possibly indicate different mechanisms of Hg sequestration; current data suggest more effective floc formation and particle settling was likely responsible for the Al treatment behavior, while either preferential MeHg sequestration or methylation suppression was potentially responsible for Fe treatment behavior. Differences in Hg sequestration behavior post-coagulation between the flocs formed by different coagulants indicate the importance of in-situ studies and the need for careful selection of coagulant treatment depending on the Hg species requiring remediation.

Effects of ferric sulfate and polyaluminum chloride coagulation enhanced treatment wetlands on Typha growth, soil and water chemistry

Land surface subsidence is a concern in many deltas worldwide as it contributes to water quality degradation, loss of fertile land and increased potential for levee failure. As a possible solution to these concerns, on-site coagulation enhanced treatment wetlands (CETWs), coagulation water treatment followed by wetland passage serving as a settling basin, were implemented in a field-scale study located on a subsided island of the Sacramento-San Joaquin Delta in northern California under three treatments; coagulation with polyaluminum chloride (PAC), coagulation with ferric sulfate and an untreated control. Because CETWs offer a relatively novel solution for water quality improvement and subsidence reversal due to its low-infrastructure requirements and in-situ nature, effects from these systems remain uncharted and they may have adverse effects on plant biomass production that also contribute to sediment accretion. This study focuses on the effect CETWs had on the growth of Typha spp.; the dominant vegetation in the wetlands. Plant growth parameters and nutrient content were measured in conjunction with soil, pore water and surface water chemistry. Soil analysis indicated there was no intermixing of newly formed flocs and original soil material. Where there was significant deposition of floc, PAC treatment reduced phosphate concentrations and ferric sulfate treatment increased total Fe concentrations in surrounding water compared to the control. Results indicated coagulation treatments had no negative effects on Typha leaf nutrient content, Typha growth or allometric parameters. Additionally, no signs of plant toxicity such as necrosis, wilting or chlorosis were observed in any of the treatments. Overall, this study suggests that CETWs are viable treatment option for water quality improvement and sediment accretion while having no negative impact on the growth of Typha plants.

Dual wave farms and coastline dynamics: The role of inter-device spacing

In dual wave farms, i.e., arrays of wave energy converters (WECs) with a dual function - generation of renewable power and mitigation of coastal erosion - the spacing between the WECs is a fundamental design parameter. The present research has the objective of establishing how this parameter affects the shoreline evolution behind the array and, on this basis, to propose and apply a method to determine the optimum spacing for coastal protection. The method is demonstrated on a beach subjected to severe erosion. Five case studies are considered: four with different inter-WEC spacings, and one without the wave farm (baseline). A spectral wave propagation model is applied to analyse the variations in significant wave height behind the WEC array. Longshore sediment transport rates are calculated, and a shoreline model is applied. We find that in all the case studies the dry beach area is greater than in the baseline (no farm) case study, which proves the capacity of the dual WEC array to mitigate the erosive trends of the system. Importantly, we obtain that the inter-WEC spacing plays a fundamental role in the evolution of the shoreline and, consequently, in the effectiveness of the WEC array for coastal protection. The case studies with intermediate spacings yield the best performance in terms of dry beach area. More generally, the benefits of dual wave farms in terms of protection of coastal properties and infrastructure, and the ensuing savings in conventional coastal defence measures (coastal structures, beach nourishment, etc.) contribute to the development of wave energy by enhancing its economic viability. The methodology presented in this paper can be used to optimize the design of dual wave farms elsewhere.

Recent sediment flux to the Ganges-Brahmaputra-Meghna delta system

The physical sustainability of deltaic environments is very much dependent on the volume of water and sediment coming from upstream and the way these fluxes recirculate within the delta system. Based on several past studies, the combined mean annual sediment load of the Ganges-Brahmaputra-Meghna (GBM) systems has previously been estimated to vary from 1.0 to 2.4 BT/year which can be separated into components flowing from the Ganges (260 to 680 MT/year) and Brahmaputra (390 to 1160 MT/year). Due to very limited data and small contribution of the Meghna system (6-12 MT/year) to the total sediment flux of the GBM system, the data of the Meghna is not considered in the analysis assuming the sediment flux from GB system as the sediment flux of GBM. However, in this paper our analysis of sediment concentration data (1960-2008) collected by Bangladesh Water Development Board shows that the sediment flux is much lower: 150 to 590 MT/year for the Ganges versus 135 to 615 MT/year for the Brahmaputra, with an average total flux around 500 MT/year. Moreover, the new analysis provides a clear indication that the combined sediment flux delivered through these two major river systems is following a declining trend. In most of the planning documents in Bangladesh, the total sediment flux is assumed as a constant value of around 1 billion tons, while the present study indicates that the true value may be around 50% lower than this (with an average decreasing trend of around 10 MT/year).

