Sinkhole Density Research Articles

Temporal variability of sinkhole hazard illustrated in the western shore of the Dead Sea

AbstractThe growing economic and societal damage caused by sinkhole activity worldwide requires the development of scientifically sound sinkhole hazard assessment approaches. Currently, there is a striking paucity of quantitative sinkhole hazard studies largely related to the incompleteness of sinkhole inventories and the lack of chronological data. Moreover, the probability of occurrence of sinkholes (i.e., sinkhole hazard) is commonly considered as a steady variable, a concept that may lead to significant hazard over- or under-estimates. The extraordinarily high frequency of sinkhole occurrence of the studied sector of the western shore of the Dead Sea has allowed us to explore for the first time the potential temporal variability of sinkhole hazard parameters. Here, we produced six multi-temporal sinkhole inventories with morphometric data between 2005 and 2021 using remote-sensed imagery. The frequency-size relationships generated for successive time intervals with a total of 667 new sinkholes reveal substantial temporal changes in the sinkhole hazard components (i.e., frequency and size). Moreover, the work illustrates that spatial redundancy (sinkholes nested within or intersecting pre-existing ones) can lead to significant hazard overestimates if not considered, especially in areas with high sinkhole density and clustering. This work discusses the limitations of some widely used methods and concepts for sinkhole hazard assessment and illustrates the advantages of detailed multi-temporal mapping for assessing frequency-size relationships and their temporal trends.

Assessing the relationship between contributing factors and sinkhole occurrence in Mexico City

Sinkholes pose a significant hazard in Mexico City (CDMX), causing substantial economic damage. While the link between sinkhole formation and groundwater extraction has been studied, specific mechanisms vary by site. Our overall aim is to characterize the phenomenon of sinkholes in CDMX. To achieve this, we create a database with 13 influencing factors, including population density, well density, distance to faults, fractures, roads, streams, elevation, slope, clay thickness, lithology, subsidence rate, geotechnical zones, and soil texture. Sinkhole locations were obtained from CDMX’s Risk Atlas (2017–2019). We shaped a susceptibility map based on statistical regression methods derived from applying linear regression models. For the susceptibility map, results showed that 40% of variables are significantly correlated with sinkhole density. Despite the regression model explained 24% of sinkhole density variability, it helped choosing variables for the susceptibility map that correlate better (89.7%). Hence, we identified that the northeast CDMX was the most susceptible zone. Therefore, the compound assessment of environmental factors is useful for the evaluation of susceptibility maps to identify prone factors for the generation of sinkholes. This framework provides relevant information for better use of the territory throughout the development of public policies.

An integrated approach combining LISA, BI-LISA, and the modified COPK method to improve groundwater management in large-scale karst areas

Groundwater protection and sustainable utilization of wells in large-scale karst areas face many challenges due to the complexity of numerous groundwater basins and aquifers. Therefore, scientific evidence is needed for groundwater management in these areas. A method combining source vulnerability, spatial autocorrelation, and bivariate spatial autocorrelation is proposed for groundwater management research. Based on the COP method (composed of flow Concentration, Overlying layers, and Precipitation regime), the COPK method is modified by replacing the sinkhole distance with sinkhole density, and incorporating land use and karst network development. The modified COPK method is used to evaluate the vulnerability of nitrate in groundwater in large-scale karst areas. Guizhou Province in China, which is a typical karst region, was evaluated as a study area. Comparatively to the classical COP method, the revised vulnerability map shows more areas of high vulnerability, mainly in the middle of the study area. According to the receiver operating characteristic curve (ROC) analysis, the modified method had a higher accuracy than the classical COP algorithm. Based on the accurate vulnerability index, vulnerability was classified using the natural break method, and the spatial autocorrelation of vulnerability clustering characteristics was studied. The source vulnerability was divided into High-High (HH), High-Low (HL), Low-High (LH), Low-Low (LL), and Not Significant (NS) clusters. This result can clarify the vulnerability clustering of groundwater in specific areas, thereby helping to improve groundwater management accuracy and efficiency. In addition, bivariate spatial autocorrelation was used to study the nitrate content of wells and source vulnerability. The bivariate local Moran's index showed a spatial negative correlation between the two. Similarly, the BI-LISA map can be used to characterize the spatial aggregation of nitrate content and well vulnerability. This result can help groundwater managers develop reasonable measures to protect water wells.

