Cave drip water response to surface meteorological conditions is
complex due to the heterogeneity of water movement in the karst unsaturated
zone. Previous studies have focused on the monitoring of fractured rock
limestones that have little or no primary porosity. In this study, we aim to
further understand infiltration water hydrology in the Tamala Limestone of
SW Australia, which is Quaternary aeolianite with primary porosity. We build on
our previous studies of the Golgotha Cave system and utilize the existing
spatial survey of 29 automated cave drip loggers and a lidar-based flow
classification scheme, conducted in the two main chambers of this cave. We
find that a daily sampling frequency at our cave site optimizes the capture
of drip variability with the least possible sampling artifacts. With the optimum
sampling frequency, most of the drip sites show persistent autocorrelation
for at least a month, typically much longer, indicating ample storage of
water feeding all stalactites investigated. Drip discharge histograms are
highly variable, showing sometimes multimodal distributions. Histogram
skewness is shown to relate to the wetter-than-average 2013 hydrological year
and modality is affected by seasonality. The hydrological classification
scheme with respect to mean discharge and the flow variation can distinguish
between groundwater flow types in limestones with primary porosity, and the
technique could be used to characterize different karst flow paths when
high-frequency automated drip logger data are available. We observe little
difference in the coefficient of variation (COV) between flow classification
types, probably reflecting the ample storage due to the dominance of primary
porosity at this cave site. Moreover, we do not find any relationship between
drip variability and discharge within similar flow type. Finally, a
combination of multidimensional scaling (MDS) and clustering by

Karst features in limestone are typically developed from the solutional dissolution of fractures and bedding planes in carbonate rocks (Arbel et al., 2010; Kurtzman et al., 2009). Worldwide, karst regions represent significant geographical areas with potentially high rates of infiltration through fractured and karstified carbonate rocks. The most usual recharge method in karstic aquifers is the faster infiltration through the deep karstic openings (Ford and Williams, 2007). Complex spatial spreading of various karst features such as solutionally widened fractures, caves, and conduits makes the monitoring and precise groundwater recharge modeling very difficult (Lange et al., 2003; Arbel et al., 2010). The upper part of karstified rock (the epikarst zone) has higher permeability than the underlying vadose zone (Klimchouk, 2004). Therefore, infiltration into the epikarst zone is faster compared to the drainage through it, and water is kept stored in this region. This stored water in the vadose zone seeps slowly and finally emerges inside caves as infiltrating drip waters (Williams, 1983).

Karstic features such as speleothems, commonly used to reconstruct paleo-environmental records, are formed due to calcite deposition from cave drip water. Therefore, the knowledge of drip water hydrology is critical to study the paleoclimatic records (Baldini et al., 2006). An early study using tipping bucket loggers formulated a relationship between maximum discharge and coefficient of variation of discharge to categorize cave discharges (Smart and Friederich, 1987), for a fractured-rock limestone system with a vertical range of approximately 140 m (GB Cave, Mendip Hills, UK). They found that the drips close to the surface have extreme coefficients of variation, whereas the drips at depths have fairly constant flow rates over time, with a significant possibility of water storage in vadose zone fractures. Thus the stalagmite record resulting from slower drips may be more closely related to the karst hydrology rather than palaeoclimate (Baldini et al., 2006). This may also be a consequence of the developed connection between the surface and the cave. Quantitative analysis of such stalagmite drip data has, in the past, used manual observations of cave drips (e.g., Baker et al., 1997). However, the recent development of automatic cave drip loggers (Collister and Mattey, 2008) has enabled generation of high temporal resolution and continuous drip discharge time series (e.g., Jex et al., 2012; Cuthbert et al., 2014; Markowska et al., 2015; Mariethoz et al., 2012), providing new opportunities for quantitative hydrological analysis.

Here we present monitoring data from Golgotha Cave located in SW Western Australia that has been extensively monitored since 2005, with the aim of better understanding karst drip water hydrogeology and the relationship between drip hydrology and surface climate. We build on the work of Mahmud et al. (2016), which presented the largest spatial and temporal survey of automated cave drip monitoring with matrix (primary) porosity published to date. This previous study consisted of data from two large chambers within this cave, measured in the period from August 2012 to March 2015, using a highly spatially (29 sites in two separate chambers) and temporally (0.001 Hz, 15 min intervals) resolved data set. In a separate study, Mahmud et al. (2015) performed morphological analysis of karstic features, based on ground-based lidar data, to identify different flow processes in karstified limestone. Based on the findings of these two studies, here we investigate the relationship between drip water hydrology and cave depth, spatial location, and stalactite type, and develop a hydrological classification scheme that is appropriate to high-frequency drip logger data and limestones with a primary porosity. This classification scheme is also compared with previous studies (Smart and Friederich, 1987; Baker et al., 1997) to examine the limitations of these previous schemes. These findings will also help better characterize and understand water movement in highly porous karst formations.

