Next Article in Journal
Dynamics of Nocturnal Evapotranspiration in a Dry Region of the Chinese Loess Plateau: A Multi-Timescale Analysis
Previous Article in Journal
The Impact of Shifts in Both Precipitation Pattern and Temperature Changes on River Discharge in Central Japan
Previous Article in Special Issue
Measurement and Calculation of Sediment Transport on an Ephemeral Stream
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Hydrology
Volume 12
Issue 7
10.3390/hydrology12070186
hydrology-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Mapping of Closed Depressions in Karst Terrains: A GIS-Based Delineation of Endorheic Catchments in the Alburni Massif (Southern Apennine, Italy)

by
Libera Esposito
,
Guido Leone
*,
Michele Ginolfi
,
Saman Abbasi Chenari
and
Francesco Fiorillo
Department of Science and Technology, University of Sannio, 82100 Benevento, Italy
*
Author to whom correspondence should be addressed.
Hydrology 2025, 12(7), 186; https://doi.org/10.3390/hydrology12070186
Submission received: 19 May 2025 / Revised: 21 June 2025 / Accepted: 4 July 2025 / Published: 10 July 2025
(This article belongs to the Special Issue Advances in Catchments Hydrology and Sediment Dynamics (Second Edition))
Browse Figures
Versions Notes

Abstract

A deep interaction between groundwater and surface hydrology characterizes karst environments. These settings feature closed depressions, whose hydrological role varies depending on whether they have genetic and hydraulic relationships with overland–subsurface flow (epigenic) or deep groundwater circulation (hypogenic). Epigenic dolines and poljes are among the diagnostic landforms of karst terrains. In this study, we applied a hydrological criterion to map closed depressions—including dolines—across the Alburni karst massif, in southern Italy. A GIS-based, semi-automatic approach was employed, combining the sink-filling method (applied to a 5 m DEM) with the visual interpretation of various informative layers. This process produced a raster representing the location and depth of karst closed depressions. This raster was then used to automatically delineate endorheic areas using classic GIS tools. The resulting map reveals a thousand dolines and hundreds of adjacent endorheic areas. Endorheic areas form a complex mosaic across the massif, a feature that had been poorly emphasized in previous works. The main morphometric features of the dolines and endorheic areas were statistically analyzed and compared with the structural characteristics of the massif. The results of the proposed mapping approach provide valuable insights for groundwater management, karst area protection, recharge modeling, and tracer test planning.

1. Introduction

The definition and delineation of karst closed depressions, including dolines, have been widely discussed in the literature. Various mapping criteria have been proposed, such as manual delineation using orthophotos [1], contour-based methods [2,3], morphometric-based methods [4,5,6,7], and hydrologically based methods [5,6,8].
Knowing the location and boundary of closed depressions is the basis of morphometric studies, and it also has practical relevance [9]. On this last point, closed depression mapping represents a key component in groundwater recharge and vulnerability assessment, delineation of protection zones for wells and springs, planning of artificial tracer tests, and sinkhole hazard analysis [10,11,12,13,14,15,16,17].
Hydrological processes play a key role in surface morphogenesis; in turn, closed depressions may represent one of the main controlling features in the infiltration process of overland-subsurface flows. From the hydrological perspective, surface (and subsurface) karst features could be distinguished in [18,19]: (i) epigenic features, having a hydrologic connection with the surface water, and forming by descending flows coming from the recharge zones; (ii) hypogenic features, having no relation with surface hydrology, and forming by ascending groundwater flows. Dolines, in particular, may have a different hydrological functioning, depending on whether they have a genetic and hydraulic relation with surface water or deep groundwater circulation [20]. Moreover, dolines are conventionally classified into solution, suffusion, and collapse based on the genetic process [21].
The literature primarily focuses on closed depressions formed by dissolution and erosion due to flowing meteoric water (overland, through-, and subcutaneous flow [22]). In particular, closed depressions of epigenic and tectonic-karst origin, such as dolines, uvalas, and poljes, are considered diagnostic features of karst terrains [23].
Morphologically, a closed depression is an area with a lower elevation than the surrounding terrain and concave upward [24]. Hydrologically, a closed depression drains a wider endorheic area, representing a closed watershed with internal runoff [25,26]. The term endorheic basin can also be found in the literature [25]. In endorheic areas, runoff is entirely absorbed through concentrated infiltration via ponors and diffuse infiltration and leakage through the cover deposits. Ponors placed at their bottom can create a direct hydraulic connection between the endorheic area and one or more springs [27]. Therefore, the amount of water conveyed toward the closed depression and contributing to groundwater recharge depends the endorheic area. Outside endorheic areas, runoff can escape and does not contribute to recharge, especially during intense rainstorms [15].
We mapped the karst closed depressions of the Alburni massif (southern Apennine, Italy). This massif is renowned for its extraordinary concentration of caves and shafts [28]. Moreover, closed depressions are widespread on its vast, gently dipping summit plateau, showing a higher concentration of such features than other karst massifs of this Apennine sector. The genesis of the closed depressions is primarily due to epigenetic and tectonic-karst processes [29]. Thus, a hydrologically based mapping approach was proposed, to delineate dolines and their associated endorheic areas. Specifically, we applied a semi-automatic approach that combines (i) the fill-based method to automatically extract closed depressions from a 5 m DEM [5] and (ii) the outermost closed contour method to isolate single dolines in compound depressions [3]. Depression mapping was supported by expert visual analysis of contour maps and digital images. Once depressions had been identified, the endorheic areas were automatically mapped using standard GIS tools.
The number of mapped dolines is much larger than that found in previous studies, which used topographic maps (1:5000 to 1:25,000 scale), providing a significant sample for morphometric analysis.
Knowing the precise location of karst closed depressions and the associated endorheic areas effectively and completely represents the surface hydrological characteristics of a karst environment. This is fundamental for research purposes and in managing and protecting the groundwater quantity and quality.

2. Study Area

The Alburni karst Massif (Figure 1) is located in the Cilento area (southern Italy), and covers about 270 km2. The mean elevation is 940 m a.s.l. and the maximum is 1742 m a.s.l. The massif is made of a thick Mesozoic carbonate sequence of Jurassic-Cretaceous age, stratigraphically covered by a Miocene flysch sequence consisting of clays and sandstones. Flysch extensively crops out in the areas surrounding the massif [30,31,32], and forms aquicludes bounding the karst aquifer. Alluvial deposits, slope breccias, and conglomeratic deposits are exposed in river valleys and at the foot of the slopes. Detailed geological information of the area can be found in the Geological Map of Italy (http://sgi.isprambiente.it/geologia100k/, accessed on 4 May 2025).
The massif is well delimited by normal faults along its boundaries. Faults caused the uplift of the Mesozoic carbonate platform in this Apennine sector, inside the deformed terrains of the chain. The karst massif represents a southwest gently dipping monoclinal structure split into several blocks [29], bounded by the Calore Lucano River (to the southwest) and the Tanagro River (to the northeast). This structure has created a vast karst plateau limited by steep slopes, occupying the summit sector.
The climate is Mediterranean. For this area, mean annual precipitation was estimated to be 1470 mm/y [16,33]. The maximum monthly precipitation occurs in November and the minimum in July. The temperature regime is almost opposite to that of precipitation: the higher temperature is between July and August while the minimum is in January. Actual evapotranspiration is about one third of the annual precipitation. Because precipitation verifies during the non-hot season, its distribution allows for the highest possible recharge [34].
The Alburni plateau has been affected by karst dissolution, forming many dolines, which have transformed this surface into an extended internal runoff area. This area provides 45% of the total mean spring discharge [16]. About 400 caves characterize the massif, with several reaching a depth of around 450 m and developing for some kilometers [35,36]. Caves are mainly concentrated in the northern-central sector.
There are four main basal spring groups, Pertosa, Auso, Castelcivita and Tanagro, and other minor springs. Spring location is shown in Figure 1, and their discharge is listed in Table 1. Systematic discharge registrations are lacking and can be found only in spot measurements in the literature and technical reports [37,38].
The Pertosa group (220–260 m a.s.l.) is located in the north-eastern sector and shows a mean discharge of 2.3 m3/s. It includes the cave of the same name (Pertosa caves), Polla Santa Domenica spring and other springs placed along the riverbed [37].
The Auso group (280 m a.s.l.) is located in the southern sector, with a variable discharge from a few L/s to several thousand L/s [35,37].
The Castelcivita group (65–94 m a.s.l.) rises along the Calore riverbed and in the homonymous caves (Castelcivita caves), with a discharge between 1 m3/s and 4 m3/s [39]. These springs are divided into a southern group, the Mulino springs (63–65 m a.s.l.), and into a northern group, the Castelcivita springs (61–65 m a.s.l.; [37,39]).
The low Tanagro group represents a preferential drainage belt. Its discharge can exceed 8.5 m3/s [36]. Water comes from the surrounding limestones and directly feeds the Tanagro River [37,40].

