Collaborating Authors


How Do AI Represent the Urban?


It's important to remember that these images aren't created from scratch. They're built from "training sets" of images that human researchers feed into the AI to help it learn and recognise patterns. If you're not familiar with how such AI apps work, this old article from 2015 does a pretty good job of explaining this. I suspect that while the process has become more sophisticated over the years, the basic principle of recursively feeding images back into neural nets until the AI "gets it" hasn't changed. Therefore, human biases do exist in the patterns chosen and images generated, which turns these images into AI interpretations of human biases.

This AI tool lets you visualize how climate change could affect your home

USATODAY - Tech Top Stories

A new tool with cutting-edge image recognition AI lets you visualize the future effects of climate change on any place in the world -- including your own home. The project, titled "This Climate Does Not Exist," lets you enter the address of your current home or your favorite travel destination and see what it could look like years later once climate change has taken its toll. You can see how Disneyland will look like covered in smog, the way extreme smog blanketed Beijing in 2014. You can see what your childhood home will look like after it is flooded by rising sea levels, the way floods devastated Indonesia in 2020 after widespread deforestation. Jakarata floods: Thousands caught in floods in Jakarta, Indonesia's sinking capital Extreme weather events due to climate change are already impacting corners of the globe.

Artificial intelligence in the real world


Charles is currently editorial director for Asia at Economist Impact. He covers a territory spanning from Australia to India. His team works with many Western multinationals from the Fortune 500 but increasingly with Asian multinationals, governments, SMEs and high-growth technology firms as well. A native Australian, Charles is currently based in Singapore and has most recently managed the regions technology research practice. He is a frequent speaker at technology events, recently giving keynote presentations at events in Singapore, Australia, Jakarta and Kuala Lumpur.

A Space Laser Shows How Catastrophic Sea Level Rise Will Be


An actual space laser is cruising 300 miles above your head right now. Launched in 2018, NASA's ICESat-2 satellite packs a lidar instrument, the same kind of technology that allows self-driving cars to see in three dimensions by spraying lasers around themselves as they roll down the street and analyzing the light that bounces back. But instead of mapping a road, ICESat-2 measures the elevation of Earth's surface with extreme accuracy. Although this space laser means you no harm, it does portend catastrophe. Today in the journal Nature Communications, scientists describe how they used ICESat-2's new lidar data to map the planet's land that's less than 2 meters above sea level, which makes it vulnerable to the creep of sea level rise.