Finally, we use a combination of multidimensional scaling (MDS) and the
popular

The cave site has been explained in detail by Treble et al. (2013). Briefly, the field site Golgotha Cave is 200 m in length and up to 25 m in width (Fig. 1), and developed in Quaternary aeolianite, which consists of wind-blown calcareous sands that were deposited along the southwest coast of Australia (Brooke et al., 2014). Vadose zone water flow, and subsequent widening by ceiling collapse, formed the cave chambers. Treble et al. (2013) described the cave site as being developed in the Spearwood system of the Tamala Limestone and is mantled by a variable thick layer of sand formation having depths of between 0.3 and 3 m. Diffuse (or matrix) flow is likely to be dominant in the Tamala Limestone formation due to its high matrix porosity as 0.3–0.5 (Smith et al., 2012). Karst in this region is also called “syngenetic” (Treble et al., 2013), implying processes like preferential vertical dissolution and varying morphology of the subsurface caprock. These processes may establish vadose zone preferential flow extending to the cave ceiling, with occasional rapid delivery of percolating waters deep into the calcarenite which end up seeping through to the cave ceiling. Therefore, this young limestone formation offers various opportunities for preferential flow into the host rock and storage within it (Brooke et al., 2014). Golgotha Cave was chosen because (a) it is located in an intensively studied karst area (Treble et al., 2013, 2015, 2016), which has over 10 years of manual and 3 years of automated drip water monitoring, (b) it contains actively growing speleothems, and (c) it is accessible year-round.

Based on previous studies at this site, we determined previously that Chamber 1 (Fig. 1b–d) is mostly dominated by matrix flow representing water flowing down and seeping through the rock matrix, characterized by both icicle-shaped and soda-straw stalactites with slow drip rates of low variability. In contrast, Chamber 2 (Fig. 1b and e) is typically controlled by fracture and combined flow, with high drip rates that are shown to vary over time depending upon the mode of water delivery to the preferential flow system. In fracture flow, water moves along the fracture orientation, forming curtain-shape stalactites in the direction of highest fracturing. Finally, combined flow is defined as the combination of conduit, matrix, and fracture flow, resulting in a circular pattern of stalactite formation.

A comprehensive description of the climate at our study site has been
presented in Treble et al. (2013). To summarize, the site
is a Mediterranean climate, associated with wet winters and dry summers.
Annual rainfall recorded at Forest Grove weather station
(Fig. 1a, 5 km away from the study site) is
1136.8

As reported in previous studies, all hydrological years have water deficit during the dry season (October to April) and significant infiltration during the wet period. Low evaporative conditions during winter should permit increased infiltration to the caves, enhancing the drip discharge response to winter rainfall. The hydrological year 2012 had roughly similar annual rainfall of 1008.6 mm to the long-term annual mean, whereas 2013 was rather wet (total rainfall of 1239.8 mm) and 2014 was a relatively dry year with a total rainfall of 943.8 mm. Recorded rainfall was significantly above average in the 2013 hydrological year for various weather stations in Western Australia (BoM, 2017). Therefore, our site had a wetter winter in 2013 with an estimated annual recharge of 858.67 mm which is very much above average (10-year mean annual recharge is 564 mm).

Data acquisition and pre-processing has been previously described in
Mahmud et al. (2016) and is concisely summarized here.
Stalagmite drip loggers (

As previously reported, drip rates in Chamber 1 are generally very low (the fastest drip rate was 25 drips per 15 min) consistent with the predominance of matrix flow in this chamber. However, it is obvious that most drip loggers exhibit a clear response to the 2013 wet winter and also indicate the substantial interannual variation in discharge between three hydrological years. All Chamber 1 drip sites (except Site 1x) show a gradual drip rate decrease during summer 2012 to winter 2013 due to below average rainfall in 2012. Then after displaying the sudden increase in all drip discharges that express the 2013 wet winter, the drip rates further reduce due to the dry 2014 hydrological year. This intra-annual variation is identified to be much greater than the interannual discharge variation of the drip sites, as previously observed in Baker et al. (1997). This suggests that high-resolution intra-annual drip rate data is helpful to obtain a complete picture of changing flow variability with recharge. The high resolution of the data sets includes precise characterization of the temporal behavior of an individual drip, illustrating the differences inherent to the drip sites.

