Negative exokarstic units as a basis for urban management: Yucatan Peninsula, Mexico

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For Tulum, the inputs have been insufficient in identifying depressions at a detailed level. This research aimed to analyze and characterize the negative exokarstic relief of the city of Tulum through morphometric parameters and urban elements that contribute to the knowledge of the man-nature relationship. From the DEM ALOS PALSAR, contour lines, elevation models, slopes, and shadows were extracted, which allowed the identification of depressions, topographic profiles, and the calculation of morphometric indices; subsequently, the distribution of depressions was analyzed concerning urban elements. The identified depressions were classified into uvalas, sinkholes, and poljes. The analysis of the topographic profiles allowed us to recognize units in the shape of "V" (64%), "U" (19%), and "Hoya" (17%). The highest concentration of type V depressions is observed in the city's central area, characterized by a medium and high population density, as well as the centralization of commercial and public establishments. The detailed characterization of depressions is a planning and management tool for the territory. morphometry depressions urban elements hazards urban planning Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Karst is a dissolution process on soluble rock (generally limestone, dolomites, marble, and gypsum) that shapes unique landscapes and aquifers characterized by rapid infiltration of rain, lack of surface water, and turbulent flow in a network of fractures, conduits, and caves [ 1 – 4 ]. Morphologically, it is divided into surface or subarea (exokarst) and underground (endokarst) geoforms [ 5 – 8 ] (Table 1 ). Table 1 Morphological classification of karst according to the scale Karst morphology * Small scale Medium scale Grand scale Negative exokarst Lapiaz and Karren sinkholes Gorges, canyons, poljes, and valleys < 1 mm at 10 m (lapiaz fields) From meters to 1 km in diameter kilometer scale Positive exokarst Mogotes, towers, cones, and pinnacles Endokarst Karst caves or cavities * The table * This table was prepared with data from Andreu et al. [ 5 ]. According to the morphological classification based on the scales, the research covers medium and grand-scale negative exokarst. The concept became popular in the scientific field at the end of the 19th century, thanks to the study of karst phenomena by Cvijić [ 9 ] and the underground investigations of Martel [ 10 ]. Since then, different disciplines have addressed the phenomenon, with physical work standing out for a long time; However, at present the description and exploration of these systems are no longer sufficient, so it is necessary to understand the complex relationships resulting from the interaction of man and karst. The importance of karst lies in the fact that it is present in 20% of the ice-free continental area of the planet [ 2 ] and more than a quarter of the world's population lives in or near karst areas [ 2 , 3 ]. In terms of ecosystem services, water resources become relevant, it is estimated that between 20% and 25% of the population partially or depends on water from karst aquifers [ 2 , 11 ]. On the other hand, karst provides resources such as soil, vegetation, and habitat for multiple species [ 12 ]. It also protects forests, rivers, and lakes against the harmful effects of acid rain [ 13 ]; in addition, unique ways of life and landscapes with scenic appearance and scientific/educational potential relevant to the development of leisure and recreation activities are developed on these systems [ 14 ]. In another context, Hernández Aguilar [ 15 ], Drew and Hötzl [ 16 ], Newton [ 17 ], and Simón et al. [ 18 ] explain that karst should be considered as a potential source of geological risk, mainly in urban areas; subsidence, for example, poses a danger to man; since the deformation that the terrain experiences during karst collapse can cause serious damage to all types of structures or even put people's lives at risk when it is generated abruptly [ 19 ]. On the contrary, these systems are highly sensitive to human disturbance and are considered among the environments of greatest vulnerability, fragility, and complexity [ 8 , 16 , 20 – 23 ]. Environmental problems related to karst areas include droughts, floods, surface collapse, pollution, and deforestation. Part of these arise from ignorance of the system, coupled with the lack of adequate planning and effective management [ 16 ]. The characteristics of karst in interaction with anthropogenic activities make urban planning a challenge [ 24 ]. Man, through the activities he carries out in the territory such as agriculture, waste disposal, and water supply, requires knowledge about karst systems [ 1 ]. Knowledge of karst zones has implications in engineering, geological threats, construction, planning, hydrogeology, neotectonics, climate change, ecology, biology, archaeology, recreation, and tourism [ 7 ]. In support of these needs, current techniques and models have provided new insights into the processes and resulting landforms [ 1 , 24 ]. The characterization and delimitation of karst zones are also important for the development of housing, transportation, and energy infrastructure, as well as to prevent contamination of aquifers and optimize the use of associated resources [ 16 , 23 ]. The surface recognition of depressions is based on the observation of aerial photographs, visual analysis of the terrain (topographic depressions, steps, areas of vegetation or anthropic fills), and observation and analysis of damage in urban areas. However, in the case of cities and places with thick vegetation, depressions cannot be detected at first glance and indirect observation methods are needed [ 18 ]. Some studies such as that of Fragoso-Servón et al. [ 25 ], have laid the foundations for the characterization of depressions in the state of Quintana Roo, and the risk studies such as that of Hernández Aguilar [ 15 ] and Pereira-Corona et al. [ 26 ]. However, the low altitudinal contrast and spatial heterogeneity of the karst require the use of technologies with higher resolution that allow the creation of cartography in greater detail and at larger scales, such as the works of Frausto-Martínez et al. [ 24 , 27 ] and Rodríguez Castillo et al. [ 28 ] in the cities of Cozumel and Playa del Carmen with Digital Elevation Models and LiDAR data. The latter is ideal for working with small territories due to the large amount of data that must be processed. Unfortunately, LiDAR data do not cover the entire Mexican territory, this includes the city of Tulum, where studies have been carried out using topographic maps and medium-scale optical images such as those of Beddows et al. [ 29 ]; Fragoso-Servón et al. [ 25 ]; INEGI [ 30 ] and Lebedeva et al. [ 31 ]. Despite this, the need to have detailed information is established in the Regulation of Cenotes and Caverns of the Municipality of Tulum [ 32 ], Quintana Roo, and in the signing of the collaboration agreement between the Autonomous University of the State of Quintana Roo and the Tulum City Council for updating the municipality's Risk Atlas [ 33 ]. Therefore, the objective of this research is to analyze and characterize the negative exokarst-type relief of the City of Tulum through morphometric parameters and its influence on the urban elements that contribute to the knowledge of the man-karst-nature relationship. 