3. Materials and Methods

3.1. Types of Karst Closed Depressions

Metric- to kilometric-scale closed depressions can be distinguished in karst terrains, namely dolines, uvalas, and poljes. These features link the surface to the underground systems of fissures, shafts and caves [23].
Dolines have diameters (or axes) from a few to a hundred meters and depths up to tens of meters [2,41]. The shape can vary in a wide range, but typical dolines are bowl-shaped (e.g., [4]), with a circular to sub-circular perimeter in plain view. Hydrologically, the doline can be viewed as a concentrated recharge point, and a descending pipe or shaft is common on the doline floor [42,43]. Dolines represent the most frequent infiltration features in a karst environment.
Uvalas and poljes are larger in size. Uvalas are compound closed depressions made of several dolines [44]. These depressions lack surface water. Moreover, sediments are scarce, the bottom is mostly undulating or pitted with dolines, and even when the bottom is nearly flat, its transition into lateral slopes is gradual [45].
A polje is a closed depression with a flat floor, karstic drainage, and steep peripheral slopes [46,47]. The tectonic setting has a significant role in their genesis, as poljes often develop parallel to the major structural trends or are associated with graben depressions [44,48]. Poljes drain meteoric water underground in two different ways: water infiltration may diffuse over the sedimentary cover or concentrate at dolines and ponors. An intermittent or permanent sinking watercourse often flows across the flat floor of poljes [43]. If the drainage is poor, the polje floor gets flooded, and a lake forms [44,47]. Flooding and sedimentation are the main processes distinguishing poljes and uvalas and contribute considerably to floor flattening and preserving the sharp transitions to the bordering slopes [45].
Figure 2 shows a zone of the Alburni karst massif, where numerous dolines, representing isolated closed depressions, are enclosed within a larger closed depression. The latter drains a closed watershed; that is the endorheic area.

3.2. Closed Depression Mapping

Figure 3 illustrates the simplified GIS-based mapping workflow. It consists of three phases, which are described in the following text.
The delineation of endorheic areas begins with the identification of closed depressions. Note that even ponors represent points of concentrated recharge and should be included in the mapping [20]. However, ponor localization generally requires field surveys.
Poljes can be easily delineated using topographic and DEM-derived contour maps. On the other hand, doline size varies in a wide range. Doline identification and delineation depend on the cartographic scale and DEM resolution. Nowadays, doline mapping primarily uses the analysis of high-resolution remotely sensed data. It may involve the visual interpretation of satellite/aerial images and DEM-derived products, including contour maps, hill-shaded DEMs, TPI raster (Topographic Position Index), surface curvature raster, and slope raster. In this case, doline delineation is based on the principle of an abrupt change in the surface slope (or curvature). Otherwise, doline mapping may involve semi-automated and automated methods. Each doline delineation method shows limitations in describing the depression rim. For example, the contour-based method assumes the doline rim to be horizontal. On the other hand, visual interpretation of orthophotos and DEM-derived products (e.g., hill shade, slope, and curvature) allows for capturing altitude variations in doline rim. However, these approaches could be highly subjective [7]. Therefore, there is no single approach that would work optimally in all circumstances [1].
A widely used automatic, quantitative mapping approach combines the hydrological processing of a DEM and morphometric analysis of the mapped depressions (e.g., [49]). (i) Hydrological processing aims to calculate the so-called “difference raster” to determine the location and depth of the closed depressions (Figure 4; [5]). This raster is obtained by subtracting the original DEM from the depression-less DEM obtained by applying a sink-filling algorithm [50,51,52]. (ii) Not all the depressions identified in such a way represent true geomorphic karst features. A method to discriminate false depressions from actual ones is selecting a set of true karst depressions and measuring their morphometric attributes. Then, an optimal threshold for each attribute is defined to filter out potentially false features with “anomalous” characteristics. Although this method reduces the number of false depressions, even true karst depressions may be eliminated.
The sink-filling algorithm virtually fills each closed depression up to the spill elevation (i.e., the elevation at which water ideally flows out of the depression). The spill point represents the shared (tangent) point between the upper rim of the closed depression and water divide of its endorheic area. Using this method, the depression upper rim is the closed contour passing through the spill point (Figure 4).
The fill-based method cannot isolate single depressions located inside a larger depression (compound depression in Figure 4). Wu et al. [3] developed an algorithm to overcome this limitation. Their method uses a vector representation of a DEM as contours and establishes the hierarchical relationship between adjacent closed isolines. Thus, the algorithm automatically extracts isolated depressions. Contours are drawn from a smoothed DEM, assuming a reasonable regular interval. Moreno-Gómez et al. [9] used the Jenks method to segment depressions, assuming irregular contour intervals.

3.3. Doline Mapping

We applied a semi-automatic approach that combines (i) the fill-based method for automatic mapping of a karst closed depression from the 5 m DEM and (ii) visual interpretation of different informative layers (Table 2); then, the corrected depression raster was used as input for the automatic delineation of endorheic areas (Figure 3).
The fill-based method automatically identifies closed depressions in the DEM, which was pre-processed using a 3-by-3 mean filter. In more detail, this method begins by calculating the depression raster, which was obtained by the difference between the filtered DEM and the sink-filled DEM (Figure 3). Since shallow depressions are likely to be false geomorphic karst features in the digital surface, the depression raster was filtered by a depth threshold of 0.65 m. This last value was calibrated from a true doline sample, mapped thanks to the 1:25,000 topographic map of IGMI (Italian Military Geographic Institute; example in Figure 5a), and represents the 99th percentile, p99th, of doline depth frequency distribution. Specifically, using the DEM, we remapped the dolines by applying the outermost closed contour method (contour interval of 0.5 m) and measured their depth. The depth distribution is shown in Figure 5b.
The fill-based method cannot isolate dolines that are contained within a larger depression (Figure 4 and Figure 6). To overcome this problem, we segmented the filtered depression raster into regularly spaced contours (contour interval = 0.5 m) and manually mapped dolines by the outermost closed contour method [2,53] (Figure 6). The outermost closed contour allows for the objective definition of the doline upper rim. However, the outermost closed contour does not represent the actual overflow contour of a depression. As such, this method provides smaller and shallower depressions than the sink-filling method.
Manual doline mapping was supported by visual inspection of Google Earth satellite images, digital orthophotos (0.5 m resolution), and 1:5000 topographic maps (Technical Chart of Campania Region). During this phase of the analysis, the results of the fill-based method were further validated (Figure 3).