In contrast, Chamber 2 drip rates present more variability between sites both in intra- and interannual discharges, except few very slow dripping sites. Of the Chamber 2 drips, the slow drip sites have the lowest coefficients of variation (COVs) and lowest discharges, indicative of matrix flow types. The timing of maximum drip rates is generally delayed in Chamber 2 versus Chamber 1: Chamber 1 drip rates typically peak in late spring/early summer (October–December) while Chamber 2 drips tend to peak a few months later (December–May), reflecting a longer water residence time. This may be a function of the thicker ceiling above Chamber 2 (40.24 versus 32.33 m) but also heterogeneity in flow paths to each chamber. Overall the drip response to the 2013 wet winter is amplified in Chamber 2 versus Chamber 1, consistent with the presence of greater fracture flow in Chamber 2.

By applying morphological analysis of ceiling features acquired by lidar data, Mahmud et al. (2015) distinguished three flow patterns (i.e., matrix flow, fracture flow, and a combination of conduit, fracture, and matrix flow) for the observed ceiling morphological features. All the drip sites were then characterized according to this flow classification in Mahmud et al. (2016), which is used here as a reference for clustering similar drip time series.

Research involving automated drip monitoring systems is increasing, for example at Cathedral Cave in Wellington (Cuthbert et al., 2014) and Harrie Wood Cave in the Snowy Mountains, Yarrangobilly (Markowska et al., 2015). The variability of the drip discharge might not only be a function of discharge itself; it could also depend on the sampling frequency. We investigate this possibility by plotting the COV versus sampling interval (the original 15 min and calculated by resampling the data at 1 h, 1 day, 1 week, and 1 month). COV is supposed to be artificially high at the high frequency of 15 min because of sampling bias that artificially increases the noise. The resampling at low frequencies is simply a way of smoothing out this noise. Using the optimum sampling frequency to minimize its effect on drip variability, we plot drip rate histograms to identify the response of drips between the flow classifications and the response to intra- and interannual variability in infiltration. We also plot the autocorrelation functions (ACFs) to investigate the relationship between the strength of correlation and the lidar-based flow type. Finally, we summarize the mean discharge of drip sites in relation to the variability in discharge using the optimum sampling frequency. These are the same drip discharge parameters as used in the classification method proposed by Friederich and Smart (1982), Fairchild et al. (2006), and Baker et al. (1997) that were based on manual drip collection at low frequency.

We employed multidimensional scaling (MDS), which allows data
dimensionality reduction, i.e., mapping complex multidimensional data on a
low-dimensional manifold. MDS is a technique that embeds a set of points in
a low-dimensional space, so that the distances between the points resemble
as closely as possible a given set of dissimilarities between the objects
they represent (Birchfield and Subramanya, 2005). MDS requires a
distance matrix to be computed in which a single scalar number
characterizes the similarity between any two time series. In our case, each
drip logger is an object and a specific distance between drip loggers is
considered to characterize the similarity between any two loggers. It takes
an input matrix giving dissimilarities between pairs of items and outputs a
coordinate matrix whose configuration minimizes a loss function. MDS is also
known as principal coordinates analysis (PCoA). MDS operates on a distance
or dissimilarity matrix (Pisani et al., 2016), which is different than
principal component analysis (PCA) that is based on a covariance matrix.
Even if PCA and MDS methods can return the same results in specific
contexts, MDS can be considered more general because it remains valid for
non-Euclidean distances, such as the distance matrix (

Construct the distance matrix

Compute the inner product matrix

Decompose

Extract the first

The corresponding Euclidean distances of the set of points, indicating
the dissimilarities of the time series, are now located in the

Choose

Compute point-to-cluster-centroid distances of all observations to each centroid. There are two steps to follow: first assign each observation to the cluster with the closest centroid. Then individually assign observations to a different centroid if the reassignment decreases the sum of the within-cluster, sum-of-squares point-to-cluster-centroid distances.

Compute the average of the observations in each cluster to
obtain

Repeat steps 2 and 3 until cluster assignments do not change, or the maximum number of iterations is reached.