Wave farm effects on the coast: The alongshore position

For wave energy to become a fully-fledged renewable and thus contribute to the much-needed decarbonisation of the energy mix, the effects of wave farms (arrays of wave energy converters) on coastal systems must be addressed. The objective of this work is to investigate the effects of wave farms on the longshore sediment transport and shoreline evolution of a gravel-dominated beach and, in particular, its sensitivity to the longshore position of the farm based on eight scenarios. Nearshore wave propagation patterns are computed by means of a spectral wave propagation model (SWAN), variations in sediment transport rates induced by the farm are calculated, and a one-line model is applied to determine the shoreline position and dry beach area. The significant wave height at breaking is reduced in the lee of the wave farm, dampening sediment transport. We find that changes in the dry beach area induced by the wave farm are highly sensitive to its alongshore position, and may result in: (i) erosion relative to the baseline scenario (without wave farm) in three of the eight scenarios, (ii) accretion in three other scenarios, and (iii) negligible effects in the remaining two. These results prove that the alongshore position of the wave farm controls the response of the beach to the extent that it may shift from accretionary to erosionary, and provide evidence of its effectiveness in countering erosion if appropriately positioned. This effectiveness opens up the possibility of using wave farms not only to generate carbon-free energy but also to manage coastal erosion, thus strengthening the case for the development of wave energy.

Protection of gravel-dominated coasts through wave farms: Layout and shoreline evolution

The impacts of wave farms (arrays of wave energy converters, or WECs) on the nearshore must be fully understood for wave technology to develop and thus contribute to a sustainable, carbon-free energy mix in the near future. The objective of this work is to investigate the role played by the farm layout on the wave propagation patterns leewards and the implications for longshore sediment transport (LST) and shoreline evolution on a gravel-dominated deltaic coast. Changes in wave propagation in four scenarios, corresponding to as many wave farm layouts, are computed by means of a spectral numerical model (Delft3D-WAVE) under (i) low-energy and storm conditions, and (ii) westerly and easterly waves - the two prevailing wave directions. On this basis, sediment transport rates are computed and changes in the shoreline position assessed using a one-line model. To quantify the impact of the wave farm on the nearshore wave conditions, sediment transport and shoreline, we define three ad hoc indicators: the non-dimensional wave height reduction, the non-dimensional LST rate reduction and the non-dimensional shoreline advance. Significant wave heights decrease in the lee of the wave farm, with the consequent reduction in LST rates. As a result, the dry beach area increases in every scenario under both westerly and easterly waves. We find that case studies with the WECs arranged on fewer rows but covering a greater stretch of coastline provide better coastal protection. These results confirm that wave farms can be used not only to generate carbon-free energy but also to protect gravel-dominated coasts.

Rice Drain Management to Reduce Seepage Exports in the Sacramento-San Joaquin Delta, California

Many deltas worldwide face subsidence issues due to increased anthropogenic activity. The Sacramento-San Joaquin delta similarly faces ongoing subsidence, more than 8 m in some areas, that increases levee failure risks and threatens the security of this water source for 25 million California residents and 1.2 million ha of agriculture. Rice ( L.) fields are an integral part of a proposed new strategy for managing subsidence because they have been shown to stop subsidence and provide an alternative crop for growers. Two important considerations for implementing rice fields are additional water requirement and the effect on water quality from mobilized dissolved organic carbon (DOC) and disinfection byproduct precursors. To understand constituent transport and potential management opportunities for rice farming, a plug flow reactor mass balance model was used to quantify surface and subsurface hydrologic pathways. Management of adjacent drainage ditch water levels under low and high scenarios were tested as a strategy to reduce seepage and water quality loads. Under high drains, groundwater met 10% of evapotranspiration (ET). Low drains resulted in a 100% increase in ET demand, which was met by surface water applied for irrigation. High drains reduced subsurface seepage by 95%. Subsurface DOC, trihalomethane, and total dissolved nitrogen loads were reduced 10-fold in high drains compared with low drains. Flow rate accounted for 74 to 90% of load variance and was the primary determinant of constituent loads. Thoughtful implementation of rice cultivation, with high water levels in adjacent drains, can be leveraged to reduce irrigation water demand and constituent load outputs.