3.4. Automatic Mapping of Endorheic Areas

Taking a watershed having centripetal drainage, the closed depression represents the volume below the contour passing through the spill point (Figure 6).
Endorheic areas can be automatically delineated using the Watershed tool. This tool was designed to delineate the watershed of a river (i.e., the upslope area that contributes to the river flow at a given section) and needs two input pieces of information: (i) the location of the drainage point, (ii) the flow directions, calculated using the Flow Direction tool. In an endorheic watershed, the drainage converges toward the closed depressions, which is represented by an ensemble of neighboring pixels. Therefore, endorheic areas can be automatically delineated considering the filtered, validated depression raster as input for running the Watershed tool [20].
A similar approach was proposed by [54], which considers the lowest pixel at the bottom of the depression as a drainage point. However, such an approach may provide a hydrologically incorrect delineation of the water divide, which often intersects the depression’s upper rim. Our modification, which considers the entire depression area, overcomes this major problem.

3.5. Morphometric Parameters

Statistical analysis was carried out considering samples of dolines and endorheic areas. In particular, for each endorheic area there are one or several dolines, so for each endorheic area and for each doline within it, the following morphometric parameters were calculated: area (A), perimeter (P), lowest, mean, and maximum elevation (Hmin, Hmean, Hmax), and longest axis orientation (θ). Other morphometric parameters were calculated, including maximum elevation difference (ΔHmax), volume (V), and Circularity Index (CI).
The maximum elevation difference, ΔHmax, is the difference between the highest point on the perimeter and the lowest point within the doline or endorheic area.
The volume, V, was calculated as:
V = A · Δ H m e a n
where ΔHmean is the mean depth, i.e., difference between the maximum elevation Hmax and mean elevation Hmean.
The Circularity Index, CI, was calculated exclusively for closed depressions:
C I = 4 π · A / P 2
The index ideally ranges from 0 (elongated shape) to 1 (circular shape).
The Minimum Bounding Geometry tool was used to measure doline (and endorheic area) orientation (or azimuth). The tool calculates the azimuth of the longest axis of the rectangle bounding the perimeter of the landform. Dolines having CI < 0.9 were considered in the analysis. Using a threshold for CI allows for minimizing the effect of nearly circular shapes. A CI threshold of 0.95 was suggested by Bauer [54]. In the studied area, dolines may still preserve a sub-circular shape for CI = 0.94–0.95, leading us to adopt a lower threshold.

4. Results

Figure 7a shows the map of dolines and endorheic areas of the Alburni karst massif. We identified 781 karst closed depressions and, correspondingly, 781 endorheic basins. Karst closed depressions include isolated dolines or compound features (see Figure 1 and Figure 6). Using the outermost closed contour method, we isolated each single doline for a total of 1001 of such features. Dolines are primarily widespread across the karst plateau. The highest doline density occurs in the north-western sector of the plateau, with a maximum density of 34 dolines/km2 (Figure 7b). Collapse dolines were identified outside the karst plateau, placed up-stream of the Auso (southern side) and Pertosa (northern side) spring groups. These dolines were not considered in the morphometric analysis.
The total internal runoff area, as delineated from our analysis, covers 117 km2—approximately 45% of the entire karst outcrop. The potential contribution of dolines to aquifer recharge depends on the extension of its catchment and can be easily visualized in our map (Figure 7a). Two main aquifer recharge mechanisms could be recognized in endorheic areas following rainfall (or snowmelt) events [22]. The first mechanism is diffuse percolation through the vadose zone, resulting in a diffuse recharge of the saturated zone. During intense and prolonged rainfall, overland and subsurface flow develops, especially when the soil is at the field capacity. Flow is conveyed toward sinking points, causing the concentrated recharge. For these reasons, wider endorheic areas provide larger concentrated recharge [15,55]. The surface hydrological features, combined with the hydraulic characteristics of the saturated zone, control the shape of the spring hydrographs.
Figure 8a–c shows the results of the doline morphometric analysis. The area (Figure 8a) ranges from 1.38 × 102 to 2.75 × 105 m2 (0.27 km2), with 50% of doline smaller than 2.29 × 103 m2. The depth ranges (Figure 8b) from 0.25 to 48.5 m, with 50% of dolines deeper than 3.86 m. The extremely low value of the minimum depth can be interpreted as an outlier. The 1st percentile, 0.53 m, could be considered in place of the minimum. Doline dimensions are in line with those found by Leone et al. [20] for dolines of the Matese karst massif. The authors applied a similar mapping approach using a 5 m DEM. Doline area and depth distributions are characterized by a high frequency of smaller-shallower dolines and a fewer larger-deeper ones, which is common for temperate zones [2,54]. According to Troester et al. [56], in temperate (and tropical) karst regions, doline depth distribution could be explained by an exponential equation. In the study area, doline distributions do not follow an exponential shape, even if they are strongly positively skewed. However, the frequency of smaller dolines should be considered underestimated, as doline mapping from DEMs has a lower detection limit in terms of area and depth, which is directly related to the DEM resolution.
Similar distributional shapes characterize endorheic watersheds (Figure 8d,e). The area (Figure 8d) ranges from 1.69 × 103 to 4 × 106 m2 (4 km2), with 50% smaller than 4.27 × 104 m2. The maximum elevation difference ranges (Figure 8e) from 5.7 to 735.7 m, with 50% of endorheic watersheds deeper than 58.8 m. Approximately 75% of the endorheic surface lies above 1000 m a.s.l.
Figure 9a shows the relationship between the horizontal and vertical sizes of the dolines. The A/P-ratio (area/perimeter-ratio) has the same dimension of the mean depth (D, a length) and can be considered a measure of the doline width. A statistically significant relationship between doline width and depth was found, highlighting that the widening and deepening processes are coupled. As discussed by Bauer [54], the widening-deepening coupling suggests a prevalent solution origin of the dolines. By contrast, the deepening of collapse dolines (cylindrical shape with steep walls, e.g., [57,58]) would not be directly coupled with area-widening.
Doline bottom elevation (Figure 8c) ranges between 604 and 1608 m a.s.l., with a mean 1165.5 m a.s.l. (and a median of 1161.3 m a.s.l.). The sample histogram highlights a multi-modal distribution. Dolines would concentrate over different main altimetric zones.
The histogram in Figure 8c shows that the doline sample may be subdivided into four sub-samples, corresponding to a lower (<875 m a.s.l.), middle-lower (875–1050 m a.s.l.), middle-upper (1050–1250 m a.s.l.), and upper zone (>1250 m a.s.l.). In particular, the two main samples characterize the middle-upper zone (N = 413 dolines) and the upper zone (N = 346 dolines). Figure 10 compares their morphometric features, considering the A/P-ratio, depth, and volume. We used the non-parametric Kolmogorov–Smirnov [59] to assess statistically significant differences between the frequency distributions shown in Figure 10. The two doline samples have different morphometric characteristics. Dolines in the middle-upper zone tend to be flatter (larger and/or shallower) than dolines in the upper zone, and the differences in the shape are statistically significant. That is, there is a higher frequency of large and shallow dolines in the mid-upper zone than in the upper zone. However, the samples show the same frequency distribution when comparing doline volumes.
Figure 9b,c illustrate the relationships between doline A/P-ratio and depth for two sub-samples. For a given value of the A/P-ratio, dolines in the mid-upper zone are generally deeper than in the upper zone.
The rose diagrams in Figure 11a,b illustrate the orientation of dolines and endorheic areas (azimuth of the major axes). Dolines with a CI < 0.9 (N = 697) exhibit a general NW-SE trend with a mode of N140-150. Endorheic areas (N = 781) have CI < 0.66 and appear elongated in three preferential directions, N40-50, N90-140, and N170-195.
The outcropping carbonate sequence is made of SW to S dipping strata, tilted by a few degrees. The major faults dissecting the karst plateau have a general N110-130 trend, as well as the main morpho-structural elements. Thus, the horizontal shape of the doline tends to follow the main discontinuity lines.
The structural setting would affect the water flow and the development of endorheic areas. Using the Flow Direction tool, we calculated the direction of the water flow across the summit plateau. As shown in Figure 11c, the water primarily flows in the S and SW directions.