Modeling intrinsic vulnerability of complex karst aquifers: modifying the COP method to account for sinkhole density and fault location

This study investigates a method of karst-aquifer vulnerability modeling that modifies the concentration-overburden-precipitation (COP) method to better account for structural recharge pathways through noncarbonate rocks, and applies advancements in remote-sensing sinkhole identification. Karst aquifers are important resources for human and agricultural needs worldwide, yet they are often highly complex and have high vulnerability to contamination. While many methods of estimating intrinsic vulnerability of karst aquifers have been developed, few methods acknowledge the complication of layered karst aquifer systems, which may include interactions between carbonate and noncarbonate rocks. This paper describes a modified version of the COP method applied to the Kaibab Plateau, Arizona, USA, the primary catchment area supplying springs along the north side of the Grand Canyon. The method involves two models that, together, produce higher resolution and greater differentiation of vulnerability for both the deep and perched aquifers beneath the Kaibab Plateau by replacing the original sinkhole distance parameter with sinkhole density. Analyses indicate that many karst regions would benefit from the methodology developed for this study. Regions with high-resolution elevation data would benefit from the incorporation of sinkhole density data in aquifer vulnerability assessments, and deeper semi-confined karst aquifers would benefit greatly from the consideration of fault location.

Evaluation of susceptibility for terrain collapse and subsidence in karst areas, municipality of Iraquara, Chapada Diamantina (BA), Brazil

The morphological evolution of the karstic systems is associated with a set of physical and chemical processes, triggered by the dissolution of the rocks, related to percolation of groundwater and surface water, which consequently open underground voids and carve out peculiar forms of relief. Due to environmental and geotechnical aspects, this system is naturally more fragile and vulnerable than other natural systems and, therefore, has increasingly received the attention of the scientific community over the past decades. The objective of the study was to delimit zones with varying degrees of susceptibility for collapses and subsidence of sinkholes in the municipality of Iraquara, Chapada Diamantina (BA), Brazil, and to understand their geological and morphological determinant factors. Geological data, karst phenomenon map, and visual analysis in the field were used to categorize zones with different types of susceptibilities to the nucleation of new sinkholes based on a Hazard Index. This index was defined from the sum of geological hazard factors, lineament density, and sinkhole density. The areas that presented the highest susceptibility for terrain collapse and subsidence corresponded to regions where carbonate rocks outcrop, with high density of photolineaments and 2.62 sinkholes/km2. Processes associated with terrain collapse and subsidence in karst areas consisted of a combination of various factors, hindering precise predictions. However, zones of different types of susceptibilities to terrain collapse and subsidence can be delimited when the relationships between these processes and their factors are understood. The Hazard Index proposed does not provide quantitative values for the probability of hazard susceptibility, but rather indicates areas that are more susceptible to terrain subsidence and collapse.

Sinkhole formation mechanisms and geostatistical-based prediction analysis in a mantled karst terrain