Optimum sampling frequency that minimizes sampling artifacts while maximizing the capture of natural variability.

We test the variability of drip discharge COV with the sampling frequency in Fig. 2, to find the optimum sampling frequency that minimizes sampling artifacts while maximizing the capture of natural variability. For high discharge, COV increases with sampling frequency, which we explain by the smaller sampling interval better capturing the actual drip variability. For low discharges, COV also increases with sampling frequency, which we explain by the variability introduced due to drip rates being less than the sampling frequency. From the data presented in Fig. 2, we can conclude that for both chambers and to compare all different types of flow, a sampling frequency of 1 day gives the minimum COV, which does not change significantly with a finer sampling frequency. Therefore, we use a sampling frequency of 1 day that minimizes sampling artifacts while maximizing the capture of natural variability. For Golgotha Cave, this would be to sum the 15 min drip rates over a 1-day period. This optimized sampling frequency is used to plot the histograms (Sect. 4.2) and ACFs (Sect. S1 in the Supplement), and to examine the drip discharge behavior with drip variability for various flow types (Sect. 4.3).

Figure 3 shows the drip rate histograms for representative drip sites and different flow categories with optimum sampling frequency of 1 day. Drip sites are organized from lowest to highest discharge in each flow classification. Slow dripping soda-straw flows (e.g., sites 2xi, 2iii, and 1v) show variation of drips with seasonality and the response to wetter recharge period with an approximate 6-month lag, which suggests the drip water is supplied from storage in the limestone formation. Among these, Site 1v displays the response to recharge in much shorter duration, the 6 months following 2013 recharge and then a shift to lower flow rates which may represent flow poaching. The histograms for icicle and combined flow systems represent unimodal skewed to bimodal distributions, indicating the shift to higher drip rates in response to the wetter 2013 hydrological year (except Site 2xiii, which shows a shift to lower drip rates). The rest of the fracture sites show bimodal or multimodal distributions. With the limited temporal scale of the analysis, it seems that the histograms with skewed distributions represent the consequences of wetter 2013 hydrological year. These skewed distributions seem to have higher drip rate response to the drier 2014–2015 period rather than the earlier normal/wetter years. This clearly denotes potential refilling of storage within the system during the 2013 wet winter, and later supplying drip water in 2014–2015 seasons. In contrast, the bimodal distribution of Site 2viii indicates the drip response to the annual cycle of wet and dry seasons of each hydrological year with an approximate 6-month lag. Several bimodal (e.g., Site 1x) and multimodal (e.g., sites 2xvi, 2vi) distributions, characterized as fracture flow, also distinguishes the dry period of 2012–2013 (having low drip rates) from the later period of 2013 wet winter (with high drip rates).

Histogram plots of both chambers' drip data according to four flow types
identified in Mahmud et al. (2016). Each histogram represents the frequencies
of the drip counts per day (the axes labels are shown in the first histogram).
Bin size is uniform for all plots and the external tick marks in

We investigate the use of ACFs to analyze drip behavior using the optimum sampling frequency of 1 day and until lags of 365 days. We do not find significant yearly autocorrelation with this limited 3 years of data. In some drips, a negative correlation occurred, but it is very insignificant and no physical process can explain a negative yearly correlation. Therefore, we plot ACFs in Fig. 4 for different flow categories with the optimum sampling frequency of 1 day and lag time of 200 days. All sites have an autocorrelation that persists for at least a month, and often much longer. However, there is no relationship between the strength or the temporal decay of the correlation and the lidar-based flow classification. This indicates the presence of ample storage in the system, supplying all stalactite types.

Autocorrelation functions of both chambers' drip data according to flow
classification of Mahmud et al. (2016).

We examine the hydrological behavior of the drips at daily resolution with
respect to mean discharge and flow variation in Fig. 5. It is clear from
Fig. 5 that there is no relationship between COV and flow type. One
soda-straw discharge (Site 2xi) has seasonal dryness, a very low
discharge, and a very high coefficient of variation due to its irregular
dripping. Otherwise, nearly all soda-straw flow, icicle flow, combined flow
and fracture flow drips have COV

Hydrological behavior of drip sites expressed in terms of daily mean discharge versus daily discharge variability calculated from the automatic drip rate data for three hydrological years. Measured drip rates are converted to volume units assuming a drip volume of 0.1433 mL (Genty and Deflandre, 1998). Blue lines and symbols reflect flow classification given in Mahmud et al. (2015).