5. Discussion

Karst, glacial, and tectonic processes have shaped the current morphological setting of the summit plateau of the Alburni massif [29], which develops along a gently dipping monoclinal structure in the SW. The plateau is displaced by faults with a limited offset [60] and is dissected into blocks, originating from small-scale horst-and-graben structures. Karstification processes have taken place recently, following the Plio-Pleistocene uplift phases and subsequent erosion of the low-permeability flysch sequence [61,62].
Different from other karst areas of southern Apennine, the carbonate sequence outcropping over the Alburni karst plateau is weakly fractured. Thanks to this condition, the stratification can be observed across the sequence. In many other places of the Apennine, fractures and recent tectonic activity have disjoined carbonate sequences, including the underground karst conduit network, and transformed the limestone rocks into breccias [34]. Locally, a poorly tectonically disturbed rock mass with horizontal or gently dipping strata seems an important factor in controlling the karstification of the plateau. These structural characteristics could explain the higher doline density characterizing the Alburni plateau compared to other karst areas in Campania, such as the Matese and Picentini mountains. In this sense, the Alburni represents a unique case in the region.
Moreover, dolines are the dominant karst features, different from the abovementioned carbonate massifs, characterized by large-scale tectonic-karst poljes [15,20,63]. Here, the doline-to-endorheic surface ratio is 8.6 dolines/km2, much higher than that observed for the Matese karst massif (2.6 dolines/km2, [20]). Nevertheless, some main closed depressions are associated with NW-SE trending tectonic valleys, limited by lateral blocks. Tectonic valleys and blocks are pitted by dolines, forming groups aligned in the direction of the structures.
As is well known, the doline mapping approach affects the results of the morphometric analyses. According to Šegina [1], the doline perimeter should not be determined by hydrological filling- or contour-based methods, which assume the doline upper surface to be horizontal. Due to the vertical irregularity of the doline rim, especially in the case of steep slopes, the authors concluded that manual delineation using orthophotos represents a more reliable method for doline delineation. However, the exclusive use of digital images in the Apennine mountains appears impractical due to the vegetation cover. On the contrary, terrain features below the vegetation cover can be captured by a DEM.
Another issue is that topographic maps are drawn using a wide contour interval, which may fail to capture dolines. However, this limitation can be mitigated by generating detailed contour maps from a high-resolution DEM.
Despite their limits (Section 3.2), previous DEM-based studies have widely employed hydrologic filling or the outermost closed contour to define depressions. Given their non-subjective nature, these methods allow comparing results obtained from different DEMs and areas (e.g., [49,64]).
Bauer [54] also tested a watershed-based doline delineation method, which was initially introduced by Jenson et al. [65]. Taking the lowest point within a depression, the perimeter is automatically delineated using the Watershed tool. This method has two main limits. First, it assimilates the closed depression to its catchment. Second, it represents dolines by a single input point, which may not provide suitable watershed mapping, especially for low-resolution DEM.
The problem of doline definition and delineation remains a topic of debate. This study considered a hydrological definition of dolines and a net separation between the closed depressions and their endorheic areas (Section 3.4). Maps coupling dolines and endorheic areas are particularly useful in water balance and vulnerability assessment: (i) endorheic areas represent zones of the karst catchment from which effective precipitation cannot escape by runoff, which infiltrates as a whole; (ii) ponors at the bottom of dolines represent a major pathway for contaminants that bypass the unsaturated zone filter and directly reach the karst network. Therefore, concentrated recharge through dolines plays a decisive role in assessing the intrinsic vulnerability of a karst aquifer, as contaminants can easily reach groundwater and be rapidly transported in karstic conduits over large distances [66]. For this reason, methods for intrinsic vulnerability assessment consider the role of flow concentration within endoreic areas and the development of the karst network among the main determinants. In particular, vulnerability assessment methods in karst environments, such as the EPIK, PI, and COP [17,67,68,69] require the location of recharge points and their watershed [70]. These methods consider the infiltration condition using a factor describing whether infiltration is diffused through a surface or concentrated through a sinking point. The role of a given doline’s catchment in aquifer vulnerability will depend on the degree to which the protective cover is bypassed. In reality, doline discharge is also connected to the characteristics of the rainfall (or snowmelt) event, since runoff activation depends on the rainfall intensity.
The Alburni massif is characterized by a large number of dolines and endorheic catchments that tend to have a limited size favoring a concentrated recharge mechanism. These unique hydrodynamic characteristics of the Alburni massif require special protection and planning measures to preserve the groundwater resource.

6. Conclusions

This study discussed the mapping of dolines and endorheic areas as hydrological elements of a karst landscape. They represent the primary landforms that connect the surface and groundwater systems. Coupling the delineation of dolines and endorheic areas allows the creation of fundamental maps for water balance estimation, hydrological modeling of runoff-infiltration processes, delineation of protection zones for wells and springs, simulation of point-source contamination, and artificial water tracer tests.
Sink-filling and outermost closed contour methods can both be considered hydrologically based mapping approaches. In this context, they represent the optimal choice in doline delineation from a DEM, since these methods identify zones in the digital surface toward which water flow converges. Modeling the rainfall-runoff process may provide additional insights into the aquifer dynamic, especially on the activation of concentrated recharge. The study primarily relies on doline mapping from remotely sensed data. Specific hydrological analysis may require field validation of a subset of mapped features.
In southern Apennine, small- to large-scale endorheic areas characterize the summit sectors of major carbonate massifs. Endorheic areas are generally adjacent, forming a vast internal runoff area that significantly contributes to groundwater recharge. The contribution of endorheic areas became particularly relevant during intense rainstorms when runoff develops.
The applied mapping method allowed the delineation of hundreds of adjacent endorheic areas, forming a mosaic across the Alburni karst plateau. A large number of concentrated infiltration points drain this karst plateau. They allow the quick transfer of meteoric water to the basal water table and, finally, to the spring outlets. This hydrological characteristic must always be considered in groundwater vulnerability evaluation because contaminants released on the karst surface can quickly reach the water table. For these reasons, the karst plateau should be preserved to ensure the quality of groundwater and optimize its management.