Efforts to evaluate sinkhole formation and quantify the controlling factors are hindered by the lack of historical observations or coarse resolution datasets in many areas. We applied recent advances in GIS-based sinkhole mapping and spatial statistics to comprehensively investigate the factors controlling sinkhole formation in the mantled karst setting of Dougherty County, Georgia. Sinkholes form at varying spatial and temporal scales within the test site. A three-stage methodology was conducted. Firstly, 275 sinkholes that formed or were enlarged between 1999 and 2011 were detected by comparing the results of sinkhole inventories derived from two DEMs acquired in 1999 and 2011. Additionally, a LiDAR DEM (1 m resolution) was used to gather a spatially detailed sinkhole inventory of 3412 sinkholes. The sinkhole inventory data was converted into sinkhole density maps for subsequent analyses. Ordinary least squares (OLS) and geographically weighted regression (GWR) spatial statistical models were applied to quantify the impact of controlling factors on sinkhole density. Controlling factors included geologic, hydrologic, anthropogenic, hydrogeologic, and geomorphologic variables. For the two sinkhole inventory datasets analyzed, overburden thickness, aquifer fluctuations, and proximity to fractures, streams, and wetlands were the most influential controlling factors on sinkhole formation (GWR p-values <0.05). Lastly, the spatial statistics results were used to 1) produce interpolated prediction sinkhole maps and 2) evaluate the accuracy of the spatial statistics' sinkhole density predictions. Results provide assessments of controlling factors on sinkhole formation and demonstrate the potential for similar applications in other karst areas with a time series of DEMs and similar ancillary datasets. The adapted GIS-based approach does not replace procedures that depend on comprehensive field surveys for both sinkhole inventory and controlling factor data acquisition, though it offers estimates for understanding sinkhole development over large areas to help select proper mitigation strategies.

Sinkholes and Residential Property Prices: Presence, Proximity, and Density

Spatial amenities, along with structural characteristics, affect residential property values. Although the bundle of structural characteristics is typically the primary value determinant, studies have shown that externalities and risk factors can adversely affect property values. We use residential property sales data from 2010 to 2014 and sinkhole data from “sinkhole alley” in Florida to examine the effect of sinkhole presence, proximity, and density on the sale price of residential real estate. Using a spatial error regression model, the results show that sinkhole proximity and sinkhole exposure (density) create a negative externality and both have a significant negative effect on house prices. While the results show a negative effect of having a sinkhole on the property, those results are not statistically significant.

Investigation of the impacts of local-scale hydrogeologic conditions on sinkhole occurrence in East-Central Florida, USA

Sinkholes occur when surface soils gradually subside or suddenly collapse into subsurface cavities and voids due to raveling and erosion of surficial soils caused by dissolution and washing-off of underlying soluble carbonate bedrock. Sinkhole occurrence is related to local-scale hydrogeologic conditions (groundwater recharge rate and hydraulic head difference between water table and potentiometric level). Historical data have shown that sinkholes are more likely to occur in the beginning of wet season and the frequency of occurrence varies seasonally. In this study, the East-Central Florida region, which is vulnerable to sinkhole hazard, is selected as the study area, and the relationships between temporal and spatial distributions of observed sinkholes and hydrogeologic conditions are quantitatively investigated. The analysis results indicate that the seasonality of sinkhole occurrence is due to the seasonal variation of rainfall and groundwater level, and sinkholes are most likely to occur when the local-scale head difference stays constant at a peak value after a sharp increase over a short period of time. In space, sinkhole density increases linearly with increases in groundwater recharge rate and hydraulic head difference.

Assessment of aquifer intrinsic vulnerability by the SINTACS method

Application of the SINTACS method for assessment of aquifer intrinsic vulnerability is demonstrated in the catchment areas of Jadro and Žrnovnica springs. Both Jadro and Žrnovnica are the springs of drinking water supplying the population of Split and surrounding settlements. A high-speed economic expansion in the catchment area accompanied with trend of increasing quantity of a number of contaminants in the spring water prompted comprehensive investigations with purpose of finalizing the Water Management Study of the Jadro and Žrnovnica springs. Results of the Study were treated by GIS tools and employed as input data for making the groundwater vulnerability map of the Jadro and Žrnovnica catchment areas. In addition to the standard method of defining the C factor which, in this case, is represented by the values of C(a), a modification is also introduced which takes into account the analysis of the sinkhole density.

Spatial analysis of geologic and hydrologic features relating to sinkhole occurrence in Jefferson County, West Virginia

In this study the influence of geologic features related to sinkhole susceptibility was analyzed and the results were mapped for the region of Jefferson County, West Virginia. A model of sinkhole density was constructed using Geographically Weighted Regression (GWR) that estimated the relations among discrete geologic or hydrologic features and sinkhole density at each sinkhole location. Nine conditioning factors on sinkhole occurrence were considered as independent variables: distance to faults, fold axes, fracture traces oriented along bedrock strike, fracture traces oriented across bedrock strike, ponds, streams, springs, quarries, and interpolated depth to groundwater. GWR model parameter estimates for each variable were evaluated for significance, and the results were mapped. The results provide visual insight into the influence of these variables on localized sinkhole density, and can be used to provide an objective means of weighting conditioning factors in models of sinkhole susceptibility or hazard risk.