The clustering results are overlaid upon the chamber ceiling images in Fig. 6 and also summarized in Tables 1 and 2 with the average drip discharges and flow type classification based on lidar. Average drip discharges are calculated from the 15 min drip rates. As mentioned above, drip logger time series are deemed similar if they are well correlated and only have a small offset with each other, and so these time series should cluster together. Most of the drip sites that are identified as matrix flow (soda-straw and icicle flow) cluster together in C1. However, three of the icicle flow sites with drip rates greater than 4 per 15 min fall in C2. The combined flow category and the fracture type usually cluster in C3 and C4, respectively. Therefore we observe that our clustering generally agrees with the morphology-based flow classification of Mahmud et al. (2016). Few of the flow classes show exceptions, for example Site 2vi is a fracture type flow and cluster in C1. This site has really high discharge with high variability, showing irregular drip rate.

One consistent feature that appears from the cluster analysis of Fig. 6 is
the spatial homogeneity of the clusters in Chamber 1, suggesting that they
are spatially connected, or that their flow paths are connected to the same
hydrological domain (the karst matrix), and supporting the overall dominant
matrix flow patterns (both soda-straw and icicle). Chamber 2 presents a
completely different situation, where it is obvious that drip sites can have
similar behavior (well correlated with a small lag), and be
spatially distinct features, separated by spans of approximately 6 m
(Fig. 6). In particular, clusters 3 and 4 are spatially scattered,
representing the presence of fractures and combined flow systems throughout
the chamber ceiling. This indicates an overall strong heterogeneity of the
flow paths between the surface and the cave for Chamber 2. Hence, in Chamber 2
we expect flow paths to be more complex with routing between multiple
stores and interconnected fracture networks potentially resulting in
non-linear response to infiltration. This is supported by drip water

Starting with the time series analysis, this research presents a methodology that can be applied globally for drip logger data. The results show that some data integration is necessary to avoid artifacts from slow drip sites. For sites where there is significant matrix flow, our study has demonstrated that the Smart and Friederich classification is not appropriate. Therefore, this study has presented alternative hydrological classification schemes that are suitable for cave sites that include matrix flow. The times series approach adopted in this study also opens the way for improved analysis and classification of hydrology time series in general, i.e., tests for histogram, autocorrelation, cluster analysis, and all of these will certainly benefit our understanding of the hydrology of karst systems.

In this study, we also extend the analysis of drip time series to multiple sites, whereby we take advantage of the ensemble of loggers to extract common properties by clustering, which would not be possible with single site analysis. The results show that by considering multiple simultaneous time series, one can make better inferences about water flow and unsaturated zone properties. The main impact is to recommend the use of spatial networks of loggers over individual loggers. It should be noted that currently, most researchers deploy only a few loggers to understand the flow to individual sites. This study also proposes a possible methodology for the analysis of such data sets.

MDS cluster groups with statistical properties of Chamber 1 drip data.

MDS cluster groups with statistical properties of Chamber 2 drip data.

Cluster group plot overlaid upon the cave ceiling for both chambers. The ceiling images are captured by lidar and the circles represent the ceiling locations of stalactites dripping on various stalagmites in both chambers (shown in Fig. 1). The color of the circles indicates individual MDS cluster group. The blue arrows in both figures show the geographic orientation and the green arrows represent the approximate transects throughout the chambers from higher to lower ceiling elevations.

Regarding the application of our findings, we believe that our methodology based
on drip logger data sets can provide direct evidence of deep drainage, and
therefore the timing of diffuse recharge, which could be used for basic
model calibration. Spatial drip data (possibly combined with lidar) is
beneficial to infer flow types (e.g., the proportion of fracture versus matrix) which could be used for model configuration to produce realistic karst
recharge (Hartmann et al., 2012), and hence large-scale
groundwater estimation (Hartmann et al., 2015). Another
potential application is the integration of flow types in groundwater models
through inverse modeling. Such data could also be used to constrain water
isotope model configurations used for forward modeling speleothem

The analysis, presented here and combined with the findings of previous work at this site, provides valuable information for paleoclimatologists and geochemists wishing to sample stalagmites. While these studies have characterized Golgotha Cave, they could be applied to any other cave system. In our previous work we (1) devised a classification for flow type based on stalactite morphology; (2) quantified the recharge response of each flow type to infiltration; (3) combined the findings of points 1–2 to estimate the total volume of cave discharge; and (4) compared cave discharge with infiltration to estimate the total recharge volume and identify highly focused areas of recharge. The current study has further developed the spatial and temporal statistical relationships between the flow sites, allowing both quantification and visualization of the hydrology between the ground surface and the cave ceiling. More generally, these studies illustrate the heterogeneity between flow sites and demonstrate methods that can be applied to any cave system for studying diffuse recharge and paleoclimate records from speleothems.

We further propose some ideas for future research that have evolved from
this study:

Combining a drip logger network with a surface weather station and soil moisture network to constrain the water balance in hydrological models. Additionally, employing sap flow meters could allow constraining tree water use.

Combining the logger network, which constrains diffuse recharge, to boreholes measuring groundwater level to understand the relative importance of diffuse and river recharge.

Combining cave drip logger data with surface geophysics data to track water movement.

Cave drip water response to surface climatic conditions is often complex due to numerous interacting drip routes with varying response times (Baldini et al., 2006). This study explores the relationship between drip water and rainfall in a SW Australian karst, where both intra- and interannual hydrological variations are strongly controlled by seasonal variations in recharge. The multi-year drip response data capture the interannual drip water variability that are likely to be greater than intra-annual variability as suggested by Baker et al. (1997). Building on previous work, we further analyze a set of statistical properties of three hydrological years of drip data under varying precipitation rates. We test the relationship between drip discharge variability and drip data sampling frequency to determine the optimum sampling frequency that maximizes the capture of natural variability with minimum sampling artifacts. Using the daily optimum sampling frequency, the histogram distributions of various drip data time series illustrate the differences between the flow classifications. Most of the drip sites show persistent autocorrelation for at least a month. The hydrological behavior of the drips is examined with respect to mean discharge and the flow types similar to the classification method proposed by previous researchers (Smart and Friederich, 1987; Baldini et al., 2006; Baker et al., 1997). The drip sites at Golgotha Cave described in this study do not fit within the drip classification method proposed by Smart and Friederich (1987) and Baker et al. (1997). These previous studies were based on manual drip counts with limited number of intermittent drip sites. Here we overcome these limitations with automated drip monitoring system.

Finally, we apply a well-developed clustering method to determine the degree of similarity between drip time series. The clustering indicates one dominating group, C1 (characterized by matrix flow type), with very slow continuous drip discharge indicating matrix porosity in the thick limestone formation. This finding concurs with the observed cave chamber morphology and lithology. Moreover, the cluster analysis agrees with the flow classification of Mahmud et al. (2016) by grouping similar flow type in one single cluster. Overall this study establishes a novel way to characterize cave hydrology, which can be obtained by applying the methodologies of Mahmud et al. (2015) and Jex et al. (2012) together. It relies on a metric that defines drip logger time series as similar if they are well correlated and only have a small offset with one another, and therefore these time series should cluster together. The MDS analysis supports this hypothesis and moreover displays the spatial patterns of the flow paths between the surface and the cave chambers. This technique shows potential for classifying, quantifying, and visualizing the observed relationships between infiltration through the fractured limestone rocks and surface climate inputs.

Over the last decade, the automation of cave drip water hydrology measurements has permitted the routine generation of continuous hydrological time series for the first time. This study demonstrates a complete methodology for such data sets, which will help better characterize karst drip water hydrogeology and understand the relationship between drip hydrology and surface climate at any cave site where such measurements are made. We demonstrate that the analysis of the time series produced by cave drip loggers generates useful hydrogeological information that can be applied generally, beyond the example presented here. The time series behavior integrates a variety of characteristics that combine the properties of epikarst (storage), fracture configuration, and recharge. The clustering approach can identify which drip behaviors are related to these cave characteristics, and their spatial relationship. Most importantly, information on cave characteristics can now be gathered at a very low cost in terms of measurement and time.

The raw data and the Matlab code to perform the data processing
and analysis are freely available in a Git repository (

The supplement related to this article is available online at:

The authors declare that they have no conflict of interest.

This paper is based on work supported by UNSW Australia, UNSW Connected Waters Initiative Research Centre, and the National Centre for Groundwater Research and Training. The authors wish to thank the individuals (Andy Spate, Alan Griffiths, Liz McGuire, Carolina Paice, Anne Wood, Monika Markowska, and others) who assisted in data acquisition at the Golgotha Cave site. Edited by: Alberto Guadagnini Reviewed by: two anonymous referees