2. Materials and Methods The urban area of Tulum, Quintana Roo-Mexico, is home to a population of 33,374 inhabitants. It has a total of 837 blocky urban AGEBS distributed over around 850.80 hectares [ 34 ] and maintains a population density of 39.22 inhabitants per km 2 (Fig. 1 ). For the study, the data from the National Housing Inventory [ 35 ] was taken as a reference. 2.2 Techniques and tools The identification and mapping of the exokarst units was carried out by processing ALOS PALSAR DEM, the high-resolution Digital Elevation Model (DEM) of the Japanese Aerospace Exploration Agency (JAXA). The model is developed from SAR (Synthetic Aperture Radar) information acquired between 2006 and 2011 by the ALOS (Advanced Land Observation Satellite) satellite and captured by the PALSAR synthetic aperture radar sensor. The data available for the entire world were downloaded at a resolution of 12.5 m on The Alaska Satellite Facility page ( https://search.asf.alaska.edu/#/ ). Following the methodology of Frausto-Martínez et al. [ 24 , 27 ], a DEM mosaic was created with 4 images that cover the municipality of Tulum, subsequently, the origin errors of the mosaic were corrected in the QGIS 3.16 software, and, in ArcGIS, a cutout was made for the study area (Fig. 2 ). Contour lines with equidistance at 1 m were extracted and smoothed from the model, which were interpolated to create an irregular network of triangles (TIN). The network was used to create four products: a) digital elevation model (DEM), b) slope model, c) hillshade model, and d) 3D elevation model. With them, the semi-automated recognition of the exokarst units of the territory began (classification of contour lines and marking of contour lines). The classification of the units was carried out taking as parameters the elongation index (relationship between major axis and minor axis), the compactness index (or Gravelius index) that relates the perimeter of the unit to the perimeter of a circle of equal area; reference indices in the morphometric studies of karst territories by Fragoso-Servón et al. [ 25 ], Frausto-Martínez et al. [ 24 ] and Kobal et al. [ 38 ]. The units were also classified according to the topographic profile, following the work of Rodríguez Castillo et al. [ 28 ]. Other parameters identified were depth, perimeter, area, and volume. Finally, a spatial analysis was carried out through the superimposition of layers: population density, economic units, vector data of highways and roads, and karst depressions. 3. Results 3.1 Morphometric analysis The model used for the analysis allowed the identification of 95 karst depressions in the urban area of the City of Tulum, Quintana Roo. In Table 2 , on average the exokarst units have a perimeter of 449 m and an area of 12,511.5 m 2 ; as for the depth, the depressions do not exceed 6 m. Table 2 Main metrics of identified depressions. Metrics Minimum Maximum Average Perimeter 131.5 1933 449 Area m 2 1325 116,424 12,511.5 Depth 1 6 2 Volume m 3 519 309,759 13,455 Major Axis 44 678 151 Minor Axis 22.5 261 80 3.1.1 Classification by elongation index The classification carried out with the elongation index shows a greater concentration of poljes-type forms with 36% presence. This shape is a large, closed depression with a flat bottom [ 39 ], a length of several kilometers, and a minimum width of 400 m with an elongated shape [ 40 ]. The units with the least frequency are the uvalas with only 11%. Shapes smaller than poljes and larger than sinkholes with an irregular bottom, intermediate genetics are presumed between a sinkhole and a polje [ 41 ] (Table 3 , Figs. 3 and 4 ). Table 3 Characterization parameters by elongation index SHAPE * INDEX CATEGORIES Round 2.0 Poljes * This table was prepared with data from Frausto-Martínez et al. [ 24 ]. 3.1.2 Compactness index This classification by the compactness index was carried out taking the parameters used by Fragoso-Servón et al. [ 25 ] who classified as poljes, those forms greater than 1 km, while sinkholes with an index from 1.04 to 1.3 and those with an index greater than 1.3 were considered uvalas. For the case study, 25% of the units were not classified because they were considered smaller shapes; however, they have an average depth of 1 m (maximum of 3 m), average perimeters of 230 m, and average areas of 4,368 m 2 ; while the major axes measure between 55.2 and 203.6 m and the minor ones between 49.6 and 228.9 m. The results show that the most common forms are the uvalas, which represent 51% of the analyzed universe. They are distributed throughout practically the entire city and a significant concentration is observed in areas of high and medium population density (Fig. 5 ), they represent the forms with the greatest depths (up to 6 meters) and are the units with the largest area and perimeter with an average of 14, 475.2.4 m 2 and 500.9 m, respectively. The major axes range between 48 m and 677.9 m, while the minor axes measure between 22.5 m and 261 m. For their part, the sinkholes are distributed around the city, with only a small amount in the center, in areas of medium population density. These depressions represent 24% of the morphometric units identified in the study, they have depths of up to 3 m. Its average surfaces are 4,748 m 2 and its perimeters are 241 m, while the major axes range between 44 and 115 m and the minor ones between 38 and 101 m. 3.1.3 Type of topographic profiles The classification of profiles was carried out based on their V, U, and Hoya shapes that respond to the depth and slope gradient. Type V units are the most frequent in the high-density area; they are distributed diagonally to the city polygon and along the highway that connects Tulum with the cities in the north of the state (Fig. 6 ). These forms are characterized by slopes with a greater degree of inclination and greater extension in the major axes. Type V units have a representativeness of 64%, average areas of 12,207 m 2 , perimeters of 466 m, and maximum depths of 6 m. While the major axes measure from 44 to 677.9 m, and the minor axes from 37.9 to 203.6 m. On the other hand, the U shapes have average areas of 6632 m 2 , perimeters of 5283 m, and maximum depths of 4 m. The major axes indicate lengths between 48 and 231 m, while the minor axes are between 37.5 and 133 m. The least frequent forms are the Hoya type, with 17%, these show average areas of 20,285 m 2 , perimeters of 560 m, maximum depths of 5 m, major axes of between 59 to 490 m, and axes minors between 22.5 and 261 m. Both the U and Hoyas forms are distributed in areas of low densities. In the coastal zone, only four depressions were recorded that represent the three shapes according to the topographic profile. 3.2 Urban context According to the latest National Housing Inventory [ 35 ], the city of Tulum contains blocks ranging from 0 to 396 inhabitants in areas from 592 m 2 to 425,947 m 2 . The tourist development of Tulum and its proximity to Playa del Carmen and Cancún has led to a significant increase in its population (mainly due to migration), growth that is reflected in the construction of new subdivisions and the sale of land in all directions, however, it is in the eastern part (city center) where the highest population density is concentrated. At the municipal level, Tulum grew by 65.3% between 2010 and 2020, currently 71% of its population lives in urban locations. According to the National Statistical Directory of Economic Units [ 42 ], in the urban area of Tulum, there are 1,886 establishments dedicated to economic, public administration, and civil activities. Of these, 88% provide private services (all types of services, other than tourism) and tourism or are engaged in commercial activities of all types. Figure 7 shows the concentration of administration establishments and public services in the central area of the city, as well as private services and commercial activities (13% of the establishments are located on some depression). The coastal area is mainly home to establishments dedicated to the provision of tourist services and the sale of products such as crafts; secondarily, establishments that provide services for different commercial activities, as well as associations and civil organizations, can also be observed. There is a greater concentration of establishments towards the south of this area. 3.3 Geological risks Geologically, the city of Tulum is part of the Yucatan Peninsula physiographic province and the Carso Yucateco subprovince. It includes a rocky plain with a rocky or cemented floor (limestone type); on the coastline, a beach topoform or bar with a rocky or cemented floor is observed [ 43 ]. The city is located within the Cenozoic era (the last geological era) and the Quaternary period and is located on a plain of marine origin, formed by sedimentary rocks, mostly limestone type with semi-consolidated sandstone deposits [ 44 ]. The location of the city on karst depressions predisposes it to danger from flooding, subsidence, and collapse, which puts life and infrastructure at risk. According to the National Risk Atlas, the urban area of Tulum is in an area that presents medium, high, and very high levels of susceptibility to karstification, which increases the geological risk, to which are added the fractures that surround the polygon of the city and the identified depressions (Fig. 8 ). 4. Discussion The main results of this research show the importance of the analysis and characterization of exokarst units in the urban context. The findings warn of the presence of type V depressions and poljes in the area with the highest population concentration, as well as fractures that surround the city which could be associated with the high rates of karstification in this area. On the other hand, the compactness index warns of a greater concentration of uvalas and sinkholes distributed around the city center; these shapes are tempting for the deposition of waste, as well as the main recharge areas and entry to groundwater [ 16 , 19 ]. In this sense and following the morphology of monocentric cities [ 45 ], Tulum is characterized by a high population density in the downtown area, as well as the highest concentration of private and public establishments. This characteristic of recent cities has begun to grow from a center [ 46 ] since in central urban spatial models the actors obtain benefits or advantages from their location concerning the core of the city [ 47 ]. In the context of the risks and dangers associated with karst phenomena, the formations of temporary karst units are not predictable, but it is possible to consider the possible spatial distribution, the alienations in the axes, for example, account for areas of greater susceptibility [ 19 ]. In areas of karst subsidence where exposure to danger cannot be avoided, measures should be applied to reduce the vulnerability of buildings [ 18 ]. As was the case of the collapse of Federal Highway 307 that exposed a flooded cavern [ 48 ] and more recently the collapse of the federal highway that connects Tulum with Playa del Carmen in 2020 [ 49 ]. Causing damage to infrastructure, exposure to the aquifer, and road problems. From an ecological perspective, sinkholes with anthropic fill are potentially dangerous areas, since they exist in the vicinity of active sinkholes, up to at least a few tens of meters away. Spatial knowledge of karst danger and land use planning focuses on avoiding or minimizing exposure to it [ 18 ]. Megaprojects, planning and management instruments, and the karst nature of the Peninsula require the joint efforts of decision-makers, entrepreneurs, researchers, and academics to create and disseminate knowledge about karst. This knowledge is of vital importance for the protection of diverse ecosystems, the availability of water, and the safety of the population and infrastructure. Much of the information available, mainly about the caves, is not freely accessible, or there is dispersed information. On a national scale, INEGI data does not reflect the reality of the territory of the Yucatán Peninsula; for a long time, the territory was treated as a surface uniform due to the lack of high elevations, however, it has already been shown that the relief of this area is heterogeneous, that karstification does not respond to patterns, and unlike positive relief, negative relief requires greater precision techniques, inputs, and specialized methods. Declarations Conflicts of Interest: “The authors declare no conflicts of interest.” Funding: “This research received no external funding” Author Contribution Conceptualization, MA and AA; methodology, FM and RC; validation, FM; formal analysis, RC; investigation, AA; resources, RC; writing—original draft preparation, MA; writing—review & editing; FM and MA; visualization, MA; supervision, FM; project administration, FM and AA. All authors have read and agreed to the published version of the manuscript.” Data Availability Statement: The original contributions of the study are included in the article, for more information you can contact the corresponding author. References De Waele, J.; Plan, L.; Audra, P. Recent Developments in Surface and Subsurface Karst Geomorphology: An Introduction. Geomorphology 2009, 106 , 1–8, doi: 10.1016/j.geomorph.2008.09.023 . Ford, D.; Williams, P. Karst Hydrogeology and Geomorphology ; John Wiley & Sons Ltd: England, 2007; ISBN 978-0-470-84996-5. 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INEGI Directorio Estadístico Nacional de Unidades Económicas (DENUE) ; Instituto Nacional de Estadísticas y Geografía: México, 2022; INEGI Conjunto de Datos Vectoriales Geológicos. Continuo Nacional. Escala 1:1,000,000 ; 2002; SEDATU Atlas de Riesgos Naturales Del Municipio de Tulum Quintana Roo. Secr. Desarro. Agrar. Territ. Urbano 2015. Korcelli, P. Theory of Intra-Urban Structure. Review and Synthesis. A Cross-Cultural Perspective. Geogr. Pol. 1975, 31 , 99–132. Borsdorf, A. Cómo Modelar El Desarrollo y La Dinámica de La Ciudad Latinoamericana. EURE Santiago 2003, 29 , doi: 10.4067/S0250-71612003008600002 . Cadwallader, M.T. Urban Geography: An Analytical Approach ; Prentice Hall, 1996; ISBN 978-0-13-341637-4. Águila, C.; Vázquez, P. Socavón en una carretera de Quintana Roo genera caos vial. La Jornada 2015. Varillas, A. Desplome En Tramo Carretero de Tulum No Fue Socavón, Es Una Cueva Acuatica. El Univers. 2020. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 27 Jun, 2024 Reviews received at journal 24 Jun, 2024 Reviews received at journal 17 Jun, 2024 Reviewers agreed at journal 16 Jun, 2024 Reviewers agreed at journal 12 Jun, 2024 Reviewers invited by journal 30 May, 2024 Editor assigned by journal 28 May, 2024 Submission checks completed at journal 28 May, 2024 First submitted to journal 21 May, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4457259","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":310566704,"identity":"1740c2a5-7ff0-4469-a010-ab2aee9390e1","order_by":0,"name":"Elsi Margarita May-Arias","email":"","orcid":"","institution":"Autonomous University of the State of Quintana Roo","correspondingAuthor":false,"prefix":"","firstName":"Elsi","middleName":"Margarita","lastName":"May-Arias","suffix":""},{"id":310566705,"identity":"bafd13cf-da5a-4f06-8026-cb8b0f5c850b","order_by":1,"name":"Oscar Frausto-Martínez","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYDAC5gMQmh8ucoCQFrYEEGnAINlAshYDuEpCWvjZeB9+5vnzR874RvK2Dx8qGOT4biSwbubBo0Wyjd1YmrfNwNjsRlrxzBlnGIwlbySw3ZyBR4vB/TYGad4Gg8RtN3KMmXnbGBI3ALXc+IBPyzE25t88fwwSN8+AaKkHa0nAr4VNmofNIHGDBERLggEhWyTb2Ngs57YZG0uceVbMOOOMhOHMMw/b8PqFn42N+cabP3Jy/O3Jmxk+VNjI8x1PPnYbX4ihOBKIJYCYsYFIDRAto2AUjIJRMAowAQD/0Uj826KzuAAAAABJRU5ErkJggg==","orcid":"","institution":"Autonomous University of the State of Quintana Roo","correspondingAuthor":true,"prefix":"","firstName":"Oscar","middleName":"","lastName":"Frausto-Martínez","suffix":""},{"id":310566706,"identity":"cc97bee1-2326-4689-b9e2-cd805cf994bb","order_by":2,"name":"José Francisco Rodríguez-Castillo","email":"","orcid":"","institution":"Autonomous University of the State of Quintana Roo","correspondingAuthor":false,"prefix":"","firstName":"José","middleName":"Francisco","lastName":"Rodríguez-Castillo","suffix":""},{"id":310566707,"identity":"c8bef336-37d3-4154-bc5a-b7803d97aba0","order_by":3,"name":"Lucinda Arroyo-Arcos","email":"","orcid":"","institution":"Autonomous University of the State of Quintana Roo","correspondingAuthor":false,"prefix":"","firstName":"Lucinda","middleName":"","lastName":"Arroyo-Arcos","suffix":""}],"badges":[],"createdAt":"2024-05-21 22:53:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4457259/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4457259/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58071814,"identity":"16c627ef-88db-4012-88d1-6feed192e0fb","added_by":"auto","created_at":"2024-06-10 19:04:02","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":138774,"visible":true,"origin":"","legend":"\u003cp\u003eLocation map of the urban area of Tulum. Own elaboration with data from INEGI [35–37]. Tulum is a coastal and tourist city in the Riviera Maya. It belongs to the municipality of the same name, in the state of Quintana Roo, and is part of the Caribbean territories of the Yucatán Peninsula.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4457259/v1/27cf2b509e754508442f44fd.jpg"},{"id":58071640,"identity":"5a638e4b-7e1a-40d6-a07c-d0772348c0d3","added_by":"auto","created_at":"2024-06-10 18:56:02","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":43152,"visible":true,"origin":"","legend":"\u003cp\u003eMethodological diagram for the identification and characterization of karst depressions. Own elaboration based on Frausto-Martínez et al. [24,27]. This methodology has been used in the cities of Cozumel and Playa de Carmen with different inputs, taking as reference morphometric indices used in watershed studies.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4457259/v1/4f5ea600c9c195c74dd5e3f9.jpg"},{"id":58072036,"identity":"1ae29ed2-7504-467c-9778-da076c68de3c","added_by":"auto","created_at":"2024-06-10 19:12:02","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":68449,"visible":true,"origin":"","legend":"\u003cp\u003eGraph of the exokarst units according to the elongation index. A greater presence of rectangular-shaped depressions is observed.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4457259/v1/18c27540beb87e741ab0f802.jpg"},{"id":58071642,"identity":"28473344-24f8-410e-af93-cd91ca2279dc","added_by":"auto","created_at":"2024-06-10 18:56:02","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":221383,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the distribution of karst units by their shape (elongation index) about the population density per block. Own elaboration based on INEGI [35]. The poljes are distributed almost throughout the city, while the uvalas are observed in areas of low and medium population density.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4457259/v1/730967dc95a4a5cb6962a61b.jpg"},{"id":58071817,"identity":"e57f55ea-db94-405a-ad17-c1d0c402014c","added_by":"auto","created_at":"2024-06-10 19:04:02","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":208763,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution map of karst units by size (compactness index). Own elaboration based on INEGI [35].\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4457259/v1/d0a1e44520d0c0905dec5c09.jpg"},{"id":58071816,"identity":"c15522d6-fd3a-4437-a976-09197b10d6a9","added_by":"auto","created_at":"2024-06-10 19:04:02","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":206433,"visible":true,"origin":"","legend":"\u003cp\u003eDepression distribution map according to the profile and the population density per block. Own elaboration based on INEGI [35].\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4457259/v1/55248dca068d9f9f865767f2.jpg"},{"id":58071646,"identity":"3bbb3991-511f-4687-8714-890744088561","added_by":"auto","created_at":"2024-06-10 18:56:02","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":240449,"visible":true,"origin":"","legend":"\u003cp\u003eMap of roads, commercial, administrative, and civil establishments linked to the presence of karst depressions. Own elaboration based on INEGI, [35,42].\u003c/p\u003e","description":"","filename":"Picture7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4457259/v1/a24a63760fcd4ab5c9ca7ab0.jpg"},{"id":58071644,"identity":"1e23d050-ac18-466a-b0c5-2a8e74e9a94b","added_by":"auto","created_at":"2024-06-10 18:56:02","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":269256,"visible":true,"origin":"","legend":"\u003cp\u003eMap of susceptibility to karstification and disturbing agents of the city of Tulum. Own elaboration based on data from INEGI, [35]and the National Risk Atlas\u003c/p\u003e","description":"","filename":"Picture8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4457259/v1/59b52165a9f5fdc9ed960c52.jpg"},{"id":58072037,"identity":"4c2542e6-f9ca-4821-9838-1b084c077691","added_by":"auto","created_at":"2024-06-10 19:12:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1871034,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4457259/v1/e913c640-c760-4e97-95fd-f453f5a7fbd3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Negative exokarstic units as a basis for urban management: Yucatan Peninsula, Mexico","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eKarst is a dissolution process on soluble rock (generally limestone, dolomites, marble, and gypsum) that shapes unique landscapes and aquifers characterized by rapid infiltration of rain, lack of surface water, and turbulent flow in a network of fractures, conduits, and caves [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Morphologically, it is divided into surface or subarea (exokarst) and underground (endokarst) geoforms [\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMorphological classification of karst according to the scale\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKarst morphology\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSmall scale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedium scale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGrand scale\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003cp\u003eexokarst\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLapiaz and Karren\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esinkholes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGorges, canyons, poljes, and valleys\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1 mm at 10 m (lapiaz fields)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrom meters to 1 km in diameter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ekilometer scale\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003cp\u003eexokarst\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eMogotes, towers, cones, and pinnacles\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndokarst\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eKarst caves or cavities\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003csup\u003e*\u003c/sup\u003e The table \u003csup\u003e*\u003c/sup\u003eThis table was prepared with data from Andreu et al. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. According to the morphological classification based on the scales, the research covers medium and grand-scale negative exokarst.\u003c/p\u003e \u003cp\u003eThe concept became popular in the scientific field at the end of the 19th century, thanks to the study of karst phenomena by Cvijić [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and the underground investigations of Martel [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Since then, different disciplines have addressed the phenomenon, with physical work standing out for a long time; However, at present the description and exploration of these systems are no longer sufficient, so it is necessary to understand the complex relationships resulting from the interaction of man and karst.\u003c/p\u003e \u003cp\u003eThe importance of karst lies in the fact that it is present in 20% of the ice-free continental area of the planet [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] and more than a quarter of the world's population lives in or near karst areas [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In terms of ecosystem services, water resources become relevant, it is estimated that between 20% and 25% of the population partially or depends on water from karst aquifers [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOn the other hand, karst provides resources such as soil, vegetation, and habitat for multiple species [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. It also protects forests, rivers, and lakes against the harmful effects of acid rain [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]; in addition, unique ways of life and landscapes with scenic appearance and scientific/educational potential relevant to the development of leisure and recreation activities are developed on these systems [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn another context, Hern\u0026aacute;ndez Aguilar [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], Drew and H\u0026ouml;tzl [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], Newton [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], and Sim\u0026oacute;n et al. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] explain that karst should be considered as a potential source of geological risk, mainly in urban areas; subsidence, for example, poses a danger to man; since the deformation that the terrain experiences during karst collapse can cause serious damage to all types of structures or even put people's lives at risk when it is generated abruptly [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOn the contrary, these systems are highly sensitive to human disturbance and are considered among the environments of greatest vulnerability, fragility, and complexity [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan additionalcitationids=\"CR21 CR22\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Environmental problems related to karst areas include droughts, floods, surface collapse, pollution, and deforestation. Part of these arise from ignorance of the system, coupled with the lack of adequate planning and effective management [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The characteristics of karst in interaction with anthropogenic activities make urban planning a challenge [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMan, through the activities he carries out in the territory such as agriculture, waste disposal, and water supply, requires knowledge about karst systems [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Knowledge of karst zones has implications in engineering, geological threats, construction, planning, hydrogeology, neotectonics, climate change, ecology, biology, archaeology, recreation, and tourism [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn support of these needs, current techniques and models have provided new insights into the processes and resulting landforms [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The characterization and delimitation of karst zones are also important for the development of housing, transportation, and energy infrastructure, as well as to prevent contamination of aquifers and optimize the use of associated resources [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe surface recognition of depressions is based on the observation of aerial photographs, visual analysis of the terrain (topographic depressions, steps, areas of vegetation or anthropic fills), and observation and analysis of damage in urban areas. However, in the case of cities and places with thick vegetation, depressions cannot be detected at first glance and indirect observation methods are needed [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSome studies such as that of Fragoso-Serv\u0026oacute;n et al. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], have laid the foundations for the characterization of depressions in the state of Quintana Roo, and the risk studies such as that of Hern\u0026aacute;ndez Aguilar [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] and Pereira-Corona et al. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, the low altitudinal contrast and spatial heterogeneity of the karst require the use of technologies with higher resolution that allow the creation of cartography in greater detail and at larger scales, such as the works of Frausto-Mart\u0026iacute;nez et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] and Rodr\u0026iacute;guez Castillo et al. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] in the cities of Cozumel and Playa del Carmen with Digital Elevation Models and LiDAR data.\u003c/p\u003e \u003cp\u003eThe latter is ideal for working with small territories due to the large amount of data that must be processed. Unfortunately, LiDAR data do not cover the entire Mexican territory, this includes the city of Tulum, where studies have been carried out using topographic maps and medium-scale optical images such as those of Beddows et al. [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]; Fragoso-Serv\u0026oacute;n et al. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]; INEGI [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] and Lebedeva et al. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite this, the need to have detailed information is established in the Regulation of Cenotes and Caverns of the Municipality of Tulum [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], Quintana Roo, and in the signing of the collaboration agreement between the Autonomous University of the State of Quintana Roo and the Tulum City Council for updating the municipality's Risk Atlas [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTherefore, the objective of this research is to analyze and characterize the negative exokarst-type relief of the City of Tulum through morphometric parameters and its influence on the urban elements that contribute to the knowledge of the man-karst-nature relationship.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe urban area of Tulum, Quintana Roo-Mexico, is home to a population of 33,374 inhabitants. It has a total of 837 blocky urban AGEBS distributed over around 850.80 hectares [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] and maintains a population density of 39.22 inhabitants per km\u003csup\u003e2\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For the study, the data from the National Housing Inventory [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] was taken as a reference.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Techniques and tools\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe identification and mapping of the exokarst units was carried out by processing ALOS PALSAR DEM, the high-resolution Digital Elevation Model (DEM) of the Japanese Aerospace Exploration Agency (JAXA). The model is developed from SAR (Synthetic Aperture Radar) information acquired between 2006 and 2011 by the ALOS (Advanced Land Observation Satellite) satellite and captured by the PALSAR synthetic aperture radar sensor. The data available for the entire world were downloaded at a resolution of 12.5 m on The Alaska Satellite Facility page (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://search.asf.alaska.edu/#/\u003c/span\u003e\u003cspan address=\"https://search.asf.alaska.edu/#/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFollowing the methodology of Frausto-Mart\u0026iacute;nez et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], a DEM mosaic was created with 4 images that cover the municipality of Tulum, subsequently, the origin errors of the mosaic were corrected in the QGIS 3.16 software, and, in ArcGIS, a cutout was made for the study area (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eContour lines with equidistance at 1 m were extracted and smoothed from the model, which were interpolated to create an irregular network of triangles (TIN). The network was used to create four products: a) digital elevation model (DEM), b) slope model, c) hillshade model, and d) 3D elevation model. With them, the semi-automated recognition of the exokarst units of the territory began (classification of contour lines and marking of contour lines).\u003c/p\u003e \u003cp\u003eThe classification of the units was carried out taking as parameters the elongation index (relationship between major axis and minor axis), the compactness index (or Gravelius index) that relates the perimeter of the unit to the perimeter of a circle of equal area; reference indices in the morphometric studies of karst territories by Fragoso-Serv\u0026oacute;n et al. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], Frausto-Mart\u0026iacute;nez et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] and Kobal et al. [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The units were also classified according to the topographic profile, following the work of Rodr\u0026iacute;guez Castillo et al. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Other parameters identified were depth, perimeter, area, and volume.\u003c/p\u003e \u003cp\u003eFinally, a spatial analysis was carried out through the superimposition of layers: population density, economic units, vector data of highways and roads, and karst depressions.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Morphometric analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe model used for the analysis allowed the identification of 95 karst depressions in the urban area of the City of Tulum, Quintana Roo. In Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, on average the exokarst units have a perimeter of 449 m and an area of 12,511.5 m\u003csup\u003e2\u003c/sup\u003e; as for the depth, the depressions do not exceed 6 m.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMain metrics of identified depressions.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetrics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerimeter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e131.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e449\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArea m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e116,424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12,511.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVolume m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e309,759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13,455\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMajor Axis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e151\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinor Axis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 Classification by elongation index\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe classification carried out with the elongation index shows a greater concentration of poljes-type forms with 36% presence. This shape is a large, closed depression with a flat bottom [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], a length of several kilometers, and a minimum width of 400 m with an elongated shape [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe units with the least frequency are the uvalas with only 11%. Shapes smaller than poljes and larger than sinkholes with an irregular bottom, intermediate genetics are presumed between a sinkhole and a polje [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacterization parameters by elongation index\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSHAPE\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eINDEX\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCATEGORIES\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSinkholes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRound or oval\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.25 to 1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSinkholes-uvalas\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOval or\u003c/p\u003e \u003cp\u003erectangular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.75 to 2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUvalas\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRectangular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePoljes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003csup\u003e*\u003c/sup\u003eThis table was prepared with data from Frausto-Mart\u0026iacute;nez et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 Compactness index\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis classification by the compactness index was carried out taking the parameters used by Fragoso-Serv\u0026oacute;n et al. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] who classified as poljes, those forms greater than 1 km, while sinkholes with an index from 1.04 to 1.3 and those with an index greater than 1.3 were considered uvalas. For the case study, 25% of the units were not classified because they were considered smaller shapes; however, they have an average depth of 1 m (maximum of 3 m), average perimeters of 230 m, and average areas of 4,368 m\u003csup\u003e2\u003c/sup\u003e; while the major axes measure between 55.2 and 203.6 m and the minor ones between 49.6 and 228.9 m.\u003c/p\u003e \u003cp\u003eThe results show that the most common forms are the uvalas, which represent 51% of the analyzed universe. They are distributed throughout practically the entire city and a significant concentration is observed in areas of high and medium population density (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), they represent the forms with the greatest depths (up to 6 meters) and are the units with the largest area and perimeter with an average of 14, 475.2.4 m\u003csup\u003e2\u003c/sup\u003e and 500.9 m, respectively. The major axes range between 48 m and 677.9 m, while the minor axes measure between 22.5 m and 261 m.\u003c/p\u003e \u003cp\u003eFor their part, the sinkholes are distributed around the city, with only a small amount in the center, in areas of medium population density. These depressions represent 24% of the morphometric units identified in the study, they have depths of up to 3 m. Its average surfaces are 4,748 m\u003csup\u003e2\u003c/sup\u003e and its perimeters are 241 m, while the major axes range between 44 and 115 m and the minor ones between 38 and 101 m.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e3.1.3 Type of topographic profiles\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe classification of profiles was carried out based on their V, U, and Hoya shapes that respond to the depth and slope gradient. Type V units are the most frequent in the high-density area; they are distributed diagonally to the city polygon and along the highway that connects Tulum with the cities in the north of the state (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). These forms are characterized by slopes with a greater degree of inclination and greater extension in the major axes.\u003c/p\u003e \u003cp\u003eType V units have a representativeness of 64%, average areas of 12,207 m\u003csup\u003e2\u003c/sup\u003e, perimeters of 466 m, and maximum depths of 6 m. While the major axes measure from 44 to 677.9 m, and the minor axes from 37.9 to 203.6 m. On the other hand, the U shapes have average areas of 6632 m\u003csup\u003e2\u003c/sup\u003e, perimeters of 5283 m, and maximum depths of 4 m. The major axes indicate lengths between 48 and 231 m, while the minor axes are between 37.5 and 133 m. The least frequent forms are the Hoya type, with 17%, these show average areas of 20,285 m\u003csup\u003e2\u003c/sup\u003e, perimeters of 560 m, maximum depths of 5 m, major axes of between 59 to 490 m, and axes minors between 22.5 and 261 m. Both the U and Hoyas forms are distributed in areas of low densities.\u003c/p\u003e \u003cp\u003eIn the coastal zone, only four depressions were recorded that represent the three shapes according to the topographic profile.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Urban context\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAccording to the latest National Housing Inventory [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], the city of Tulum contains blocks ranging from 0 to 396 inhabitants in areas from 592 m\u003csup\u003e2\u003c/sup\u003e to 425,947 m\u003csup\u003e2\u003c/sup\u003e. The tourist development of Tulum and its proximity to Playa del Carmen and Canc\u0026uacute;n has led to a significant increase in its population (mainly due to migration), growth that is reflected in the construction of new subdivisions and the sale of land in all directions, however, it is in the eastern part (city center) where the highest population density is concentrated. At the municipal level, Tulum grew by 65.3% between 2010 and 2020, currently 71% of its population lives in urban locations.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAccording to the National Statistical Directory of Economic Units [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], in the urban area of Tulum, there are 1,886 establishments dedicated to economic, public administration, and civil activities. Of these, 88% provide private services (all types of services, other than tourism) and tourism or are engaged in commercial activities of all types.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows the concentration of administration establishments and public services in the central area of the city, as well as private services and commercial activities (13% of the establishments are located on some depression). The coastal area is mainly home to establishments dedicated to the provision of tourist services and the sale of products such as crafts; secondarily, establishments that provide services for different commercial activities, as well as associations and civil organizations, can also be observed. There is a greater concentration of establishments towards the south of this area.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Geological risks\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eGeologically, the city of Tulum is part of the Yucatan Peninsula physiographic province and the Carso Yucateco subprovince. It includes a rocky plain with a rocky or cemented floor (limestone type); on the coastline, a beach topoform or bar with a rocky or cemented floor is observed [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe city is located within the Cenozoic era (the last geological era) and the Quaternary period and is located on a plain of marine origin, formed by sedimentary rocks, mostly limestone type with semi-consolidated sandstone deposits [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe location of the city on karst depressions predisposes it to danger from flooding, subsidence, and collapse, which puts life and infrastructure at risk. According to the National Risk Atlas, the urban area of Tulum is in an area that presents medium, high, and very high levels of susceptibility to karstification, which increases the geological risk, to which are added the fractures that surround the polygon of the city and the identified depressions (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe main results of this research show the importance of the analysis and characterization of exokarst units in the urban context. The findings warn of the presence of type V depressions and poljes in the area with the highest population concentration, as well as fractures that surround the city which could be associated with the high rates of karstification in this area.\u003c/p\u003e \u003cp\u003eOn the other hand, the compactness index warns of a greater concentration of uvalas and sinkholes distributed around the city center; these shapes are tempting for the deposition of waste, as well as the main recharge areas and entry to groundwater [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this sense and following the morphology of monocentric cities [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], Tulum is characterized by a high population density in the downtown area, as well as the highest concentration of private and public establishments. This characteristic of recent cities has begun to grow from a center [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] since in central urban spatial models the actors obtain benefits or advantages from their location concerning the core of the city [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the context of the risks and dangers associated with karst phenomena, the formations of temporary karst units are not predictable, but it is possible to consider the possible spatial distribution, the alienations in the axes, for example, account for areas of greater susceptibility [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In areas of karst subsidence where exposure to danger cannot be avoided, measures should be applied to reduce the vulnerability of buildings [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. As was the case of the collapse of Federal Highway 307 that exposed a flooded cavern [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] and more recently the collapse of the federal highway that connects Tulum with Playa del Carmen in 2020 [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Causing damage to infrastructure, exposure to the aquifer, and road problems.\u003c/p\u003e \u003cp\u003eFrom an ecological perspective, sinkholes with anthropic fill are potentially dangerous areas, since they exist in the vicinity of active sinkholes, up to at least a few tens of meters away. Spatial knowledge of karst danger and land use planning focuses on avoiding or minimizing exposure to it [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMegaprojects, planning and management instruments, and the karst nature of the Peninsula require the joint efforts of decision-makers, entrepreneurs, researchers, and academics to create and disseminate knowledge about karst.\u003c/p\u003e \u003cp\u003eThis knowledge is of vital importance for the protection of diverse ecosystems, the availability of water, and the safety of the population and infrastructure. Much of the information available, mainly about the caves, is not freely accessible, or there is dispersed information. On a national scale, INEGI data does not reflect the reality of the territory of the Yucat\u0026aacute;n Peninsula; for a long time, the territory was treated as a surface uniform due to the lack of high elevations, however, it has already been shown that the relief of this area is heterogeneous, that karstification does not respond to patterns, and unlike positive relief, negative relief requires greater precision techniques, inputs, and specialized methods.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflicts of Interest:\u003c/h2\u003e \u003cp\u003e\u0026ldquo;The authors declare no conflicts of interest.\u0026rdquo;\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003e\u0026ldquo;This research received no external funding\u0026rdquo;\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, MA and AA; methodology, FM and RC; validation, FM; formal analysis, RC; investigation, AA; resources, RC; writing\u0026mdash;original draft preparation, MA; writing\u0026mdash;review \u0026amp; editing; FM and MA; visualization, MA; supervision, FM; project administration, FM and AA. All authors have read and agreed to the published version of the manuscript.\u0026rdquo;\u003c/p\u003e\u003ch2\u003eData Availability Statement:\u003c/h2\u003e \u003cp\u003eThe original contributions of the study are included in the article, for more information you can contact the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDe Waele, J.; Plan, L.; Audra, P. 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Acta de Cabildo Del Ayuntamiento de Tulum.; 2018;\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUQROO Firman Convenio UQROO y Ayuntamiento de Tulum Para Actualizar Documento de Atlas de Riesgos Available online: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://saladeprensa.uqroo.mx/noticias/5380/\u003c/span\u003e\u003cspan address=\"https://saladeprensa.uqroo.mx/noticias/5380/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed on 28 October 2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eINEGI \u003cem\u003eCenso de Poblaci\u0026oacute;n y Vivienda 2020\u003c/em\u003e; INEGI: M\u0026eacute;xico, 2020;\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eINEGI \u003cem\u003eInventario Nacional de Viviendas. 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El Univers. 2020.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-geoscience","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Geoscience](https://www.springer.com/journal/44288)","snPcode":"44288","submissionUrl":"https://submission.nature.com/new-submission/44288","title":"Discover Geoscience","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"morphometry, depressions, urban elements, hazards, urban planning","lastPublishedDoi":"10.21203/rs.3.rs-4457259/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4457259/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe morphological characterization of large-scale depressions, with high-resolution contributions and in urban contexts, has been carried out in the cities of Cozumel and Playa del Carmen. For Tulum, the inputs have been insufficient in identifying depressions at a detailed level. This research aimed to analyze and characterize the negative exokarstic relief of the city of Tulum through morphometric parameters and urban elements that contribute to the knowledge of the man-nature relationship. From the DEM ALOS PALSAR, contour lines, elevation models, slopes, and shadows were extracted, which allowed the identification of depressions, topographic profiles, and the calculation of morphometric indices; subsequently, the distribution of depressions was analyzed concerning urban elements. The identified depressions were classified into uvalas, sinkholes, and poljes. The analysis of the topographic profiles allowed us to recognize units in the shape of \"V\" (64%), \"U\" (19%), and \"Hoya\" (17%). The highest concentration of type V depressions is observed in the city's central area, characterized by a medium and high population density, as well as the centralization of commercial and public establishments. The detailed characterization of depressions is a planning and management tool for the territory.\u003c/p\u003e","manuscriptTitle":"Negative exokarstic units as a basis for urban management: Yucatan Peninsula, Mexico","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-10 18:55:58","doi":"10.21203/rs.3.rs-4457259/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-27T07:16:34+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-24T18:26:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-17T21:51:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"252969749781466109899819957064813621396","date":"2024-06-17T01:13:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"100253800725181673469964486251020674686","date":"2024-06-12T20:27:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-30T15:56:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-28T11:32:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-28T11:31:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Geoscience","date":"2024-05-21T22:45:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-geoscience","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Geoscience](https://www.springer.com/journal/44288)","snPcode":"44288","submissionUrl":"https://submission.nature.com/new-submission/44288","title":"Discover Geoscience","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9d9fa952-8609-4725-8fc6-da71da29aa47","owner":[],"postedDate":"June 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-08-06T05:30:30+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-10 18:55:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4457259","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4457259","identity":"rs-4457259","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}