Relations between sinkhole density and anthropogenic contaminants in selected carbonate aquifers in the eastern United States

The relation between sinkhole density and water quality was investigated in seven selected carbonate aquifers in the eastern United States. Sinkhole density for these aquifers was grouped into high (>25 sinkholes/100 km2), medium (1–25 sinkholes/100 km2), or low (<1 sinkhole/100 km2) categories using a geographical information system that included four independent databases covering parts of Alabama, Florida, Missouri, Pennsylvania, and Tennessee. Field measurements and concentrations of major ions, nitrate, and selected pesticides in samples from 451 wells and 70 springs were included in the water-quality database. Data were collected as a part of the US Geological Survey (USGS) National Water-Quality Assessment (NAWQA) Program. Areas with high and medium sinkhole density had the greatest well depths and depths to water, the lowest concentrations of total dissolved solids and bicarbonate, the highest concentrations of dissolved oxygen, and the lowest partial pressure of CO2 compared to areas with low sinkhole density. These chemical indicators are consistent conceptually with a conduit-flow-dominated system in areas with a high density of sinkholes and a diffuse-flow-dominated system in areas with a low density of sinkholes. Higher cave density and spring discharge in Pennsylvania also support the concept that the high sinkhole density areas are dominated by conduit-flow systems. Concentrations of nitrate-N were significantly higher (p < 0.05) in areas with high and medium sinkhole density than in low sinkhole-density areas; when accounting for the variations in land use near the sampling sites, the high sinkhole-density area still had higher concentrations of nitrate-N than the low sinkhole-density area. Detection frequencies of atrazine, simazine, metolachlor, prometon, and the atrazine degradate deethylatrazine indicated a pattern similar to nitrate; highest pesticide detections were associated with high sinkhole-density areas. These patterns generally persisted when analyzing the detection frequency by land-use groups, particularly for agricultural land-use areas where pesticide use would be expected to be higher and more uniform areally compared to urban and forested areas. Although areas with agricultural land use and a high sinkhole density were most vulnerable (median nitrate-N concentration was 3.7 mg/L, 11% of samples exceeded 10 mg/L, and had the highest frequencies of pesticide detection), areas with agricultural land use and low sinkhole density still were vulnerable to contamination (median nitrate-N concentration was 1.5 mg/L, 8% of samples exceeded 10 mg/L, and had some of the highest frequencies of detections of pesticides). This may be due in part to incomplete or missing data regarding karst features (such as buried sinkholes, low-permeability material in bottom of sinkholes) that do not show up at the scales used for regional mapping and to inconsistent methods among states in karst feature delineation.

Evaluating and comparing methods of sinkhole susceptibility mapping in the Ebro Valley evaporite karst (NE Spain)

Multiple sinkhole susceptibility models have been generated in three study areas of the Ebro Valley evaporite karst (NE Spain) applying different methods (nearest neighbour distance, sinkhole density, heuristic scoring system and probabilistic analysis) for each sinkhole type separately (cover collapse sinkholes, cover and bedrock collapse sinkholes and cover and bedrock sagging sinkholes). The quantitative and independent evaluation of the predictive capability of the models reveals that: (1) The most reliable susceptibility models are those derived from the nearest neighbour distance and sinkhole density. These models can be generated in a simple and rapid way from detailed geomorphological maps. (2) The reliability of the nearest neighbour distance and density models is conditioned by the degree of clustering of the sinkholes. Consequently, the karst areas in which sinkholes show a higher clustering are a priori more favourable for predicting new occurrences. (3) The predictive capability of the best models obtained in this research is significantly higher (12.5–82.5%) than that of the heuristic sinkhole susceptibility model incorporated into the General Urban Plan for the municipality of Zaragoza. Although the probabilistic approach provides lower quality results than the methods based on sinkhole proximity and density, it helps to identify the most significant factors and select the most effective mitigation strategies and may be applied to model susceptibility in different future scenarios.