Author Contributions

Conceptualization, G.L. and F.F.; methodology, L.E., G.L. and F.F.; software, G.L., M.G. and S.A.C.; formal analysis, G.L.; data curation, G.L., M.G. and S.A.C.; figure preparation, G.L.; writing—original draft preparation, all the authors.; writing—review and editing, all the authors; supervision, L.E. and F.F. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Digital orthophotos and IGMI topographic map (1:25,000 scale) can be accessed via WMS services at https://gn.mase.gov.it/portale/servizio-di-consultazione-wms, accessed on 4 May 2025; CTR maps (1:5000 scale) are available at https://sit2.regione.campania.it/servizio/carta-tecnica-regionale, accessed on 4 May 2025; 5 m DEM is not public; cave location is provided by Federazione Speleologica Campana, available at https://www.fscampania.it/catasto-2/catasto/, accessed on 4 May 2025.

Acknowledgments

The Authors sincerely thank the Editor and the anonymous Reviewers for their constructive feedback and valuable suggestions.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Šegina, E.; Benac, Č.; Rubinić, J.; Knez, M. Morphometric Analyses of Dolines—The Problem of Delineation and Calculation of Basic Parameters. Acta Carsologica 2018, 47, 23–33. [Google Scholar] [CrossRef]
  2. Bondesan, A.; Meneghel, M.; Sauro, U. Morphometri Analysis of Dolines. Int. J. Speleol. 1992, 21, 1–55. [Google Scholar] [CrossRef]
  3. Wu, Q.; Deng, C.; Chen, Z. Automated Delineation of Karst Sinkholes from LiDAR-Derived Digital Elevation Models. Geomorphology 2016, 266, 1–10. [Google Scholar] [CrossRef]
  4. Kozar, M.D.; McCoy, K.J.; Weary, D.J.; Field, M.S.; Pierce, H.A.; Schill, W.B.; Joung, J.A. Geohydrology and Water Quality of the Leetown Area, West Virginia; U.S. Geological Survey Open-file Report; U.S. Geological Survey: Raystown, WV, USA, 2007.
  5. Doctor, D.H.; Young, J.A. An Evaluation of Automated GIS Tools for Delineating Karst Sinkholes and Closed Depressions From 1-Meter Lidar-Derived Digital Elevation Data. In Proceedings of the 13th Multidisciplinary Conference on Sinkholes and the Engineering and Environmental Impacts of Karst, Carlsbad, NM, USA, 6–10 May 2013; pp. 449–458. [Google Scholar]
  6. Hofierka, J.; Gallay, M.; Bandura, P.; Šašak, J. Identification of Karst Sinkholes in a Forested Karst Landscape Using Airborne Laser Scanning Data and Water Flow Analysis. Geomorphology 2018, 308, 265–277. [Google Scholar] [CrossRef]
  7. Čeru, T.; Šegina, E.; Gosar, A. Geomorphological Dating of Pleistocene Conglomerates in Central Slovenia Based on Spatial Analyses of Dolines Using LiDAR and Ground Penetrating Radar. Remote Sens. 2017, 9, 1213. [Google Scholar] [CrossRef]
  8. Stefanovski, S.; Kokalj, Ž.; Stepišnik, U. Sky-View Factor Enhanced Doline Delineation: A Comparative Methodological Review Based on Case Studies in Slovenia. Geomorphology 2024, 465, 109389. [Google Scholar] [CrossRef]
  9. Moreno-Gómez, M.; Liedl, R.; Stefan, C. A New GIS-Based Model for Karst Dolines Mapping Using LiDAR; Application of a Multidepth Threshold Approach in the Yucatan Karst, Mexico. Remote Sens. 2019, 11, 1147. [Google Scholar] [CrossRef]
  10. Andreo, B.; Vías, J.; Durán, J.J.; Jiménez, P.; López-Geta, J.A.; Carrasco, F. Methodology for Groundwater Recharge Assessment in Carbonate Aquifers: Application to Pilot Sites in Southern Spain. Hydrogeol. J. 2008, 16, 911–925. [Google Scholar] [CrossRef]
  11. Goldscheider, N.; Meiman, J.; Pronk, M.; Smart, C. Tracer Tests in Karst Hydrogeology and Speleology. Int. J. Speleol. 2008, 37, 27–40. [Google Scholar] [CrossRef]
  12. Goldscheider, N. Delineation of Spring Protection Zones. In Groundwater Hydrology of Springs; Krasic, N., Stevanovic, Z., Eds.; 2010; pp. 305–338. [Google Scholar]
  13. Gutiérrez, F.; Galve, J.P.; Lucha, P.; Castañeda, C.; Bonachea, J.; Guerrero, J. Integrating Geomorphological Mapping, Trenching, InSAR and GPR for the Identification and Characterization of Sinkholes: A Review and Application in the Mantled Evaporite Karst of the Ebro Valley (NE Spain). Geomorphology 2011, 134, 144–156. [Google Scholar] [CrossRef]
  14. Gutiérrez, F.; Parise, M.; De Waele, J.; Jourde, H. A Review on Natural and Human-Induced Geohazards and Impacts in Karst. Earth-Sci. Rev. 2014, 138, 61–88. [Google Scholar] [CrossRef]
  15. Fiorillo, F.; Pagnozzi, M.; Ventafridda, G. A Model to Simulate Recharge Processes of Karst Massifs. Hydrol. Process. 2015, 29, 2301–2314. [Google Scholar] [CrossRef]
  16. Fiorillo, F.; Pagnozzi, M.; Addesso, R.; Cafaro, S.; D’Angeli, I.M.; Esposito, L.; Leone, G.; Liso, I.S.; Parise, M. Uncertainties in Understanding Groundwater Flow and Spring Functioning in Karst. In Geophysical Monograph Series; Currell, M.J., Katz, B.G., Eds.; Wiley: New York, NY, USA, 2022; pp. 131–143. ISBN 978-1-119-81859-5. [Google Scholar]
  17. Marín, A.I.; Martín Rodríguez, J.F.; Barberá, J.A.; Fernández-Ortega, J.; Mudarra, M.; Sánchez, D.; Andreo, B. Groundwater Vulnerability to Pollution in Karst Aquifers, Considering Key Challenges and Considerations: Application to the Ubrique Springs in Southern Spain. Hydrogeol. J. 2021, 29, 379–396. [Google Scholar] [CrossRef]
  18. Palmer, A.N. Origin and Morphology of Limestone Caves. Geol. Soc. Am. Bull. 1991, 103, 1–21. [Google Scholar] [CrossRef]
  19. Klimchouk, A.B. Hypogene Speleogenesis: Hydrogeological and Morphogenetic Perspective; Special paper/National Cave and Karst Research Institute; National Cave and Karst Research Inst: Carlsbad, NM, USA, 2007; ISBN 978-0-9795422-0-6. [Google Scholar]
  20. Leone, G.; Catani, V.; Pagnozzi, M.; Ginolfi, M.; Testa, G.; Esposito, L.; Fiorillo, F. Hydrological Features of Matese Karst Massif, Focused on Endorheic Areas, Dolines and Hydroelectric Exploitation. J. Maps 2022, 19, 2144497. [Google Scholar] [CrossRef]
  21. Gutiérrez, F.; Guerrero, J.; Lucha, P. A Genetic Classification of Sinkholes Illustrated from Evaporite Paleokarst Exposures in Spain. Environ. Geol. 2008, 53, 993–1006. [Google Scholar] [CrossRef]
  22. Gunn, J. Point-Recharge of Limestone Aquifers—A Model from New Zealand Karst. J. Hydrol. 1983, 61, 19–29. [Google Scholar] [CrossRef]
  23. Sauro, U. Closed Depressions in Karst Areas. In Encyclopedia of Caves; Academic Press: Cambridge, MA, USA, 2012; pp. 140–155. [Google Scholar]