Planned relocation: Pluralistic and integrated science and governance


Although relocation of human populations is nothing new, global environmental changes such as climate change, sea level rise, and land use change are increasing the likelihood of relocation for potentially millions of people, especially in coastal regions. Globally, sea level rise alone could place 340 million people on land projected to be below annual flood levels by 2050 ([ 1 ][1]). The need for relocation will increase because of such risks, the lack of funding for protection and accommodation strategies, and/or the reality that sea walls and other measures will eventually be ineffective. Thus, current approaches to planned relocation such as buyouts for individual households are likely to be “woefully inadequate” in the future ([ 2 ][2]). We discuss how science, governance, and their interactions need to evolve to make planned relocation a strategic option that leaves people, communities, and the environment better off. The starting point is to acknowledge that relocation involves a physical transition away from locations exposed to global change hazards, as well as the need for transformation of institutions, social networks, cultural associations, economic relationships, and other aspects of a community's way of life. Given that relocation is a life-altering change, organizations such as the United Nations (UN) High Commission on Refugees mandate that it needs to be planned and implemented with meaningful engagement of affected parties and carried out to improve (or at least maintain) their quality of life. To ensure responsiveness to changing conditions and preferences, relocation should be part of a flexible, nested, and interconnected set of adaptation strategies that also include coping (reactive, short-term risk-reduction measures) and incremental adjustments (measures to increase resistance and/or resilience) ([ 3 ][3]). How to combine these different measures into a strategic portfolio of policies and actions places demands on science and governance to support open-ended adaptive planning processes that manage trade-offs across interests, uncertainties in knowledge, and institutional ambiguity created by overlapping jurisdictions, authorities, and expertise. Planned relocation is a complex social dilemma that involves many structural, perceptual, economic, and interpersonal dynamics that discourage collective action. It will involve resolving fraught questions such as what decision processes are used, who relocates (and when), how are they compensated, where will they move, what assistance is provided (and to whom) in receiving communities, how abandoned wastes and environmental legacies are remediated, and how agreements are monitored and enforced. There is no single best approach to move a community—stakeholders with conflicting objectives will see it differently even when they share basic world views. The interaction of social and environmental triggers and lack of a preferred pathway make planned retreat similar to other global change dilemmas. But the potential scope, existential character of needed transformations, and complexity of governance challenges make it especially demanding. Despite the immensity of the challenge, it is vital now to constructively engage science and governance to plan physical transitions and socioeconomic transformations that reduce risk and make people, communities, and the environment better off. Here, we offer several ideas for improving governance partnerships in developing strategies for planned relocation. ### Eliminate perverse incentives and establish inclusive governance Existing institutions and processes of governance will be stretched to address the challenges of planning and implementing relocation in a way that meets basic humanitarian principles and good practices. This is because current mixes of policies, institutions, and relationships are responsible for producing the prevailing distribution of privilege and vulnerability in society. Although climate change plays a role, it amplifies present challenges that are an amalgam of past governance, entrenched inequities, and norms. The sheer potential scale of relocation globally is beyond anything our modern global society has experienced. For example, the megacity Jakarta is actively considering relocation because of growing climate hazards, aquifer subsidence, and the density of a highly vulnerable low-income population. These challenges are not limited to the developing world, as evidenced by the mounting annual damages and recovery costs of climate extremes on populations in the United States. Improving governance will require addressing structural inequalities and many perverse incentives and behavioral dynamics that continue to drive people to settle in areas exposed to hazards. Innovations are needed to address organizational silos, poor planning and risk communication, psychological attachments to place, and dependence on continued occupation for tax revenues. These challenges can be exacerbated with well-intentioned coping strategies (e.g., the “levee effect” that reduces accurate perception of risk). In the United States, for example, federal programs including subsidization of beach nourishment, the National Flood Insurance Program, and the federalization of natural disaster recovery encourage settlement of risky areas. Planned relocation toolkits ([ 4 ][4]) are beginning to emerge that orient the challenge within domestic legal frameworks and international organizations (e.g., the UN Office for Disaster Risk Reduction) and the experiences garnered from existing national efforts (e.g., Fiji's efforts to move 46 villages). Making and implementing decisions in which communities voluntarily relocate will require inclusive, deliberative processes that emphasize transparency, engagement, trust building, accountability, and an interactive approach for engaging with science. Policy or legislative frameworks are critical to defining long-term targets and providing credible commitments to maintain the continuity of objectives across institutions and political mandates ([ 5 ][5]). Strategies will need to accommodate changing circumstances (new scientific evidence, technological change, new preferences) and the management of implementation tactics based on expert advice, monitoring and reporting, and accountability. In most countries, new institutions and funding are required to improve access to expert advice, coordination, and consultation. Governance frameworks for relocation will need to include periodic communication about future risks, engagement with private sector and civil society, and oversight mechanisms to monitor and enforce the implementation of agreed plans. ### Diverse perspectives in problem framing Defining the problem and its context is the central challenge posed by planned relocation. Framing a problem establishes what is prioritized (and what is treated as unimportant), what the objectives are, and what questions will be asked and answered. Framing is often contested, and to avoid marginalizing communities, it needs to incorporate diverse perspectives, start from the specific local context of ongoing systemic challenges, enhance stakeholders' agency, and bring together diverse sources of knowledge ([ 6 ][6], [ 7 ][7]). It is particularly challenging to carefully analyze the diverse stakeholders and