Sinkhole hazard assessment in Minnesota using a decision tree model

An understanding of what influences sinkhole formation and the ability to accurately predict sinkhole hazards is critical to environmental management efforts in the karst lands of southeastern Minnesota. Based on the distribution of distances to the nearest sinkhole, sinkhole density, bedrock geology and depth to bedrock in southeastern Minnesota and northwestern Iowa, a decision tree model has been developed to construct maps of sinkhole probability in Minnesota. The decision tree model was converted as cartographic models and implemented in ArcGIS to create a preliminary sinkhole probability map in Goodhue, Wabasha, Olmsted, Fillmore, and Mower Counties. This model quantifies bedrock geology, depth to bedrock, sinkhole density, and neighborhood effects in southeastern Minnesota but excludes potential controlling factors such as structural control, topographic settings, human activities and land-use. The sinkhole probability map needs to be verified and updated as more sinkholes are mapped and more information about sinkhole formation is obtained.

The origin, typology, spatial distribution and detrimental effects of the sinkholes developed in the alluvial evaporite karst of the Ebro River valley downstream of Zaragoza city (NE Spain)

AbstractThree types of sinkhole have been mapped in a 50 km2 stretch of the Ebro River valley downstream of Zaragoza: large collapse sinkholes, large shallow subsidence depressions and small cover‐collapse sinkholes. The sinkholes relate to the karstification of evaporitic bedrock that wedges out abruptly downstream, giving way to a shale substratum. Twenty‐three collapse sinkholes, up to 50 m in diameter by 6 m deep, and commonly hosting saline ponds, have been identified in the floodplain. They have been attributed to the upward stoping of dissolutional cavities formed within the evaporitic bedrock by rising groundwater flows. Twenty‐four large shallow subsidence depressions were mapped in the floodplain. These may reach 850 m in length and were formed by structurally controlled interstratal karstification of soluble beds (halite or glauberite? and gypsum) by rising groundwater flow and the progressive settlement of the overlying bedrock and overburden sediments. A total of 447 small cover‐collapse, or dropout, sinkholes have been recognized in a perched alluvial level along the southern margin of the valley. These sinkholes result from the upward propagation of voids through the alluvial mantle caused by the downward migration of detrital sediments into dissolutional voids. The majority of these sinkholes, commonly 1·5–2 m in diameter, are induced by human activities. Over the karstic bedrock, there is a significant increase in sinkhole density downstream. This is interpreted as being a result of the evaporitic bedrock wedging out and the convergence of the groundwater flow lines in the karstic aquifer. The collapse sinkholes in this area, locally with a probability of occurrence higher than 140 sinkholes/km2/year, cause substantial damage to the linear infrastructures, buildings and agriculture, and they might eventually cause the loss of human lives. Copyright © 2006 John Wiley &amp; Sons, Ltd.

Karst database implementation in Minnesota: analysis of sinkhole distribution

This paper presents the overall sinkhole distributions and conducts hypothesis tests of sinkhole distributions and sinkhole formation using data stored in the Karst Feature Database (KFD) of Minnesota. Nearest neighbor analysis (NNA) was extended to include different orders of NNA, different scales of concentrated zones of sinkholes, and directions to the nearest sinkholes. The statistical results, along with the sinkhole density distribution, indicate that sinkholes tend to form in highly concentrated zones instead of scattered individuals. The pattern changes from clustered to random to regular as the scale of the analysis decreases from 10–100 km2 to 5–30 km2 to 2–10 km2. Hypotheses that may explain this phenomenon are: (1) areas in the highly concentrated zones of sinkholes have similar geologic and topographical settings that favor sinkhole formation; (2) existing sinkholes change the hydraulic gradient in the surrounding area and increase the solution and erosional processes that eventually form more new sinkholes.