  24. Klingseisen, B.; Metternicht, G.; Paulus, G. Geomorphometric Landscape Analysis Using a Semi-Automated GIS-Approach. Environ. Model. Softw. 2008, 23, 109–121. [Google Scholar] [CrossRef]
  25. Bonacci, O.; Andrić, I. Karst Spring Catchment: An Example from Dinaric Karst. Environ. Earth Sci. 2015, 74, 6211–6223. [Google Scholar] [CrossRef]
  26. White, W.B. Karst Hydrology: Recent Developments and Open Questions. Eng. Geol. 2002, 65, 85–105. [Google Scholar] [CrossRef]
  27. Valente, A.; Catani, V.; Esposito, L.; Leone, G.; Pagnozzi, M.; Fiorillo, F. Groundwater Resources in a Complex Karst Environment Involved by Wind Power Farm Construction. Sustainability 2022, 14, 11975. [Google Scholar] [CrossRef]
  28. Santo, A.; Giulivo, I.; Russo, N.; Prete, S. Grotte e Speleologia Della Campania; Elio Sellino editore: Avellino, Italy, 2005; ISBN 88-88991-32-8. [Google Scholar]
  29. Brancaccio, L.; Civita, M.; Vallario, A. Prime Osservazioni Sui Problemi Idrogeologici Dell’Alburno (Campania). Boll. Della Soc. Geol. Dei Nat. Napoli 1973, 82, 13–35. [Google Scholar]
  30. Scandone, P. Studi Di Geologia Lucana: Carta Dei Terreni Della Serie Calcareo-Silico-Marnosa e Note Illustrative. Boll. Soc. Nat. 1972, 81, 225–300. [Google Scholar]
  31. Ippolito, F.; Ortolani, F.; Russo, M. Struttura Marginale Tirrenica Dell’Appennino Campano: Reinterpretazione Di Dati Di Antiche Ricerche Di Idrocarburi. Mem. Della Soc. Geol. Ital. 1973, 12, 227–250. [Google Scholar]
  32. Patacca, E.; Scandone, P. Geology of the Southern Apennines. Ital. J. Geosci. 2007, 7, 75–112. [Google Scholar]
  33. Pagnozzi, M.; Fiorillo, F.; Esposito, L.; Leone, G. Hydrological Features of Endorheic Areas in Southern Italy. Ital. J. Eng. Geol. Environ. 2019, 85–91. [Google Scholar] [CrossRef]
  34. Fiorillo, F. Spring Hydrographs as Indicators of Droughts in a Karst Environment. J. Hydrol. 2009, 373, 290–301. [Google Scholar] [CrossRef]
  35. Bellucci, F.; Giulivo, I.; Pelella, L.; Santo, A. Carsismo Ed Idrogeologia Del Settore Centrale Dei Mt. Alburni (Sa). Geol. Tec. 1991, 3, 5–12. [Google Scholar]
  36. Bellucci, F.; Giulivo, I.; Pelella, L.; Santo, A. Monti Alburni: Ricerche Speleologiche; De Angelis: Rome, Italy, 1995. [Google Scholar]
  37. Celico, P.; Pelella, L.; Stanzione, D.; Aquino, S. Sull’idrogeologia e l’idrogeochimica Dei Monti Alburni. Geol. Romana 1994, 30, 687–698. [Google Scholar]
  38. Ducci, D.; De Masi, G.; Delli Priscoli, G. Contamination Risk of the Alburni Karst System (Southern Italy). Eng. Geol. 2008, 99, 109–120. [Google Scholar] [CrossRef]
  39. Santo, A. Idrogeologia Dell’area Carsica Di Castelcivita (M. Alburni-SA). Geol. Appl. Idrogeol. 1993, 27, 663–673. [Google Scholar]
  40. Bolognini, M.; Celico, P.; Tescione, M.; Aquino, S. La Produttività Deii Pozzi in Acquiferi Carbonatici Molto Carsificati: L’esempio Dei Monti Alburni (SA). Geol. Romana 1994, 30, 671–686. [Google Scholar]
  41. Bonacci, O. Karst Hydrology with Special References to the Dinaric Karst; Springer: Berlin/Heidelberg, Germany, 1987. [Google Scholar]
  42. Klimchouk, A. The Formation of Epikarst and Its Role in Vadose Speleogenesis. In Speleogenesis: Evolution of Karst Aquifers; National Speleological Society: Huntsville, AL, USA, 2000. [Google Scholar]
  43. Veress, M. Covered Karsts; Springer Geology: Dordrecht, The Netherlands, 2016; ISBN 978-94-017-7516-8. [Google Scholar]
  44. Ford, D.; Williams, P. Karst Hydrogeology and Geomorphology; John Wiley & Sons Ltd.: West Sussex, UK, 2007; ISBN 978-1-118-68498-6. [Google Scholar]
  45. Ćalić, J. Karstic Uvala Revisited: Toward a Redefinition of the Term. Geomorphology 2011, 134, 32–42. [Google Scholar] [CrossRef]
  46. Gams, I. The polje; The problem of definition; With special regard to the Dinaric karst. Z. Fuer Geomorphol. 1978, 22, 170–181. [Google Scholar]
  47. Bonacci, O. 6.11 Poljes, Ponors and Their Catchments. In Treatise on Geomorphology; Elsevier: Amsterdam, The Netherlands, 2013; pp. 112–120. ISBN 978-0-08-088522-3. [Google Scholar]
  48. Field, M. Lexicon of Cave and Karst Terminology with Special Reference to Environmental Karst Hydrology; EPA National Center for Environmental Assessment: Washington, DC, USA, 2002; p. 214.
  49. De Carvalho, O.; Guimarães, R.; Montgomery, D.; Gillespie, A.; Trancoso Gomes, R.; De Souza Martins, É.; Silva, N. Karst Depression Detection Using ASTER, ALOS/PRISM and SRTM-Derived Digital Elevation Models in the Bambuí Group, Brazil. Remote Sens. 2013, 6, 330–351. [Google Scholar] [CrossRef]
  50. O’Callaghan, J.F.; Mark, D.M. The Extraction of Drainage Networks from Digital Elevation Data. Comput. Vis. Graph. Image Process. 1984, 28, 323–344. [Google Scholar] [CrossRef]
  51. Planchon, O.; Darboux, F. A Fast, Simple and Versatile Algorithm to Fill the Depressions of Digital Elevation Models. Catena 2002, 46, 159–176. [Google Scholar] [CrossRef]
  52. Wang, L.; Liu, H. An Efficient Method for Identifying and Filling Surface Depressions in Digital Elevation Models for Hydrologic Analysis and Modelling. Int. J. Geogr. Inf. Sci. 2006, 20, 193–213. [Google Scholar] [CrossRef]
  53. Verbovšek, T.; Gabor, L. Morphometric Properties of Dolines in Matarsko Podolje, SW Slovenia. Environ. Earth Sci. 2019, 78, 396. [Google Scholar] [CrossRef]
  54. Bauer, C. Analysis of Dolines Using Multiple Methods Applied to Airborne Laser Scanning Data. Geomorphology 2015, 250, 78–88. [Google Scholar] [CrossRef]
  55. Fiorillo, F.; Pagnozzi, M. Recharge Processes of Matese Karst Massif (Southern Italy). Environ. Earth Sci. 2015, 74, 7557–7570. [Google Scholar] [CrossRef]
  56. Troester, J.W.; White, E.L.; White, W.B. A comparison of sinkhole depth frequency distributions in temperate and tropical karst regions. In Sinkholes: Their geology, engineering, and environmental impact; Beck, B.F., Ed.; Balkema: Rotterdam, The Netherlands, 1984; pp. 65–73. [Google Scholar]
  57. Fiorillo, F.; Leone, G.; Pagnozzi, M.; Catani, V.; Testa, G.; Esposito, L. The Upwelling Groundwater Flow in the Karst Area of Grassano-Telese Springs (Southern Italy). Water 2019, 11, 872. [Google Scholar] [CrossRef]
  58. Caramanna, G.; Ciotoli, G.; Nisio, S. A Review of Natural Sinkhole Phenomena in Italian Plain Areas. Nat. Hazards 2008, 45, 145–172. [Google Scholar] [CrossRef]
  59. Borradaile, G. Statistics of Earth Science Data: Their Distribution in Time, Space, and Orientation; Springer: Berlin/Heidelberg, Germany, 2003; ISBN 978-3-642-07815-6. [Google Scholar]