the types of knowledge that are pivotal to understanding and framing planned relocation (e.g., capturing perspectives from the relocating, receiving, and remaining populations). Problem framing could consider the need for expertise, tactical engagement, and sustained advocacy to catalyze plans into transformative actions ([ 6 ][6], [ 8 ][8]). In addition, emerging innovations in computational social science and “coproduction” of research (in which stakeholder communities are involved in different aspects of the scientific process) offer opportunities for formalizing stakeholder analysis. Analyses could improve stakeholder identification, categorization, and relationship (power) mapping. ### Account for power dynamics Decades of research in planning, public administration, sustainability science, and science and technology studies have examined how to improve the relevance and effectiveness of science to inform planning and policy for a wide range of social, environmental, and sustainability challenges. Several prominent strands of this work focus on coproduction as being more than a means to produce science, providing a mechanism to generate public goods, services, and institutions ([ 7 ][7]). Accordingly, the design of coproduction processes is not just about how the interactions of policy-makers, stakeholders, and scientists affect the usability of science. It is also about the process of social change—how epistemologies, social and cultural norms, institutions and policies, and power relationships among communities and stakeholders interact to determine who is involved in the process, which types of knowledge are seen as legitimate, what is produced, and what outcomes result. For challenges as fraught as planned relocation, this more expansive approach provides a foundation for codeveloping knowledge and action. It requires engaging multiple perspectives on values and knowledge where the actors involved in coproduction of planned retreat must work together to explore normative and political differences inherent in their different visions of the future ([ 6 ][6]). A critique of coproduction processes is that they can depoliticize discourse by using scientific arguments to evoke universalized ideas of what is “best.” They can be structured as if all participants have an equal role when in fact governments, large nongovernmental organizations, and economic interests have disproportionate power and greater opportunities for participation ([ 7 ][7]). This is not just a process issue but can also affect the outcomes of coproduction—for example, favoring the use of narrow cost-benefit framings that conclude that protective measures such as beach nourishment or construction of sea walls are economically justified only for high-value assets. Empirically informed awareness of the diverse roles and dimensions of power in coproduction and social change offers an avenue for rebalancing problematic relationships that lead to inequality or exclusion, or at least avoiding their unintended consequences ([ 7 ][7]). Modest steps such as providing funding to enable underserved communities to participate in coproduction, or formalizing the participation of Indigenous advisory councils, can also help level the playing field ([ 9 ][9]). ### Diversify knowledge sources and types To support planned relocation, science needs to deliver not just technical solutions but also knowledge of how to relocate and transform communities, including the willingness and capacities of different groups and institutions to support fundamental change over time ([ 6 ][6]). Providing this knowledge will require a transdisciplinary approach to research that broadens the array of scientific disciplines and other sources of knowledge engaged. Government bodies and stakeholders (e.g., real estate interests, businesses, community-based organizations) will need to be integrated into research not just as “users” but as knowledge holders and experts in community needs, preferences, norms, and evolving capacity to implement solutions. When relocation involves Indigenous communities, rather than integrating traditional knowledge into Western science, scientists involved in coproduction arrangements should foster mutual respect on the multiple ways of knowing, by engaging in tribal avenues, such as regional newsletters and talking circles at tribal meetings ([ 9 ][9], [ 10 ][10]). Informing social and economic transformation will require research into the capacities and values of different populations and institutions. This requires understanding issues such as what will motivate people to make changes, the capacity of individuals and institutions to act on their preferences, and how current conditions and path dependencies affect the viability of future options ([ 6 ][6]). It will be necessary to “think critically about outcomes as well as processes, about institutional and process designs, [and] about power and performance” ([ 11 ][11]). ### Sample from a range of plausible futures to evaluate decision options Science can better inform action if it stops trying to predict inherently unpredictable phenomena. Currently, many decision-makers frame their questions to scientists as “what will happen,” and scientists respond with “projections” (possibilities based on assumptions about future radiative forcing), which are often used as predictions. This framing, in addition to putting science in the dangerous position of speculating, is not necessarily as helpful to decision-makers as “what if” questions about the consequences of options under many plausible futures. Science can be more useful by changing the objective of collaboration from “predict then act” to the exploration of hypothetical questions about what short-term actions would be consistent with long-term objectives and perform well under a diverse range of plausible futures ([ 12 ][12]). As a specific example, the State of Louisiana has been confronting sea level rise, land subsidence, accelerating losses of coastal lands, and increasing risks from storm surge. The state has initiated an innovative and collaborative planning process that budgets $50 billion in a portfolio of projects to be adaptively implemented over the next 50 years ([ 13 ][13]). Unlike traditional cost-benefit–driven risk planning efforts based on a specific expected future (“what will happen”), the Louisiana master plan has engaged broad stakeholder participation to map what project investments hold immediate benefits while providing flexibility to confront a broad range of plausible future scenarios that could reshape their investment priorities as well as future stakeholder needs (“what if” planning). This approach recognizes that many types of uncertainty will impede judgment and decision-making ([ 12 ][12]). The natural stressors that can trigger the need for evacuation are uncertain because they are emergent, compounding, and cascading outcomes of complex human–environment interactions. But the implications of changes in future values and behaviors are also uncertain and arguably just as important for evaluating decision options. Even in well-documented historical instances of relocation, it is difficult to understand how outcomes emerged from the actions taken—let alone anticipate with any certainty how desired outcomes arise from future actions ([ 14 ][14]). One important opportunity is to more widely apply decision-making under deep uncertainty (DMDU) methods ([ 12 ][12]). These exploratory approaches draw on local-scale stakeholders' knowledge of the key factors and dynamics (human and natural) and provide a promising mechanism for informing planned relocation. Models and scenarios serve as focal points to build shared understanding about the potential implications of the different values and options preferred by stakeholders. ### Social learning to build local capacity Relocation is a complex process that will benefit from expanding the range of intermediaries and services available to facilitate production and application of knowledge. Those involved will need to know not only what scientifically robust sources of information are available for the hazards they face, but also how this information should be used to assess vulnerability, revise flood maps or zoning, evaluate financial risks to reset insurance rates and bond ratings, redesign infrastructure systems, update capital improvement and other plans, or establish thresholds and monitoring systems to trigger the next phase of agreed measures. Much attention has focused on providing climate scenarios and data, but to meet the needs of relocation, the range of services must be expanded. Needed services include not only identifying good practices in engineering, financial risk, and other technical analyses but also supporting transformation, capacity building, and establishment of standards for different types of deliberative and analytic processes. Research, case studies, and pilot projects are testing approaches to meet these challenges, and a useful next step is to organize evaluation and social learning to establish good practices and technical guidance. One option is to incorporate evaluation into assessments such as the Intergovernmental Panel on Climate Change and the US National Climate Assessment to establish a knowledge foundation for climate services. This would create standards for services delivered through international organizations, the private sector, academia, and public agencies (to ensure availability of services for underserved, low-income communities) ([ 15 ][15]). Another is an open-source wiki for climate solutions that would enable a more diverse range of knowledge holders to interact and curate guidance on good practices on an ongoing basis, emphasizing sources of credible information. Another opportunity is to expand the use of intermediaries—individuals and institutions that facilitate interactions between stakeholders and experts ([ 8 ][8]). Many intermediary skillsets are necessary for the different stages of deliberative planning, financing, tactical implementation, and ex-post monitoring of relocation actions. Given the potential for contested needs and values, it is important that intermediaries be aware of how they can unintentionally affect power relationships or outcomes—for example, by using types of knowledge, analysis metrics, or visualizations that favor the perspectives of one group or another. A “critical pragmatic approach” highlights the importance of this awareness and of designing and critically evaluating deliberative processes where conflicts between parties are not reduced to simple consensus-driven debates ([ 11 ][11]). A variety of measures are needed to increase the number and efficacy of intermediaries, including professional certification; greater recognition, including in promotion and tenure processes; and increased funding. ### Harness emerging innovations in community science and data analytics Innovations in community science, sensing, and data analytics hold great promise in providing insights for planned relocation if privacy, equity, and other concerns such as maladaptive applications of generic algorithmic or sensing tools are addressed ([ 15 ][15]). Combining these innovations with monitoring investments in socioeconomic data offers the potential to better capture the interdependent evolution of human and natural systems that shape the experiences and prospects of populations facing relocation. For example, high-resolution models of flooding magnitude and extent might be available for an area, but data are missing on how inequities in agency and justice interact with exposure to hazards to shape the prospects of using planned relocation to improve people's lives. These innovations will increase the utility of standard modes of multidisciplinary scientific research that combine hazard predictions, engineering, financial, and other analyses to inform technical solutions that contribute to physical transitions. Additional methodological advances that have not yet been fully exploited include improved projections of hazards at various spatial scales; research on coastal habitat loss and nature-based solutions; new data sources, indicator-based assessments, and demographic modeling to identify vulnerable populations; and practice standards for using global change risk analytics in engineering and other professions. This contextualized technical knowledge can provide insights for sequencing transitional risk reduction and protection measures. Revolutionizing the role of science to focus on conditions that will affect the ability of society to identify just relocation pathways, build agency, and implement strategies under uncertainty will require a “pluralistic and integrated approach to action-oriented knowledge” ([ 6 ][6]). Such an approach will increase confidence in the ability of communities to successfully navigate planned relocation on the massive scales at which it is likely to be required. It must build a more ethical and responsible approach that serves those affected. 1. [↵][16]1. S. A. Kulp, 2. B. H. Strauss , Nat. Commun. 10, 4844 (2019). [OpenUrl][17] 2. [↵][18]1. J. Carey , Proc. Natl. Acad. Sci. U.S.A. 117, 13182 (2020). [OpenUrl][19][FREE Full Text][20] 3. [↵][21]1. N. Chhetri, 2. M. Stuhlmacher, 3. A. Ishtiaque , Environ. Res. Commun. 1, 015001 (2019). [OpenUrl][22] 4. [↵][23]1. E. Ferris , Int. Organ. Migr. (2017). 5. [↵][24]World Bank, “World Bank Reference Guide to Climate Change Framework Legislation” (Washington, DC 2020); . 6. [↵][25]1. G. Caniglia et al ., Nat. Sustain. 4, 93 (2021). [OpenUrl][26] 7. [↵][27]1. C. Wyborn et al ., Annu. Rev. Environ. Resour. 44, 319 (2019). [OpenUrl][28][CrossRef][29] 8. [↵][30]1. P. Kivimaa, 2. W. Boon, 3. S. Hyysalo, 4. L. Klerkx , Res. Policy 48, 1062 (2019). [OpenUrl][31] 9. [↵][32]1. J. K. Maldonado, 2. B. Colombi, 3. R. Pandya 1. P. Cochran et al ., in Climate Change and Indigenous Peoples in the United States: Impacts, Experiences and Actions, J. K. Maldonado, B. Colombi, R. Pandya, Eds. (Springer, 2014; ), pp. 49–59. 10. [↵][33]1. N. Latulippe, 2. N. Klenk , Curr. Opin. Environ. Sustain. 42, 7 (2020). [OpenUrl][34] 11. [↵][35]1. J. Forester , Plann. Theory 12, 5 (2013). [OpenUrl][36] 12. [↵][37]1. V. A. W. J. Marchau, 2. W. E. Walker, 3. P. J. T. M. Bloemen, 4. S. W. Popper , Decision Making under Deep Uncertainty: From Theory to Practice (Springer Nature, 2019); . 13. [↵][38]1. J. R. Fischbach, 2. D. R. Johnson, 3. D. G. Groves , Environ. Res. Commun. 1, 111001 (2019). [OpenUrl][39] 14. [↵][40]1. K. de Koning, 2. T. Filatova , Environ. Res. Lett. 15, 034008 (2020). [OpenUrl][41] 15. [↵][42]1. R. H. Moss et al ., Weather Clim. Soc. 11, 465 (2019). 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Senior Data Scientist - Demand Generation


About the Role As our Senior Data Scientist, you'll be an integral player in the Demand and Incentives Data Team based in Jakarta. With the latest cutting-edge data science tech at your disposal, you'll focus your efforts on bringing our incentive systems to the next level, employing various quantitative techniques such as Machine Learning, Optimization, Simulation, Bayesian Techniques to drive asymmetric values for our businesses at Gojek. You'll be heavily involved in ideation, research, and building prototypes, and the folks in the Data Science Platform will bring your models to production. Your efforts will directly influence the stability and scalability of Gojek's demand & incentives stream, and thus to company's top and bottom line as a whole. What You Will Do Drive the long term vision of the Machine Learning-based incentive systems and own its implementation end-to-end Enhance the technical excellence of the team and bring the data science products in your stream to the next level Work with other Data Scientists, Machine Learning Engineers, and Business users to build, deploy, and scale data science solutions for incentive systems Utilize your experience in data science, machine learning, software engineering, distributed systems to develop these systems; work with the platform team to take the systems to production Work with Business teams to continuously refine and improve the systems to cater to Gojek's ever-evolving needs What You Will Need At least 5 years of experience as a Data Scientist/ Machine Learning Engineer, with solid understanding of Data Science and Machine Learning fundamentals and experience taking Data Science models into production Experience in Python, R, Golang/Java, Unix; along with knowledge of good software design principles and TDD Working knowledge of Cloud-based solutions (GCP/ AWS), Stream Data Processing Frameworks (Beam) and mature Deep Learning frameworks (e.g.

Machine Learning Engineer - Merchant Platform


About the Role Fasten your helmet and climb on board if you're ready to be our Machine Learning Engineer. In this role, you'll be a crucial player within the Merchant Platform, using and building machine learning as a microservice, integrating it with the core service, and establishing data pipelines for structured & unstructured data. In close collaboration with the Data Science, Data Engineering, and Product Engineering teams, you'll get your hands dirty in complex ML, data pipeline, and service product tech stacks. By automating processes and integrating ML models into our products & services, your efforts will help ensure a robust and efficient Merchant Platform for Gojek. What You Will Do Collaborate with Data Science team to gather the requirement for model parameter Build the feature extraction script to automate the process for the ML model Collaborate with product engineer to integrate ML model into product/service Process data from streaming/raw data based on user needs Collaborate with other Data Engineers to develop data and model pipelines Design distributed systems to apply machine learning and data science techniques What You Will Need At least 2 years of experience as a Software Engineer or ML Engineer, with fluency and experience in Clojure, Elixir, Python, or Java Basic knowledge in data science, and familiarity with ML libraries such as Pandas, Scikit, or Tensorflow Proven track-record in building large-scale, high-throughput, low-latency production systems Experience building data stream processes Familiarity with SQL and NoSQL Database Ability to implement CI/CD and TDDAbout the Team Our Merchant Platform team is a big family of around 60 people based across Jakarta, Yogyakarta, and India.

Check Out These Cool New Features In Google Maps Brought To Reality By Artificial Intelligence


Google announces a series of updates in Google Maps using artificial intelligence (AI) that have been or are about to be released in the coming year. Live View uses augmented reality (AR) cues to avoid those situations where one starts walking in the undesired direction. Live View is powered by global localization. It is an AI technology that uses AI to scan billions of Street view images and understand your exact orientation. Thus, it helps Live View understand an object's precise altitude and placement of some of the trickiest-to-navigate places indoors, like airports and malls.

Surge Pricing, Artificial Intelligence, and Responsibility


On my first work trip to Jakarta 14 January 2016 for Grab, multiple terrorist bombs exploded a couple of miles from the GrabBike office where I had just arrived. People were fleeing cafes and restaurants around the attack site. My new colleagues were shaken, glad to be safe, looking to help. There was news of crowds on the streets trying to get away, confirmed by a spike in booking requests from the blocks around the explosion. My colleagues remembered the 2002 Bali bombings, and knew we should get people to spread out.