A framework for identifying and selecting long term adaptation policy directions for deltas

Deltas are precarious environments experiencing significant biophysical, and socio-economic changes with the ebb and flow of seasons (including with floods and drought), with infrastructural developments (such as dikes and polders), with the movement of people, and as a result of climate and environmental variability and change. Decisions are being taken about the future of deltas and about the provision of adaptation investment to enable people and the environment to respond to the changing climate and related changes. The paper presents a framework to identify options for, and trade-offs between, long term adaptation strategies in deltas. Using a three step process, we: (1) identify current policy-led adaptations actions in deltas by conducting literature searches on current observable adaptations, potential transformational adaptations and government policy; (2) develop narratives of future adaptation policy directions that take into account investment cost of adaptation and the extent to which significant policy change/political effort is required; and (3) explore trade-offs that occur within each policy direction using a subjective weighting process developed during a collaborative expert workshop. We conclude that the process of developing policy directions for adaptation can assist policy makers in scoping the spectrum of options that exist, while enabling them to consider their own willingness to make significant policy changes within the delta and to initiate transformative change.

Imaging Multi-Age Construction Settlement Behaviour by Advanced SAR Interferometry

This paper focuses on the application of Advanced Satellite Synthetic Aperture Radar Interferometry (A-DInSAR) to subsidence-related issues, with particular reference to ground settlements due to external loads. Beyond the stratigraphic setting and the geotechnical properties of the subsoil, other relevant boundary conditions strongly influence the reliability of remotely sensed data for quantitative analyses and risk mitigation purposes. Because most of the Persistent Scatterer Interferometry (PSI) measurement points (Persistent Scatterers, PSs) lie on structures and infrastructures, the foundation type and the age of a construction are key factors for a proper interpretation of the time series of ground displacements. To exemplify a methodological approach to evaluate these issues, this paper refers to an analysis carried out in the coastal/deltaic plain west of Rome (Rome and Fiumicino municipalities) affected by subsidence and related damages to structures. This region is characterized by a complex geological setting (alternation of recent deposits with low and high compressibilities) and has been subjected to different urbanisation phases starting in the late 1800s, with a strong acceleration in the last few decades. The results of A-DInSAR analyses conducted from 1992 to 2015 have been interpreted in light of high-resolution geological/geotechnical models, the age of the construction, and the types of foundations of the buildings on which the PSs are located. Collection, interpretation, and processing of geo-thematic data were fundamental to obtain high-resolution models; change detection analyses of the land cover allowed us to classify structures/infrastructures in terms of the construction period. Additional information was collected to define the types of foundations, i.e., shallow versus deep foundations. As a result, we found that only by filtering and partitioning the A-DInSAR datasets on the basis of the above-mentioned boundary conditions can the related time series be considered a proxy of the consolidation process governing the subsidence related to external loads as confirmed by a comparison with results from a physically based back analysis based on Terzaghi’s theory. Therefore, if properly managed, the A-DInSAR data represents a powerful tool for capturing the evolutionary stage of the process for a single building and has potential for forecasting the behaviour of the terrain–foundation–structure combination.

Introducing a new framework for mapping subsidence vulnerability indices (SVIs): ALPRIFT

Proof-of-concept (PoC) is presented for a new framework to serve as a proactive capability to mapping subsidence vulnerability of Shabestar plain of approximately 500km² overlaying an important aquifer supporting a region renowned for diversity of agricultural products. This aquifer is one of 12 in East and West Azerbaijan provinces, Northwest Iran, which surround the distressed Lake Urmia, with its water table declined approximately 4m in between 2004 and 2014. The decline of water table in aquifers undermines their soil texture and structure by exposure to pressures under their weight and thereby induce or trigger land subsidence. Inspired by the DRASTIC framework to map intrinsic aquifer vulnerability to anthropogenic pollution, the paper introduces the ALPRIFT framework for subsidence, which comprises the seven data layers of Aquifer media (A), Land use (L), Pumping of groundwater, Recharge (R), aquifer thickness Impact (I), Fault distance (F) and decline of water Table (T). The paper prescribes rates to account for local variations and weights for the relative importance of the data layers. The proof-of-concept for ALPRIFT is supported by the correlation of Subsidence Vulnerability Indices (SVIs) with measured subsidence values, which renders a value of 0.5 but improves significantly to 0.86 when using fuzzy logic. Similar improvements are suggested by the ROC/AUC performance metric.

Integrating complex numerical approaches into a user-friendly application for the management of coastal environments

This paper presents a software platform to compute the total water level, one of the key variables for the environmental management of coastal zones. The platform integrates six modules: (1) simulation of deep-water wave variables, storm surge and river flow; (2) wave downscaling; (3) wave propagation; (4) contribution of the river discharge; (5) astronomical tide; and (6) total water level. It was applied to three case studies in southern Spain. The first case study consisted of designing the extension of a fluvial marina in a highly dynamic area (Guadalete estuary, Cádiz), and the maximum number of floating docks to avoid flooding events was obtained. The second case study involved calculating the operation conditions for navigation purposes in an inlet with sedimentation problems (Punta Umbría, Huelva), and a relationship between the percentage of operation hours and the dredged volume was obtained. The third case study consisted of estimating the number of overwash events as a function of the height of the berm on a deltaic beach with erosion issues (Guadalfeo, Granada), and a simple design curve to help managers during the decision-making process of artificial nourishment projects was provided. These results highlight the potential of the developed software, whose methodology is feasibly extensible to other coastal areas worldwide, to help managers handle a wide range of environmental problems related to the total water level. This is especially relevant due to the expected sea level rise in the coming years.

Stabilization of global temperature at 1.5°C and 2.0°C: implications for coastal areas

The effectiveness of stringent climate stabilization scenarios for coastal areas in terms of reduction of impacts/adaptation needs and wider policy implications has received little attention. Here we use the Warming Acidification and Sea Level Projector Earth systems model to calculate large ensembles of global sea-level rise (SLR) and ocean pH projections to 2300 for 1.5°C and 2.0°C stabilization scenarios, and a reference unmitigated RCP8.5 scenario. The potential consequences of these projections are then considered for global coastal flooding, small islands, deltas, coastal cities and coastal ecology. Under both stabilization scenarios, global mean ocean pH (and temperature) stabilize within a century. This implies significant ecosystem impacts are avoided, but detailed quantification is lacking, reflecting scientific uncertainty. By contrast, SLR is only slowed and continues to 2300 (and beyond). Hence, while coastal impacts due to SLR are reduced significantly by climate stabilization, especially after 2100, potential impacts continue to grow for centuries. SLR in 2300 under both stabilization scenarios exceeds unmitigated SLR in 2100. Therefore, adaptation remains essential in densely populated and economically important coastal areas under climate stabilization. Given the multiple adaptation steps that this will require, an adaptation pathways approach has merits for coastal areas.This article is part of the theme issue 'The Paris Agreement: understanding the physical and social challenges for a warming world of 1.5°C above pre-industrial levels'.

Climate change impacts on hydrology and water resources of Indian River basin

Anthropogenic greenhouse gas emission is altering the global hydrological cycle due to change in rainfall pattern and rising temperature which is responsible for alteration in the physical characteristics of river basin, melting of ice, drought, flood, extreme weather events and alteration in groundwater recharge. In India, water demand for domestic, industrial and agriculture purposes have already increased many folds which are also influencing the water resource system. In addition, climate change has induced the surface temperature of the Indian subcontinent by 0.48 ºC in just last century. However, Ganges–Brahmaputra–Meghna (GBM) river basins have great importance for their exceptional hydro-geological settings and deltaic floodplain wetland ecosystems which support 700 million people in Asia. The climatic variability like alterations in precipitation and temperature over GBM river basins has been observed which signifies the GBM as one of the most vulnerable areas in the world under the potential impact of climate change. Consequently, alteration in river discharge, higher runoff generation, low groundwater recharge and melting of glaciers over GBM river basin could be observed in near future. The consequence of these changes due to climate change over GBM basin may create serious water problem for Indian sub-continents. This paper reviews the literature on the historical climate variations and how climate change affects the hydrological characteristics of different river basins.