  60. Santangelo, N.; Santo, A. Endokarst Processes in the Alburni Massif (Campania, Southern Italy): Evolution of Ponors and Hydrogeological Implications. Z. Für Geomorphol. 1997, 41, 229–247. [Google Scholar] [CrossRef]
  61. Santo, A. Alcune Osservazioni Sul Carsismo Ipogeo Dei M. Alburni. Appennino Meridionale Annu. Club Alpino Ital. Sez. Napoli 1988, 1, 71–88. [Google Scholar]
  62. Incoronato, A.; Nardi, G.; Ortolani, F. Assetto Strutturale Dei Massicci Carbonatici Della Campania Meridionale. Implicazioni Idrogeologiche. Rend. Accad. Sci. Fis. E Mat. Della Soc. Naz. Sci. Lett. E Arti Napoli 1978, 45, 479–493. [Google Scholar]
  63. Aucelli, P.P.C.; Cesarano, M.; Di Paola, G.; Filocamo, F.; Rosskopf, C.M. Geomorphological Map of the Central Sector of the Matese Mountains (Southern Italy): An Example of Complex Landscape Evolution in a Mediterranean Mountain Environment. J. Maps 2013, 9, 604–616. [Google Scholar] [CrossRef]
  64. Zumpano, V.; Pisano, L.; Parise, M. An Integrated Framework to Identify and Analyze Karst Sinkholes. Geomorphology 2019, 332, 213–225. [Google Scholar] [CrossRef]
  65. Jenson, S.K.; Domingue, J.O. Extracting Topographic Structure from Digital Elevation Data for Geographic Information-System Analysis. Photogramm. Eng. Remote Sens. 1988, 54, 1593–1600. [Google Scholar]
  66. Goldscheider, N. Karst Groundwater Vulnerability Mapping: Application of a New Method in the Swabian Alb, Germany. Hydrogeol. J. 2005, 13, 555–564. [Google Scholar] [CrossRef]
  67. Goldscheider, N.; Klute, M.; Sturm, S.; Hötzl, H. The PI Method—A GIS-Based Approach to Mapping Groundwater Vulnerability with Special Consideration of Karst Aquifers. Z. Angew. Geol. 2000, 46, 157–166. [Google Scholar]
  68. Doerfliger, N.; Jeannin, P.-Y.; Zwahlen, F. Water Vulnerability Assessment in Karst Environments: A New Method of Defining Protection Areas Using a Multi-Attribute Approach and GIS Tools (EPIK Method). Environ. Geol. 1999, 39, 165–176. [Google Scholar] [CrossRef]
  69. Vías, J.M.; Andreo, B.; Perles, M.J.; Carrasco, F.; Vadillo, I.; Jiménez, P. Proposed Method for Groundwater Vulnerability Mapping in Carbonate (Karstic) Aquifers: The COP Method: Application in Two Pilot Sites in Southern Spain. Hydrogeol. J. 2006, 14, 912–925. [Google Scholar] [CrossRef]
  70. Goldscheider, N.; Drew, D. Methods in Karst Hydrogeology; International Contributions to Hydrogeology; Taylor & Francis: London, UK, 2007; ISBN 978-0-415-42873-6. [Google Scholar]
Hydrology 12 00186 g001
Figure 1. (a) Map of the Italian Peninsula and (b) hydrogeological map of the Alburni karst massif (from [16], modified).
Figure 1. (a) Map of the Italian Peninsula and (b) hydrogeological map of the Alburni karst massif (from [16], modified).
Hydrology 12 00186 g001
Hydrology 12 00186 g002
Figure 2. Dolines, compound closed depression and relative endoreic area, for a zone of the Alburni karst massif. The compound closed depression is defined by the highest closed contour (1093.5 m a.s.l.) passing through the spill (or overflow) point.
Figure 2. Dolines, compound closed depression and relative endoreic area, for a zone of the Alburni karst massif. The compound closed depression is defined by the highest closed contour (1093.5 m a.s.l.) passing through the spill (or overflow) point.
Hydrology 12 00186 g002
Hydrology 12 00186 g003
Figure 3. Mapping workflow (simplified). Blue, input data; pink, output raster; white, calculation by GIS tools (except visual analysis); green, output layers of karst hydrological features. 1Minus tool subtracts the value of a raster (DEM) from the value of another raster (sink-filled DEM). 2Flow Direction tool creates a raster of flow direction from each cell to its downslope neighbor. 3Watershed tool determines the contributing area above the closed depression using the flow direction raster and the closed depression location.
Figure 3. Mapping workflow (simplified). Blue, input data; pink, output raster; white, calculation by GIS tools (except visual analysis); green, output layers of karst hydrological features. 1Minus tool subtracts the value of a raster (DEM) from the value of another raster (sink-filled DEM). 2Flow Direction tool creates a raster of flow direction from each cell to its downslope neighbor. 3Watershed tool determines the contributing area above the closed depression using the flow direction raster and the closed depression location.
Hydrology 12 00186 g003
Hydrology 12 00186 g004
Figure 4. Automatic identification of karst closed depressions by sink-filling approach: (a) original DEM; dashed white line is the trace of the cross-section A-B; (b) sink-filled DEM; (c) filtered difference raster (arbitrary threshold depth = 1 m), representing closed depressions; depression depth is measured from the spill elevation. Abbreviations: CCD, compound closed depression; ICD, isolated closed depression. (d) Cross-section A-B showing the topographic profile, runoff directions, and spill elevation of ICD or CCD.
Figure 4. Automatic identification of karst closed depressions by sink-filling approach: (a) original DEM; dashed white line is the trace of the cross-section A-B; (b) sink-filled DEM; (c) filtered difference raster (arbitrary threshold depth = 1 m), representing closed depressions; depression depth is measured from the spill elevation. Abbreviations: CCD, compound closed depression; ICD, isolated closed depression. (d) Cross-section A-B showing the topographic profile, runoff directions, and spill elevation of ICD or CCD.
Hydrology 12 00186 g004
Hydrology 12 00186 g005
Figure 5. (a) Extract of the 1:25000 IGMI topographic map showing the location of some dolines (“–” symbol). (b) Box-plot showing the frequency distribution of doline depths, based on features mapped by the IGMI map (186 samples). The 99th percentile, p99th, is 0.65 m.
Figure 5. (a) Extract of the 1:25000 IGMI topographic map showing the location of some dolines (“–” symbol). (b) Box-plot showing the frequency distribution of doline depths, based on features mapped by the IGMI map (186 samples). The 99th percentile, p99th, is 0.65 m.
Hydrology 12 00186 g005
Hydrology 12 00186 g006