Mapping the global threat of land subsidence


Subsidence, the lowering of Earth's land surface, is a potentially destructive hazard that can be caused by a wide range of natural or anthropogenic triggers but mainly results from solid or fluid mobilization underground. Subsidence due to groundwater depletion ([ 1 ][1]) is a slow and gradual process that develops on large time scales (months to years), producing progressive loss of land elevation (centimeters to decimeters per year) typically over very large areas (tens to thousands of square kilometers) and variably affects urban and agricultural areas worldwide. Subsidence permanently reduces aquifer-system storage capacity, causes earth fissures, damages buildings and civil infrastructure, and increases flood susceptibility and risk. During the next decades, global population and economic growth will continue to increase groundwater demand and accompanying groundwater depletion ([ 2 ][2]) and, when exacerbated by droughts ([ 3 ][3]), will probably increase land subsidence occurrence and related damages or impacts. To raise awareness and inform decision-making, we evaluate potential global subsidence due to groundwater depletion, a key first step toward formulating effective land-subsidence policies that are lacking in most countries worldwide. A large-scale systematic literature review reveals that during the past century, land subsidence due to groundwater depletion occurred at 200 locations in 34 countries [see supplementary materials (SM)]. However, subsidence extent is only known for one-third of these records, information on the impacts is scarce, and mitigation measures were implemented only in a few locations. In China, widespread subsidence affects cities developed in the main sedimentary basins. In Indonesia, coastal subsidence in Jakarta is so severe that government authorities are planning to move the capital to the island of Borneo. In Japan, subsidence affected several cities during the 20th century, including more than 4 m of subsidence in Tokyo, before groundwater management practices mitigated further subsidence. Iran currently hosts some of the fastest-sinking cities in the world (25 cm year--1) because of unregulated groundwater pumping. In Europe, the greatest impact of subsidence occurs in the Netherlands, where subsidence is primarily responsible for placing 25% of the country below the mean sea level and increasing the flooding risk. Subsidence in the Po River Plain in Italy started during the second half of the 20th century and currently threatens 30% of the Italian population, contributing to recurrent coastal flooding during extreme high tides in Venice. In North America, intense groundwater depletion triggers subsidence from California's Central Valley, with as much as 9 m of subsidence in the past century, to the Atlantic and Gulf of Mexico coastal plains in the United States, where subsidence is increasing flooding risk. In México, subsidence rates are among the highest worldwide (as much as 30 cm year-1), affecting small structurally controlled intermontane basins where the main urban centers developed, causing an important but unaccounted economic impact. Spatial analysis of subsidence locations identified in our global database (see SM) reveals that subsidence has preferentially occurred in very flat areas where unconsolidated sediments accumulated in alluvial basins or coastal plains, and where urban or agricultural areas developed in temperate or arid climates characterized by prolonged dry periods. Land subsidence has generally occurred in water-stressed basins, where the combination of groundwater withdrawal and natural groundwater discharge outpaced groundwater recharge, resulting in groundwater storage losses, groundwater depletion, and compaction of susceptible aquifer systems. In the affected basins, land subsidence mainly occurred in highly populated areas, with half of documented occurrences in areas susceptible to flooding. In coastal zones, the combined effects of absolute sea-level rise and land subsidence contribute to relative sea-level rise ([ 4 ][4]). The contribution from land subsidence may exceed the contribution from absolute sea-level rise by a factor of 10 or more and could be especially critical for 21% of the geographic locations identified in our database, where land elevation is less than 1 m above the mean sea level. On the basis of the spatial analysis findings, a global model is proposed to combine the main variables influencing subsidence to identify environmental settings favoring land subsidence and the anthropogenic factors leading to groundwater depletion (see SM). Statistical analyses of lithology, land-surface slope, land cover, and Koppen-Geiger climate classes are used to predict global subsidence susceptibility at a spatial resolution of 1 km2. The probability of groundwater depletion is estimated by identifying urban and irrigated areas suffering water stress and where groundwater demand is high. The analyses do not consider subsidence magnitude and rate, owing to the lack of this information at a global scale. Hence, the combination of subsidence susceptibility and the probability of groundwater depletion is used to predict a “proxy” of subsidence hazard, which permits identification of exposed areas where the probability of land subsidence occurrence is high or very high. Even though these results do not necessarily translate to direct impacts or damages, they are useful for identifying potential subsidence areas where further local-scale analysis is necessary. T he comparison of our model predictions with an independent validation dataset reveals a 94% capability to distinguish between subsidence and nonsubsidence areas, according to the value of the area under the receiver operating characteristic curve (see SM). The global exposure to potential subsidence is evaluated by calculating the number of inhabitants living in potential subsidence areas, i.e., subsidence hazard proxy, and the equivalent gross domestic product (GDP). T his “proxy” of exposed assets is calculated assuming that GDP per capita is homogeneous within each country. Finally, the evolution of potential global subsidence and the related exposure is predicted for 2040 for a global change scenario based on steady population growth and increasing greenhouse gas emissions (Shared Socioeconomic Pathways 2, Representative Concentration Pathway 8.5), which accounts for the greatest sea-level rise projections. ![Figure][5] Potential global subsidence The color scale indicates the probability intervals classified from very low (VL) to very high (VH), for every 30-arcsec resolution pixel (1 km by 1 km at the Equator). The white hatched polygons indicate countries where groundwater data is unavailable, and the potential subsidence only includes information on the susceptibility. See maps of other regions in supplementary materials. GRAPHIC: N. DESAI/ SCIENCE Our results suggest that potential subsidence threatens 12 million km2 (8%) of the global land surface with a probability greater than 50% (MH to VH in the figure). Potential subsidence areas are concentrated in and near densely urban and irrigated areas with high water stress and high groundwater demand, overlying some of the largest and most depleted aquifer systems ([ 5 ][6]) in Asia (e.g., North China Plain) and North America (e.g., Gulf of Mexico coastal plain); coastal and river delta areas worldwide (e.g., Vietnam, Egypt, or the Netherlands); and inland sedimentary basins of México, Iran, and the Mediterranean countries. Potential subsidence is lower in Africa, Australia, and South America, owing to the lower groundwater depletion ([ 6 ][7]). In central Africa, potential subsidence only includes information on the susceptibility, as groundwater depletion is unknown. In this region, subsidence susceptibility (see fig. S6) could be useful to prevent subsidence impacts on developing cities that during the next decades could rely more on the available groundwater resources. To evaluate the exposure to potential subsidence, we focus on areas where the potential subsidence probability is high or very high (see the figure). The cumulative potential subsidence area amounts to 2.2 million km2, or 1.6% of the land; includes 1.2 billion inhabitants, or 19% of the global population; and has an exposed GDP of US$ 8.19 trillion, or 12% of the global GDP. Hi gh-income countries account for 62% of the global exposed GDP but only 11% of the global exposed population, whereas low-income countries account for 54% of the global exposed population and 12% of the global exposed GDP. It is expected that the capability of low-income countries to implement the political, regulatory, and socioeconomic measures necessary to prevent and mitigate subsidence impact will be less than that for high-income countries. Potential subsidence threatens 484 million inhabitants living in flood-prone areas, 75% of whom live in fluvial areas and 25% of whom live near the coast. This number of threatened inhabitants corresponds to 50% of the global population exposed to flooding hazards according to previous estimates ([ 7 ][8]), demonstrating the importance of considering potential subsidence in global flooding risk analyses. Most of the global population exposed to potential subsidence live in Asia (86%), which is about 10 times the combined exposed population of North America and Europe (9%). The results indicate that 97% of the exposed global population is concentrated in 30 countries (see SM). India and China share the top two rankings of potential subsidence in terms of spatial extent and exposed population. Egypt and the Netherlands have the largest populations living in potential subsidence areas that are below the mean sea level. The greatest population densities in potential subsidence areas occur in Egypt and Indonesia, whereas the relative exposure per country, measured as the exposed population normalized by the total population, is greater than 30% for Egypt, Bangladesh, Netherlands, and Italy. The United States ranks first in terms of GDP exposed to potential subsidence, owing to its high GDP per capita. Combination of the aforementioned metrics permits derivation of a potential subsidence index ranking (see SM). Seven of the first ten ranked countries have the greatest subsidence impact, accounting for the greatest amount of reported damages (Netherlands, China, USA, Japan, Indonesia, México and Italy). During this century, climate change will cause serious impacts on the world's water resources through sea-level rise, more frequent and severe floods and droughts, changes in the mean value and mode of precipitation (rain versus snow), and increased evapotranspiration. Prolonged droughts will decrease groundwater recharge and increase groundwater depletion, intensifying subsidence. The global potential subsidence is predicted for 2040 using the same subsidence metrics and available global projections of water stress, water demand variations, climate, and population (see SM). Although predicted potential subsidence areas increase only by 7% globally, the threatened population is predicted to rise by 30%, affecting 1.6 billion inhabitants, 635 million of whom will be living in flood-prone areas. These changes will not be homogeneous. Between 2010 and 2040, the predicted population exposed to potential subsidence increases more than 80% in the Philippines, Iraq, Indonesia, México, Israel, Netherlands, Algeria, and Bangladesh. The increase will be moderate, less than 30%, for China, the United States, Italy, and Iran. Potential subsidence is forecasted to decrease in Japan and Germany, owing to effective groundwater management policies and population declines. Finally, potential subsidence is predicted to emerge in high-latitude northern countries like Canada and to increase in extent in Russia or Hungary, where climate change will favor longer dry seasons. Further advancements in the global evaluation of subsidence can be made when a global historical database on subsidence rate, magnitude, and extent has been compiled, which could be largely sourced from continental monitoring of surface displacements using satellite radar imagery ([ 8 ][9]). Widespread continuous monitoring of subsidence will permit better evaluation of the potential impact of land subsidence, especially in countries like Indonesia, México, and Iran, where local studies revealed the highest subsidence rates worldwide, but the national dimension of subsidence is still unknown. Further research also is necessary to evaluate the cost of damage caused by current and historical subsidence worldwide. The combination of damage information with hazard estimates will permit improved assessments of potential loss and design of cost-effective countermeasures. Presently, annual subsidence costs are only published for China (US$ 1.5 billion) and the Netherlands (US$ 4.8 billion) ([ 9 ][10]). The greater subsidence costs in the Netherlands owe to the exposed population below the mean sea level and the large investments made to prevent flooding. Our model, which does not yet consider mitigation measures, likely overestimates potential subsidence exposure in the Netherlands and Japan, where groundwater management has effectively controlled subsidence over the past decades ([ 10 ][11]). Our results identify 1596 major cities, or about 22% of the world's 7343 major cities that are in potential subsidence areas, with 57% of these cities also located in flood-prone areas. Moreover, subsidence threatens 15 of the 20 major coastal cities ranked with the highest flood risk worldwide ([ 11 ][12]), where potential subsidence can help delimit areas in which flooding risk could be increased and mitigation measures are necessary. Overall, potential global subsidence results can be useful to better define the spatial extent of poorly documented subsidence occurrences, discover unknown subsiding areas, prevent potential subsidence impacts wherever groundwater depletion occurs, and better identify areas where subsidence could increase the flooding risk. In any of these scenarios, an effective land-subsidence policy should include systematic monitoring and modeling of exposed areas, evaluation of potential damages, and cost-benefit analyses permitting implementation of adequate mitigation or adaptation measures. These measures should consider groundwater regulation and strategic long-term measures, such as the development of alternative water supplies and the protection and (or) enhancement of natural or artificial recharge of aquifers. Considering that the potential subsidence may affect 635 million inhabitants living in flood-prone areas in 2040, it is of prime importance that potential subsidence is quantified and systematically included in flood risk analyses and related mitigation strategies. [][13] 1. [↵][14]1. D. L. Galloway, 2. T. J. Burbey , Hydrogeol. J. 19, 1459 (2011). [OpenUrl][15] 2. [↵][16]1. J. S. Famiglietti , Nat. Clim. Chang. 4, 945 (2014). [OpenUrl][17] 3. [↵][18]1. K. E. Trenberth , Clim. Res. 47, 123 (2011). [OpenUrl][19][CrossRef][20][Web of Science][21] 4. [↵][22]1. J. P. M. Syvitski et al ., Nat. Geosci. 2, 681 (2009). [OpenUrl][23][CrossRef][24][Web of Science][25] 5. [↵][26]1. P. Döll, 2. H. Müller Schmied, 3. C. Schuh, 4. F. T. Portmann, 5. A. Eicker , Water Resour. Res. 50, 5698 (2014). [OpenUrl][27][CrossRef][28][PubMed][29] 6. [↵][30]1. R. G. Taylor et al ., Nat. Clim. Chang. 3, 322 (2013). [OpenUrl][31] 7. [↵][32]1. B. Jongman, 2. P. J. Ward, 3. J. C. J. H. Aerts , Glob. Environ. Change 22, 823 (2012). [OpenUrl][33] 8. [↵][34]1. R. Lanari et al ., Remote Sens. 12, 2961 (2020). [OpenUrl][35] 9. [↵][36]1. T. H. M. Bucx, 2. C. J. M. Van Ruiten, 3. G. Erkens, 4. G. De Lange , in Proceedings of the International Association of Hydrological Sciences 372, 485 (2015). [OpenUrl][37] 10. [↵][38]1. K. A. B. Jago-on et al ., Sci. Total Environ. 407, 3089 (2009). [OpenUrl][39][CrossRef][40][PubMed][41] 11. [↵][42]1. S. Hallegatte, 2. C. Green, 3. R. J. Nicholls, 4. J. Corfee-Morlot , Nat. Clim. Chang. 3, 802 (2013). [OpenUrl][43] 12. [↵][44]1. G. Herrera, 2. P. Ezquerro , Global Subsidence Maps, figshare (2020); 10.6084/m9.figshare.13312070. Acknowledgments: Four anonymous peer reviewers and S. E. Ingebritsen (U.S. Geological Survey) helped to improve the manuscript. Funding for this study was provided partly by the Spanish Research Agency (AQUARISK, PRX19/00065, TEC2017-85244-C2-1-P projects) and PRIMA RESERVOIR project, and by all the institutions represented in the Land Subsidence International Initiative from UNESCO. G.H.-G., P.E., R.T., M.B.-P, and J.L.-V. designed the study, performed the analysis, and wrote the initial manuscript with input from all other authors. R.M.M., E.C.-C., and M.R. advised on the susceptibility analysis. R.M.M., J.L., P.T., and G.E. advised on hazard analysis. D.C.-F., J.L., P.T., E.C.C., G.E., D.G., W.C.H., N.K., M.S., L.T., H.W., and S.Y. advised on global exposure analysis. R.T., M.B.P., R.M.M., J.L., P.T., W.-C.H., N.K., L.T., H.W., and S.Y. contributed essential data for the analysis. All the authors edited and revised the manuscript through the different reviews. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. government. The authors declare no competing interests. All data included in this study are available at figshare ([ 12 ][45]). 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