Global climate change and local land subsidence exacerbate inundation risk to the San Francisco Bay Area

The current global projections of future sea level rise are the basis for developing inundation hazard maps. However, contributions from spatially variable coastal subsidence have generally not been considered in these projections. We use synthetic aperture radar interferometric measurements and global navigation satellite system data to show subsidence rates of less than 2 mm/year along most of the coastal areas along San Francisco Bay. However, rates exceed 10 mm/year in some areas underlain by compacting artificial landfill and Holocene mud deposits. The maps estimating 100-year inundation hazards solely based on the projection of sea level rise from various emission scenarios underestimate the area at risk of flooding by 3.7 to 90.9%, compared with revised maps that account for the contribution of local land subsidence. Given ongoing land subsidence, we project that an area of 125 to 429 km² will be vulnerable to inundation, as opposed to 51 to 413 km² considering sea level rise alone.

An integrated methodology to forecast the efficiency of nourishment strategies in eroding deltas

Many deltas across the globe are retreating, and nearby beaches are undergoing strong erosion as a result. Among soft and prompt solutions, nourishments are the most heavily used. This paper presents an integrated methodology to forecast the efficiency of nourishment strategies by means of wave climate simulations, wave propagations with downscaling techniques, computation of longshore sediment transport rates and application of the one-line model. It was applied to an eroding deltaic beach (Guadalfeo, southern Spain), where different scenarios as a function of the nourished coastline morphology, input volume and grain size were tested. For that, the evolution of six scenarios of coastline geometry over a two-year period (lifetime of nourishment projects at the study site) was modelled and the uncertainty of the predictions was also quantified through Monte Carlo techniques. For the most efficient coastline shape in terms of gained dry beach area, eight sub-scenarios with different nourished volumes were defined and modelled. The results indicate that an input volume around 460,000m³ is the best strategy since nourished morphologies with higher volumes are more exposed to the prevailing storm directions, inducing less efficient responses. After setting the optimum coastline morphology and input sediment volume, eleven different nourished grain sizes were modelled; the most efficient coastline responses were obtained for sediment sizes greater than 0.01m. The availability of these sizes in the sediment accumulated upstream of a dam in the Guadalfeo River basin allows for the conclusion that this alternative would not only mitigate coastal erosion problems but also sedimentation issues in the reservoir. The methodology proposed in this work is extensible to other coastal areas across the world and can be helpful to support the decision-making process of artificial nourishment projects and other environmental management strategies.

An improved database of coastal flooding in the United Kingdom from 1915 to 2016

Coastal flooding caused by extreme sea levels can produce devastating and wide-ranging consequences. The 'SurgeWatch' v1.0 database systematically documents and assesses the consequences of historical coastal flood events around the UK. The original database was inevitably biased due to the inconsistent spatial and temporal coverage of sea-level observations utilised. Therefore, we present an improved version integrating a variety of 'soft' data such as journal papers, newspapers, weather reports, and social media. SurgeWatch2.0 identifies 329 coastal flooding events from 1915 to 2016, a more than fivefold increase compared to the 59 events in v1.0. Moreover, each flood event is now ranked using a multi-level categorisation based on inundation, transport disruption, costs, and fatalities: from 1 (Nuisance) to 6 (Disaster). For the 53 most severe events ranked Category 3 and above, an accompanying event description based upon the Source-Pathway-Receptor-Consequence framework was produced. Thus, SurgeWatch v2.0 provides the most comprehensive and coherent historical record of UK coastal flooding. It is designed to be a resource for research, planning, management and education.

Managing coastal environments under climate change: Pathways to adaptation

This paper deals with the question of how to manage vulnerable coastal systems so as to make them sustainable under present and future climates. This is interpreted in terms of the coastal functionality, mainly natural services and support for socio-economic activities. From here we discuss how to adapt for long term trends and for short terms episodic events using the DPSIR framework. The analysis is presented for coastal archetypes from Spain, Ireland and Romania, sweeping a range of meteo-oceanographic and socio-economic pressures, resulting in a wide range of fluxes among them those related to sediment. The analysis emphasizes the variables that provide a higher level of robustness. That means mean sea level for physical factors and population density for human factors. For each of the studied cases high and low sustainability practices, based on stakeholders preferences, are considered and discussed. This allows proposing alternatives and carrying out an integrated assessment in the last section of the paper. This assessment permits building a sequence of interventions called adaptation pathway that enhances the natural resilience of the studied coastal systems and therefore increases their sustainability under present and future conditions.