Figure 6. Mapping criterion of dolines and endorheic areas. Two dolines (black lines) can be detected inside a larger depression (compound depression, yellow line). The endorheic area (red line is the water divide) represents the closed catchment of a single doline or compound depression. The spill (or overflow) point represents the shared (tangent) point between the upper rim of the closed depression and its endorheic area (red dot).
Figure 6. Mapping criterion of dolines and endorheic areas. Two dolines (black lines) can be detected inside a larger depression (compound depression, yellow line). The endorheic area (red line is the water divide) represents the closed catchment of a single doline or compound depression. The spill (or overflow) point represents the shared (tangent) point between the upper rim of the closed depression and its endorheic area (red dot).
Hydrology 12 00186 g006
Hydrology 12 00186 g007
Figure 7. (a) Map of dolines and endorheic areas of the Alburni karst massif. (b) Map of doline density.
Figure 7. (a) Map of dolines and endorheic areas of the Alburni karst massif. (b) Map of doline density.
Hydrology 12 00186 g007
Hydrology 12 00186 g008
Figure 8. Probability density distribution, f(x), and relative cumulative frequency, F(x), of dolines (a–c) and endorheic areas (d,e) for different morphometric features (area, maximum depth, and bottom elevation). The main statistics are provided by figures.
Figure 8. Probability density distribution, f(x), and relative cumulative frequency, F(x), of dolines (a–c) and endorheic areas (d,e) for different morphometric features (area, maximum depth, and bottom elevation). The main statistics are provided by figures.
Hydrology 12 00186 g008
Hydrology 12 00186 g009
Figure 9. (a) Correlation between doline area/perimeter ratio, A/P), and mean depth, D (log–log plot): (a) whole sample; (b) mid-higher elevation dolines (1050–1250 m a.s.l.); (c) higher elevation dolines (>1250 m a.s.l.).
Figure 9. (a) Correlation between doline area/perimeter ratio, A/P), and mean depth, D (log–log plot): (a) whole sample; (b) mid-higher elevation dolines (1050–1250 m a.s.l.); (c) higher elevation dolines (>1250 m a.s.l.).
Hydrology 12 00186 g009
Hydrology 12 00186 g010
Figure 10. Cumulative frequency distribution (log x-scale) of two doline data sets, sampled in the mid-upper zone (1050–1250 m a.s.l., N = 413) and upper zone (>1250 m a.s.l., N = 345) of the karst massif. Different morphometric parameters were analyzed: (a) area/perimeter ratio, A/P; (b) mean depth; (c) volume. The figure also shows the results of the Kolmogorov–Smirnov test (KS-test) (p-value < 0.05, significant difference between the two distributions; p-value > 0.05, no significant difference between the two distributions).
Figure 10. Cumulative frequency distribution (log x-scale) of two doline data sets, sampled in the mid-upper zone (1050–1250 m a.s.l., N = 413) and upper zone (>1250 m a.s.l., N = 345) of the karst massif. Different morphometric parameters were analyzed: (a) area/perimeter ratio, A/P; (b) mean depth; (c) volume. The figure also shows the results of the Kolmogorov–Smirnov test (KS-test) (p-value < 0.05, significant difference between the two distributions; p-value > 0.05, no significant difference between the two distributions).
Hydrology 12 00186 g010
Hydrology 12 00186 g011
Figure 11. Rose diagram showing the orientation of the major axes of dolines (a) and endorheic areas (b). (c) Bar plot showing the percentage of the internal runoff area in relation to flow direction.
Figure 11. Rose diagram showing the orientation of the major axes of dolines (a) and endorheic areas (b). (c) Bar plot showing the percentage of the internal runoff area in relation to flow direction.
Hydrology 12 00186 g011
Table 1. Springs of the Alburni karst massif, and related discharge values if available (nd, no data; from [16], modified); spring location is shown in Figure 2.
Table 1. Springs of the Alburni karst massif, and related discharge values if available (nd, no data; from [16], modified); spring location is shown in Figure 2.
LabelSpring NameSpring GroupElevation
m a.s.l.		Mean Discharge
m3/s
PSPPertosaPertosa1951.1
SPTPetina6470.1
PSDSanta Domenica243nd
PERPertosa Caves2631.1
AUSAuso ResurgenceAuso2801
FESFestola280nd
RMCMulino di Castelcivita ResurgenceCastelcivita65nd
GDCCastelcivita Caves941 to 4
STNTanagroLower Tanagro204>8.5
SCRControneOther springs1000.1
SP1Postiglione 15700.1
SP2Postiglione 25700.1
SP3Postiglione 35700.1
SCFCafaro180nd
FSSFontana Scorzo Sicignano3630.01
SALAuletta235nd
LSRLavatoio San Rufo6690.01
SSRSan Rufo636nd
ASRAbbotituro San Rufo6720.01
SVOValetorno848nd
GDADell’Acqua Caves875nd
Table 2. Table of the main topographic and geo-thematic data used for mapping.
Table 2. Table of the main topographic and geo-thematic data used for mapping.
DataYearResolution
or Scale		Author
or Provider
Digital Elevation Model (DEM)-5 m-
Digital orthophoto2006, 20120.5 mGeoportale Nazione, Ministero dell’Ambiente e della Sicurezza Energetica (MASE)
Google Earth satellite images2016–2023-Google
Technical Regional Chart (CTR)20041:5000Territorial Informative System of Campania Region
Topographic Map19951:25,000Italian Military Geographic Institute (IGMI)
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Esposito, L.; Leone, G.; Ginolfi, M.; Chenari, S.A.; Fiorillo, F. Mapping of Closed Depressions in Karst Terrains: A GIS-Based Delineation of Endorheic Catchments in the Alburni Massif (Southern Apennine, Italy). Hydrology 2025, 12, 186. https://doi.org/10.3390/hydrology12070186

AMA Style

Esposito L, Leone G, Ginolfi M, Chenari SA, Fiorillo F. Mapping of Closed Depressions in Karst Terrains: A GIS-Based Delineation of Endorheic Catchments in the Alburni Massif (Southern Apennine, Italy). Hydrology. 2025; 12(7):186. https://doi.org/10.3390/hydrology12070186

Chicago/Turabian Style

Esposito, Libera, Guido Leone, Michele Ginolfi, Saman Abbasi Chenari, and Francesco Fiorillo. 2025. "Mapping of Closed Depressions in Karst Terrains: A GIS-Based Delineation of Endorheic Catchments in the Alburni Massif (Southern Apennine, Italy)" Hydrology 12, no. 7: 186. https://doi.org/10.3390/hydrology12070186

APA Style

Esposito, L., Leone, G., Ginolfi, M., Chenari, S. A., & Fiorillo, F. (2025). Mapping of Closed Depressions in Karst Terrains: A GIS-Based Delineation of Endorheic Catchments in the Alburni Massif (Southern Apennine, Italy). Hydrology, 12(7), 186. https://doi.org/10.3390/hydrology12070186

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop