E&EG Journal Volume XXIX, Number 4 by Association of Environmental & Engineering Geologists (AEG)

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THIS PUBLICATION IS PRINTED ON ACID-FREE PAPER EDITORS ABDUL SHAKOOR Kent State University Kent, OH 44242 ashakoor@kent.edu

ERIC PETERSON Department of Geography, Geology, and the Environment Illinois State University Normal, IL 61790 309-438-5669 ewpeter@ilstu.edu

KAREN E. SMITH, Editorial Assistant, kesmith6@kent.edu

OOMMEN, THOMAS Board Chair, Michigan Technological University SASOWSKY, IRA D. University of Akron

ASSOCIATE EDITORS ACKERMAN, FRANCES Ramboll Americas Engineering Solutions, Inc. BASTOLA, HRIDAYA Lehigh University BEGLUND, JAMES Montana Bureau of Mines and Geology BRUCKNO, BRIAN Virginia Department of Transportation CLAGUE, JOHN Simon Fraser University, Canada DEE, SETH University of Nevada, Reno FRYAR, ALAN University of Kentucky GARDNER, GEORGE Massachusetts Department of Environmental Protection GHOSH, SUMAN California Department of Conservation

HAUSER, ERNEST Wright State University KEATON, JEFF WSP USA MAY, DAVID USACE-ERDC-CHL POPE, ISAAC Book Review Editor SANTI, PAUL Colorado School of Mines SCHUSTER, BOB SHLEMON, ROY R.J. Shlemon & Associates, Inc. STOCK, GREG National Park Service SWANSON, SUSAN (SUE) Beloit College ULUSAY, RESAT Hacettepe University, Turkey WEST, TERRY Purdue University

Environmental & Engineering Geoscience NOVEMBER 2023

VOLUME XXIX, NUMBER 4

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Cover photo View of the landslide that destroyed the Munnar College building, Kerala, India (August 20th, 2018). Photo credit: Thomas Oommen. See article on page 245.

Volume XXIX, Number 4, November 2023

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ADVISORY BOARD WATTS, CHESTER “SKIP” F. Radford University HASAN, SYED University of Missouri, Kansas City NANDI, ARPITA East Tennessee State University

ENVIRONMENTAL & ENGINEERING GEOSCIENCE

Environmental & Engineering Geoscience (ISSN 1078-7275) is published quarterly by the Association of Environmental & Engineering Geologists (AEG) and the Geological Society of America (GSA). Periodicals postage paid at AEG, 3053 Nationwide Parkway, Brunswick, OH 44212 and additional mailing offices.

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Environmental & Engineering Geoscience Volume 29, Number 4, November 2023 Table of Contents 231

Geologic Model for Alluvium-Buttressed Landslides Philip L. Johnson, Samuel W. Nolan, and Patrick O. Shires

245

Assessing the Improvement of a Sparse Rain Gauge Network in a Landslide Hotspot in Kerala, India—A Multi-Criteria Approach C. L. Vishnu, Thomas Oommen, Snehamoy Chatterjee, A. Rajaneesh, and K. S. Sajin Kumar

257

Effect of Various Aspects of Joints on Permeability of Jointed Glacial Till, Northeastern Ohio Aleksandar Prvanovic and Abdul Shakoor

275

Topographic Controls on the Duration of Sinkhole Flooding in Central Tennessee, USA Mark Abolins and Albert Ogden

291

The Chemistry of Cave Ice: Two Examples from Slovenia Devin F. Smith, W. Berry Lyons, Susan A. Welch, Matija Zorn, Jure Tičar, Matej Lipar, Anne E. Carey

309

Book Review Thomas Oommen

Geologic Model for Alluvium-Buttressed Landslides PHILIP L. JOHNSON SAMUEL W. NOLAN PATRICK O. SHIRES Cotton, Shires and Associates, Inc., 646 University Avenue, Los Gatos, CA 95032

Key Terms: Landslides, base level, fluvial incision, fluvial aggradation ABSTRACT Large, deep-seated landslides typically occur in hillside settings without any natural buttressing, and many of these landslides remain relatively unstable and prone to reactivation. However, where large, deep-seated landslides have moved into incised valleys that subsequently experienced alluvial aggradation, a natural buttress of alluvium may cover the toes of these landslides, increasing stability. This study presents three examples of large, deep-seated landslides that are buttressed by Quaternary alluvium. The McCracken Hill Landslide in southern California and the Potrero Canyon Landslide Complex in central California are proximal to the Pacific coast. The Knights Valley Landslide Complex in northern California is much farther inland than the other examples. We analyzed the stability of one of the example landslides to demonstrate that a buttress of alluvium increases stability. In most settings, base level primarily controls alluvial aggradation. Base-level rise may result from either climatically driven late Quaternary eustatic sea-level rise or local factors such as damming of streams or downstream tectonic uplift. Late Quaternary eustatic sealevel rise caused alluvial aggradation at the McCracken Hill and Potrero Canyon sites. Downstream tectonic uplift likely caused local base-level rise and alluvial aggradation at Knights Valley. INTRODUCTION Engineering geologists working in southern California during the 1960s and 1970s first noted large, deep-seated landslides that are buttressed by alluvial sediments (Stout, 1969, 1977; Leighton, 1976). However, the geologic literature contains few descriptions of this type of landslide. The few published accounts are primarily brief descriptions in field trip guidebooks and publications focused on regional engineering geology (Rasmussen, 1982; Hansen, 1989). Corresponding author email: pjohnson710@gmail.com

Alluvium-buttressed landslides are important because they are more stable than landslides that lack natural buttressing. Due to this increased stability, these ancient landslides tend to be currently inactive, leading to degradation and re-vegetation of scarps and lateral margins. As a result, alluvium-buttressed landslides are more difficult to recognize than recently active landslides. Also, the conditions that lead to natural buttressing of landslides (deposition of alluvial sediment over the toe of a landslide) are unique, so alluvium-buttressed landslides are less common than landslides without a natural buttress. Thus, this important class of landslides receives little attention. The purposes of this study were to describe three examples of alluvium-buttressed landslides that have been investigated through deep subsurface investigation in order to understand how these landslides became buttressed by alluvium and to propose a geologic model for alluviumbuttressed landslides. Though all of the examples used for this study are located in California, alluvium-buttressed landslides are likely to be found in other regions. As awareness of alluvium-buttressed landslides grows, geologists may recognize additional examples elsewhere. EXAMPLES OF ALLUVIUM-BUTTRESSED LANDSLIDES In this section, we summarize the results of geologic investigations of three deep-seated landslides that are buttressed by Quaternary alluvial sediments. A simplified map of the western United States (Figure 1) shows the locations of the three example sites. The McCracken Hill Landslide is located in the southern California community of San Juan Capistrano, adjacent to the Pacific coast. The Potrero Canyon Landslide Complex, in the Carmel Valley region of central California, is approximately 3.7 mi (6 km) inland from the Pacific coast. The Knights Valley Landslide Complex is farther inland, within the Coast Ranges of northern California. The landslide mapping for this study utilized interpretation of stereo-pair aerial photographs, evaluation of light detection and ranging (LiDAR)–based topographic maps, and field mapping. Subsurface investigation of the subject landslides consisted of a combination of core borings and

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Figure 1. Location map for the three ﬁeld areas used in this study.

downhole logging of large-diameter borings. The primary purposes of the exploratory borings were the characterization of the subsurface geology and identification of the depth to landslide rupture surfaces to facilitate preparation of detailed geologic cross sections. In addition, representative samples of earth materials were collected from the borings for geotechnical laboratory testing. McCracken Hill Landslide The McCracken Hill Landslide is a large, deep-seated landslide that is approximately 0.9 mi (1.5 km) wide and 0.6 mi (1 km) long. However, alluvium covers a significant portion of the landslide toe, so the full length of the landslide is likely greater than that exposed at the ground surface. A prominent graben at the head of the landslide is approximately 660 to 820 ft (200 to 250 m) wide, and the adjacent arcuate main scarp is approximately 230 ft (70 m) high. The McCracken Hill Landslide is clearly visible in stereo-pair aerial photographs, digital raster elevation models, and in the field. Vedder et al. (1957) first mapped this landslide, and later published geologic mapping (Edgington, 1974) included the McCracken Hill Landslide. Mapping and subsurface investigation of the McCracken Hill Landslide and other nearby landslides shows that these deep-seated landslides formed within the Upper Miocene to Pliocene Capistrano Formation. Previous studies of the Capistrano Formation focused on coastal bluffs that expose coarse-grained channel-form beds that were deposited in a subsea fan setting (Walker, 1975; Campion et al., 2005). However, in the McCracken Hill area, the Capistrano Formation consists primarily of thick intervals of massive siltstone with local, thin beds of sandstone and very thin beds of claystone that are typically 0.8 to 1.2 in. (2 to 3 cm) thick. The Capistrano Formation tends to fail along the weak claystone beds (Hansen, 1989) in block-slide– type failures. Numerous large, deep-seated landslides that 232

moved along bedding in this manner are found in the communities of San Juan Capistrano and San Clemente. Bedding within the Capistrano Formation dips shallowly to the west and toward San Juan Creek in the McCracken Hill area. Given the strong tendency toward sliding on weak claystone beds within the Capistrano Formation, this bedding orientation favored failure of deep-seated landslides (such as the McCracken Hill Landslide) into the channel of San Juan Creek when the channel was deeply incised. At present, San Juan Creek flows across a broad (approximately 0.5 mi [0.8 km] wide) low-relief valley floor that is underlain by thick alluvial deposits. South of the McCracken Hill Landslide, San Juan Creek discharges to the Pacific Ocean. A map of the McCracken Hill Landslide (Figure 2) shows the alluvium-filled valley of San Juan Creek and the locations of exploratory borings used for this study. The shaded relief topographic base map used for Figure 2 shows the current topography as modified by extensive grading for highways and urban development. The authors reviewed stereo-pairs of historical aerial photographs, conducted field mapping, logged core samples, downhole logged large-diameter borings, and logged exploratory trenches and slot cuts as part of a multi-phase engineering geologic study of the McCracken Hill Landslide and secondary slope failures into the graben of the landslide. Exploratory borings within the alluvium that overlies the toe of the landslide indicated that the thickness of alluvium is greater than 144 ft (45 m) in the valley of San Juan Creek, forming a significant buttress against the toe of the McCracken Hill Landslide. Cross-section AA 0 (Figure 3) shows the basal rupture surface of the McCracken Hill Landslide as determined by the logging of deep core borings. The basal rupture surface is a weak shear zone with highly polished surfaces and high-plasticity clay gouge. This shear zone follows a shallowly dipping bed of claystone that is interbedded with very thick beds of siltstone. The cross section also shows the graben at the head of the landslide and the buttress of alluvium at the toe. Potrero Canyon Landslide Complex The Potrero Canyon Landslide Complex is located on the south flank of Carmel Valley, approximately 3.7 mi (6 km) inland from the Pacific Ocean (Figure 4). The Carmel River flows through Carmel Valley and drains an extensive watershed south and east of the landslide complex. It discharges directly to Carmel Bay and the Pacific Ocean. The western reach of Carmel Valley aligns with an offshore submarine canyon, Carmel Canyon, which is a tributary to the larger Monterey Submarine Canyon system. It appears that Carmel Canyon was the downstream reach of Carmel Valley during previous sea-level lowstands.

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Alluvium-Buttressed Landslide Model

Figure 2. Map of the McCracken Hill Landslide with the location of cross-section A-A 0 . The locations of deep core borings logged by the authors are designated with LC numbers. The locations of selected borings logged by others are designated with DE numbers. The mapping of alluvium and beach deposits is modiﬁed from Tan et al. (1999).

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Figure 3. Cross-section A-A 0 through the McCracken Hill Landslide. The borings used to prepare the cross section are shown with vertical lines. The dashed blue lines represent possible potentiometric surfaces. The colluvial sediments that ﬁll the graben of the McCracken Hill Landslide are designated as Col.

Presently, Carmel Valley has a broad (approximately 0.6 mi [0.9 km] wide) low-relief ﬂoor, underlain by thick alluvium. Exploratory borings in Carmel Valley indicated that the thickness of alluvial sediment exceeds 131 ft (40 m) in the vicinity of the Potrero Canyon Landslide Complex and reaches a maximum of 180 ft (55 m) downstream, near the coast (CDWR, 2004). Upstream, the thickness diminishes to a minimum of approximately 30 ft (9 m) in the upper reaches of Carmel Valley. Clark et al. (1974, 1997) and Dupre (1990) previously mapped the Potrero Canyon Landslide Complex on the south ﬂank of Carmel Valley. The landslide mapping for this study included some re-interpretation of the previous landslide mapping based on interpretation of stereo-pair

aerial photographs and LiDAR imagery as well as ﬁeld mapping. The landslide complex consists of multiple landslides that moved northward into Carmel Valley (Figure 5). However, there are multiple adjacent landslides that combined to form a larger landslide complex that is approximately 2.3 mi (3.7 km) wide. Geologic mapping for this study and by Clark et al. (1997) indicated that this landslide complex formed in siltstone and siliceous mudstone of the Miocene Monterey Formation. In this area, bedding in the Monterey Formation dips shallowly toward the north, and many of the large landslides are translational blocks that moved along bedding as deep-seated block slides. Our subsurface investigation focused on a single deep-seated landslide

N 0 0

0.5

1.0 km 0.5 mi

Carmel Bay

Carmel River Carmel Valley Potrero Canyon Landslide Complex

Area of Fig. 5 Figure 4. Map of Carmel Valley showing the locations of the Potrero Canyon Landslide Complex, Carmel Bay, and Carmel River.

234

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Alluvium-Buttressed Landslide Model

Figure 5. Map of the Potrero Canyon Landslide Complex showing the locations of the relevant exploratory borings and cross-section B-B 0 .

plasticity clay gouge. The orientations of the bounding polished surfaces are similar to bedding orientations within the underlying siltstone, indicating that the landslide moved along bedding. Within Landslide B, the rock that overlies the shear zone consists of siltstone of the Monterey Formation. Slope debris composed of angular blocks of siltstone with a matrix of clayey sand overlies the deepseated landslide block. As shown on cross-section B-B 0 (Figure 6), the toe of Landslide B interﬁngers with Quaternary alluvial sediment in Carmel Valley, which resulted in buttressing of Landslide B. The displaced siltstone of the landslide contrasts

(labeled Landslide B) that failed off of a larger landslide, labeled Landslide A (Figure 5). Mapping for this study showed several other secondary landslides that also failed off of Landslide A. Though we did not have the opportunity to investigate Landslide A, it appears that the basal rupture surface of this larger landslide is signiﬁcantly deeper than that of Landslide B. Thus, a thicker deposit of alluvium should form the buttress to Landslide A. The results of subsurface investigation for this study indicated that Landslide B moved along a basal rupture surface shear zone characterized by polished and striated surfaces that bound a 1-in.-thick (2.5-cm-thick) high-

B 200

ELEVATION (METERS)

Potrero Canyon Landslide Complex LD-2 (Proj. 8m NW)

100

SD-2 (Proj. 35m SE) LD-1

SD-1 (Proj. 14m SE)

SD-3 (Proj. 32m SE)

Carmel Valley

Landslide B 0 ?

Landslide A ?

Col

Qal ?

N51E

Monterey Formation

-100

Figure 6. Cross-section B-B 0 through a portion of the Potrero Canyon Landslide Complex. The dashed blue line indicates the estimated elevation of the potentiometric surface.

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Knights Valley 0

5 km

3 mi

Fig. 8

Ru ss

ian

River

Fig. 11

Pacific Ocean

Santa Rosa

Figure 7. Map of the stream channels that drain Knights Valley via the Russian River to the Paciﬁc Ocean.

with the alluvial sand and gravel composed of quartz, feldspar, biotite, and granitic and metamorphic rock fragments derived from the Salinian complex crystalline rocks exposed upstream (Wagner et al., 2002; Rosenberg and Wills, 2016). As a result, the landslide debris was easily distinguished from the alluvium in the borings drilled during subsurface investigation. Knights Valley Landslide Complex Knights Valley is a broad, alluvium-filled valley within the eastern Russian River watershed (Figure 7) in California, approximately 27 mi (44 km) east-northeast from the Pacific coast. However, the distance from the valley to the coast is approximately 50 mi (81 km) if measured along the circuitous stream channels that drain Knights Valley via the Russian River. Clearly, Knights Valley is much farther upstream from the Pacific Ocean than the other two examples. The Knights Valley Landslide Complex is located on the southern flank of Knights Valley as mapped by McLaughlin et al. (2004) during regional geologic mapping. We used interpretation of aerial imagery and field reconnaissance for our landslide mapping of the area (Figure 8). The landslide complex is approximately 0.4 mi (0.6 km) long and 1 mi (1.6 km) wide with multiple landslides (situated side by side) that moved northward into Knights Valley. Several of these landslides display a pattern of deep-seated failure high on the slope followed by secondary deep-seated failure of the lower to middle portion of 236

the older landslide mass. This pattern produced a stepped topography (Figure 9). This study focused on the westernmost landslide within the complex. The mapping for this study (Figure 8) and cross-section C-C0 (Figure 9) showed that the upper landslide (labeled Landslide C) is approximately 1,700 ft (520 m) long and 1,310 ft (400 m) wide with a prominent main scarp and low-relief surface at the head of the landslide. The bedrock geology consists of tuff of the Pliocene Sonoma Volcanics overlying Pliocene fluvial and lacustrine strata (pebbly sandstone, siltstone, and claystone). Landslide movement apparently occurred in block-slide mode with the basal rupture surface following the weak claystone and siltstone beds. The basal shear zone gouge consists of weak, high-plasticity clay that is bounded by polished surfaces. In the downslope reaches of Landslide C, the basal shear zone overlies a deposit of dark brown silty clay with matrix-supported angular clasts of tuff, which we interpret as colluvium or earth-flow deposits that occupied the lower slope and were overrun by Landslide C. It appears that the toe of Landslide C overran these surficial deposits prior to deposition of the alluvial sediment that currently forms the floor of Knights Valley. Alluvium now fills the valley to a thickness of over 100 ft (30 m), forming a natural buttress to Landslide C. McLaughlin et al. (2004) mapped the alluvium in Knights Valley as late Pleistocene to Holocene in age. Based upon that age range, we expect that Landslide C moved into Knights Valley during late Pleistocene time.

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Tsv

Qya

Alluvium (Late Pleistocene to Holocene)

Tsv

Sonoma Volcanics (Pliocene)

Tsv

Landslide with headscarp

Figure 8. Map of the Knights Valley Landslide Complex showing the location of cross-section C-C 0 .

During the latest portion of fluvial aggradation, a lower landslide (labeled Landslide D) failed off of Landslide C and into the valley. This placed Landslide D over the alluvial sediment that buttresses Landslide C. The final stages of aggradation deposited a roughly 2-m-thick buttress of alluvial sediment at the toe of Landslide D. Multiple core borings were drilled through Landslide D and into the underlying alluvial sediments. Radiocarbon dating of alluvium immediately beneath Landslide D recovered from a core boring (Boring B-7 in Figure 9) indicated an age of 18,890 6 50 calibrated year B.P. (Beta Analytic, 2012). SLOPE STABILITY ANALYSES Buttressing is a common engineering measure used to stabilize landslides. Therefore, a buttress of alluvium should also increase landslide stability. To confirm the stabilizing effect of these natural buttresses, we performed a simple

limit equilibrium slope stability analysis on one example, the McCracken Hill Landslide. Slope Stability Analysis Methods We evaluated the stability of the example using a commercially available slope stability analysis computer program that utilizes the method of Morgenstern and Price (1965). The program divides the sliding mass into slices and calculates stabilizing forces and driving forces. The input parameters needed for the limit equilibrium slope stability analysis include the ground surface topography, subsurface geology, groundwater potentiometric surface, and material properties (shear strength and unit weight) of the earth materials. Cross-section A-A0 (Figure 3) provides the topographic profile and subsurface geology for the analysis. We specified fixed nodes along the basal rupture surface where borehole data constrain the rupture surface depth in the cross section. The program then searched for the most critical rupture surface that fit the

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C’

300

Tsv

ELEVATIONS IN METERS

? ?

B-9

B-6 (proj. 19m NE)

200

(proj. 84m NE)

B-10 (proj. 27m SW)

B-11

(proj. 170m NE)

B-5

B-7

LANDSLIDE C

(proj. 30m NE)

LANDSLIDE D 100

? ?

Qal ?

N26W

Qsd

Qal

Alluvium (Holocene and late Pleistocene undifferentiated)

Qsd

Slope Debris

Tsv

Sonoma Volcanics (Pliocene)

Fluvial and lacustrine strata of Humbug Creek (Pliocene)

Potentiometric surface

Old landslides (late Pleistocene)

Figure 9. Cross-section C-C 0 through the Knights Valley Landslide Complex. The vertical lines indicate the locations of exploratory core borings. The dashed blue line represents the approximate elevation of the potentiometric surface.

constraints of the cross section. We optimized these searches by allowing the program to vary unfixed nodes within a depth range of 10 ft (3 m) along the basal rupture surface until it identified the most critical basal rupture surface. For this analysis, we utilized material shear strengths and unit weights determined by geotechnical laboratory testing, and Table 1 summarizes these values. The shear strength of the landslide basal rupture surface was evaluated using torsional ring shear testing. We also performed back-calculation analysis to confirm the basal rupture surface shear strength. This analysis assumed a factor of safety (FS) of 1.0 (i.e., the ratio of resisting forces over driving forces) without the buttress of alluvium. For the purpose of back-calculation analysis, we estimated that the potentiometric surface at the time of failure Table 1. Summary of material properties for the McCracken Hill Landslide.

Material Bedrock/landslide debris Basal rupture surface Alluvium Artiﬁcial ﬁll

Wet Unit Weight [pcf (19 kN/m3)]

Effective Friction Angle (°)

Effective Cohesion [psf (10 kPa)]

120 120 120 120

45* 8 35 30

200 0 0 200

*Anisotropic strength function: friction angle along bedding planes is 35°, and friction angle across bedding planes is 45°.

238

was similar to the high-groundwater condition shown in Figure 3. The resulting analysis indicated a basal rupture shear strength that closely matched that determined by laboratory testing. To calculate the FS of the landslide under both existing conditions and prior to deposition of the buttress, we considered a range of possible groundwater conditions, as shown in Figure 3. We considered a low-groundwater condition with a potentiometric surface below the basal rupture surface of the landslide. We also considered a high-groundwater condition with a potentiometric surface at the likely upper limit. Last, we ran the analysis with an intermediate potentiometric surface approximately halfway between the high- and low-groundwater scenarios. Results of Slope Stability Analysis Table 2 summarizes the results of our limit equilibrium slope stability analysis. Depending on groundwater level, Table 2. Results of slope stability analyses. Groundwater Condition

FS without Buttress

FS with Buttress

High groundwater Intermediate groundwater Low groundwater

1.0 1.2 1.5

2.3 3.0 3.3

FS ¼ factor of safety.

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Alluvium-Buttressed Landslide Model

the analysis indicates that FS increased by 120 percent to 150 percent with the deposition of the buttress of alluvium over the toe of the landslide. These results confirm that a buttress of alluvium increases stability. GEOLOGIC MODEL We propose a model for the natural buttressing of large, deep-seated landslides by late Quaternary sediments. When landslides move into incised stream channels that subsequently aggrade, deposition of alluvial sediment over the toes of the landslides forms a natural buttress. The primary factor controlling alluvial incision and aggradation in most geologic settings is base level. As base level falls, streams incise due to increased channel steepness and stream power. As base level rises, those formerly incised stream channels aggrade and deposit sediment in response to decreased stream gradient and reduced stream power. A rise of base level that triggers substantial alluvial aggradation can result in alluvial buttressing of landslides. Potential causes for rising base level range from global sea-level rise to local events such as downstream tectonic uplift or damming of streams (Leopold and Bull, 1979). Perhaps the most widely recognized type of base-level rise is sea-level rise. Changes in sea level are typically described in terms of relative sea level (RSL), which is a measurement of prehistoric sea level relative to current mean sea level at a given location. At most locations, RSL changes primarily reflect changes in the volume of ocean water due to growth or melting of glacial ice, known as eustatic sea-level changes. During the Quaternary Period, glacial advance and retreat caused multiple transgressive and regressive cycles (Muhs et al., 2003) over roughly 100,000 year intervals. Blum and Tornquist (2000) found that the effects of sea-level change on late Quaternary fluvial systems extend roughly 25 to 250 mi (40 to 400 km) upstream, depending upon the overall gradient of the specific stream system. Stout (1969, 1977) and Leighton (1976) attributed alluvial buttressing to aggradation related to late Quaternary sea-level rise; we refer to this concept of landslide buttressing as the eustatic model. Since that concept was initially proposed, multiple studies have refined the timing of Quaternary RSL changes, and we have utilized that improved geochronology to update the eustatic model for alluvium-buttressed landslides. The global RSL curve for the past 200,000 years (Figure 10) indicates that sea level reached a highstand of up to þ23 ft (þ7 m) at 126 to 116 ka (Kopp et al., 2009, 2013; Dutton and Lambeck, 2012; and Kemp et al., 2015) during the Last Interglacial Period (LIG). Following this highstand, RSL gradually declined (Waelbrock et al., 2002; Grant et al., 2014). Beginning at 31 ka, RSL dropped rapidly from 270 ft ( 82 m) at 31 ka to 390 ft ( 119 m)

Figure 10. Relative sea level (RSL) from 200 ka to present. Based on Quaternary sea-level curve from Grant et al. (2014), modiﬁed with data from Kopp et al. (2009, 2013), Dutton and Lambeck (2012), and Lambeck, et al. (2014). Abbreviations: LGM ¼ Last Glacial Maximum, LIG ¼ Last Interglacial Period, PGM ¼ Penultimate Glacial Maximum.

at 29 ka (Lambeck et al., 2014; Yokohama et al., 2018). From 29 ka to 21 ka, RSL declined more gradually during the Last Glacial Maximum (LGM; the maximum advance of continental glaciers during latest Pleistocene time). Sea level reached a minimum lowstand of 440 ft ( 134 m) at 21 ka. From 21 ka to 7 ka, RSL rose from 440 ft ( 134 m) to 13 ft ( 4 m) (Lambeck et al., 2014) as continental glaciers retreated. From 7 ka to 2 ka, RSL rose from 13 ft ( 4 m) to approximately the current sea level. During late Quaternary time, global base-level changes caused significant changes to coastal landscapes. During the rapid decline in RSL approaching the LGM, stream gradients steepened, and valleys incised. During the LGM lowstand, the Pacific shore shifted westward, eventually reaching more than 28 mi (45 km) beyond the mouth of the modern San Francisco Bay (Cochrane et al., 2015). Following the LGM, the rapid rise in RSL led to filling of incised stream valleys with late Quaternary sediments as alluvial systems responded to the rise in base level (King et al., 2019). Sediment-filled late Quaternary coastal valleys have been described extensively at locations in North America, Europe, Asia, Australia, and elsewhere (Amorosi and Colalongo, 2005; Fielding et al., 2005; Simms et al., 2010; Tanabe et al., 2015; and Breda et al., 2016). Due to the global nature of late Quaternary sea-level rise, sediment-filled paleo-valleys are common in many coastal regions worldwide (Warne and Stanley, 1995; Stanley and Warne, 1997). It is also important to recognize that large landslides could have moved into incised valleys during a previous sea-level lowstand. The lowstand that preceded LGM coincided with the Penultimate Glacial Maximum (PGM) (Figure 10), which occurred at approximately 160 ka to 135 ka (Grant et al., 2014; Rohling et al., 2017). However, it is likely that the scarps and lateral margins of a stable, alluvium-buttressed landslide of roughly PGM age would be more degraded than one that underwent movement during the LGM. In addition, the RSL lowstand elevation during the PGM was approximately 328 ft ( 100 m) (Grant

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et al., 2014), which is considerably higher than the lowstand during the LGM. Thus, deeper scouring during the LGM lowstand may have removed much of the sediment deposited between the PGM and LIG from alluvial valleys. LIG highstand deposits may remain locally as alluvial terraces preserved on the ﬂanks of alluvial valleys, because the LIG highstand was higher than current mean sea level. Though we have not found examples of pre-LIG alluviumbuttressed landslides, they may exist, and large landslide complexes that were active during the LGM may have a history of prior activity that could extend to the PGM. Another factor that may have contributed to slope instability during late Pleistocene time is the wet climate associated with glaciation. Studies of Pleistocene pluvial lakes in western North America have indicated a change to a wetter climate associated with glacial advance beginning at 70 ka and continuing through the LGM (Ali et al., 2022). High precipitation during late Pleistocene time would tend to promote landslide activity. In fact, many large, deep-seated landslide complexes have their origins in the Pleistocene Epoch. In contrast to global base-level changes related to eustatic sea-level changes, local base-level changes are limited to individual watersheds, streams, or reaches of streams. For instance, where streams are dammed, either due to natural or anthropogenic causes, aggradation commonly occurs upstream of the dam (Leopold and Bull, 1979; Hewitt, 1998; and Fortugno et al., 2017). Similarly, downstream tectonic uplift can result in upstream aggradation due to local base-level rise. Burbank and Anderson (2001) described an example of local base-level rise due to active tectonic uplift of the Attock Range in northern Pakistan. The Kabul River aggrades upstream of the range where sediment is trapped due to rising base level. Downstream of the aggrading reach, the river crosses the uplifting range and incises into the bedrock uplift. Because local base level may control an individual reach of a stream system, one reach of a given stream may aggrade while another incises. Thus, local base-level rise can initiate local alluvial aggradation that is independent of global RSL. DISCUSSION OF EXAMPLES AND APPLICATION OF GEOLOGIC MODELS McCracken Hill Landslide Given the short distance (approximately 0.7 to 1.1 mi [1.2 to 1.8 km]) from the McCracken Hill Landslide to the shoreline and the direct connection of San Juan Creek to the Paciﬁc coast, it is apparent that the rise of sea level from the LGM to late Holocene highstand controlled the aggradation of San Juan Creek. Based upon data from deep exploratory borings, it appears that the McCracken Hill Landslide moved into the incised valley during or shortly after the LGM. Stout (1969, 1977) 240

described radiocarbon dating of wood collected from a boring at the toe of another alluvium-buttressed landslide located along San Juan Creek; the reported age was 17,180 6 750 years B.P. This age fits with alluvial aggradation and deposition of alluvial strata during the early stages of post-LGM sea-level rise. Given the close connection between San Juan Creek and the Pacific coast, along with the timing of emplacement of other alluviumbuttressed landslides within this watershed, the McCracken Hill Landslide appears to fit the eustatic model for alluvium-buttressed landslides. Potrero Canyon Landslide Complex At the Potrero Canyon Landslide Complex, Landslide A likely moved into the incised channel of the Carmel River at or near sea-level lowstand, and significant alluvial aggradation followed during post-LGM sea-level rise. After the deposition of alluvium at the toe of Landslide A, a younger landslide (Landslide B) moved over that alluvium. Continued alluvial deposition buried the toe of Landslide B and further buttressed Landslide A. Based upon the depth of burial of the landslide within Carmel Valley alluvium, it appears likely that Landslide B moved into the valley during early Holocene time. However, the sediment recovered from beneath Landslide B was not suitable for dating using radiocarbon methods, so the exact timing remains unknown. Based upon the results of subsurface investigation of the Potrero Canyon Landslide Complex, the interfingering of Landslide B with the alluvium of Carmel Valley, and the close connection of Carmel Valley and the Carmel River with the Pacific coast, this example also appears to fit the eustatic model for alluvium-buttressed landslides. Knights Valley Landslide Complex Based upon available radiocarbon dating, it appears that Landslide D moved into Knights Valley shortly after the LGM, approximately 19 ka. Based on time versus RSL curves by Lambeck et al. (2014), it appears that RSL at 19 ka was approximately 377 ft ( 115 m). These data imply that deposition of most of the alluvium that fills Knights Valley and forms a buttress to Landslide C occurred during (or prior to) the LGM lowstand. This does not appear to fit the eustatic model for alluvium-buttressed landslides. Alluvial fans with their source in the Mayacamas Mountains north of Knights Valley filled the valley with alluvial sediment (Figure 11). Though gradients clearly diminish where these streams enter the valley, leading to loss of carrying capacity and deposition of sediment, the origin of the topographic valley and the accommodation space that allowed for accumulation of Quaternary sediments is less certain. Three streams drain Knights Valley: Bidwell Creek, Redwood Creek, and an unnamed creek

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Alluvium-Buttressed Landslide Model

2 km 1 mi

m ca aa M

ive

n R

ex Al

t ul Fa

r-R

Kni

oo w

alle

aul Bidwell Creek t

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Figure 11. Map showing Knights Valley, streams that drain Knights Valley, and nearby Quaternary faults. The mapping of Quaternary faults was adapted from the Quaternary Fault and Fold Database of the United States (U.S. Geological Survey, 2014).

that drains the northwest end of the valley (Figure 11). Downstream from Knights Valley, these streams ﬂow through a set of hills, known as the Bald Hills, through narrow V-shaped valleys incised into bedrock of the Jurassic to Cretaceous Franciscan Complex (Blake et al., 2002; McLaughlin et al., 2004; and Graymer et al., 2007). These streams emerge into broader alluvial valleys after they cross the Maacama Fault; the same fault also offsets the stream channels in a right-lateral sense. Paleo-seismic studies of the Maacama fault showed evidence of Holocene fault-rupturing events north of the study area (Larsen et al., 2005; Prentice et al., 2014). We hypothesize that the uplift of the Bald Hills and incision of stream channels may have been related to Quaternary activity of the Maacama Fault on the west side of the hills and the Knights Valley Reverse Fault on the east side, causing accumulation of alluvium in Knights Valley. Mapping by Oskin et al. (2016) indicated that reverse faulting at the northern end of the active West Napa Fault connects to the Knights Valley Fault. McLaughlin et al. (2019) described thrust faulting actively uplifting Franciscan Complex bedrock east of the Maacama Fault in the Ukiah-Hopland area north of Knights Valley. Similar thrust/reverse faulting east of the Maacama Fault may have caused uplift of the Bald Hills, leading to reach-scale ﬂuvial incision in the Bald Hills and aggradation upstream in Knights Valley. In contrast to the other two examples, deposition of alluvial sediment in Knights Valley does not appear to have been a response to post-LGM base-level rise. Two lines of evidence lead to that conclusion. First, the streams that drain Knights Valley are incised into Franciscan

Complex bedrock downstream from the valley. It is unlikely that thick alluvium could accumulate in Knights Valley and not directly downstream, if aggradation were controlled by sea-level rise. Second, limited dating of Knights Valley alluvial sediment indicates that deposition of the buttress at the toe of Landslide C occurred when sea level was relatively low, and upstream reaches, such as Knights Valley, should have remained incised. Therefore, alluvial aggradation in Knights Valley was likely due to local base-level rise related to late Quaternary tectonic uplift of the Bald Hills. CONCLUSIONS This study described examples of alluvium-buttressed landslides as well as the potential origins of the alluvial buttresses. In all examples, deposition of alluvium over the toes of large, deep-seated landslides resulted from alluvial aggradation related to base-level rise. In the case of the eustatic model, a rapid drop in base level approaching the LGM caused late Pleistocene alluvial downcutting. Locally, large, deep-seated landslides moved into those incised stream valleys, approaching, during, or shortly after sea-level lowstand. The wet climate during late Pleistocene time likely increased the potential for such large-scale slope failures. During post-LGM base-level rise, alluvial aggradation resulted in deposition of thick alluvial strata over the toes of these landslides, forming natural buttresses that increased the stability of the landslides. The McCracken Hill Landslide and Potrero Canyon Landslide Complex are examples of alluvium-buttressed landslides in coastal valleys where post-LGM sea-level rise

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controlled alluvial aggradation and buttressing of Pleistocene landslides. The Knights Valley Landslide Complex is an example of an alluvium-buttressed landslide in a location where local base-level rise caused local aggradation. Specifically, accumulation of alluvium appears to have been related to downstream tectonic uplift. The Knights Valley example is an exception to the more broadly applicable eustatic model for alluvium-buttressed landslides. Limit equilibrium slope stability analyses demonstrated that a buttress of alluvium has a stabilizing effect on deep-seated landslides. Though the slope stability analysis completed for this study showed a significant increase in stability, detailed analysis of other alluvium-buttressed landslides may yield a different result. Thus, it is important to complete thorough geologic investigation, geotechnical laboratory testing, and slope stability analyses to evaluate the stability of alluvium-buttressed landslides. ACKNOWLEDGMENTS We thank Kevin Harmon, Taylor Honnette, Ryan Reynolds, and Jamie Smith for drafting the illustrations. We wish to thank Suzanne Hecker for her review of an earlier version of this manuscript. We also thank three anonymous reviewers and the journal editor for their thoughtful comments. REFERENCES ALI, G. A. H.; LIN, K.; HEMMING, S. R.; COX, S. E.; RUPRECHT, P.; ZIMMERMAN, S. R. H.; STINE, S.; AND WANG, X., 2022, Emergence of wet conditions in the Mono Basin of western USA coincident with inception of the Last Glaciation: Geological Society of America Bulletin, Vol. 134, pp. 2267–2279. AMOROSI, A. AND COLALONGO, M. L., 2005, The linkage between alluvial and coeval nearshore marine successions: Evidence from the late Quaternary record of the Po River Plain, Italy. In Blum, M. D.; Marriot, S. B.; and Leclair, S. F. (Editors), Fluvial Sedimentology VII: Special Publication 35, International Association of Sedimentologists, Oxford, U.K., pp. 257–276. Beta Analytic, 2012, Calibration of Radiocarbon Age: unpublished laboratory report, Beta Analytic, Miami, FL, dated February 2012. BLAKE, M. C.; GRAYMER, R. W.; AND STAMSKI, R. E., 2002, Geologic Map and Map Database of Western Sonoma, Northernmost Marin, and Southernmost Mendocino Counties, California: U.S. Geological Survey Miscellaneous Field Studies Map MF-2402, 1 sheet. BLUM, M. D. AND TORNQUIST, T. E., 2000, Fluvial response to climate and sea level change: A review and looking forward: Sedimentology, Vol. 47, pp. 2–48. BREDA, A.; AMOROSI, A.; ROSSI, V.; AND FUSCO, F., 2016, Late-glacial to Holocene depositional architecture of the Ombrone paleovalley system (southern Tuscany, Italy): Sea-level, climate and local control in valley-fill variability: Sedimentology, Vol. 63, pp. 1124–1148. BURBANK, D. W. AND ANDERSON, R. S., 2001, Tectonic Geomorphology: Blackwell Science, Malden MA, 274 p. CAMPION, K. M.; SPRAGUE, A. R.; AND SULLIVAN, M. D., 2005, Architecture and Lithofacies of the Capistrano Formation (Miocene–Pliocene), San Clemente, California: Book 100, Pacific Section, Society for Sedimentary Geology (SEPM), Fullerton, CA, 42 p.

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Assessing the Improvement of a Sparse Rain Gauge Network in a Landslide Hotspot in Kerala, India—A Multi-Criteria Approach C. L. VISHNU THOMAS OOMMEN* SNEHAMOY CHATTERJEE Department of Geological and Mining Engineering and Sciences, Michigan Technological University, Houghton, MI 49931

A. RAJANEESH K. S. SAJIN KUMAR Department of Geology, University of Kerala, Thiruvananthapuram, 695581 Kerala, India

Key Terms: Rain Gauge Optimization, Satellite Precipitation Estimates, Interpolation, Multi-Criteria Analysis, Conditional Merging, Cross-Validation ABSTRACT A statistically sufficient number of rainfall estimates is necessary to model precipitation-induced landslide hazards accurately. This is particularly important where existing gauges are scanty and widespread, such as in Kerala State of southern India, an area characterized by heavy monsoon rains and thus inherently prone to massive landslides. To identify potential new gauge sites, we evaluated general slope stability, landslide density, and land cover. We optimized locations for installing new rain gauges through a sequential process based on interpolation errors; this was validated by comparing the resulting statistics with a random selection. Based on these procedures, we installed eight new rain gauges. We also used a satellitegauge algorithm (conditional merging) to assess the effect of rain gauge network expansion for precipitation measurements. The Pearson correlation coefficient indicated statistically significant measurement improvement after the new rain gauge installation. We applied the leave-one-out cross-validation (LOOCV) test to improve rain gauge modeling. We divided the study area into rain gauge influence sections and calculated the root mean square error (RMSE) for each. We found that the optimized expanded rain gauge network locally produced a 20–25 percent reduction of RMSE compared with the original gauge distribution. *Corresponding author email: toommen@mtu.edu

INTRODUCTION Landslides occur on unstable slopes when a triggering event in the form of rainfall, snow melting, or seismic activity acts on a slope. Worldwide, average annual property loss of about three billion U.S. dollars and casualties of about 500 people are reported due to landslides (Froude and Petley, 2018). One strategy for reducing the effects of landslides is modeling their mechanisms and generating early warning alerts. In developing countries, such modeling is often limited by a lack of accurate data, which can generate false predictions, rendering the model ineffective (Dash and Gladwin, 2007; Sorensen and Sorensen, 2007; and Haynes et al., 2018). The primary source of uncertainty in modeling raintriggered landslides is the sparsity in rainfall data rendered by the lack of dense rain gauge networks. The World Meteorological Organization (WMO) standards (WMO, 2008) for rain gauge density recommend the minimum number of rain gauge stations as one in every 250 km2 for mountainous regions, every 575 km2 for interior plains and hilly/undulating regions, every 900 km2 for coastal regions, and every 10,000 km2 for the polar regions. A rainfall product with a sufﬁcient temporal resolution is necessary to model rain-triggered landslides (Hong et al., 2007). Satellite precipitation data could be considered as alternatives to rain gauges in sparse data regions owing to their better spatial representation of precipitation. However, they often lack the needed accuracy and require additional calibration, which in turn requires robust rain gauge data. Thus, it becomes necessary to improve rain gauge networks by adding new gauges in optimal locations. Expansion of a sparse rain gauge network requires the identiﬁcation of optimal locations for installing new rain gauges. Many rain gauge optimization techniques are found in literature, such as the use of principal component analysis

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Figure 1. (a) Location map of Idukki district, Kerala, India. (b) Landslide and rain gauge distribution in Idukki over the background of a Shuttle Radar Topography Mission (SRTM) digital elevation model.

(PCA) by Dai et al. (2017), the application of a greedy ranking algorithm on satellite rainfall data by Huang et al. (2020), the use of artiﬁcial neural networks by Adhikary et al. (2018), and genetic algorithm–based sequential optimization by Anctil et al. (2006), to name a few. However, most of these studies worked on ﬁnding the best combination of rain gauges from already dense networks (Feki et al., 2017; Yeh et al., 2017; Wu et al., 2020; Bertini et al., 2021; and Tekleyohannes et al., 2021). A common theme in rain gauge optimization algorithms is the utilization of kriging variances (Xu et al., 2015; Chebbi et al., 2017; Zandkarimi et al., 2018; and Salleh et al., 2019). However, kriging is heavily dependent on the availability of a robust variogram, which may not be possible in sparse data regions. In such situations, other geospatial interpolation techniques like inverse distance weighting (IDW) may have to be considered. Studies by Dirks et al. (1998), Ly et al. (2010), and Yang et al. (2015) compared different spatial interpolation techniques for rainfall estimation and found that IDW performed better or as good as other techniques like spline and ordinary kriging. Thus, in this study, we propose a rain gauge optimization procedure for sparse data regions using IDW. Since the available number of rain gauges was low to start with, we developed a candidate set of potential rain gauge locations using a multi-criteria approach. As the objective of 246

rain gauge network expansion was improved landslide modeling, the candidate rain gauge locations were chosen such that they were situated in zones of high landslide activity. Such zones were identiﬁed by overlaying landslide density, slope stability, and land-cover layers. We also present a case study of a sparse data region where the proposed method was utilized to install new rain gauges. The case study also assessed the improvement in landslide modeling brought about by rain gauge network expansion. STUDY AREA DESCRIPTION In this study, we considered the mountainous district of Idukki, in the state of Kerala, India, as an ideal scenario for testing our rain gauge optimization process (Figure 1a). Idukki receives annual average rainfall of about 4000 mm and is subject to frequent landslide activity every year (Abraham et al., 2019, 2021; Hao et al., 2020; Jacinth Jennifer and Saravanan, 2020, 2021; Sajinkumar et al., 2020; Jones et al., 2021; Lalitha et al., 2021; and Sajinkumar and Oommen, 2021). Idukki receives most of the annual rainfall during the southwest monsoons from June to September. In an anomalously high monsoon rainfall in 2018 (Vishnu et al., 2019, 2020; Sajinkumar et al., 2022) that brought in 40 percent more rainfall than what was normally expected, the district of Idukki witnessed

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2,223 landslides (Hao et al., 2020, 2022). With a population of 1.11 million (»254 inhabitants/km2), the higher elevations in Idukki are under heavy anthropogenic stress (Jones et al., 2021). This population pressure is continuously on the rise, contributing to increased landslide risk and necessitating the development of a landslide early warning system. However, this requires the development of a robust rain gauge network with placement in strategic locations. Prior to our study, Idukki’s rain gauge network consisted of five rain gauges over an area of 4366 km2 (Figure 1b), while the WMO standards would require at least 17. DATA An extensive landslide database for 2018, compiled by Hao et al. (2020), comprising 2,223 landslides was used in this study. A slope susceptibility map of Idukki was prepared using the Geographic Information System Tool for Infinite Slope Stability Analysis (GIS-TISSA) (Escobar-Wolf et al., 2021) and the Advanced Land Observing Satellite (ALOS) Phased Array L-Band Synthetic Aperture Radar (PALSAR) elevation data. A landcover map of the area was prepared from Landsat 8 Operational Land Imager (OLI) data. The five rain gauges in existence prior to this study are maintained by the India Meteorological Department (IMD), and the data were procured directly from them. Satellite precipitation, in the form of Global Precipitation Measurement (GPM) Integrated Multi-Satellite Retrievals for GPM– Final (IMERG-F), was utilized as the source of rainfall measurement. GPM, the successor to the Tropical Rainfall Measuring Mission (TRMM), is a satellite constellation using active radar, passive microwave, and infrared imaging to acquire global precipitation measurements for over 90 percent of Earth’s surface. GPM provides four levels of data products ranging from intercalibrated and geolocated brightness temperatures in level 1 to gridded research products in level 4 (Hou et al., 2014). The gridded IMERG products have two near-real-time versions: a 4-hour latency early product (IMERG-E) and a 14-hour latency late (IMERG-L) product, and one post-real-time final product (IMERG-F) at a latency of about 45 days (Sun et al., 2018). The products are available at hourly, daily, and monthly precipitation rates at a spatial resolution of 0.1° or roughly 100 km2. IMERG-F uses a monthly gauge correction from the Climate Prediction Center (CPC) and is generally observed to be the most accurate of the three versions (Sungmin and Kirstetter, 2018; Zhao et al., 2018). GPM IMERG-F monthly data were utilized for the rain gauge optimization process proposed in this study because accuracy was more important than latency. The average monsoon rainfall

from 2015 to 2019 was input as the precipitation data to perform the optimization process. METHODOLOGY A Multi-Criteria Approach to Optimize Rain Gauge Network Expansion For the optimization process, it is necessary to have a set of candidate rain gauge locations from which to choose. The following process was used to create a set of such locations. The study area was divided into grids that coincided with the GPM IMERG-F pixels, and the centers of these grids were considered as candidate rain gauge locations. Each grid had an area of 0.1° 3 0.1° or approximately 100 km2, and there were 71 such pixels in the study area. To obtain a wider pool of potential locations from which to choose, each grid was improved to four times the resolution, thus increasing the number of possible locations to 1,136 and improving the resolution of each grid to 0.025° or approximately 6.25 km2. Since precipitation values for each of these grids were required for the analysis, the GPM IMERG-F 5-year monsoon averages were subjected to four times resolution improvement, using the well-known geostatistical method of area-to-point kriging (ATPK) (Wang et al., 2015; Chen et al., 2018; and Xu et al., 2020). Once candidate locations for new rain gauges were identified in the above manner, landslide hotspots were identified using a multi-criteria approach that utilized landslide density, slope stability, and land cover as the constituent layers. The landslide density layer, with a grid size of 6.25 km2, was prepared from the 2018 landslide database. The slope stability was computed with GIS TISSA, using the ALOS PALSAR digital elevation model. This was later aggregated into 6.25 km2 grids with each grid having a value equal to the mean of the contributing pixels. A set of potential rain gauge locations was developed through the following manner: (1) All the grid points corresponding to a landslide density of more than one were chosen as candidate rain gauge locations; (2) all grid points outside this threshold but having a slope stability factor of safety (FOS) of 1.25 or less were added to the previous set of candidate rain gauge locations; and (3) any grid points in the set of candidate locations that fell within areas of forest cover were removed. This omission step was required to ensure the optimal allocation of resources to areas of human settlement. This two-step addition and one-step omission process resulted in the final set of candidate rain gauge locations. Once the set of candidate rain gauge locations was finalized, a sequential process was applied to select 12 optimum locations. The following steps explain the sequential process:

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Figure 2. Workﬂow of the methodology for the sequential optimization process.

(1) The ﬁve rain gauge locations existing at the beginning of the study were used for the ﬁrst iteration of the sequential process. The pixel values of the precipitation product corresponding to the rain gauge locations were extracted and interpolated using IDW over the Idukki district boundary. The resultant raster had a resolution of 0.025° 3 0.025° to correspond with the resolution of the landslide density and slope stability layers. (2) The difference between the precipitation product (GPM IMERG-F, monsoon averages) and the IDW 248

interpolated product was computed, squared, and considered the error of estimation due to interpolation. If the precipitation product is termed as P and the interpolated product as I, the estimation error (E) can be computed as: 2

E ¼ ðP I Þ :

(1)

(3) IDW assigns weights according to the inverse of distance such that closer locations have more weight while more distant locations have lesser weight.

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Thus, areas “influenced” by a particular sample point should have an interpolated value closer to that of that sample point. Ideally, if each grid of the GPM IMERG-F were a sample point, then the error of interpolation (E) should be zero. Since the number of samples is less than the total number of grids, interpolated values will be different from the original values, with larger differences in locations farther from the available sample points. Thus, the grid with the largest estimation error (E) should correspond to the location farthest from the influence of the sample points (existing rain gauges in this case). So, the candidate rain gauge location corresponding to the maximum error of estimation was identified as an optimum rain gauge location and was added to the existing rain gauge network. (4) In iteration two, the expanded rain gauge network (now with six rain gauges) was used to perform the interpolation. As in step two, the error of estimation was computed, and a new optimum rain gauge location was identified as in step three. Steps one to three were repeated in further iterations until 12 new rain gauge locations were identified, thereby expanding the existing rain gauge network to 17 rain gauges. (5) Once the existing rain gauge network was expanded to the minimum required standard, IDW interpolation of the expanded network was carried out, and errors of estimation were computed as in step two. To test the efficiency of the sequential optimization algorithm, it was compared with a random selection process. The latter was carried out by selecting 12 rain gauges at random from the set of potential rain gauge locations. These locations were added to the existing rain gauge network. The IDW interpolation of these 17 points was carried out, and errors of estimation were computed as in step 2. Then, the errors of estimation of the sequential process and this random process were compared. Ten such random estimations were computed and compared with the sequential process to make sure that the sequential optimization of the rain gauge locations was better than random selection. A workflow of the methodology is given in Figure 2. Validation After identifying optimal points in the above manner, rain gauges were installed in the field. The installation was carried out with assistance from the state disaster management authority. The rain gauges were installed on government-owned properties, and hence some changes in installation locations were necessary. Only eight rain gauges could be installed due to budget constraints. All the rain gauges were automatic weather stations, capable of transmitting data every 15 minutes. Customization of

data transmission to every 5 minutes during heavy rainfall is also possible. A two-step validation process was performed utilizing the data from the newly installed rain gauges, to assess the improvement in the rain gauge network post-expansion. In the first step, the improvement in the precipitation estimation capability was assessed, and in the second step, the improvement in the landslide modeling capability was assessed. A satellite calibration procedure, called conditional merging (Pegram and Clothier, 2001; Sinclair and Pegram, 2005; and Vishnu et al., 2021), was utilized to assess the precipitation estimation capability. Satellite precipitation in the form of GPM IMERG data was calibrated using the initial five rain gauges and the expanded 13 rain gauges separately, and the root mean square error (RMSE) from cross-validation was compared. The conditional merging process was carried out in the following steps: (i) The rain gauge observations were interpolated to create a continuous gridded rainfall product that provided the best linear unbiased rainfall estimate (Irg). (ii) The satellite pixel values corresponding to the rain gauge locations were interpolated to create a continuous gridded rainfall product (Srg). (iii) The continuous rainfall product thus obtained (Srg) was subtracted from the original satellite product (S). This difference (S Srg) gave a gridded error product. (iv) The error product obtained in step (iii) was added to the rainfall product obtained in step (i) (Irg). The result was a rainfall gridded product that followed the mean field of the rain gauge interpolation while preserving the spatial variability of the satellite product. The resulting conditionally merged product can be represented as: CM ¼ Irg þ S Srg ;

(2)

where CM is the conditionally merged rainfall at each grid, Irg is the interpolation product of the rain gauge observations, S is the satellite (gridded) product, and Srg is the interpolation product of satellite estimations in the location of rain gauges. Cumulative monthly precipitation for October 2021 was used for this assessment. This period was chosen because this was one of the earliest months with steady data transmission from the newly installed rain gauges. Conditional merging was initially performed with precipitation measurements from the initial ﬁve rain gauges and later using rainfall observations from all 13 rain gauges of the expanded network. These two products were compared with the actual rain gauge observations to check for improvement in rainfall estimation. To assess the improvement in the landslide modeling capability of the expanded rain gauge network, an application of leave-one-out cross-validation (LOOCV) was

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procedure, and its spatial resolution was improved to 0.025° (approximately 6.25 km2/pixel) from 0.1°. The 2018 landslide database from Hao et al. (2020), being the most elaborate list of landslides in Idukki, was used to identify all the pixels with at least one landslide activity. The GIS TISSA–based slope stability model was considered for every 6.25 km2 grid, and all grids with at least 10 percent of the area being classiﬁed as unstable (FOS ,1) were used for the validation. Next, LOOCV was performed on the GPM IMERG conditionally merged products with ﬁve and 13 rain gauges separately. The following steps explain the crossvalidation process adopted:

Figure 3. Thiessen polygons were used to divide the study area into areas of inﬂuence of the rain gauges. The names of the stations are as follows: 1—Manjumala; 2—Arakkulam; 3—Peermade; 4— Myladumpara; 5—Thodupuzha; 6—Alakode; 7—Idukki; 8— Pallivasal; 9—Munnar; 10—Pazhampallichal; 11—Erattayar; 12— Elappara; 13—Santhanpara.

used. First, the entire study area was divided into grids of 6.25 km2. The GPM cumulative precipitation for October 2021 was subjected to resolution improvement using the same ATPK method discussed in the optimization

(i) One rain gauge from the network was removed, and conditional merging was performed on the GPM precipitation product using the remaining rain gauges. There were five such iterations for the initial network and 13 for the expanded network. (ii) Thiessen polygons were created to represent the rain gauge influence zones. For the first product, there were five such polygons, and for the expanded network, there were 13 such polygons. During each iteration, the value of the conditionally merged pixels falling within the Thiessen polygon of the left-out rain gauge was stored, and at the end of all iterations, a separate precipitation product was created with those pixel values. (iii) Both the LOOCV products obtained at the end of step (ii) were compared with actual rain gauge observations. For this purpose, both products were divided into 13 Thiessen polygons, representative of the expanded network (Figure 3). Each pixel within a particular polygon was differenced from the rain gauge observation corresponding to that

Figure 4. Resolution improvement carried out on the coarse-resolution GPM IMERG-F monsoon average.

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Figure 5. (a) Landslide density of Idukki district prepared from 2018 landslide database. (b) Factor of safety for slope stability prepared using GIS-TISSA. (c) Land-cover map of Idukki district.

polygon, and this difference was squared. The sum of these squared errors was computed and averaged, and their square root was computed as the RMSE of estimation.

To validate the change in landslide modeling brought about by the improvement in the rain gauge network, the RMSE of estimation of only those pixels that reported at least one landslide or had at least 10 percent of the area classiﬁed as unstable was considered. RESULTS AND DISCUSSION

Figure 6. The set of candidate rain gauge locations selected using the multi-criteria approach.

The coarse- and fine-resolution GPM IMERG-F monsoon average precipitation products are shown in Figure 4. Though the primary purpose of this resolution improvement was to increase the number of candidate rain gauge locations, the resolution-improved product also improved the gridded precipitation by capturing the finer changes in rainfall distribution. Resolution improvement increased the number of rain gauge grid points from 71 to 1,136. The landslide density map prepared is shown in Figure 5a, and the slope stability map is shown in Figure 5b. It was observed that many regions outside of recorded landslide occurrences had relatively low slope stability. Such slopes have potential for future landslide occurrences, and they were included in the set of candidate rain gauge locations. Figure 5c shows the land-cover map of Idukki district, which was used to filter out candidate locations within forested areas. The forested areas are predominantly spread in the northern and southern ends of the Idukki district, and less landslide activity was observed in these regions. The multi-criteria approach to select a set of candidate rain gauge locations resulted in 107 locations (Figure 6). Twelve rain gauge locations were selected from this set

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Figure 7. Proposed network expansion with 17 rain gauges including the ﬁve existing rain gauges and 12 newly identiﬁed locations.

using the sequential optimization technique. Thus, the existing rain gauge network was expanded from ﬁve to 17 proposed locations (Figure 7). The estimation error was computed in each step and continuously reduced through the iterations. With the initial network, the RMSE was 62.11 mm. At the end of the iterations, the RMSE was 38.48 mm. A 38 percent reduction in RMSE was observed. The sequential optimization process was compared with 10 random selections of 12 rain gauges each by comparing the RMSE of respective estimation errors (Table 1). The sequential optimization process recorded better RMSE of estimation than all 10 random Table 1. Comparison of the estimation errors of random selection with sequential optimization. Optimization

RMSE (mm)

Random 1 Random 2 Random 3 Random 4 Random 5 Random 6 Random 7 Random 8 Random 9 Random10 Sequential

43.33 48.84 46.71 43.70 43.41 45.88 45.03 46.27 48.04 44.19 38.48

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Figure 8. Improved rain gauge network after installing eight new rain gauges.

selections. While the RMSE of the sequential process was 38.48 mm, the average RMSE of the 10 random selections was 45.54 mm. The existing rain gauge network in Idukki was expanded utilizing the approach discussed in the Methodology section. Figure 8 shows the expanded rain gauge network with the locations of the five original stations and eight newly installed stations marked separately. Two calibrated GPM IMERG precipitation products, conditionally merged using five rain gauges and 13 rain gauges, respectively, were compared to assess the improvement in precipitation estimation due to expansion of the rain gauge network. It was found that the calibrated GPM IMERG had a Pearson’s correlation coefficient of 0.81 with the rain gauge observations when conditionally merged with five rain gauges, which improved to 0.99 when conditionally merged with 13 rain gauges. While both the calibrated products showed a significant improvement from the uncalibrated GPM IMERG, which reported a Pearson’s correlation coefficient of 0.53 with the rain gauge observations, it was observed that expanding the rain gauge network increased the correlation. Table 2 shows the rainfall estimated by each of the two conditionally merged products, the uncalibrated GPM estimation, and the rain gauge observations in each of the 13 rain gauges.

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Rain Guage Network Optimization, Kerala, India Table 2. Comparison of performance of the GPM cumulative precipitation for October 2021 before and after conditional merging with ﬁve and 13 rain gauges, respectively.

Stations Alakode Arakkalam Elappara Erattayar Manjumala Pazhampallichal Pallivasal Santhanpara Peermade Thodupuzha Munnar Idukki Myladumpara

Uncalibrated GPM (mm)

Rain Gauge (mm)

GPM Conditionally Merged with Five Rain Gauges (mm)

GPM Conditionally Merged with 13 Rain Gauges (mm)

648.11 526.80 350.18 407.81 458.21 429.12 360.37 328.56 530.85 763.51 328.56 482.01 339.82

808.10 960.70 759.70 402.60 570.30 701.20 578.30 344.40 1,022.9 798.60 406.20 687.90 501.10

853.31 821.46 856.90 630.72 898.73 667.83 507.05 468.42 1,021.62 799.36 408.87 687.26 501.29

812.20 960.39 758.47 416.72 586.80 700.96 576.97 351.82 1,014.43 828.64 414.94 719.24 500.82

To assess the effect of improving the rain gauge network on landslide modeling, the LOOCV product was compared with the landslide occurrence and slope stability layers in the study area. Figure 9 shows the landslide occurrence and slope stability grids considered for validation. In landslide occurrence and unstable slopes, there was a significant reduction of errors in most stations when conditional merging was performed with the expanded network of 13 rain gauges than when performed with five rain gauges. In the landslide occurrence scenario, the average RMSE decreased from 201.10 mm to 145.32 mm upon expanding the rain gauge network. The coefficient value of cross-validation also significantly improved from 0.24 to 0.71. In the unstable slope scenario, the average RMSE decreased from 184.23 mm to 143.64 mm with the R value improving from 0.26 to 0.69. Tables 3 and 4 show the RMSE of estimation at each of the 13 rain gauges.

In both scenarios, the Manjumala station showed an increase in RMSE when the rain gauge network was expanded. In the unstable slope scenario, apart from Manjumala, three more stations showed increased RMSE upon network expansion. However, out of the three, Thodupuzha had only one unstable pixel; thus, this aberration could not be considered. In Pazhampallichal, the change in RMSE was negligible. In Alakkode, there was a 10 mm increase in RMSE. Incidentally, the actual locations of these rain gauges differed from the proposed locations due to practical construction considerations. Such anomalies point to the sensitive nature of the sequential algorithm and could be an issue that may be ﬁxed with the addition of more rain gauges in the future. Moreover, since only eight out of 12 proposed rain gauges could be installed, this does not represent a “full-ﬂedged” product, and hence these anomalies should not be considered as proof of model inaccuracy.

Figure 9. (a) Percentage of unstable slope and (b) the number of landslides per grid, computed to validate the improvement in landslide modeling brought about by improving the rain gauge network.

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Vishnu, Oommen, Chatterjee, Rajaneesh, and Sajin Kumar Table 3. RMSE of the cross-validation product developed for the initial network with ﬁve rain gauges and the expanded network with 13 rain gauges for pixels with at least one landslide occurrence. Landslides

Station Alakode Arakkalam Elappara Erattayar Manjumala Pazhampallichal Pallivasal Santhanpara Peermade Munnar Idukki Myladumpara Thodupuzha

RMSE of LOOCV Precipitation Product with 13 Rain Gauges (mm)

RMSE of LOOCV Precipitation Product with Five Rain Gauges (mm)

89.62 216.38 88.63 221.32 203.42 105.12 55.73 136.20 318.74 96.82 109.95 101.86 —

100.63 216.50 162.31 274.61 192.75 109.30 83.13 280.98 374.72 231.07 118.03 269.22 —

The general deduction from the assessment of the performance of the expanded rain gauge network is that increasing the number of rain gauges signiﬁcantly improved the precipitation estimation capabilities. Though it is only logical to assume that this statement is a foregone conclusion, one should also look into the fact that the optimal allocation of new rain gauge locations is necessary. The anomalous rise in RMSE upon network expansion in the Alakkode station is testimony to the fact that the optimal location of rain gauge installation is more important than the additional number of rain gauges.

Table 4. RMSE of the cross-validation product developed for the initial network with ﬁve rain gauges and the expanded network with 13 rain gauges for pixels with at least 10 percent of the area classiﬁed as unstable (FOS ,1). Unstable Slopes

Station Alakode Arakkalam Elappara Erattayar Manjumala Pazhampallichal Pallivasal Santhanpara Peermade Munnar Idukki Myladumpara Thodupuzha

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RMSE of CrossValidation with 13 Rain Gauges

RMSE of CrossValidation with Five Rain Gauges

95.20 198.86 78.81 230.32 230.36 115.18 58.87 161.41 303.90 98.88 118.79 104.08 72.60

45.48 202.70 160.37 282.21 228.58 113.39 80.37 303.20 363.97 223.47 121.08 262.93 7.21

CONCLUSION This study used a simple interpolation-based sequential optimization process to select rain gauge locations to expand an existing sparse network. The optimization process selected 12 from 107 candidate rain gauge locations using a multi-criteria approach that utilized landslide density, slope stability, and land-cover layers. This approach was designed to focus on the landslide occurrence and slope instability in the study area because the objective of network expansion was to aid landslide early warning. The sequential optimization process was compared with 10 random selections, and it was found having much better RMSE than random selections. Once the optimal locations were identified, rain gauges were installed in the field, and an assessment of the effect of network expansion on precipitation estimation and landslide modeling was made. A satellite-gauge merging process, called conditional merging, was utilized to create continuous precipitation products from the rain gauge network. Two such conditionally merged products were generated: one with the initial five rain gauges and the other with the improved network of 13 rain gauges. The expanded network showed a better correlation coefficient with the actual rain gauge observations. An application of LOOCV was developed to assess the performance of satellite-gauge merged products on locations of landslide occurrence and slope instability, which also indicated the superiority of the expanded network. This study shows the need for and advantage of having dense rain gauge networks for assessing raininduced landslide and slope failures. It also proposes a simple interpolation-based sequential optimization process for adding rain gauges in a sparse rain gauge network where frequently used geostatistical methods like kriging cannot be used due to a lack of samples. However, the sequential method may tend to localize the optimization process and hence have a limitation over a simultaneous global optimization technique. The development of such an optimization technique is beyond the scope of this paper and is the ground for future research. ACKNOWLEDGMENTS The authors thank the Society of Exploration Geophysicists (SEG) Geoscientists Without Borders (GWB), for funding this study (SEG GWB #201907008). REFERENCES ABRAHAM, M. T.; POTHURAJU, D.; AND SATYAM, N., 2019, Rainfall thresholds for prediction of landslides in Idukki, India: An empirical approach: Water, Vol. 11, No. 10, p. 2113. ABRAHAM, M. T.; SATYAM, N.; SHREYAS, N.; PRADHAN, B.; SEGONI, S.; ABDUL MAULUD, K. N.; AND ALAMRI, A. M., 2021, Forecasting landslides using SIGMA model: A case study from Idukki, India: Geomatics, Natural Hazards and Risk, Vol. 12, No. 1, pp. 540–559.

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Eﬀect of Various Aspects of Joints on Permeability of Jointed Glacial Till, Northeastern Ohio ALEKSANDAR PRVANOVIC Hull & Associates, 300 Business Center Drive, Pittsburgh, PA 15205

ABDUL SHAKOOR* Kent State University, Kent, OH 44242

Key Terms: Glacial Till, Joints, Permeability, JointFilling Material, Aperture ABSTRACT Glacial till bluffs along Lake Erie’s shoreline, northeastern Ohio, contain three sets of joints that influence the hydraulic behavior of till mass like joints in rock influence the hydraulic behavior of rock mass. According to the Unified Soil Classification System, the till classifies as low plasticity silt to low plasticity clay, with a clay content of 36–41 percent. Mineralogically, it consists of quartz, illite, and kaolinite. The joints in till are either open or filled with sand, silt, or both from the overlying lacustrine material or with disintegrated till from the joint walls. We used double-ring infiltration and dye tests to estimate field permeability. Laboratory permeability tests were performed on dry and saturated samples of till material, with manually created joints that were either kept open or filled with sand or disintegrated till material to simulate field conditions. Field and laboratory test results showed that permeability of jointed till depended upon whether the joints were open or filled, the nature of the joint-filling material, and the water content of the till (dry versus saturated) and that permeability generally decreased with increasing test time. Dry samples exhibited a substantially larger decrease in permeability than saturated samples, with the largest decrease occurring during the first 24 hours. Open joints in dry samples collapsed during the test procedure, and the permeability of these samples was generally similar to the permeability of the collapsed till material. Permeability tests on saturated samples with open joints were inconclusive. We present a comparison of field and laboratory test results. INTRODUCTION Joints in soil can significantly influence the engineering and hydraulic behavior of soil masses similar to

*Corresponding author email: ashakoor@kent.edu

the manner in which rock joints influence the engineering and hydraulic behavior of rock masses. However, the engineering and hydraulic significance of soil joints is not fully recognized or understood (Kirkaldie and Talbot, 1992). The current practice in soil engineering does not distinguish between soil properties and soil mass properties. This is because soils at most construction sites do not contain joints. However, in the northern states where jointed glacial till is abundantly exposed, particularly along the Lake Erie shoreline (Figure 1a), and where engineering structures are located on jointed till, the presence of joints significantly influences the engineering and hydraulic behavior of till, especially its permeability (Williams and Farvolden, 1967; Allred, 2000; and Clarke, 2018). Soil joints are particularly important in projects dealing with slope stability, sanitary landfills, waste disposal facilities, drainage facilities, and contaminant transport. Water movement through soil joints can lead to problems of slope instability due to rapid buildup of pore-water pressure along a potential failure plane, seepage of water into foundation excavations, water loss from reservoirs and canals, an increased rate of foundation settlement, and enhanced infiltration and migration of contaminants (Kirkaldie, 1988; Blanchard et al., 1993; Allred, 2000; and Helmke, 2003). In the past in northeastern Ohio, designing and constructing landfills on glacial till was based on the assumption that till was impermeable or mostly impermeable because of its clayey nature, i.e., till was considered an aquiclude or aquitard. However, this assumption is questionable because the permeability of jointed till is significantly influenced by the presence and various aspects of joints (orientation, spacing, pattern, aperture, open or filled, nature of filling material, water content of till in the vicinity of joints, and hydraulic activity). Literature on aquitards transmitting contaminants to the underlying aquifers, through joints in the aquitards (Jørgensen et al., 2004; Cherry, 2006; Rowe and Booker, 2011; and Mosthaf et al., 2021), negates the assumption that glacial till can be considered a hydraulic barrier.

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Traditional methods for determining the permeability of glacial till involve laboratory tests performed on small samples of intact glacial till, without taking into account the presence of joints. However, the permeability of jointed glacial till can be several orders of magnitude greater than the permeability of intact glacial till (Grisak and Cherry, 1975; Babcock, 1977; Kirkaldie and Talbot, 1992; Jones, 1993; Jorgensen et al., 1998; Highman and Shakoor, 1998; Allred, 2000; Fisher, 2002; and Helmke, 2003). In some cases, the joints can act as open channels and pathways for the vertical ﬂow of surface and ground water (Kirkaldie and Talbot, 1992; Highman and Shakoor, 1998; and Prvanovic, 2015). OBJECTIVES The speciﬁc objectives of this study were as follows:

Figure 1. (a) Bluff stratigraphy along the Lake Erie shoreline, showing glacial till (gray) overlain by lacustrine material (tan). (b) A prominent, nearly vertical, joint set perpendicular to the bluff face. The figure also shows traces of a horizontal joint set and some irregularly developed jointing. A third joint set that parallels the bluff face is not visible in the figure. The second vertical joint from the left and portions of some other joints are filled with disintegrated material. The 5-gallon (19 L) plastic bucket (36 cm tall) and the hammer (32 cm long) are for scale in the upper and lower pictures, respectively.

Many older landfills in northeastern Ohio are located in areas underlain by glacial till and are responsible for causing soil and groundwater contamination (EPA, 2007). Joints in till provide pathways for pollutants to reach the aquifers, although traditionally, till has been considered an effective barrier to watercarried contaminants (Kirkaldie, 1988; Jørgensen and Foged, 1994; Helmke, 2003; and Jørgensen et al., 2004). (The current practice of designing landfills in Ohio requires the use of either synthetic or compactedclay liners.)

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1. Characterize glacial till material in terms of its geological and engineering properties, including Atterberg limits, grain size distribution, minerology, swelling potential, and permeability of intact material. 2. Investigate various aspects of joints in the field, such as orientation, spacing, pattern, aperture, open or filled, nature of filling material, water content of till in the vicinity of joints, and hydraulic activity. 3. Estimate permeability of jointed glacial till in the field using double-ring infiltration and dye test methods and relate any changes in field permeability to the corresponding changes in joint aspects. 4. Investigate the effect of relevant aspects of joints on permeability of till material in the laboratory. 5. Compare the results of field and laboratory investigations. RESEARCH METHODS Selecting the Study Area A stretch of glacial till bluffs along the Lake Erie shoreline in Lake and Ashtabula counties, east of Cleveland, Ohio, was chosen for this research. This area was selected because of its good exposures of jointed till (Figure 1b). The wave action from Lake Erie helps remove the weathered material, exposing fresh till. Three individual sites, designated sites 1, 2, and 3, were selected for investigating various aspects of joints and their influence on in situ permeability (Figure 2). Site 1 is 3.2 km east of Fairport Harbor, near Painesville Township Park, site 2 is 10.5 km east of Fairport Harbor, and site 3 is approximately 48 km east of Fairport Harbor and 2.5 km north of North Kingsville, near Sunset Park. Large block samples of

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Figure 2. Location of the study area.

glacial till were collected from the three sites for laboratory investigations. Field Investigations Description of Glacial Till Most glacial till in northeastern Ohio is the result of Wisconsinan glaciation. The glacial deposits of Lake and Ashtabula counties, which include those of the study area (Figure 2), belong to the main Erie lobe (White, 1980). Multiple ice advances and retreats resulted in eight types of glacial till that diﬀer in color, lithological composition, and weathering horizons (White, 1980). The Ashtabula Till, the focus of this study, is named for its distinctive exposures near the town of Ashtabula (Figure 2). It is the youngest till in the area, deposited during the last ice advance of the Erie lobe. It stretches in a belt, 5.0–6.5 km wide, along the Lake Erie shoreline, and its thickness varies from approximately 15 m along the lake bluﬀs to only a few meters a short distance inland (White, 1980). The Ashtabula Till is underlain by the Hiram Till (not exposed at the study sites) and is overlain by lacustrine sand and silt deposits (Figure 1a).

Mapping the Joint Aspects We used the detailed line survey (DLS) method (Piteau and Martin, 1977) to evaluate various aspects of glacial till joints. The DLS method consisted of stretching a 30-m-long measuring tape horizontally across the bluff face at each of the three sites and recording all aspects of the joints intercepted by the tape. The DLS was performed close to the bluff toe, about 0.5–0.7 m above the beach, for easy access (the bluffs are quite steep). Visual observations revealed that the joint pattern and aspects did not change with height. A limitation of the DLS method is that it misses horizontal joints parallel to the survey line (Park and West, 2002). However, since horizontal joints are less frequent than vertical joints and are generally tight (Figure 1b), the DLS method was considered to be appropriate for mapping joints. Field Permeability Tests Since the vertical permeability of glacial till is higher than the horizontal permeability because of the abundance of vertical joints and the tightness of the less frequent horizontal joints, we decided to use the

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a pick at site 3. The benches were approximately 1.5–2 m long and 1.2–1.5 m wide. We removed any loose soil so that hard till, with or without joints, was exposed at the bench surfaces. The double-ring infiltration test apparatus consisted of two tapered aluminum rings (Figure 3a). The smaller (inner) ring had a radius of 0.3 m and a height of 0.6 m, whereas the larger (outer) ring had radius and height both equal to 0.6 m. Rings with these dimensions provide sufficiently accurate permeability data (Perroux and White, 1988). We used ASTM D3385 (ASTM, 1996) to conduct the double-ring infiltration test on both jointed and nonjointed till surfaces. In some cases, because of high infiltration rate, it was necessary to transfer lake water to the rings using a gasoline-powered water pump. We attached polyvinyl chloride pipes with valves to the hose and adjusted the valves to provide different flow rates to maintain the same water levels in the inner and outer rings (Figure 3a). Dye tests (Figure 3b) were conducted on selected excavated benches at sites 2 and 3. The tests could not be performed at site 1 because the portion of the bluff with excavated benches was eroded away by wave action during a storm event. Dye tests, conducted in conjunction with double-ring tests, can provide information about the water flow pathways in a jointed medium. The tests were helpful in measuring the range of velocities of the infiltrating water, evaluating the degree of joint filling (qualitatively), and obtaining information about the hydraulic activity of individual joints and joint systems. The dye tests consisted of adding sodium fluorescein, a widely used tracer in hydrogeology, to the water injected into selected joints exposed on a bench and recording the amount of time the dyed water took to emerge on the bluff face below the bench. Small concentrations of sodium fluorescein give water a fluorescent yellowish-green color that is easy to detect (Figure 3b). About 19 L of water were mixed with a small amount of the sodium fluoresceine tracer. The tracer–water mixture was carefully introduced into the selected joint or joints in a trickling manner to avoid turbulent flow and to simulate natural conditions such as rainwater infiltration. Laboratory Investigations Figure 3. Field permeability tests: (a) double-ring infiltration test and (b) dye test. Note the bucket (36 cm tall) for scale.

double-ring inﬁltration and dye tests (Figure 3) to estimate permeability at the three sites. To conduct the tests, horizontal benches, perpendicular to the bluﬀ face, were created using a backhoe at sites 1 and 2 and

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Laboratory investigations included the Atterberg limits test following ASTM D4318, grain size distribution analysis by the wet sieving method following ASTM D1140, x-ray diﬀraction analysis, a swelling test following ASTM D4546, and a series of permeability tests following ASTM D2434 and D2435 (ASTM, 1996). The purpose of the Atterberg limits test was to classify the till material according

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to the Unified Soil Classification System (USCS; Casagrande, 1948; Holtz et al., 2011), whereas the purpose of the grain size distribution test was to determine its clay content. X-ray diffraction analysis was conducted to determine the mineralogical composition of till material. The swelling test was performed to evaluate the effect of swelling, if any, on joint aperture upon wetting of the till material. Permeability tests, the focus of this study, were performed to investigate the effect of various aspects of joints and water content (dry versus saturated) on permeability of the till mass. Permeability Tests Both constant head permeability tests following ASTM D2434 and falling head permeability tests following ASTM D2435 (ASTM, 1996) were performed on till samples, depending upon the types of samples and the aspects investigated. A modified version of the compaction permeameter was used for permeability testing. The modification consisted of using a metallic ring, 10 cm in diameter and 5 cm in height, instead of the full height of the compaction mold (11.6 cm). This modification was necessary to shorten the time required for saturation of dry intact till because of its very low permeability (10−7 –10−8 cm/s) and considering the large number of permeability tests. Sample preparation for permeability tests involved the following steps: 1. Block samples of till, collected in the field, were dry cut with a saw until they were close to the size of the permeameter mold and then trimmed, using a knife and a file, to fit the permeameter mold. 2. A thin saw was used to create joints in the samples. Sand paper was used to rub the joint surfaces to create joints of the desired aperture. For tests involving saturated till samples, the joints of the desired aperture were created after saturating the samples in the permeameter. 3. In the case of a single joint, the two halves of the sample were placed in the mold using two metal separators in the joint (Figure 4), which also provided a uniform aperture across the full length of the joint. Molten wax was then poured along the perimeter of the sample to seal the annular space between the sample and the mold and to limit the water flow exclusively through the joint (Figure 4). A similar procedure was followed for samples with more than one joint. 4. Metal separators were taken out and, using a funnel, the joint space was filled with filling material in 5-mm-thick layers. Filling material used for the tests included fine-grained sand, medium-grained sand, and disintegrated till material. After placing each

Figure 4. Sample with metal separators to maintain the desired aperture and with annular space around the sample ﬁlled with paraﬃn wax.

layer of filling material, water was poured gently into the joint space, using a laboratory wash bottle, until the layer was saturated. This procedure continued until the joint was completely filled. 5. The permeameter was then assembled and either a constant head or a falling head test was performed to measure the permeability of the sample. The permeability was calculated upon stabilization of water flow, which took from 5 to 15 days depending upon the type of the test. 6. Permeability tests were also performed on dry and saturated samples with open joints (no filling material). 7. Prior to the tests on jointed till samples, the permeability of various types of filling material was determined. Joint Aspects Investigated The joint aspects investigated during the permeability tests included the number of joints, joint pattern (i.e., a single joint, two parallel joints, or two joints intersecting perpendicular to each other to simulate joints perpendicular and parallel to the bluff face), joint aperture, open or filled, nature of filling material, and water content of till (dry versus saturated). Field observations indicated that joints with apertures greater than 5 mm exhibited turbulent flow, rendering Darcy’s law inapplicable. However, in our laboratory study, we used an aperture range of 1–7 mm for different tests because complete filling of joints and the use of porous stones in the permeameters prevented any turbulent flow. The lacustrine sediments that overlie the till bluffs along the Lake Erie shoreline cause piping of silt and

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Figure 5. Closeup view of a joint ﬁlled with a mixture of disintegrated till and the overlying lacustrine material (that piped in after heavy rain). The material moved down the open joint like a mudﬂow. The hammer (32 cm long) serves as a scale. The aperture at the bottom of the picture, where joint walls can be seen, is approximately 40 mm.

sand into the open till joints (Figure 5). Therefore, fine and medium sand, obtained from sieving the lacustrine material, were used as joint-filling material. Glacial till disintegrates into its constituent particles (clay, silt, sand, and gravel) when exposed to water (Figure 6). Under natural conditions, this usually happens during and after precipitation, following dry periods. Field observations revealed that disintegrated till filled the joints either partially or completely (Figure 1b), affecting the overall permeability of the till mass. Therefore, disintegrated till was also used as a joint-filling material. Contrary to dry till, saturated till disintegrates little when exposed to water (Figure 7). Therefore, it was considered necessary to investigate the influence of water content on permeability of jointed till. Two extreme situations with respect to water content were investigated: dry till samples (0 percent water content) and saturated till samples (13–16.3 percent water content). For tests on saturated samples with open joints, joints were cut in three patterns so that all samples had

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Figure 6. Disintegration of the dry till sample upon interaction with water (a) after 1 minute, (b) after 10 minutes, and (c) after 50 minutes.

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the set parallel to the bluff face could not be recorded because of dense vegetation on top of the bluff. The joint set perpendicular to the bluff face strikes NWSE and dips either NE or SW at steep angles (70–90 degrees). In addition, joints with random orientations are present. The average spacing of joints perpendicular to the bluff face is 0.8 m, the aperture varies from 1–30 mm, the vertical extent ranges from 0.3–0.5 m, and they are either open or partially filled with disintegrated till material or lacustrine material from above (Prvanovic, 2015). Like site 1, the dominant joint set at site 2 is perpendicular to the bluff face with similar orientations. The average spacing of these joints is 0.76 m, the aperture varies from 1–15 mm, and the vertical extent ranges from 0.7–8 m. The joint surfaces are mostly rough, and the joints are either open or partially filled with disintegrated till material (Prvanovic, 2015). In addition to the joint sets perpendicular and parallel to the bluff face, a horizontal joint set is present at site 3. The orientation of the set perpendicular to the bluff face is the same as at sites 1 and 2 (NW-SE with steep dips to the NE or SW). The average spacing of the joints perpendicular to the bluff face is 0.63 m, the aperture varies from 1–70 mm, and the vertical extent ranges from 0.4–4.5 m. The joint surfaces are rough, and the joints are either open or partially filled with disintegrated till material. The horizontal joint set has an aperture of less than 1–2 mm (Prvanovic, 2015). We noticed some water seeping through the joints during DLS mapping at site 1 but none at sites 2 and 3. Double-Ring Infiltration Test Results

Figure 7. Disintegration of the saturated till sample upon interaction with water (a) after 1 day and (b) after 15 days.

the same open joint area: a single joint with a 4-mm aperture; two parallel joints, each with a 2-mm aperture; and two perpendicular joints, each with a 2-mm aperture. FIELD INVESTIGATION RESULTS Joint Aspects At site 1, two nearly vertical joint sets are present: one perpendicular to the bluff face and the other parallel to the bluff face. The set parallel to the bluff face can be seen on the bluff face where till blocks, bounded by the joint sets, have fallen. However, the aspects of

Complete results of double-ring infiltration tests for two benches at site 1, three benches at site 2, and three benches at site 3, including both intact and jointed till, are provided in Prvanovic (2015). For brevity, we present a summary of the findings. For intact till (no visible joints on the bench surface), the permeability was calculated by converting the water infiltration rate to permeability using the Southwest Florida Water Management District Excel spreadsheet (Andreyev and Wiseman, 1989). For jointed till, the permeability was initially expressed as the infiltration rate in liters per second and then converted to permeability units (cm per second) using Meinzer’s conversion, where 1 gallon/d/ft2 (1 gallon = 3.79 L and 1 foot = 30.55 cm) corresponds to 4.72 × 10−5 cm/s (Fetter, 1998). The results indicated that field permeability of intact till (1.87 × 10−5 to 9.53 × 10−5 cm/s) was two to three orders of magnitude higher than the permeability of intact till samples tested in the laboratory (1.03 × 10−7 to 3.90 × 10−8 cm/s). Allred (2000) found similar

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results. The surface area of the laboratory samples was smaller than the till area tested in the field, with no joints present in the laboratory samples. The difference between field and laboratory values of permeability of intact till could be attributed to the possible presence of tight joints in the intact till mass, which were not exposed on the bench surface. For jointed till mass, the infiltration tests indicated an increase in permeability (flow rate) for joints with apertures greater than 5 mm (infiltration of 0.1–0.9 L/s). For these joints, water flow became turbulent, resulting in aperture increase and removal of filling material. Joints with apertures greater than 5 mm and no or minimal filling material tended to act as open conduits for fluid flow. For joints with apertures of less than 3 mm, the field permeability ranged from 1.27 × 10−2 to 5.46 × 10−4 cm/s and decreased with testing time. Thus, the field permeability of jointed till highly depends upon joint aperture and the degree and nature of joint filling.

of 27 and average plasticity index of 7 (Prvanovic, 2015), the glacial till at the study sites was classiﬁed as low plasticity silt to low plasticity clay, according to USCS (Casagrande, 1948; Holtz et al., 2011). Weight percentages of gravel, sand, silt, and clay-sizes particles, as indicated by grain size distribution analysis, varied between 4 and 6, 26 and 29, 26 and 32, and 36 and 41 percent, respectively. On average, the silt and clay matrix constituted up to 69 percent of the till by weight. Mineralogically, the till consisted of quartz, illite, kaolinite, and trace chlorite, as indicated by xray diﬀraction analysis. The amount of axial swelling varied between 4.5 and 6.4 percent. Previous research (Grisak and Cherry, 1975; Babcock, 1977; Kirkaldie and Talbot, 1992; Highman and Shakoor, 1998; Jørgensen et al., 1998; Allred, 2000; Fisher, 2002; and Helmke, 2003), as well as the results of this study, indicated that intact glacial till from the Great Lakes region is impermeable, with permeability on the order of 10−6 –10−8 cm/s.

Dye Test Results Dye tests were conducted only at sites 2 and 3 because a portion of the bluff at site 1, containing the benches, was washed away by wave action during a storm event. For site 2, where aperture of joints used for injection of dye ranged from 1–3 mm, the flow velocities ranged from 0.12–5.70 cm/s. For site 3, where the aperture range was 2–5 mm, the flow velocities range was 0.94–8.23 cm/s (Prvanovic, 2015). After performing a dye test on two joints at site 3, both with 4- to 5-mm apertures, the bluff face was excavated, which revealed that the two joints were hydraulically connected. The dye tests indicated a range of water flow velocities (0.12–8.23 cm/s) and, indirectly, permeability. Because of the limited number of dye tests and their shorter durations, compared to double-ring infiltration tests, and the different concepts on which the two tests are based, it was not possible to directly relate the results of the two tests. However, water velocities calculated from dye tests suggested that permeability was greatly influenced by the degree of joint filling, because joints of same apertures exhibited substantial differences in permeability values depending upon the degree of filling (Prvanovic, 2015). Dye tests were also helpful in evaluating hydraulic connectivity between adjacent joints. LABORATORY TEST RESULTS Characterizing Till Material Based on Atterberg limits, determined for two samples from each site, with an average liquid limit

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Permeability Test Results with Fine and Medium Sand as Filling Material The average permeability values for fine-grained and medium-grained sand used as filling material were 3.81 × 10−3 and 6.68 × 10−3 cm/s, respectively. The lacustrine sediment overlying the glacial till was used to extract fine and medium sand fractions as filling material. The lacustrine material also contains abundant silt; however, in the field, the silt fraction, when piped into fractures, is easily washed out by subsequent rain events. A total of 23 tests were performed on dry and saturated samples with fine-grained and mediumgrained sand as filling material (14 and 9 tests, respectively), with joint apertures varying from 1–4 mm. Tables 1 and 2 show the initial permeability values, average permeability values after stabilization, change between initial and final permeability values, and joint apertures after tests for all samples, with the joint-filling material consisting of fine-grained sand (Table 1) and medium-grained sand (Table 2). Figure 8 shows permeability change over time. Tables 1 and 2 and Figure 8 indicate that for dry samples, permeability noticeably decreased during the first 48 hours of the tests and stabilized after 3–10 days. Upon stabilization, the average permeability values for dry samples ranged between 2.11 × 10−3 and 3.29 × 10−3 cm/s for samples with fine sand as joint-filling material and between 1.67 × 10−3 and 3.43 × 10−3 cm/s for samples with medium sand as joint-filling material. These values are slightly below the initial permeability values for fine and medium sand. However, permeability values for saturated samples at the end of the tests remained practically the same as their initial permeability values.

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Permeability of Jointed Glacial Till Table 1. Permeability test results for samples with fine-grained sand as joint-filling material. Aperture (mm)/Water Content/Sample No.

Initial Permeability (cm/s)

Average Final Permeability (cm/s)

Permeability Change (%)

1/dry/1 9.93 × 10−3 2.82 × 10−3 − 71.6 −3 1/dry/2 9.60 × 10 2.69 × 10−3 − 72.0 1/sat (15.8%*)/3 1.07 × 10−2 1.08 × 10−2 No change 1/dry/4 8.33 × 10−3 2.36 × 10−3 − 71.7 Average permeability (cm/s) of dry samples with 1-mm joint apertures = 2.62 × 10−3 2/dry/1 5.39 × 10−3 2.15 × 10−3 − 60.1 2/dry/2 4.61 × 10−3 2.13 × 10−3 − 53.8 2/dry/3 4.54 × 10−3 2.05 × 10−3 − 54.8 2/sat (15.7%*)/4 3.83 × 10−3 3.87 × 10−3 No change Average permeability (cm/s) of dry samples with 2-mm joint apertures = 2.11 × 10−3 3/7.5/1 1.24 × 10−2 6.21 × 10−3 − 49.9 3/dry/2 1.01 × 10−2 2.84 × 10−3 − 71.9 3/sat (16.3%*)/3 1.07 × 10−2 1.06 × 10−2 No change 3/sat (12.8%*)/4 1.08 × 10−2 9.22 × 10−3 − 14.6 −3 3/dry/5 8.45 × 10 3.29 × 10−3 − 61.1 3/dry/6 9.81 × 10−3 2.37 × 10−3 − 75.8 Average permeability (cm/s) of dry samples with 3-mm joint apertures = 2.83 × 10−3

Aperture after Test (mm)

Aperture Reduction due to Swelling (%)

0.8 0.75–0.85 1.0 0.8

20 15–25 0 20

1.5 1.5 1.0–2.0 2.0

25 25 0–50 0

2.0–2.5 1.5–2.0 3.0 2.5–3.0 1.5–2.0 1.5–2.0

17–33 33–50 0 0–17 33–50 33–50

*Water content at saturation (sat).

At the end of the tests, the joint apertures for the dry samples showed a decrease of 20–50 percent compared to their pretest values. Saturated samples showed no decrease in aperture. Permeability Test Results with Disintegrated Till as Filling Material The average permeability of the disintegrated till was found to be 1.13 × 10−5 cm/s, 2–2.5 orders of magnitude higher than the permeability of intact till (10−6 – 10−8 cm/s). Eight permeability tests were performed on till samples containing joints ﬁlled with disintegrated till material: three on dry samples with joint apertures of 5 mm, one on a dry sample with a joint aperture of 7 mm, and four on saturated samples with

joint apertures of 5 mm. The smaller aperture sizes could not be tested because of coarse sand and gravel in the disintegrated till material. The duration of the tests varied between 7 and 14 days, and each test was considered complete when permeability became constant or nearly constant. Tables 3 and 4 show the initial and final permeability values, as well as any changes in aperture, during the tests for dry samples (Table 3) and saturated samples (Table 4), whereas Figures 9 and 10 show the changes in permeability over time. Because of the rapid initial change in permeability for dry samples (Figure 9c), the permeability was measured continuously during the first 0–135 or 0–380 minutes, depending on the test. Figure 9 shows a decrease in permeability for dry samples by almost an order of magnitude during the first 35 minutes of the test. The average fi-

Table 2. Permeability test results for samples with medium-grained sand as joint-filling material. Aperture (mm)/Water Content/Sample No.

Initial Permeability (cm/s)

Average Final Permeability (cm/s)

Permeability Change (%)

2/dry/1 4.33 × 10−3 1.55 × 10−3 − 64.2 2/sat (16.2%*)/2 1.08 × 10−2 1.07 × 10−2 No change 2/dry/3 4.64 × 10−3 1.92 × 10−3 − 58.6 Average permeability (cm/s) of dry samples with 2-mm joint apertures = 1.74 × 10−3 3/sat (15.5%*)/1 5.27 × 10−2 4.53 × 10−2 − 14.0 3/dry/2 2.8 × 10−2 2.63 × 10−3 − 90.6 3/dry/3 2.59 × 10−2 4.22 × 10−3 − 83.7 Average permeability (cm/s) of dry samples with 3-mm joint apertures = 3.43 × 10−3 4/dry/1 8.91 × 10−3 1.51 × 10−3 − 83.2 −2 4/dry/2 1.11 × 10 2.07 × 10−3 − 81.3 4/dry/3 8.42 × 10−3 1.42 × 10−3 − 83.1 Average permeability (cm/s) of dry samples with 4-mm joint apertures = 1.67 × 10−3

Aperture after Test (mm)

Aperture Reduction due to Swelling (%)

1.5 2.0 1.5–2.0

25 0 0–25

2.5–3.0 2.0 2.0–2.5

0–17 33 17–33

3.0 2.5–3.0 2.5–3.0

25 25–37 25–37

*Water content at saturation (sat).

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Figure 8. Summary of permeability-change trends for all samples with joints ﬁlled with (a) ﬁne-grained sand and (b) medium-grained sand. The legends show aperture and water content (dry versus saturated) of the samples. Table 3. Permeability test results for dry samples with disintegrated till as joint-filling material. Aperture (mm)/Water Content/Sample No. 5/dry/1 5/dry/2 5/dry/3 7/dry/4 Average values

Initial Permeability (cm/s)

Average Final Permeability (cm/s)

Initial vs. Final Permeability Ratio

Aperture after Test (mm)

Aperture Reduction due to Swelling (%)

1.01 × 10−3 5.90 × 10−4 1.96 × 10−3 3.7 × 10−4 9.83 × 10−4

4.54 × 10−6 2.61 × 10−6 6.50 × 10−6 1.03 × 10−6 3.67 × 10−6

222.5 226.1 301.6 359.2 227.4

3.5–4.5 3.5–4.5 3.5–4.0 4.0–4.5 —

10–30 10–30 20–30 35–43 —

Table 4. Permeability test results for saturated samples with disintegrated till as joint-filling material. Aperture (mm)/Sample No. 5/1 5/2 5/3 5/4 Average values

Initial Permeability (cm/s)

Average Final Permeability (cm/s)

Initial vs. Final Permeability Ratio

Aperture after Test (mm)

Aperture Reduction due to Swelling (%)

6.09 × 10−5 7.68 × 10−5 6.99 × 10−5 1.35 × 10−4 8.56 × 10−5

7.19 × 10−6 1.49 × 10−5 9.07 × 10−6 9.21 × 10−6 1.01 × 10−5

8.7 5.1 7.6 15.7 9.3

4.5–5.0 4.5–5.0 4.5–5.0 NA ∼4.7

0–10 0–10 0–10 NA ∼5

NA = not applicable.

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Figure 9. Permeability versus time change for dry samples with disintegrated till as joint-filling material (a) during the whole 9-day period, (b) from day 2 to day 9, and (c) during the first 380 minutes. The legend shows joint apertures for different samples.

nal permeability was in the range of 10−6 cm/s, within an order of magnitude lower than the permeability of disintegrated till material (1.13 × 10−5 cm/s). In the case of saturated samples, a smaller decrease in perme-

ability over time was observed (Figure 10). The average permeability of saturated samples upon stabilization was similar to the permeability of disintegrated till.

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Figure 10. Permeability change over time for saturated samples with joints ﬁlled with disintegrated till material. Joint aperture was 5 mm for all four samples.

Permeability Test Results with No Filling Material Three permeability tests were performed on till samples containing joints with no ﬁlling material: one on a dry sample with a 2-mm aperture and two on dry samples with 5-mm apertures. The duration of the tests varied from 7.3 to 11.6 hours, and each test was considered complete when permeability became constant or nearly constant. In addition to permeability tests, photographs were taken to document any changes in joint aperture (joint “healing”) upon completion of the tests. Table 5 summarizes the test results, and Figure 11 shows permeability change over time for all three tests. The initial permeability of all three samples was generally consistent, varying between 4.17 × 10−2 and 7.08 × 10−2 cm/s. In the tests on dry samples with open joints, the initial permeability values were not truly initial values. This is because water passing through open joints caused some immediate disintegration of the joint walls, causing a small decrease in the true initial permeability. The high coeﬃcient of determination (R2 ) values (0.96 to 0.99; Figure 11b) indicate a consistent pattern of permeability change over time in all three samples.

By the end of the tests, joints in all samples had collapsed. Figure 12 shows a sample with a 2-mm aperture joint before and after the test. The overall drop of permeability was about three orders of magnitude. Upon stabilization, permeability varied within a relatively close range of 2.65 × 10−5 to 8.11 × 10−5 cm/s. This final permeability was slightly higher than the permeability of disintegrated till because of the relatively loose nature of disintegrated till that filled in the open joints during the tests. Four permeability tests were performed on samples with three joint patterns: a single 4-mm aperture joint, two 2-mm aperture joints parallel to each other, and two 2-mm aperture joints perpendicular to each other. For each joint pattern, two tests were performed using porous stones in the permeameter and two were performed without porous stones. The purpose of the tests without porous stones was to simulate field conditions. Duration of the tests varied from 5–9 days, and each test was considered complete when permeability became constant or nearly constant. Table 6 shows the initial and final permeability values, as well as the final joint apertures for samples with different joint patterns and varying joint apertures. Figure 13 illustrates the variation in permeability

Table 5. Permeability test results for dry samples with open joints. Aperture (mm)/Sample No. 2/1 5/2 5/3

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Initial Permeability (cm/s)

Average Final Permeability (cm/s)

Initial vs. Final Permeability Ratio

Aperture after Test (mm)

Aperture Reduction due to Swelling (%)

4.17 × 10−2 4.53 × 10−2 7.08 × 10−2

8.12 × 10−5 2.65 × 10−5 5.55 × 10−5

513.5 1,709.4 1,275.7

0 0 0

100 100 100

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Figure 11. Change of permeability over time for the three dry samples with open joints of varying apertures (a) during the entire test period and (b) between the ﬁrst 20 minutes and the end of the test.

Table 6. Summary of permeability test results on samples with a single 4-mm aperture joint, two 2-mm aperture joints parallel to each other, and two 2-mm aperture joints perpendicular to each other. Aperture (mm)/Sample No.

Initial Permeability (cm/s)

Average Final Permeability (cm/s)

5.63 × 10−1 2.70 × 10−1 5.30 × 10−2 4.50 × 10−2

5.13 × 10−1 7.44 × 10−2 7.42 × 10−3 1.72 × 10−2

No Yes, minimal Yes, signiﬁcant Yes, minimal

1.39 × 10−1 1.95 × 10−2 2.70 × 10−1 1.70 × 10−1

1.49 × 10−2 8.58 × 10−3 5.26 × 10−2 1.65 × 10−2

Yes Not visible Yes Yes, signiﬁcant

9.93 × 10−2 3.73 × 10−2 1.67 × 10−1 3.00 × 10−1

8.91 × 10−2 1.38 × 10−2 6.23 × 10−2 1.42 × 10−1

Yes Yes Yes, signiﬁcant Yes

Single 4-mm aperture joint 4/2* 4/3* 4/4 4/1 2-mm aperture parallel joints 2/1 2/2 2/3* 2/4* 2-mm aperture cross-joints 2/1 2/2 2/3* 2/4*

Partial Joint Collapse

*Samples tested without porous stones.

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the joints, changing the geometry of the space within the joints available for water ﬂow. Some decrease in aperture was observed upon completion of the tests, but no apparent pattern of closure and collapse could be discerned. DISCUSSION

Figure 12. Dry sample with a 2-mm aperture open joint (a) before the permeability test and (b) after the permeability test.

during the test period. Figure 14 shows the degrees of aperture closure and partial joint-wall collapse after completing the test on samples with two joints perpendicular to each other. Samples with porous stones had lower initial permeability than those without porous stones and their permeability upon stabilization was one to two orders of magnitude lower than that of samples without porous stones (Table 6). Although samples with porous stones generally exhibited a more uniform change of permeability throughout the test, no consistent pattern of permeability change was observed due to diﬀerent degrees and randomness of partial joint-wall collapse (Figure 14), and repositioning of the collapsed material. Samples without porous stones exhibited substantial and irregular variations in permeability change as the energy of water ﬂow caused repositioning of the collapsed material within

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Based on field and laboratory tests, it is obvious that the overall permeability of jointed till depends on (1) whether the joints are open or filled, (2) the type of filling material, (3) whether the till is dry or saturated, and (4) the joint aperture at the end of the test. The results also demonstrate that the permeability is time dependent, because joint aspects change with time as water flows through them. One would expect the permeability of jointed till to be controlled primarily by the permeability of the filling material. However, laboratory test results indicated that regardless of the type of filling material, the change in permeability of the jointed till was strongly influenced by the initial water content of the till samples, i.e., dry versus saturated samples. This is because dry till disintegrates quickly during flow of water through joints, whereas saturated till does not disintegrate. Because intact till is practically impermeable, it is unlikely to become saturated, even after prolonged rainfall. However, the joint walls can become saturated, or nearly saturated, if rainwater continues to move through joints for an extended period. Tables 1 and 2 and Figure 8 show the permeability changes for all samples with joints filled with fine-grained and medium-grained sand. The average permeability of fine-sand filling material (3.81 × 10−3 cm/s) is about 34 percent higher than the average final permeability for dry samples with joints filled with fine sand (2.52 × 10−3 cm/s), whereas the average permeability of medium-sand filling material (6.68 × 10−3 cm/s) is approximately 66 percent higher than the average final permeability for dry samples with joints filled with medium sand (2.28 × 10−3 cm/s). These trends can be attributed partly to swelling of the samples upon wetting, resulting in an aperture decrease, and partly to disintegration of joint-wall material, filling the pore spaces between sand grains. Since medium sand has larger pores than fine sand, it is easier for disintegrated till material to fill the pores in medium sand, thereby resulting in a larger decrease in permeability. Similar trends were observed for dry samples with joints filled with disintegrated till material (Table 3 and Figure 9). More than 90 percent of the reduction in permeability occurred during the first 24 hours of the tests. Swelling and aperture changes did not occur in saturated laboratory samples with joints filled with disin-

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Figure 13. Comparison of change in permeability over time for saturated samples with open joints with diﬀerent apertures and joint patterns: (a) single joint with a 4-mm aperture, (b) two parallel joints each with a 2-mm aperture, and (c) two perpendicular joints each with a 2-mm aperture.

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Figure 14. Apertures after completion of the test for samples with two perpendicular joints with a 2-mm aperture: (a) and (b) porous stones used and (c) and (d) no porous stones used.

tegrated till material. Despite the absence of aperture change, permeability exhibited a decrease with increasing time (Table 4 and Figure 10). However, the reduction in permeability was smaller than that for the dry samples and was most likely caused by densification of disintegrated till material due to percolation of water. Permeability tests on dry samples with open joints resulted in disintegration or collapse of joint walls and complete closure of joints regardless of the initial aperture. Table 5 shows that the average final permeability is controlled by the permeability of collapsed till material and is slightly higher (5.44 × 10−5 cm/s) than the average permeability of disintegrated till (1.13 × 10−5 cm/s). It is likely that in the case of open joints, disintegrated till did not fill the joints as completely as when disintegrated till was placed in the joints manually. Comparison of Field and Laboratory Permeability Test Results The double-ring infiltration test indicated that field permeability values varied between 1.27 × 10−2 and 272

5.46 × 10−4 cm/s for joints with apertures of less than 3 mm. The joint-filling material at the three sites was mostly disintegrated till that filled the joints to varying degrees. In comparison, the initial and final values of laboratory permeability for disintegrated till–filled joints, with less than 5-mm apertures (except for one joint), averaged 9.83 × 10−4 and 3.67 × 10−6 cm/s, respectively (Table 3). Thus, the laboratory values are substantially lower than the field values despite larger joint apertures. This difference in field and laboratory values can be attributed to (1) field joints were only partially filled with disintegrated till material, whereas joints in the laboratory samples were completely filled; (2) some joints in till beneath the benches on which double-ring tests were conducted could have been open; (3) the till area influenced by the double-ring tests contained more interconnected joints than the joints in the laboratory samples; and (4) flow of water through joints in the laboratory samples most likely densified the disintegrated till material, considering the duration of the laboratory tests.

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Permeability of Jointed Glacial Till

Both field and laboratory tests demonstrated that the permeability of jointed glacial till was strongly influenced by its water content and the type of jointfilling material, whereas the role of aperture was less significant. Scale Effects Scale effects should be kept in mind when comparing the results of field and laboratory tests. The laboratory samples were smaller than the till areas affected by infiltration of water during the double-ring infiltration tests or percolation of dye during the dye tests. The laboratory samples did not completely simulate the joint pattern in the field, nor did they represent the interconnectivity of the joints in the field outcrops. The larger the field area, the more joints (some completely open) and the more interconnections. These scale effects help explain why the field permeability values are significantly higher than the laboratory permeability values. This indicates the need to adjust laboratory values based on the scale effects. Although laboratory tests performed on small samples do not completely simulate the hydraulic behavior of in situ till mass, the results of laboratory tests provide an insight about the role of joint aspects on permeability of jointed till, especially the role of jointfilling material and the degree of saturation. The preceding discussion suggests that measuring permeability of intact till in a laboratory, which is usually done to demonstrate low permeability for permitting purposes, may significantly underestimate the field permeability of the jointed till mass. Consequently, containment structures placed on jointed till, such as sanitary landfills, may release the contaminants they are supposed to hold. Therefore, design and construction of containment facilities on jointed glacial till should carefully consider the effect of the presence of joints, joint aspects, and the degree of saturation on the hydraulic behavior of the till mass. Limitations of Research The research presented herein has the following limitations: 1. The joints in the laboratory samples were filled with fine sand, medium sand, and disintegrated till, all in a loose state. However, the filling material in the field may have hardened to varying degrees, depending upon how long ago it was deposited. 2. Joints in the laboratory were completely filled with the filling material, whereas joints in the field can be either partially or completely filled. 3. Completely disintegrated till was used in the laboratory, whereas in the field, the till material

filling the joints may be in varying states of disintegration. 4. The laboratory samples were smaller than the field outcrops; therefore, the differences in laboratory and field results are largely due to scale effects. 5. We used simple joint patterns in the laboratory, whereas the field joint patterns may be more complex in terms of interconnectivity of joints. 6. We ignored the effect of horizontal joints in the laboratory study because of their low frequency and generally tight nature. However, they can provide a hydraulic connection between vertical joints in the field. CONCLUSIONS The following conclusions can be drawn from the results of this study: 1. Field permeability of jointed glacial till, as determined by the double-ring infiltration tests, varied between 1.27 × 10−2 and 5.46 × 10−4 cm/s for joints with apertures of less than 3 mm and filled with disintegrated till to varying degrees. Field permeability decreased with increasing duration of the test. 2. Permeability of dry till samples in the laboratory, with fine sand, medium sand, and disintegrated till as the joint-filling material (apertures of 1–7 mm), exhibited a decrease with time, with the largest decrease occurring during the first 24 hours. 3. Permeability of dry laboratory samples with open joints also decreased with increasing time. Water flow through open joints resulted in complete closure of the joints due to disintegration of joint walls. The final permeability of these samples was generally similar to the permeability of the disintegrated till matrix. 4. Initial joint aperture did not appear to influence the permeability of either dry or saturated samples. The apertures decreased during the tests, especially for dry samples, causing a decrease in final permeability values. 5. The laboratory permeability values are significantly lower than the field permeability values because of scale effects. 6. Overall, permeability of jointed glacial till depends on whether the joints are filled or unfilled, the type of filling material, and the initial water content in the vicinity of joints (dry versus saturated). REFERENCES Allred, B. J., 2000, Survey of fractured glacial till geotechnical characteristics: Hydraulic conductivity, consolidation and

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Prvanovic and Shakoor shear strength: Ohio Journal Science, Vol. 100, No. 3–4, pp. 63–72. Andreyev, N. E. and Wiseman, L. P., 1989, Stormwater Retention Pond Analysis in Unconfined Aquifers, Permitting Guidelines for Southwest Florida Water Management District: Jammal and Associates, FL, 33 p. ASTM (American Society for Testing and Materials), 1996, Annual Book of ASTM Standards, Soil and Rock (1), Vol. 4, Section 4, Conshohocken, PA, 1000 p. Babcock, E. A., 1977, A comparison of joints in bedrock and fractures in overlying Pleistocene lacustrine deposits, Central Alberta: Canadian Geotechnical Journal, Vol. 14, pp. 357–366. Blanchard, P. E.; Kitchen, N. R.; and Heidenreich, L., 1993. Ground water transport of nitrate in loess and glacial till. In Annual Geological Society of America (GSA) North–Central Section Meeting (program with abstracts): Rolla, MO, Vol. 26, No. 3, p. 8. Casagrande, A., 1948, Classification and identification of soils: Transactions American Society Civil Engineers, Vol. 113, pp. 901–930. Cherry, J. A., 2006, Contaminant Transport through Aquitards—A State-of-the-Science Review: American Water Works Association, IWA Publishing, 126 p. Clarke, B. J., 2018, The engineering properties of glacial tills: Geotechnical Research, Vol. 5, No. 4, pp. 262– 277. EPA (Environmental Protection Agency), 2007, personal communication, Ohio EPA, Twinsburg, OH. Fetter, C. W., 1988, Applied Hydrogeology, 2nd ed.: Charles E. Merrill and Co., Columbus, OH, 592 p. Fisher, H., 2002, Hydraulic conductivity of Ohio’s glaciated soil, its implications and suggestions for future studies: Ohio Journal Science, Vol. 102, No. 5, pp. 106–109. Grisak, G. E. and Cherry, J. A., 1975, Hydrologic characteristics and response of fractured till and clay confining a shallow aquifer: Canadian Geotechnical Journal, Vol. 23, No. 12, pp. 23–43. Helmke, M. F., 2003, Studies of Solute Transport Through Fractured Till in Iowa: Ph.D. Dissertation, Iowa State University, Ames, IA, 222 p. Highman, T. A. and Shakoor, A., 1998, Bluff erosion along the Lake Erie shoreline, northeast Ohio, as influenced by soil joints: Environmental Engineering Geoscience, Vol. 4, No. 2, pp. 195–207. Holtz, R. D.; Kovacs, W. D.; and Sheahan, T. C., 2011, An Introduction to Geotechnical Engineering, 2nd ed.: Prentice Hall, Upper Saddle River, NY, 853 p.

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Jones, L., 1993, A comparison of pumping and slug tests for estimating the hydraulic conductivity of unweathered Wisconsinan age till in Iowa: Ground Water, Vol. 31, No. 6, pp. 896–904. Jørgensen, P. R. and Foged, N., 1994, Pesticide leaching in intact blocks of clayey till. In 13th International Conference on Soil Mechanics and Foundation Engineering: New Delhi, India, pp. 1661–1664. Jørgensen, P. R.; Mckay, L.; and Spliid, N., 1998, Evaluation of chloride and pesticide transport in a fractured clayey till using large undisturbed columns and numerical modeling: Water Resources Research, Vol. 34, No. 4, pp. 539–553. Jørgensen, P. R.; Mckay, L. D.; and Kistrup, J. P., 2004, Aquifer vulnerability to pesticide migration through till aquitards. Ground Water, Vol. 42, No. 6–7, pp. 841–855. Kirkaldie, L., 1988, Potential contaminant movement through soil joints: Bulletin Association Engineering Geologists, Vol. 25, No. 4, pp. 520–524. Kirkaldie, L. and Talbot, J. R., 1992, The eﬀects of soil joints on soil mass properties: Bulletin Association Engineering Geologists, Vol. 29, No. 4, pp. 415–430. Mosthaf, K.; Rolle, M.; Petursdottir, U.; Aamand, J.; and Jørgensen, P. R., 2021, Transport of tracers and pesticides through fractured clayey till: Large undisturbed column (LUC) experiments and model-based interpretation. Water Resources Research, Vol. 57, No. 5. https://doi.org/10.1029/2020WR028019 Park, H. J. and West, T. R., 2002, Sampling bias of discontinuity orientation caused by linear sampling technique: Engineering Geology, Vol. 66, pp. 99–110. Perroux, K. M. and White, I., 1988, Designs for disc permeameters: Soil Science Society America Journal, Vol. 52, pp. 1205– 1215. Piteau, D. R. and Martin, D. C., 1977, Description of detail line engineering geology mapping method: Rock slope engineering, part G: Federal Highway Administration Manual FHWA-1397-208, Portland, OR, 29 p. Prvanovic, A., 2015, Influence of Soil Joints on Permeability of Glacial Till: Ph.D. Dissertation, Department of Geology, Kent State University, Kent, OH, 222 p. Rowe, R. K. and Booker, J. R., 2011, Contaminant migration through fractured till into an underlying aquifer. Canadian Geotechnical Journal, Vol. 27, No. 4, pp. 484–495. White, G. W., 1980, Glacial geology of Lake County, Ohio: Ohio Department of Natural Resources Report of Investigations No. 117, Columbus, OH, 20 p. Williams, R. E. and Farvolden, R. N., 1967, The inﬂuence of joints on the movement of ground water through glacial till: Journal Hydrology, Vol. 5, pp. 163–170.

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Topographic Controls on the Duration of Sinkhole Flooding in Central Tennessee, USA MARK ABOLINS* ALBERT OGDEN Geosciences Department, Middle Tennessee State University, Murfreesboro, TN 37132

Key Terms: Sinkhole Flooding, Carbonate Karst, Remote Sensing, LiDAR, DTM, Geographic Information Systems ABSTRACT Daily 3-m cell size Dove multispectral satellite images were used to estimate the duration of flooding in topographic depressions in the approximately 12-km2 Shores Road focus area, central Tennessee. Flooding happened after rainfall events February 4–6 and 10–12, 2020. The filling and draining of 60 topographic depressions (mostly sinkholes) were observed by visually inspecting images for eight dates. The average duration of inundation was approximately 15 days, and all depressions were dry 30 days after the end of the first event. Satellite observations were consistent with ground-based water elevation measurements in three sinkholes and eight wells. Depression volume, elevation, and depth were estimated with a 0.76-m cell size LiDAR 3D Elevation Program (3DEP) digital terrain model (DTM). Long-duration (21 or more days) inundation was statistically more likely in larger-volume, lower-elevation, and deeper depressions at the p , 0.01 level. When combined in a probit model, these three factors accurately sorted 80 percent of depressions into long-duration and short-duration categories, and the accurately classified depressions accounted for approximately 95 percent of depression volume. Duration of inundation was not related (p . 0.05) to percentage of depression covered by slow-permeability soils, as determined from the Soil Survey Geographic Database. This study shows how to use repeat Dove satellite imagery and a LiDAR 3DEP DTM to assess how multiple topographic factors contribute to the duration of inundation in sinkholes. INTRODUCTION Many factors affect carbonate sinkhole formation, including topography, bedrock lithology, soil type and depth, depth of the water table, and the presence of subsurface conduits (e.g., Cahalan and Milewski, 2018; Boo *Corresponding author email: mark.abolins@mtsu.edu

et al., 2020). However, relatively little has been published about controls on the duration of sinkhole flooding. The purpose of this study is to statistically evaluate the relationship between inundation duration and sinkhole volume, elevation, and depth within the approximately 12-km2 Shores Road focus area inside the urban growth boundary (UGB) of the rapidly urbanizing community of Murfreesboro, TN. In addition, we statistically evaluate the relationship between inundation duration and percentage of the sinkhole covered by slow-permeability soil as determined from the Soil Survey Geographic Database (SSURGO; TN149 Metadata, 2022). Volume, elevation, and depth were identified as potentially predictive based on previous studies in central Tennessee (Bradley and Hileman, 2006) and southern Illinois (Panno et al., 2013). A non-statistical, fieldbased study of sinkholes near Murfreesboro, TN (Bradley and Hileman, 2006), showed that sinkholes that were at low elevations and near the water table retained water longer and revealed longer-duration flooding in shallow sinkholes at a range of elevations. In addition, Bradley and Hileman (2006) inferred that some sinkholes were better connected to subsurface conduits than others. Bradley and Hileman (2006) did not quantitatively evaluate volume as a factor in duration of inundation, but Panno et al. (2013) found that large-volume sinkholes drained relatively quickly in the southern Illinois sinkhole plain. This study leverages relatively new satellite imagery and LiDAR digital terrain model (DTM) datasets to statistically evaluate relationships. Since 2016, Dove 3-m cell size multispectral images have been used to observe the rise and fall of floodwaters (e.g., Cooley et al., 2017). These images can be used as a substitute for repeat aerial photography and can be downloaded by academics at no cost. The volume, minimum elevation, and depth of flooded depressions can be estimated with the Tennessee LiDAR DTM (Tennessee Department of Finance & Administration, 2022) and similar U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) DTMs (Arundel et al., 2015). The Tennessee LiDAR DTM has a 0.762-m cell size, and the vertical accuracy is Quality Level 2. Together, Dove satellite imagery and the Tennessee LiDAR DTM make it possible to evaluate the

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Figure 1. (A) Location of areas subject to sinkhole flooding in relation to the eastern United States, Tennessee, and the Central Basin. (B) Location of the Shores Road focus area in relation to the City of Murfreesboro and its town square, the Murfreesboro urban growth boundary (UGB), and the areas where Bradley and Hileman (2006) studied sinkhole flooding (U.S. Geological Survey [USGS] areas). Arrows indicate flow paths for groundwater dye traces. Arrow tails are dye injection points, and arrowheads are dye detection points. SR ¼ Stones River; S ¼ Stewarts Creek; O ¼ Overall Creek. Dye traces are from Ogden (2020) and Abolins and Ogden (2021).

relationship between a sinkhole’s duration of inundation and its volume, minimum elevation, and maximum depth. STUDY AREA Murfreesboro is on the southeast side of the Nashville Metropolitan Statistical Area and grew in population by approximately 40 percent between 2010 and 2020, attaining a population of nearly 153,000. In the years ahead, the city is expected to expand into the UGB. Because sinkhole ﬂooding happens in parts of the UGB, the Rutherford County, TN, Planning Commission sponsored a study in 2019–2020 (Ogden, 2020). The purpose of the study was to characterize the sinkhole ﬂooding problem in an effort to inform decisions about development, explore potential solutions (e.g., Ogden, 1995), and investigate the 276

relationship between flooding and well water elevations. However, that study was largely restricted to field measurements of the elevation of water in six wells and three sinkholes before and during a sinkhole flooding event that happened during February 2020. In the study described in this paper, the authors use repeat Dove imagery to estimate the duration of flooding within 60 topographic depressions (mostly sinkholes), permitting the statistical evaluation of factors that determine sinkhole flooding. The approximately 12-km2 Shores Road focus area is situated about 12.5 km west–northwest of the Murfreesboro Town Square (Figure 1). As shown in Figure 1, Bradley and Hileman (2006) documented sinkhole flooding in three areas close to the town square. Murfreesboro is in the Central Basin, which is a lowland within the Interior Low Plateaus geomorphic province. Much of the basin is underlain by limestone, and karst is widespread.

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HYDROGEOLOGIC SETTING Geologic mapping (Wilson, 1965) indicates that Late Ordovician Ridley Limestone is at the surface throughout the entire focus area except for small areas where the overlying Lebanon Limestone aquitard caps hills along the northern, western, and southwestern edges (Figure 2). Ridley Limestone consists of an aquitard sandwiched between two aquifers and is underlain by the Pierce Limestone aquitard (Figure 3). Based on the elevation of the Lebanon–Ridley contact, the thicknesses of stratigraphic units, and the elevation of the land surface, Abolins and Ogden (2023) concluded that the informally defined lower Ridley karst aquifer and the informally defined lower Ridley confining layer are at the surface in much of the study area. Pierce Limestone is also likely at the surface in some areas previously mapped as Ridley Limestone, because Pierce Limestone has been misinterpreted as the lower Ridley confining layer at some central Tennessee locations (Farmer and Hollyday, 1999). During wet times, groundwater commonly perches on top of Pierce Limestone and flows through conduits in the lower Ridley karst aquifer to springs that occur at or near the Ridley–Pierce contact (Rima et al., 1977). Because water flow through the aquifer is rapid compared with porous media, the Ridley Limestone aquifer generally dries up during late summer and early fall in the focus area. Much flooding likely happens within Ridley Limestone immediately above the aquitards, because elsewhere in central Tennessee, contaminants have moved along the contact between the informally defined upper Ridley Limestone aquifer and the informally defined lower Ridley confining layer (Crawford and Ulmer, 1994). In addition, the longest central Tennessee cave system, Snail Shell and Nanna Caves, is close to the contact between Ridley and Pierce Limestones (Crawford, 1988; Abolins and Ogden, 2021). Groundwater dye tracing shows that the focus area contains two groundwater basins (Figure 2). A sinking stream flows into the western basin from the south, and groundwater then flows northwest to Stewarts Creek, a tributary of the Stones River. From the eastern basin, groundwater flows northeast to Overall Creek, another tributary of the Stones River. Sinkhole flooding begins about when the West Fork of the Stones River exceeds flood stage (Bradley and Hileman, 2006), an event that occurred 13 times between October 1, 2007, and August 8, 2022, which averages to one event per approximately 417 days. The maximum stage of the river on February 6, 2020, was the fifth highest out of these 13 events, indicating that the 2020 event was relatively typical and a little large. Stage and daily precipitation from February 1 to March 7, 2020, are shown in Figure 4, and they can be

compared with stage and precipitation during the three events studied during 2001–2002 by Bradley and Hileman (2006). The 2020 maximum stage (5.88 m) exceeded that of two of the 2001–2002 events. In addition, during two 2020 precipitation events, the 72-hour precipitation exceeded that of the smallest 2001–2002 event. These two events happened February 4–6 (9.91 cm) and February 10–12 (8.08 cm). Based on stage and precipitation, the 2020 event was comparable to the 2001–2002 events, and the 2020 event was a relatively typical sinkhole flood. METHODS Observing the Filling and Draining of Topographic Depressions The filling and draining of topographic depressions (mostly sinkholes) were observed with Dove PS2 (Dove-C) 3-m cell size multispectral satellite data. The authors examined three types of complementary images: (1) color-infrared (CIR), (2) normalized difference water index (NDWI), and (3) change detection. See McFeeters (1996) for more information about NDWI. Cloud-free images were available for February 2 (before the storms), February 8 (between the first and the second storm events), and on six dates between February 17 and March 7 inclusive. The authors observed each depression on all image dates to document filling and draining. They also observed each depression on 2018 and 2021 National Agricultural Imagery Program (NAIP) natural color and CIR digital air photos to identify permanent water bodies. For each of the Dove image dates, each depression was coded as dry or partially wet. • CIR. In these composites, red is band 4 (near-infrared: 0.780–0.860 mm), green is band 3 (visible red: 0.590– 0.670 mm), and blue is band 2 (visible green: 0.500– 0.590 mm). On these images, vegetation is red and water is dark. band 2 band 4 • NDWI. NDWI ¼ band 2 þ band 4. This provides a number between 1 and 1, with higher numbers generally indicating water and developed land. NDWI is high for both water and developed land because both lack vegetation. • Change detection. A dry NDWI image is subtracted from a wet NDWI image, creating an image in which most high-value cells indicate a change from dry to wet. The change image reduces confusion between water and developed land, because developed land does not change between these two. Therefore, developed land has a low value on the change detection image, and water has a high value.

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Figure 2. (A) Topography of the Shores Road focus area. Wilson (1965) mapped Ridley Limestone throughout this area except for small peripheral areas where Lebanon Limestone (Olb) overlies Ridley Limestone. Water elevation levels were measured in the indicated sinkholes and wells on the ground during late 2019 and early 2020 (Ogden, 2020). See Figure 1 for location and groundwater dye trace detection points. See Tennessee Department of Finance & Administration (2022) for speciﬁcations of 0.76-m cell size vertical Quality Level 2 topographic data. (B) Contour map (2.5-m interval) showing numerous closed depressions.

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others came from the 0.5-m interval map. All geospatial work was performed in ArcGIS. Field Measurements of Sinkhole and Well Water Elevations Sinkhole and well water elevations were measured on the ground when permitted by landowners and when ﬂooding did not prevent access. Staff gauges were placed in the sinkholes, and well water levels were measured using an electric line water level meter. Statistical Evaluation

Figure 3. Principal stratigraphic units within and below the focus area (Crawford, 1988; Wilson, 1965). C-Horizons are important in speleogenesis and contaminant transport.

Identification of Topographic Depressions For each area covered by water on the images, the authors delineated the corresponding topographic depression by manually selecting the topographic contour best approximating the extent and shape of the inundated area. The Tennessee LiDAR DTM was contoured with a 0.25m and a 0.5-m contour interval. Some best-ﬁt contours were selected from the 0.25-m contour interval map, and

Pair-wise Mann-Whitney U-tests (e.g., Johnson and Thrailkill, 1973) are used to evaluate the relationship between inundation duration and each of the following: volume, maximum depth, minimum elevation, and percentage of slow-permeability soils. Depressions are divided into two groups: long-duration inundation and short-duration inundation. Then, a one-tailed statistical test is used to determine, for example, whether differences between the volumes of sinkholes in the two groups are probably explained by chance. Parameters with a statistically signiﬁcant difference are then used in a probit model (e.g., Fustos et al., 2017) of inundation duration. The model uses the statistically signiﬁcant parameters to predict whether a depression is subject to short-duration or long-duration inundation.

Figure 4. Stage of the West Fork of the Stones River, TN (USGS03428200), and daily precipitation, February 1 to March 8, 2020. Vertical dashed lines indicate dates of satellite images.

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Figure 5. Color-infrared (CIR) images showing dry conditions on February 2 (A) and wet conditions on February 8 (B). Water is blue, roads and buildings are cyan and white, bare ﬁelds are light brown, grass (including cropland) is red, and forest is dark reddish-brown. Inundated topographic depressions are outlined in white. See Figure 1 for location.

RESULTS Filling and Draining of Topographic Depressions Images reveal widespread inundation on February 8 (Figures 5–7) and February 17 (Figure 7), and this inundation persisted through some time between February 29 and March 7. In some depressions that were dry on February 2, peak inundation was observed on February 8, and in others, peak inundation was observed on February 17. See Appendix A (https://www.aegweb.org/e-egsupplements) for inundation duration, volume, depth, and minimum elevation for each depression. Figures 5–7 show the kinds of images used to observe filling and draining, but their scale in this paper does not reveal the full extent of flooding in some depressions. For example, flooding beneath trees in some depressions is visible 280

when the images are enlarged in the ArcGIS geographic information system. The total duration of inundation within each of the 65 depressions is shown in Figure 8. Because all depressions except for permanent water bodies were dry on March 7, the duration of inundation is known to be 29 days or less, and the duration of inundation for some depressions is known to be at least 23 days. Depressions were generally not full for the entire time. Rather, duration of inundation is based on the depression being at least partially full. The most common durations of inundation were 2–10 and 11–15 days, and only four nonpermanent features partially held water for 27 or more days. Examination of NAIP 2018 and 2021 orthophotos shows that permanent water bodies (inundation duration of 34 days) cover a small part of the focus area, because

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Figure 6. Normalized difference water index (NDWI) images showing dry conditions on February 2 (A) and wet conditions on February 8 (B). Water and developed land are dark blue. Inundated topographic depressions are outlined in black. See Figure 1 for location.

there are only five comprising an area of only approximately 0.026 km2 (about 1.8 percent of the area of waterfilled depressions). These five were excluded from further analysis, and most or all are probably farm ponds. Of the other 60, most are probably sinkholes, because they are within an internally drained area. Within this group of 60, there are probably four exceptions within and adjacent to the I-840/Veterans Parkway interchange in the northeast corner of the map. The spatial association of those four with the interchange and their uniformly flat bottoms on LiDAR images indicate that they were constructed. Some Shores Road focus area structures flood as shown in Figure 9A and B, and flooding blocks access to some homes as shown in Figure 9B. Relationships Involving Volume and Depth of Topographic Depressions Most large water-covered areas are within well-defined topographic depressions approximating the shape and extent of the inundated area. Excluding the five permanent

depressions, the remaining 60 depressions range in area from 229 m2 to 0.49 km2. However, the 26 depressions holding water for 21 or more days accounted for 40 percent of the depressions, approximately 85 percent of the depression area, and approximately 93 percent of the depression volume. Outside of well-deﬁned topographic depressions, inundation persisted in two sizeable areas. One (Feature 1 in Figure 7) was along a sinking stream, and the other was along both sides of a private farm road (Feature 2 in Figure 7). Depression Volume The nine largest-volume depressions all had volumes exceeding 29,820 m3 and partially held water for at least 21 days (Figure 8). Those nine alone comprise 78 percent of the depression area and approximately 89 percent of the depression volume. Of the nine, seven are sinkholes and two are human-made basins along I-840. In addition, the seven smallest-volume depressions had volumes of less than 275 m3, and all held water for 20 or

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Figure 7. NDWI change detection images for February 8 (A) and February 17 (B). The dry February 2 NDWI image was subtracted from each. Inundation along a sinking stream (Feature 1) and along both sides of a farm road (Feature 2) do not happen within well-deﬁned topographic depressions.

fewer days. In summary, the largest-volume depressions held water for a long time, and the smallest-volume depressions held water for a shorter amount of time. The volume results suggest that 21 days is a natural break point for placing the depressions in a long-inundation duration (at least 21 days) group and a short-duration (,21 days) group. Minimum Depression Elevation The 11 highest-elevation depressions are all in the short-duration group, but inundation duration varies among low-elevation depressions. For example, four of the 11 lowest-elevation depressions are in the shortduration group and seven are in the long-duration group. 282

Maximum Depression Depth The 13 shallowest depressions are all in the shortduration group, but inundation duration varies in the deepest depressions. For example, six of the 13 deepest depressions are in the short-duration group and seven are in the long-duration group. Ground-Based Water Level Measurements Sinkhole Water Elevations Water levels were measured in three sinkholes between February 1 and March 7 (Figure 10A), recording the impact of the precipitation events, as well as the continued rise of water in some sinkholes after the ﬁrst event. These sinkholes were chosen for their accessibility. See

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Figure 8. Inundation duration. Bold outlines surround the nine most voluminous inundation features. All are sinkholes (A) except for two humanmade basins (B) along I-840. See Ogden (2020) for data on the groundwater dye trace marked with an asterisk. Other dye traces are from Abolins and Ogden (2021). See Figure 9A for an orthophoto of 2010 ﬂooding at Well D and Figure 9B for an unmanned aerial vehicle (UAV) oblique photo of 2020 ﬂooding at UAV. See Figure 1 for location.

Abolins and Ogden (2023) for data. The Baker sinkhole (86°300 51.06800 W, 35°510 32.90400 N) was dry on February 2, and water elevations peaked on February 6–7, declined slightly on February 7–9, and then peaked again on February 14. That sinkhole then dried again and was dry on all measurement dates from February 21 to March 6. The response of the Able sinkhole (86°32 0 26.35700 W, 35°51 019.37600 N) was similar, but water levels rose by 1.3 m on February 6–7, declined on February 7–9, and attained a maximum for the entire time of measurement on February 14. In addition, there was water in the sinkhole on March 2 during a 2-day (March 1–2), 4.17cm rainfall event. Like the Able sinkhole, the Charlie sinkhole (86°30 0 39.07700 W, 35°51 0 3.58100 N) gained water on February 6–7, and like the Baker sinkhole, water levels peaked on February 7. However, Baker sinkhole water levels differed from those of the other two in that levels declined between February 7 and February 14, and water still remained in the sinkhole on February 21 and March 6. Well Water Elevations Water levels were measured in seven wells between February 1 and March 7 (Figure 10B), documenting the impact of precipitation on water levels within the Murfreesboro Limestone aquifer beneath the Pierce Limestone aquitard. See Abolins and Ogden (2023) for data. Water levels rose

in six of the seven wells on February 2–7, peaking on February 7. The water level in the seventh well, Well F, was not measured on February 2, but it was at an elevation of 179.8 m on January 12 and rose to 183.1 m on February 7. In all wells except Well D, water levels declined on February 7–21, and levels declined further from February to March 6. No measurements were possible in Well D after February 7 because of sinkhole flooding. ANALYSIS AND INTERPRETATION Statistical Analysis of Inundation Duration Mann-Whitney U-test results (Table 1) indicate that at the p , 0.01 level, small-volume depressions, high-elevation depressions, and shallow depressions hold water for less time. The relationship between inundation duration and percentage of depressions covered by slow-permeability soil is not statistically significant at the p , 0.05 level. The lack of a statistically significant relationship indicates that for the most part, in the Shores Road focus area, SSURGO (TN149 Metadata, 2022) cannot be used to predict inundation duration. Part of the reason for this might be that moderate- to slow-permeability soils cover the entire area; therefore, permeability may not vary much. Variations in soil thickness might be important, but soil thickness data are not available for the focus area. In addition, it is possible that within the focus area,

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Figure 9. (A) Orthophoto of May 2010 flooding of residences near Well D on Figure 8. This was associated with the second-highest stage, 2007 to present, on the West Fork of the Stones River, so flooding was probably more extensive than in 2020. Source: City of Murfreesboro, TN. (B) Oblique unmanned aerial vehicle (UAV) view of February 13, 2020, flooding at UAV on Figure 8. Source: Rutherford County, TN.

SSURGO is not detailed enough or accurate enough for this analysis. A probit model of inundation duration based on depression volume, minimum elevation, and maximum depth accurately assigns 80 percent of depressions to either longduration or short-duration groups, and depressions accurately assigned to one of these two account for 95 percent of depression volume. This model has a chi-square of 25.8 and is statistically signiﬁcant at the p , 0.01 level. As described here, the model is an improvement over simply considering each parameter in isolation, and the model underscores variability in inundation duration with respect to elevation and depth: • Volume. The model correctly assigns the nine largestvolume depressions to the long-duration group and the seven smallest-volume depressions to the shortduration group.

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• Minimum elevation. The model correctly assigns all 11 of the highest-elevation depressions to the shortduration group. For the 15 lowest-elevation depressions, the model correctly assigns nine to the long-duration group and ﬁve to the short-duration group, and the model incorrectly assigns one long-duration depression to the short-duration group. • Maximum depth. The model accurately assigns the 12 shallowest depressions to the short-duration group. However, the accuracy for the 12 deepest depressions is 75 percent, underscoring variability in inundation duration with respect to depth. For the 12 deepest depressions, the model accurately assigns ﬁve to the longduration group and four to the short-duration group, and the model inaccurately assigns two short-duration depressions to the long-duration group and one longduration depression to the short-duration group.

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Figure 10. Water elevations in sinkholes (A) and wells (B). See Figure 2 for locations of sinkholes and wells.

In summary, the added value of the probit model is most apparent in its ability to sort a high percentage of low-elevation and deep depressions into the correct groups. There are three plausible explanations for the 20 percent of depressions that are misclassiﬁed. First, karst environments are heterogeneous; therefore, two depressions can drain at different rates even though they have the same volume, minimum elevation, and maximum depth, because the two can differ in the extent to which they are connected to subsurface conduits. Second, the elevation of the water table varies, so the bottom of one depression can be at the water table and the bottom of a second depression can be well above the water table, even though both share the same minimum elevation. Third, soil thickness can differ in two sinkholes, even if

they share all other characteristics, and sinkholes filled with thick soils are more likely to retain water than those filled with thin soils. Interpretation and Discussion Comparison with Other Geographic Areas The statistical analysis reveals both similarities and differences between the Shores Road focus area and the other areas where duration of sinkhole flooding has been studied. A similarity between the Shores Road focus area and the nearby USGS focus areas (Bradley and Hileman, 2006) is that longer-inundation duration was more likely at lower elevations. Likely explanations

Table 1. One-tailed Mann-Whitney U-test results for factors affecting inundation duration. Volume (m3)* Days of inund. Max. Mean Min. SD p

,21 23,209 4,116 66 5,969

21 1,148,997 99,101 278 262,376 0.00008

Min. Elevation (m)* ,21 188.7 183.3 175.9 2.97

21 185.9 181.1 177.7 2.16 0.00084

Max. Depth (m)* ,21 21 7.01 7.32 1.56 2.76 0.31 0.61 1.61 1.94 0.00131

Slow-Permeability Soil (%)** ,21 100 53.4 0.0652 41.2

21 100 62.5 0.0650 34.4 0.24196

*Signiﬁcant at the p , 0.01 level. **Not signiﬁcant at the p , 0.05 level. The dataset consisted of 39 depressions in the ,21 days of inund. group (short duration), and 21 depressions in the 21 days of inund. group (long duration). Min. ¼ minimum; Max. ¼ maximum; inund. ¼ inundation; SD ¼ standard deviation.

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Figure 11. Relationships between volume and depth (A) and minimum elevation (B) in 60 topographic depressions in the Shores Road focus area, central Tennessee.

include that more low-elevation depressions are close to the water table, water ﬂows from high-elevation to lowelevation depressions, or some combination of these. Another similarity is that longer-duration ﬂooding was more likely in deeper depressions. Part of the reason for this might be that the bottoms of these depressions are closer to the water table. The similarities described in this paragraph are apparent even though Bradley and Hileman (2006) studied only the draining of sinkholes over intervals of 8–13 days after the 2001–2002 storms, showing that some insights into long-term relationships can be gleaned from shorter-duration studies. Another similarity between the Shores Road focus area and the nearby USGS focus areas is that many shallow depressions held water for 11 or more days. However, the large number of depressions in this study and the longer duration of repeat satellite observations provided additional insights. Although 10 of the 12 shallowest Shores Road depressions held water for 11 or more days, two of small volume (142–185 m3) and at high elevations (186–189 m) drained in 2–10 days, and all drained completely in less than 21 days, placing all 12 depressions in the short-inundation duration group in this study. Indeed, 286

deeper Shores Road depressions were statistically more likely to retain water for 21 or more days. This study found that the most statistically signiﬁcant relationship is that inundation duration is generally longer in larger-volume sinkholes. This relationship might be partially explained by the moderate correlation between volume and depth (Figure 11A), which contrasts with the weak correlation between volume and minimum elevation (Figure 11B). The relationship between volume and depth is consistent with the inference that large volume sinkholes drain slowly because the bottoms of many of these sinkholes are probably near the water table after a rainfall event, although the relationship does not prove this inference. In addition, it is plausible that larger-volume sinkholes take longer to drain because they are not proportionally more connected to subsurface conduits. Conceptually, a larger-volume sinkhole will take longer to drain than a smaller-volume one if each drains through a single conduit and both conduits share the same characteristics (e.g., diameter). For whatever combination of reasons, the rate at which sinkholes drain falls just short of increasing in a 1:1 proportion with volume, because the slope of the log10 volume–log10 subsurface discharge relationship is 0.93 6 0.045 (95 percent conﬁdence interval), as shown in Figure 12. The relationship between volume and inundation duration is probably generalizable to some other areas, because limited published data (Naughton et al., 2012) suggests that larger-volume Irish turloughs are also statistically (p , 0.01) more likely to remain inundated longer than smallervolume turloughs (Table 2). Because many Irish turloughs are larger in volume than the Shores Road sinkholes, the statistical comparison excludes turloughs that have a volume exceeding that of the most voluminous Shores Road depression. In addition, many of the Irish turloughs retain water longer, so the short-duration group includes those holding water for 7–36 days and the long-duration group includes those holding water for 45–91 days. The Irish results suggest that the relationship between volume and inundation duration extends to karst depressions holding water for up to 3 months. On the southern Illinois sinkhole plain, the relationship between volume and inundation duration is the opposite of the relationship observed in the focus area and in Ireland. In southern Illinois, Panno et al. (2013) found that the sinkholes that drained rapidly had volumes of 10,000–1 million m3 and sinkholes that drained slowly had volumes of 1,000–100,000 m3. Panno et al. (2013) thought the large-volume southern Illinois sinkholes drained quickly because they emptied into large-diameter (e.g., 7 m) cave passages and the water table is typically relatively deep there. In contrast, the smaller-volume sinkholes are drained by smaller-diameter (e.g., 0.15–0.30 m) conduits subject to clogging by glacial sediments. Volume

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Figure 12. Relationship between depression volume and the rate at which Shores Road sinkholes empty. As used here, subsurface discharge includes evapotranspiration. Subsurface discharge was calculated by dividing the volume of the depression by the number of days the depression held water. A middle value was used for the duration of inundation. For example, if a depression held water for 21–22 days, then the inundation duration was 21.5 days.

is important in Illinois, but the nature of the relationship is different, underscoring the importance of evaluating the relationship between inundation duration and volume in a particular area of interest. Relationship between Ground-Based Measurements and Remotely Sensed Data Ground-based measurements at all three sinkholes (Figure 10A) support a rise in water levels because of rainfall, drying in less than 15 days in some sinkholes, and different hydrologic responses to the second rainfall event among different sinkholes. In response to the second event, water levels rose in the Able sinkhole, remained at the same elevation in the Baker sinkhole, and declined in Charlie sinkhole. The elevation of water in all wells also rose after the ﬁrst rainfall event (February 4–6), Table 2. One-tailed Mann-Whitney U-test results for the volume of short-inundation and long-inundation duration Irish turloughs. Volume (m3) Days of inund. N Max. Mean Min. SD p

7–36

45–91

7 878,000 560,125 356,000 203,458

8 1,077,000 851,000 636,000 166,863 0.00639

Volume is the volume of water, not the topographic depression. Short-inundation turloughs are Ballinderreen, Caranavoodaun, Carrowreagh, Kilglassaun, Lough Aleenaun, Rathnalulleagh, Skealoghan, and Turloughmore. Long-inundation turloughs are Ardkill, Brierﬁeld, Croaghill, Lisduff, Lough Gealain, Roo West, and Termon South. See Naughton et al. (2012) for data. inund. ¼ inundation; Min. ¼ minimum; Max. ¼ maximum; SD ¼ standard deviation.

and the elevation fell between that event and February 21 in those wells in which measurements could be made (Figure 10B). The decline is consistent with the decline observed for water levels in many depressions in the February 21 image. In addition, the well water elevations are consistent with water elevations declining at different rates in different locations. For example, the water elevation in Well A declined by only 0.76 m from February 7–21 (»0.05 m/d), but the elevation then declined by 6.13 m from February 21 to March 7 (»0.44 m/d). In contrast, the elevation of water in Well G declined by 5.45 m from February 7–21 (»0.39 m/d), and then declined by only 0.40 m from February 21 to March 7 (»0.03 m/d). However, the elevation of water in the wells is not necessarily directly related to the elevation of water in the sinkholes, because the wells tap into the Murfreesboro Limestone aquifer below the Pierce Limestone aquitard and the sinkholes are above the aquitard in Ridley Limestone. SUMMARY Mann-Whitney U-tests show that the duration of sinkhole ﬂooding in 60 central Tennessee topographic depressions can be largely explained by volume, minimum elevation, and maximum depth at the p , 0.01 level. In the 12-km2 central Tennessee focus area, the nine largestvolume (.29,820 m3) depressions held water for 21 or more days (long duration), and the seven smallest-volume (,275 m3), 10 highest-elevation (.186 m), and 12 shallowest (,0.4 m deep) depressions held water for less than 21 days (short duration). A probit model combining all three parameters correctly sorted 80 percent of depressions into the long-duration and short-duration groups, and correctly classiﬁed depressions accounted for approximately 95 percent of depression volume. The added value of the

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model is most apparent in its ability to sort many low-elevation and deep depressions into the correct groups. The probit model is significant at the p , 0.01 level. These statistical tests show that repeat Dove satellite imagery and a LiDAR 3DEP DTM can provide insights into topographic controls on the duration of sinkhole flooding. RECOMMENDATIONS Although it is prudent in many settings to avoid development in and near sinkholes that flood for any duration, inundation duration could be used to guide mitigation and land use strategies. For example, sinkholes that drain relatively quickly would generally be candidates for mitigation strategies, because these strategies are more likely to eliminate or greatly reduce flooding where the problem is less severe. Mitigation strategies include drilling storm water drain wells to divert water to the subsurface, removing soil-clogging sinkholes, and installing standpipes (e.g., Ogden, 1995). In contrast, areas of long-duration flooding might be best used as open space (e.g., Brody and Highfield, 2013). Unfortunately, most Shores Road focus area sinkholes are not good candidates for implementation of mitigation strategies, because the water table is probably shallow (Abolins and Ogden, 2023). During high rainfall events, the water table likely rises, intersecting the sinkhole bottoms. Therefore, enhancing flow from the sinkholes to the subsurface will not reduce flooding. In contrast, raising driveways to prevent floodwater from blocking access to structures might make sense at some locations. We make four recommendations to geoscientists seeking to distinguish between sinkholes that flood for a long amount of time and those that drain quickly. The relationships discussed earlier are likely related in part to a hydrogeologic setting in which a shallow water table is perched above a shallow (,10-m depth) bedrock aquitard. Relationships may be different in geologic settings where the water table is deeper, glacial till mantles the sinkholes, or the hydrogeology differs in other ways. 1. Imagery. To discover relationships in a specific area, we recommend using repeat 3-m cell size imagery to document the draining of sinkholes over the entire period that they hold water, and we recommend using this information to divide the sinkholes into long-inundation duration and short-duration categories. 2. DTM. We recommend using 3DEP DTMs (1-m cell size or less) to estimate depression volume, depth, and minimum elevation. 3. Statistics. We recommend using Mann-Whitney U-tests to check for statistically significant relationships between inundation duration and depression volume, depth, and minimum elevation. Geoscientists are encouraged to use knowledge 288

about specific areas to assess relationships between inundation duration and additional parameters like soil permeability and thickness if relevant data of sufficiently high resolution and quality exist. Parameters that have a statistically significant relationship should be incorporated into a multivariate probit model of inundation duration. 4. Ground truth. We recommend measuring well water elevation and the elevation of water in sinkholes at a few locations. The probit model will help planners and engineers identify sites where sinkhole flooding is most likely to persist for long amounts of time. Four specific recommendations are described briefly here: 1. Volume. Larger-volume Shores Road depressions are statistically more likely to hold water longer than smaller-volume Shores Road depressions, so volume should be included in the analysis of inundation duration. This relationship is generalizable to some other geographic areas and to karst depressions that flood for a longer amount of time. For example, larger-volume Irish turloughs are also statistically more likely to retain water longer, and some of them flood for as much as 3 months (Naughton et al., 2012). Another reason to include volume in the analysis of inundation duration is that the relationship works in a different way in some geologic settings. For example, Panno et al. (2013) describe large-volume southern Illinois sinkholes as draining more quickly than small-volume ones. 2. Multiple variables. Although the relationship between volume and inundation duration is the most statistically significant, inundation duration is also related to depression depth and minimum elevation at the p , 0.01 level. In addition, the correlation between volume and depth is moderate and the correlation between volume and minimum elevation is weak, suggesting that depth and minimum elevation provide additional information about inundation duration. Therefore, all three should be included in a multivariate probit model of inundation duration. 3. SSURGO slow-permeability soils. In the Shores Road depressions, the percentage of depressions covered by slow-permeability soils is not statistically related to the duration of inundation at the p , 0.05 level. This is for determination of the percentage of coverage from SSURGO. Therefore, SSURGO slow-permeability soils were not used in the inundation duration probit model for the Shores Road area, although researchers are encouraged to statistically evaluate the possibility that soils data

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of sufficient resolution and quality might be useable in other hydrogeologic settings. 4. Duration of inundation study. After a rainfall event, we recommend documenting the duration of inundation over the entire time that the depressions hold water, because draining of shallow depressions was not fully characterized in a shorter 13-day study of sinkhole flooding near the Shores Road area (Bradley and Hileman, 2006). Specifically, shallow depressions in that study were described as retaining water for a long amount of time. This study showed that 10 of the 12 shallowest Shores Road depressions held water for 11 or more days, but all dried in less than 21 days. In contrast, deeper Shores Road depressions were statistically more likely to hold water for more than 21 days. ACKNOWLEDGMENTS The authors thank Rutherford County, TN, for supporting the measurement of Shores Road focus area sinkhole and well water elevations during 2019–2020. REFERENCES ABOLINS, M. AND OGDEN, A., 2021, Application of the global SRTM and AW3D30 digital elevation models to mapping folds at cave sites: International Journal of Speleology, Vol. 50, pp. 75–89, https://scholarcommons.usf.edu/ijs/vol50/iss1/7/ ABOLINS, M. AND OGDEN, A., 2023, Sinkhole flooding above a shallow bedrock aquitard in a rapidly-urbanizing community, central Tennessee, USA: Geomorphology, Vol. 425, 108586, https://www.sciencedirect. com/science/article/pii/S0169555X23000065?via%3Dihub ARUNDEL, S. T.; ARCHULETA, C. M.; PHILLIPS, L. A.; ROCHE, B. L.; AND CONSTANCE, E. W., 2015, 1-meter digital elevation model specification: U.S. Geological Survey Techniques and Methods, book 11, chap. B7, 25 p. with appendixes, http://dx.doi.org/10.3133/tm11B7 BOO, H. N.; KIM, Y. J.; AND YOUN, H., 2020, Identification and quantitative analysis of sinkhole contributing factors in Florida’s Karst: Engineering Geology, Vol. 271, 105610, https://doi.org/10.1016/j. enggeo.2020.105610 BRADLEY, M. W. AND HILEMAN, G. E., 2006, Sinkhole Flooding in Murfreesboro, Tennessee, 2001–02: U.S. Geological Survey Scientific Investigations Report 2005-5281, Reston, VA, 38 p., https:// pubs.usgs.gov/sir/2005/5281/PDF/SIR20055281.pdf BRODY, S. D. AND HIGHFIELD, W. E., 2013, Open space protection and flood mitigation: A national study: Land Use Policy, Vol. 32, pp. 89–95. CAHALAN, M.D. AND MILEWSKI, A.M., 2018, Sinkhole formation mechanisms and geostatistical-based prediction analysis in a mantled karst terrain: Catena, Vol. 165, pp. 333–344, https://doi.org/10. 1016/j.catena.2018.02.010

COOLEY, S. W.; SMITH, L. C.; STEPAN, L.; AND MASCARO, J., 2017, Tracking dynamic northern surface water changes with high-frequency Planet CubeSat imagery: Remote Sensing, Vol. 9, No. 12, 1306, https://doi.org/10.3390/rs9121306 CRAWFORD, N. C., 1988, Karst hydrology investigation in the vicinity of the Campus-Injector Complex for the proposed Middle Tennessee Site of the Superconducting Super Collider. In Crawford, N. C. and Barr, T. C. (Editors), Tennessee White Paper: Hydrology of the Snail Shell Cave—Overall Creek Drainage Basin and Ecology of the Snail Shell Cave System: Tennessee Department of Conservation, Nashville, TN, 203 p., 7 plates. CRAWFORD, N. C. AND ULMER, C. S., 1994, Hydrogeologic investigations of contaminant movement in karst aquifers in the vicinity of a train derailment near Lewisburg, Tennessee: Environmental Geology, Vol. 23, pp. 41–52. FARMER, J. J. AND HOLLYDAY, E. F. P., 1999, Regional Sub-surface Correlation of the Pierce Limestone and Adjacent Limestones of Middle Tennessee: U.S. Geological Survey Report of Investigations 47, 21 p. FUSTOS, I.; ABARCA-DEL-RIO, R.; AVILA, A.; AND ORREGO, R., 2017, A simple logistic model to understand the occurrence of flood events into the Biobio River Basin in central Chile: Journal of Flood Risk Management, Vol. 10, pp. 17–29. JOHNSON, J. T. AND THRAILKILL, J., 1973, Variables affecting well success in a Kentucky limestone aquifer: Journal of Hydrology, Vol. 20, pp. 327–333. MCFEETERS, S. K., 1996, The use of the normalized difference water index (NDWI) in the delineation of open water features: International Journal of Remote Sensing, Vol. 17, pp. 1425–1432. NAUGHTON, O.; JOHNSTON, P. M.; AND GILL, L. W., 2012, Groundwater flooding in Irish karst: The hydrological characterisation of ephemeral lakes (turloughs): Journal of Hydrology, Vol. 470–471, pp. 82–97. OGDEN, A., 1995, Investigation of sinkhole flooding problems in Knoxville, Tennessee. In Beck, B. (Editor), Karst Geohazards: Engineering and Environmental Problems in Karst Terrane: A.A. Balkema, Rotterdam, the Netherlands, pp. 291–296. OGDEN, A., 2020, Ground Water (Dye) Tracing and Well Water Level Monitoring along Shores Road and Royal Glen Subdivision to Determine Hydrogeologic Causes and Potential Solutions to Sinkhole Flooding Problems: Report to Rutherford County Planning Commission, Murfreesboro, TN, 38 p. PANNO, S. V.; KELLY, W. R.; ANGEL, J. C.; AND LUMAN, D. E., 2013, Hydrogeologic and topographic controls on evolution of karst features in Illinois’ sinkhole plain: Carbonates and Evaporites, Vol. 28, pp. 13–21. RIMA, D. R.; MORAN, M. S.; AND WOODS, E. J., 1977, Ground-water supplies in the Murfreesboro area, Tennessee: U.S. Geological Survey Water-Resources Investigation 77-86, 76 p. Tennessee Department of Finance & Administration, 2022, Tennessee Department of Finance and Administration Strategic Technology Solutions: Electronic document, available at https://lidar.tn.gov/ pages/what-is-lidar TN149 Metadata, 2022, Soil Survey Geographic (SSURGO) database for Rutherford County, Tennessee: Electronic document, available at https://websoilsurvey.sc.egov.usda.gov/ WILSON, C. W., Jr., 1965, Geologic map of the Rockvale Quadrangle, Nashville, TN: Tennessee Division of Geology, Nashville, TN.

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The Chemistry of Cave Ice: Two Examples from Slovenia DEVIN F. SMITH* W. BERRY LYONS SUSAN A. WELCH School of Earth Sciences, The Ohio State University, Columbus, OH 43210-1398

MATIJA ZORN JURE TIČAR MATEJ LIPAR Anton Melik Geographical Institute, ZRC SAZU, Novi trg 2, Ljubljana, 1000, Slovenia

ANNE E. CAREY School of Earth Sciences, The Ohio State University, Columbus, OH 43210-1398

Key Terms: Karst, Ice Cave, Slovenia, Oxygen-18, Deuterium, Ice Geochemistry, Southeastern Alps ABSTRACT Cave ice can contain a wealth of paleo-climatic and geochemical information that is rapidly being lost with the melting of the temperate zone cryosphere. The karst areas of Slovenia host over 260 ice caves. We collected samples for stable water isotope, major ion, and nutrient analyses from two Slovenian ice caves. Samples included two shallow ice cores in Sne zna Cave, collected »5 m apart, and an ice face profile in Iva ci ceva Cave. All ice isotopic ratios reflected modern precipitation that could be described by high-elevation meteoric water lines. An offset suggested that fractionation and mixing processes of melted ice affected the isotopic signals. Cation concentrations of ice in both caves showed Ca Mg . Na . K. The high Ca21 and Mg21 contents and elevated HCO32 concentrations indicate that CaCO3 dissolution within the local karst landscape is a primary control on ice chemistry. Low concentrations and inconsistent profile patterns of other major ions and nutrients suggest atmospheric deposition and vadose zone leaching were also primary sources of ions to the ice. Differences in Cl2 and SO422 profile concentrations at similar depths in Sne zna Cave imply that ice melting, water mixing, and re-freezing processes can affect the primary climatic signal stored in the ice. While temperate ice caves can be repositories of climatic information, secondary diagenetic processes that affect ice chemical composition alter the original signal. In addition to chemical analysis, physical processes within the caves must be studied *Corresponding author email: smith.11880@osu.edu

at a small spatial scale to understand and interpret ice chemistry. INTRODUCTION Ice caves occur in bedrock with perennial ice accumulation (Lipar et al., 2021b) that is hoar, stalactitic, massive (floored and stratified), and extrusive (Yonge and Macdonald, 1999). The preservation of ice in these caves is dependent on cave climate, which is closely tied to the climate and environment outside of the cave. Ice caves are a widely distributed portion of the cryosphere, but the ice in many caves is rapidly disappearing (Pers oiu and Onac, 2019). Ice caves are found throughout the Northern Hemisphere, often occurring at lower elevations and in locations where surface ice is absent, such as in Central Europe (Kern and Pers oiu, 2013). Permafrost conditions at high latitudes or high elevations enhance ice cave preservation, but ice caves have also been identified in locations as warm as the Mediterranean climate of Turkey (Pers oiu and Onac, 2019). During the past 20 to 25 years, studies have investigated ice caves around the world (Citterio et al., 2004; Fórizs et al., 2004; Clausen et al., 2007; Fang et al., 2010; Kern et al., 2011b; Pers oiu et al., 2011; Sancho Marcén et al., 2012; Higham and Palmer, 2017; Yonge et al., 2017; Carey et al., 2019, 2020; and Smirnov and Sokolov, 2022). Ice may be created by snow compaction, freezing of karst drip water, or condensation of water vapor. Climatic factors that are modulated by seasonality, such as temperature and precipitation, control the rate of ice accumulation and stability within ice caves (Pers oiu and Pazdur, 2011). The preservation and accumulation of the ice formed are also facilitated by the geographic location and geomorphologic setting of a particular cave. When caves are located at lower latitudes, mechanisms such as cold air trapping,

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unidirectional air flow, and evaporative cooling preserve ice masses (Perșoiu and Onac, 2019). In order to understand the formation processes and preservation of ice in caves, the subsequent diagenetic processes (e.g., melting, re-freezing, mixing, sublimation, evaporation) must be analyzed for each individual cave, or even for a specific location within a cave (Clausen et al., 2007; Pers oiu et al., 2011). Water can enter a cave quickly through the vadose zone via fissures in the rock or slowly through water drip. Snow can accumulate through vertical and inclined cave entrances, and once in the cave, snow may melt and then freeze as ice or directly form ice through densification (Lipar et al., 2021b). Differences in the fast inflow and slow inflow of water delivery change the mechanisms that drive ice formation and, consequently, the chemical composition of ice. Mixing of incoming water and meltwater from older precipitation may further complicate interpretations (Kern et al., 2011a), as these physical hydrologic alterations can alter ice chemical compositions. In addition, evaporation can concentrate a chemical compound or cause chemical precipitation. Progressive freezing in the top ice layers may also concentrate solutes in residual water, which is then frozen as a solute-enriched bottom layer of ice (Citterio et al., 2004). Nevertheless, ice caves can provide an archive of chemical solute and stable isotope composition in recent precipitation (Carey et al., 2019, 2020). There is discussion on the extent to which the ice found in caves may also serve as paleo-climate archives and indicators of climate change (Pers oiu et al., 2011; Zorn et al., 2020). Recent studies have demonstrated the utility of ice caves as climate archives when paired with analysis of ice formation, diagenesis, and dating methods (Pers oiu et al., 2017; Sancho et al., 2018; and Racine et al., 2022a). Cave ice d18O and d2H compositions and other climate proxies, such as pollen and microfossils stored in the ice, can be used to reconstruct climate conditions during the time of ice formation (Baõdaõlutaõ et al., 2020). Many of the ice caves now under investigation have entrances that have only recently been uncovered as surface snow and ice cover have melted (Zorn et al., 2020). The possible future rapid disappearance of ice caves as paleo-climate archives (Kern and Pers oiu, 2013) has ironically been made possible by the current warming climate, which has melted surface ice and snow and allowed access to the ice caves. The present and the future of these cave ice deposits are closely tied to climate. Ablation of ice within these caves has been related to the increases in percolation of warm water within the karst system (Holmlund et al., 2005), to ice melt through warming temperatures (Fórizs et al., 2004), and to enhanced summer rainfall (Pers oiu et al., 2021). Increased warming, atmospheric perturbations, and enhanced precipitation events in Central Europe may well lead to a potentially rapid reduction of cave ice deposits in the region over the next decades (Colucci et al., 2016). This 292

cave ice loss would coincide with the rapid loss of small glaciers that has occurred in the karst regions of northern Italy and Slovenia over the past century (Triglav-Čekada et al., 2014; Čekada et al., 2016; and Lipar et al., 2021a). More than 260 caves with permanent ice features have been found and mapped in Slovenia (Mihevc, 2018; Speleological Association of Slovenia, 2022; and Blatnik et al., 2023). We have previously reported on results of coarsely sampled vertical profiles from Paradana Cave, Sne zna Cave, and Iva ci ceva Cave (Carey et al., 2019, 2020). Here, we present results of the geochemical analysis of detailed profiles from Iva ci ceva Cave and Sne zna Cave, wherein we measured stable isotopic compositions along with major ions and nutrient elemental concentrations. Iva ci ceva Cave is the highest cave in Slovenia with known stratified layers of ice and is geographically located in the northwest region of Slovenia, which hosts the majority of ice caves. These data were compared to Sne zna Cave, which is a tourist cave with stratified layers of ice and is located further east. To our knowledge, this is the first attempt to drill ice cores from ice caves in Slovenia. The goal of this research was to investigate the utility of cave ice as a paleo-climatic proxy through the quantification of stable water isotopic compositions and chemical concentrations of the ice to identify patterns and discern environmental variables that control ice chemistry. We hypothesized that the ice would reflect precipitation chemistry that has been altered by interactions with the vadose zone and epikarst as it flows into the caves. We tested this hypothesis by collecting samples for chemical analysis from a 2 m exposure in Iva ci ceva Cave at the end of the 2019 melt season during the brief time when the cave is safely accessible for sampling and from two shorter (36 cm) ice cores drilled in Sne zna Cave to explore differences on a small spatial scale. To investigate the meteorological influence on ice chemistry, we compared the data to pristine snow and firn chemistry of Alto dell’Ortles (3,830 m above sea level [a.s.l.]) in Italy. Mt. Ortles is among the highest summits in the Eastern European Alps, and the snow/ firn layers demonstrate evidence of preserving the climate record (Gabrielli et al., 2010). BACKGROUND Stable water isotopes oxygen (18O/16O; d18O) and hydrogen (2H/1H; d2H) are useful natural tracers that can be used as a climate proxy to reconstruct paleo-climate. Previous studies have linked the isotopic composition of ice in caves to winter ambient temperature changes and changes in moisture sources (Perșoiu, 2018). The utility of d2H and d18O as climate proxies is limited by the fractionation effects that control the meteoric source water and occur during ice formation and by secondary diagenetic processes. Stable water isotope data are reported in delta notation (d):

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Cave Ice Chemistry, Slovenia 18

d O ð%Þ ¼

O=16 Ox

O=16 OVSMOW

! 1 3 1; 000

(1)

in reference to the Vienna Standard Mean Ocean Water (VSMOW) standard. The global meteoric water line (GMWL) linear relationship between d2 H and d18O is well deﬁned in precipitation (Craig, 1961): d2 H ¼ 8 3 d18 O þ 10:

composition. Meltwater will likely be inﬂuenced by evaporation prior to re-freezing, and the evaporative inﬂuence will result in enrichment of 2H and 18O in the residual water (Craig and Gordon, 1965). Similar to isotopic fractionation during ice formation, the degree of residual water fractionation from evaporation is determined by ambient variables. These processes further alter the initial d2H and d18O values of the source water.

(2)

This equation describes the average d2H–d18O relationship for global precipitation. On a local or regional scale, the d2H–d18O relationship can be characterized by local meteoric water lines, where the d2H–d18O divergence from the GMWL is caused by geographic, topographic, and/or meteorologic variables that are driven by kinetic and equilibrium liquid–vapor fractionation (Dansgaard, 1964). The effects that control local precipitation include the (1) latitude effect, where increasing latitude corresponds to a decrease in temperature and greater fractionation; (2) continental effect, where heavier isotopologues preferentially condense as an air mass travels inland from the ocean; (3) altitude effect, where heavier isotopologues preferentially condense as an air mass cools with increasing altitude; and (4) amount effect, where heavier isotopologues are preferentially condensed from cloud vapor, so precipitation incorporates lighter isotopologues with continuous rainfall (Dansgaard, 1964). All of these effects cause continual loss of the heavier isotopes of 2H and 18O in the precipitation and depletion of d2H and d18O signatures. The combination of effects can cause the localized isotopic signatures of meteoric waters that feed ice caves. The deuterium excess (d-excess ¼ d2H 8 3 d18O) values preserved in ice can also provide information on the original moisture source of the water (Racine et al., 2022b). When meteoric waters enter a cave as water or snow, further fractionation of source water occurs during ice formation. Under equilibrium conditions, the heavier isotopes (2H and 18O) are preferentially incorporated into ice due to differences in bond strength between heavy and light isotopes (Clark and Fritz, 1997). However, the degree of fractionation is dependent on ambient variables that may speed up or slow down the freezing process, creating kinetic fractionation conditions. Rapid freezing may result in a lesser degree of fractionation between heavy and light isotopes in the ice and residual water (Jouzel et al., 1999; Souchez and Tison, 1987). Partial melting and refreezing can cause additional isotopic fractionation through the same enrichment process. Seasonal temperature ﬂuctuations within a cave can result in ice melting, mixing of ice layers, and partial re-freezing of ice (Citterio et al., 2004; Clausen et al., 2007; and Ersek et al., 2018). The mixing, melting, and re-freezing of ice cause the isotopic composition of ice to deviate from the source water isotopic

SAMPLING AND ANALYTICAL METHODOLOGY Sample Collection Sne zna Cave (Cave Register No. 1254) is situated at 1,504 m elevation, below the tree line in northern Slovenia (Figure 1). The cave ceiling thickness, from the surface to the roof of the chamber, is 40–50 m in the Entrance Chamber, where samples were collected from massive floor ice. The source of water to the cave is drip from water that has traveled through the vadose zone and epikarst. Samples from Sne zna Cave (Figure 2) were collected from the 10m-deep floor ice, which represents approximately 400 m3 of ice (Mihevc, 2018). We sampled at two locations »5 m apart using a SIPRE ice auger that had been rinsed in deionized water and wrapped in plastic wrap prior to use. Cores were drilled to the depth of a rock layer incorporated into the ice, which we assumed to be widespread throughout the ice block since the inflow of debris was constant from the entrance slopes and walls of the cave. Core A was 36 cm in length, and core B was 24 cm in length. The ice cores were returned to the laboratory, sectioned, and wrapped in new Al-foil for subsequent storage under freezing conditions prior to sampling for analysis. Both cores were then cut into 2 cm sections that were melted into precleaned high-density polyethylene (HDPE) bottles. Deionized water was placed in contact with the Al-foil, and multiple samples of this water were collected and analyzed as “blanks” along with the melted ice samples (see below). Iva ci ceva Cave (Cave Register No. 2399) is situated at 2,471 m elevation, above the tree line in northwest Slovenia (Figure 1). The cave ceiling thickness is 60–70 m, and samples were taken from a stratified section of wall ice within the cave that is fed by drip water and condensation within the cave (Novak and Kustor, 1983). There was no massive floor ice in the section of the cave where samples were collected. Wall ice samples from Iva ci ceva Cave were collected from the ice face described in Carey et al. (2020); however, this data series is much more detailed (40 samples collected every 5 cm over 200 cm) (Figure 3) than the six samples published in Carey et al. (2020) over about the same distance. An »1–2 cm layer of ice was removed from the ice face in Iva ci ceva Cave before samples were drilled with Petzl Laser ice screws and chipped directly into polyethylene bags, with nitrile gloves worn during sample collection. The ice screws

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Figure 1. Map of Slovenia showing karst areas, ice cave locations sampled in this study, Mount Triglav (2,864 m a.s.l.), and Global Network of Isotopes in Precipitation (GNIP) and Slovenian Network of Isotopes in Precipitation (SLONIP, 2023) sample locations (Komac and Urbanc, 2012; Geodetic Administration of the Republic of Slovenia, 2022; IAEA/WMO, 2022; Speleological Association of Slovenia, 2022).

had a diameter of 1.4 cm. Therefore, the edges of consecutive samples were 3.6 cm apart, and the center of each sample was 5 cm apart. After melting, aliquots for ion and nutrient analyses were filtered through 0.45 lM pore-size Millipore filters into HDPE bottles that had been clean with 18.2 MX deionized water. Aliquots for isotope analyses were poured directly into 20 mL vials with no headspace. These water samples were shipped to The Ohio State University (Columbus, OH, USA) and refrigerated at 4°C prior to chemical and isotopic analyses within 1 week of arrival. d2H and d18O Analysis The 2H/1H and 18O/16O ratios of melted ice samples, here reported as d2H and d18O values relative to the VSMOW standards (d2H ¼ 0%, d18O ¼ 0%), were measured on a Picarro Wavelength Scanned-Cavity Ring Down Spectroscopy Analyzer Model L2130-I. Seven injections of 2.3 lL were analyzed per sample. The first three injections were discarded to avoid memory effects, while the last four injections were averaged to produce uncorrected d2H and d18O values. Samples were corrected by internal laboratory 294

standards that had been calibrated to VSMOW at the Institute of Arctic and Alpine Research (INSTAAR) at the University of Colorado at Boulder through analysis by a dual-inlet mass spectrometer. Internal laboratory standards were Colorado (d2H ¼ 126.3%, d18O ¼ 16.53%), Nevada (d2H ¼ 104.80%, d18O ¼ 14.20%), Ohio (d2H ¼ 61.80%, d18O ¼ 8.99%), and Florida (d2H ¼ 9.69%, d18O ¼ 2.09%). Instrumental precision was 0.34% and 0.028% for d2H and d18O, respectively. In-run accuracy was 2.0% and 0.35% for d2H and d18O, respectively. Ice d2H and d18O data were compared to the GMWL (Craig, 1961), Slovenia regional meteoric water line (RMWL), Kredarica meteoric water line (MWL), Rate ce MWL, and Zgornja Radovna MWL. The Slovenia RMWL was determined from samples collected at Ljubljana (n ¼ 336, 1981–2010) at 282 m a.s.l., Kozina (n ¼ 39, 2000–2003) at 484 m a.s.l., and Portoro z (n ¼ 84, 2000–2006) at 2 m a.s.l., reported by the International Atomic Energy Agency (IAEA) as part of the Global Network of Isotopes in Precipitation (GNIP) program (IAEA/WMO, 2022; Vre ca et al., 2006, 2011). The unweighted Slovenian RMWL was calculated though an ordinary least square regression

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Cave Ice Chemistry, Slovenia

Figure 2. Floor ice in the Sne zna Cave where ice samples were taken (photo: Matej Lipar).

method (Hughes and Crawford, 2012). The Kredarica (2514 m a.s.l), Rate ce (864 m a.s.l), and Zgornja Radovna (750 m a.s.l) MWLs were calculated from samples collected as part of the Slovenian Network of Isotopes in Precipitation (SLONIP) (Vre ca et al., 2022; SLONIP, 2023). SLONIP is a platform that provides detailed information on the isotopic composition of precipitation from eight locations in the country to capture differences driven by climate, geography, and precipitation sources (Vre ca et al., 2022). The reported precipitation weighted MWLs were calculated with a reduced major access (RMA) regression (Crawford et al., 2014). Chemical Analyses Un-acidiﬁed melted ice samples were analyzed for major cations (Na2þ, Mg2þ, Kþ, Ca2þ) and anions (Cl , SO42 ) using ion chromatography following previously described methods (Welch et al., 2010). Nutrients (NNO3 þ NO2 , N-NH4þ, and H4SiO4 [dissolved silica]) were measured calorimetrically using a Skalar SANþþ nutrient auto-analyzer system according to methods recommended by the manufacturer. N-NO3 þ NO2 and NNH4þ are hereafter referred to as NO3 þ NO2 and

NH4þ. Bicarbonate (HCO3 ) values were calculated using the major cation and anion charge imbalance approach (Welch et al., 2010). Propagated error from instrument precision and accuracy was #10 percent for Ca2þ, Mg2þ, Cl , and N-NO2 þ NO3 . Propagated error for SO4 was 22 percent. Na, K, and H4SiO4 had errors ,5 percent. Analytical blanks (N ¼ 5) were run on deionized water in contact with Al-foil used in the ﬁeld. Blanks for ice collection bags were also analyzed (Carey et al., 2020). All analytes in the blanks were below the limits of detection, with the exception of Ca2þ (0.9 micromole, lM) and Mg2þ (3.5 lM). All chemical analyses from this study are reported in Supplemental Material Table S1. Correlations between chemical components were conducted in JMP Statistical Software Version Pro 15 using a pairwise method. RESULTS Ice d2H and d18O Values Sne zna Cave d2H and d18O proﬁles for cores A and B ranged from 99.6% to 67.6% and from 14.1% to 9.99%, respectively. Deuterium excess (d-excess)

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Figure 3. Wall ice proﬁle sampled in Iva ci ceva Cave (photo: Jure Ti car).

ranges for cores A and B were þ11.1% to þ13.4% and þ11.3% to þ12.8%, respectively (Supplemental Material Table S1). The ice profiles had mean values for d2H, d18O, and d-excess of 81.2%, 11.69%, and þ12.3% in core A and 78.8%, 11.36%, and þ12% in core B. These mean d2H, d18O, and d-excess values were not significantly different (a ¼ 0.05), but core A showed a larger range of variation in d2H and d18O ( 31.9%, 4.13%) than core B ( 13.6%, 1.63%). The mean d2H ( 80.3%) and d18O ( 11.57%) values of all the Sne zna Cave ice analyses were significantly different than the mean values of the Iva ci ceva Cave wall ice profile of 296

60.7% and 9.29%, which were enriched in 2H and 18 O in comparison to the Sne zna Cave samples. Iva ci ceva Cave d2H and d18O values ranged from 75.2% to 50.0% and from 11.1% to 7.93%, respectively (Supplemental Material Table S1). The d-excess values for Iva ci ceva Cave proﬁle ranged from þ12.1% to þ15.7% (mean þ13.6%). As illustrated in Figure 4, the d18O and d2H data from all proﬁles fall above the GMWL and the Slovenia RMWL (Craig, 1961; IAEA/WMO, 2022). Sne zna Cave ice samples plot on or above the Rate ce and Zgornja Radovna MWLs and below the Kredarica MWL. Iva ci ceva

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Figure 4. Plot of Sne zna Cave and Iva ci ceva Cave ice d2H and d18O values showing the global meteoric water line (d2H ¼ 8 3 d18O þ 10), Slovenia regional meteoric water line (d2H ¼ 7.94 3 d18O þ 9.03), Kredarica MWL (d2H ¼ 8.42 3 d18O þ 18.98), Rate ce MWL (d2H ¼ 8.04 3 d18O þ 11.7), and Zgornja Radovna MWL (d2H ¼ 7.98 3 d18O þ 11.13). Weighted mean values for Kredarica, Rate ce, and Zgornja Radovna are also shown.

Cave samples plot above all other MWLs, and either on or below the Kredarica MWL (Figure 4). The regression line of all Sne zna Cave ice samples is d2H ¼ 7.82 3 d18O þ 7.6, and the regression line of Iva ci ceva Cave ice samples is d2H ¼ 7.80 3 d18O þ 11.7. The Iva ci ceva Cave ice intercept matches that of the Rate ce MWL, but the cave ice slope is lower than the MWL. The most enriched isotopic values occurred at profile depths of 20, 70, and 150–155 cm for the Iva ci ceva Cave samples (Figure 5). The most depleted isotopic compositions for Iva ci ceva Cave occurred at depths of 5, 30–35, 55, 105–110, 125, 170, and 200 cm, in the profile; however, these variations are considered minor. The most enriched values for Sne zna Cave occurred at depths of 12 cm and 36 cm (core A) and 4 cm and 12 cm (core B). The most depleted Sne zna Cave values were at depths of 8 cm (core A) and 24 cm (core B). Ice Inorganic Chemistry The ice profiles for Sne zna Cave had higher concentrations of both sulfate (SO42 ) and chloride (Cl ) than concentrations observed in Iva ci ceva Cave, with maximum values of »18 lM and »20 lM at »10 cm depth in the Sne zna Cave cores A and B, respectively. All the SO42 and Cl values measured for Iva ci ceva Cave were ,5 lM. The concentrations of Ca2þ were similar for ice from both caves, with the highest values of »300 lM at 5 cm and »100 cm depth in the Iva ci ceva Cave. NO3 þ NO2

concentrations were the most variable, ranging between »9 and 1 lM, with both the highest and lowest values observed in the Iva ci ceva Cave ice. For Sne zna Cave, ice NO3 þ NO2 concentrations ranged only from 1 to 5.5 lM (Figure 5 and Supplemental Material Table S1). As with the stable isotope profiles, there was no consistent pattern for the ion concentrations with depth in the Sne zna Cave cores. Except for the bottom 20 cm, Cl and SO42 concentrations in the Iva ci ceva Cave profile were universally low, whereas Ca2þ and NO3 þ NO2 concentrations showed much more compositional variation. Pearson correlation coefficients (r) of stable water isotopes, major ions, and nutrient species for all sampled ice profiles are presented in Table 1. Ice d2H and d18O values were negatively correlated with chemical ion concentrations at the 95 percent confidence interval, except for Ca2þ. The depleted isotopic values (lower H and O isotope ratios) corresponded to higher chemical concentrations of Cl , SO42 , Naþ, Kþ, and Mg2þ. However, these correlations were not as robust or consistent when analyzing relationships for ice from each cave individually (Supplemental Material Tables S2 and S3). These same grouping of ions all had significant positive relationships with each other (Table 1). In contrast, Ca2þ had weak, positive, or negative relationships with these major constituents, with the exception of Mg2þ (r ¼ 0.36, p , 0.05). Ca2þ and H4SiO2 also had a significant positive relationship (Table 1). NO3 þ NO2 only showed weak positive and negative relationships with other constituents. NH4þ had

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Figure 5. Ice chemistry proﬁles for cores A and B from Sne zna Cave and Iva ci ceva Cave ice. The diagrams are expanded for the top 36 cm of ice to better visualize the patterns of all the isotopic and compositional proﬁles. Error bars represent propagated error of measurement accuracy and precision.

significant positive relationships with most major cations, except Ca2þ. Concentrations of NH4þ and Cl were also significantly correlated. We interpreted these relationships with caution because they are driven by 22 percent of the samples from Sne zna Cave having NH4þ concentrations .1 lM. NH4þ concentrations in Sne zna Cave core profiles ranged from ,1 to 4.6 lM, whereas NH4þ concentrations at Iva ci ceva Cave were ,1.2 lM (Supplemental Material Table S1). Correlations among data from both Sne zna Cave and Iva ci ceva Cave ice were not consistent between the two sites (Supplemental Material Tables S2 and S3). Many of the correlations observed in Sne zna Cave ice were not observed for Iva ci ceva Cave ice samples. Sne zna Cave cores A and B had significant negative correlations between Cl and stable water isotopes, and among Cl and major ions Naþ, Kþ, Ca2þ, and nutrients NO3 þ 298

NO2 , NH4þ, and H4SiO4. Dissolved silica (H4SiO4) also had significant positive correlations with Mg2þ and Ca2þ and significant negative correlations with stable water isotopes (Supplemental Material Table S2). NO3 þ NO2 had significant positive correlations with Naþ and Kþ. Concentrations of Cl , SO42 , Naþ, and Kþ for Iva ci ceva Cave ice had significant positive correlations with each other, and NO3 þ NO2 concentration in Iva ci ceva Cave ice also had a significant positive relationship with Kþ (Supplementary Table 3). DISCUSSION Cave Ice d2H and d18O The d2H and d18O values for ice in Sne zna Cave and Iva ci ceva Cave reflect modern precipitation, but additional

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Cave Ice Chemistry, Slovenia Table 1. Correlations of chemical variations for ice cores A and B from Sne zna Cave and Iva ci ceva Cave ice. Correlation values (r) highlighted in gray are signiﬁcant (a ¼ 0.05). Correlation was estimated by pairwise method, with four samples excluded due to below detection limit values.

d2H (%) Cl (lM) SO42 (lM) Naþ (lM) Kþ (lM) Mg2þ (lM) Ca2þ (lM) N-NH4þ (lM) N-NO3 þ NO2 (lM) H4SiO4 (lM) HCO3 (lM)

d18O (%)

d2 H (%)

Cl (lM)

SO42 (lM)

Naþ (lM)

Kþ (lM)

Mg2þ (lM)

Ca2þ (lM)

N-NH4þ (lM)

N-NO3 þ NO2 (lM)

H4SiO4 (lM)

1.00 0.82 0.68 0.65 0.70 0.57 0.06 0.42 0.05 0.04 0.04

0.84 0.70 0.67 0.72 0.56 0.10 0.44 0.04 0.07 0.01

0.74 0.81 0.83 0.55 0.16 0.61 0.07 0.21 0.05

0.58 0.55 0.39 0.16 0.20 0.09 0.17 0.13

0.89 0.37 0.17 0.50 0.14 0.23 0.09

0.44 0.21 0.56 0.23 0.17 0.11

0.36 0.37 0.01 0.01 0.51

0.17 0.11 0.31 0.98

0.12 0.10 0.07

0.03 0.10

0.29

environmental processes played roles in controlling their isotopic compositions. The d18O profile of Iva ci ceva Cave wall ice shows greater variation than the Sne zna Cave floor ice profiles (Figure 5). Due to differences in geographic location and geomorphological setting, we did not expect these data sets to reflect the same isotopic enrichment or depletion patterns. Furthermore, we cannot constrain the ages of ice in either cave, and the ice in these two locations could represent different time frames. The mean Iva ci ceva Cave ice d2H and d18O values were similar to the mean of congelation ice (d2H ¼ 61.99%, d18O ¼ 9.25%) measured in a cave ice sample (M-17; Cave Register No. 5878) also in the Julian Alps in northwest Slovenia (Racine et al., 2022b). Sne zna Cave d2H and d18O values were isotopically depleted in comparison to Cave M-17. However, the high (.10%) d-excess values of both Iva ci ceva Cave and Sne zna Cave ice samples resembled those reported by Racine et al. (2022b), which are characteristic of a west and central Mediterranean moisture source (Gõmez-Hernández et al., 2013; Kern et al., 2020). As shown in the d2H-d18O plot (Figure 4), the Iva ci ceva Cave samples were isotopically enriched in comparison to Sne zna Cave samples, and while the slopes of the regression lines were similar, the intercepts differed by »4%. Both cave ice data sets plotted above the Slovenia RMWL (IAEA/WMO, 2022), indicating that the RMWL from lower elevations does not adequately describe the precipitation source of ice to the cave. This finding supports the purpose of the SLONIP program, which seeks to characterize the localized variation of Slovenian precipitation isotopic compositions (Vre ca et al., 2022). It is important to measure the stable isotopic composition of localized precipitation to identify the initial isotopic composition of ice source water. Iva ci ceva Cave d2H and d18O The mean Iva ci ceva Cave ice d2H and d18O values are similar to the mean Rate ce ( 65.3%, 9.55%) and

Zgornja Radovna ( 60.6%, 8.94%) values, but the data plot above those MWLs (Figure 4). At 2,471 m a.s.l., the isotopic composition of water entering Iva ci ceva Cave is probably better described by the Kredarica MWL (Figure 4). The weighted mean precipitation isotopic composition at Kredarica (n ¼ 34) is lower than the Iva ci ceva Cave samples, but this mean precipitation value likely accurately reﬂects the source water to ice in Iva ci ceva Cave. Freezethaw cycles alter the d2H and d18O values of the ice via fractionation and mixing processes through the preferential incorporation of heavy isotopes into ice under nonequilibrium conditions, partial ice melting and freezing at different rates, and evaporation of melt water that enriched the residual water in heavy isotopes (Craig and Gordon, 1965; Souchez and Tison, 1987; Jouzel et al., 1999; Citterio et al., 2004; Clausen et al., 2007; and Ersek et al., 2018). The Iva ci ceva Cave ice samples plot below the Kredarica MWL and are described by a regression line slope ,8. The regression line of the ice sample offset from the MWL is termed the freezing line (Jouzel and Souchez, 1982; Souchez and Jouzel, 1984). The freezing line is idealized for a “lake ice model,” where an isotopically uniform water body freezes from the top down. The initial frozen layer of surface ice is enriched in 18O and 2H because of the preferential incorporation of heavy isotopes into ice. The frozen surface creates a closed system in which the underlying water freezes from the top, and the resulting ice is increasingly depleted due to the continual incorporation of heavy isotopes into newly created ice (Citterio et al., 2004). Therefore, the freezing line can be extrapolated to identify the isotopic composition of the initial water. This “lake ice model” is not ideal for Iva ci ceva Cave wall ice because the system was not sealed off by an ice boundary layer. However, this same fractionation process has also been observed for open systems (Jouzel and Souchez, 1982; Souchez and Jouzel, 1984), where ice formed by thin layers of water freezing can be described by an isotopic freezing line (Souchez

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et al., 2000). This idea of open-system fractionation is supported by the lack of an inverse d-excess–d2H (or d18O) relationship, which would arise under equilibrium conditions while freezing (Supplemental Material Figure S1). The absence of the inverse d-excess–d2H relationship indicates that incomplete freezing of the water deposited and/or rapid freezing due to quick temperature changes resulted in non-equilibrium processes within the cave (Souchez et al., 2000; Pers oiu et al., 2011). The Kredarica precipitation weighted mean isotopic composition plots on the Iva ci ceva Cave ice data regression line (Supplemental Material Figure S2), which implies that it represents the initial source of ice in Iva ci ceva Cave. In addition to the subsequent freezing of thin water layers along the ice wall, it is possible that the regression line describing the Iva ci ceva Cave ice data may also be caused by ice melt and the subsequent evaporation of lighter isotope water prior to re-freezing. Seasonal temperature fluctuations can cause melting and mixing of layers, where the meltwater undergoes an evaporative influence. Mean annual air temperature at the Mt. Kredarica meteorological station (2,514 m a.s.l.) above the Iva ci ceva Cave is 0.7°C (1991–2020; ARSO, 2023). While the assumed average annual temperature is below freezing in the portion of the cave covered in ice, the maximum temperature measured in the cave where ice is located is .0°C, and there is a seasonal freeze-thaw cycle that occurs in the caves (Novak and Kustor, 1983). Therefore, the preferential evaporation of 1H and 16O from meltwater likely causes further enrichment of the residual water, which is eventually frozen again due to seasonal temperature fluctuations in the cave (Clausen et al., 2007). Sne zna Cave d2H and d18O Seasonal freeze-thaw events likely altered the stable isotopic compositions in Sne zna Cave (Mihevc, 2018). Papi and Pipan (2011) reported freeze-thaw activity at the site in Sne zna Cave where our samples were collected, and elevated temperatures may have caused ice to melt and evaporation of standing water. The mean annual air temperature at the Krvavec meteorological station (1,740 m a.s.l.), located approximately 20 km SW from the Sne zna Cave, is 3.8°C (1991–2020; ARSO, 2023). The average annual temperature of the Entrance Chamber in Sne zna Cave is 5°C (62°C), whereas in other parts of the cave, the average annual temperature is »4.5°C (Mihevc, 2018). There can also be interaction between ice and meltwater when the ice is close to the melting point, further altering the initial stable isotopic composition (Clausen et al., 2007). The Sne zna Cave samples plot between the Kredarica and Rate ce and Zgornja Radovna MWLs (Figure 4). The Sne zna Cave ice is likely not described by the MWLs presented in Figure 4. Work reported in the Adriatic300

Pannonian region identified the altitude effect as the main driver of precipitation isotopic depletion of 1.2%/km for d18O and 7.9%/km for d2H, which is essentially what we observed from differences in Kredarica and Rate ce and Zgornja Radovna weighted precipitation means (Kern et al., 2020). Situated at an elevation between these collection sites, and in a different geographic location (Figure 1), the combination of continental and altitude effects on precipitation in Sne zna Cave is not captured in the Kredarica and Rate ce and Zgornja Radovna MWLs. We observed that Sne zna Cave floor ice values were more isotopically depleted compared to the wall ice in Iva ci ceva Cave (Figure 3). While the Sne zna Cave samples were floor ice samples, the lack of depletion with depth indicates that the “lake ice model” closed-system freezing process, discussed above, is also not applicable to describe these data. This is further supported by the lack of a d-excess–d2H relationship in these samples (Souchez et al., 2000; Pers oiu et al., 2011). We explain the observed depletion of Sne zna Cave floor ice in two potential ways: (1) the cold air trap nature of Sne zna Cave, where air descends into the cave from a single entrance, compared to continual wind flow through Iva ci ceva Cave, which is reported at a quarter of the external wind velocity (Novak and Kustor, 1983; Papi and Pipan, 2011; and Perșoiu, 2017), and (2) an enhanced seasonal bias through an ice chimney fed by vadose zone drip water (Mihevc, 2018). Ice formation in Sne zna Cave may be biased towards early spring, when isotopically depleted snowmelt increases water percolation through the vadose zone, and this drip water freezes within the cave (Pers oiu et al., 2021). An enhanced seasonal bias is also likely at Sne zna Cave because it is below the tree line, which can result in a greater proportion of water percolating through the vadose zone during early spring months, when evapotranspiration is low, and ice and snow are melting (Jasechko et al., 2014). Kern et al. (2011a) also observed a seasonal bias of ice formation due to snowmelt in an Austrian cave, and because of fractionation processes during freezing, they found samples that were enriched in 2 H and 18O compared to average local precipitation. This has been observed elsewhere; for example, Fórizs et al. (2004) observed d18O values in the uppermost ice in Focul Viu Cave, Romania, that were more positive than the annual mean of local precipitation due to partial melting and re-freezing of ice. It is likely that we observed enrichment fractionation of residual ice for both d2H and d18O from depleted cool-month (October–March) precipitation. The monthly weighted mean isotopic composition of cool-month precipitation at Kredarica are lower than 75% and 10%, for d2H and d18O (Vre ca et al., 2022). Other work has shown that partial freezing in the vadose zone, where the heavier isotopes are concentrated in ice in the surface, results in isotopically depleted drip water

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delivery to karst ice caves (Ersek et al., 2018). Enriched ice near the surface will melt and contribute to water flow into the cave in the early spring (ARSO, 2023). However, Ersek et al. (2018) observed that the water availability from partial melting is low, and so this is likely only a small contribution to ice formation in the cave. This water may mix and affect the original primary isotopic signature (Ersek et al., 2018). Regardless, the primary source of this water would be from depleted winter precipitation. These processes could explain the 2H and 18O depletion that we observed in Sne zna Cave ice core profiles compared to Iva ci ceva Cave, where the geomorphologic setting of Iva ci ceva Cave and consequent air flow may also cause a greater range of seasonal isotopic signatures there. We clearly observed that neither Sne zna Cave nor Iva ci ceva Cave exhibited a consistent pattern of enrichment or depletion with depth in floor or wall ice. Previous studies by Pers oiu et al. (2011, 2017), and Ersek et al. (2018) have suggested that the isotopic composition of cave ice should reflect a dampened local meteoric water signature that fluctuates both over the short term (seasonally) and long term (decadal temperature changes). The isotopic composition of the two Sne zna Cave core profiles had the same d2H and d18O values at the ice surface (top of profile), indicating the same source of water. However, the profile patterns differed until approximately 12 cm depth, where they displayed similar isotopic compositions until 22 cm depth (Figure 5). Although these cores were collected in close proximity (»5 m apart), the data suggest differences in freezing-thaw processes on a small horizontal scale, which could also be influenced by the influx of water from ice masses thawing on the entrance slope. Differences in isotopic composition in the upper portion of the two Sne zna Cave ice core profiles imply that fractionation and mixing processes caused by differences in the freezing rate, refreezing, meltwater enrichment, and mixing of ice layers during formation played a role in the observed ice isotopic compositions. Cave Ice d2H and d18O in Context The age of the Sne zna Cave and Iva ci ceva Cave ice cores cannot be constrained by the isotopic composition of the ice due to mixing and fractionation processes. However, the ice data in both caves can be contextualized for the region and climatic variations. The cave ice d18O results from this study are compared in Figure 6 with meteoric precipitation, glacier ice, snow, and ice d18O values from Gabrielli et al. (2010), Vre ca and Malenšek (2016) Carey et al. (2020), and Vre ca et al. (2022) for Slovenia. Locations near the coast receive the most enriched 18O precipitation, and the average d18O values for each location decrease with increasing elevation

Figure 6. Plot of arithmetic mean d18O for ice at Sne zna Cave and Iva ci ceva Cave versus cave elevation. Shown for comparison are average d18O values for local ice and snow samples in the vicinity of the two caves (Carey et al., 2020), the Mt. Ortles (Italian Alps) ice pit in the Italian Tyrol (Gabrielli et al., 2010), regional precipitation from locations in Slovenia and Croatia from the SLONIP database (Vre ca and Malenšek, 2016, additional reference therein), and weighted precipitation means at Kredarica, Rate ce, and Zgornja Radovna (Vre ca et al., 2022; SLONIP, 2023).

(Kern et al., 2020). Analyses from the Iva ci ceva Cave and ice and meltwater near Triglav Glacier (Figure 1) were published in Carey et al. (2020). These previously published values closely resemble the average Iva ci ceva Cave ice proﬁle d18O values in this study, but a difference in mean values between Iva ci ceva Cave d18O data from this study and data presented by Carey et al. (2020) is likely due to the different sampling resolution on the same ice wall. In this study, 40 samples were taken over 2 m, and Carey et al. (2020) presented six samples from the same 2 m (Carey et al., 2020). Overall, Iva ci ceva Cave samples from both studies differ from the overarching pattern of decreasing 18O/16O ratios with elevation (Figure 6). The Sne zna Cave ice core d18O results exhibit a better ﬁt with the pattern of d18O depletion with increased elevation. The rainfall sample at a similar elevation (Zavi zan, Croatia, 1,594 m) is near the coast of the Adriatic Sea, which likely is the cause for 18O-enriched precipitation there (Vre ca and Malenšek, 2016). Ice Cave Geochemistry Although caves associated with permanent ice are found throughout Central Europe (Perșoiu and Onac, 2019), with more than 260 in Slovenia alone (Speleological Association of Slovenia, 2022), there are few published studies on the chemistry of the ice, with a much greater focus on ice d18O and d2H data as climate proxies. The work that does exist suggests that the primary inputs of ions to cave ice are derived from three main sources: (1) the original

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Figure 7. Plot of (Ca þ Mg) and (HCO3 þ SO4) for ice samples from Sne zna Cave and Iva ci ceva Cave, with the 1:1 equivalence line shown for reference.

precipitation, (2) water that has percolated through the vadose zone, and (3) water derived from the karst system (Citterio et al., 2004; Clausen et al., 2007; Kern et al., 2011b; and Carey et al., 2019). The cation concentrations in ice from both the Sne zna Cave and Iva ci ceva Cave varied as follows: Ca Mg . Na . K, strongly suggesting that the primary source of Ca2þ and probably Mg2þ was from the dissolution of CaCO3 within the karst system or carbonate bedrock itself, as previously described by Citterio et al. (2004), Clausen et al. (2007), and Kern et al. (2011b) for caves in Italy, Slovakia, and Croatia, respectively. This supposition is supported by our data from these Slovenian caves on a Ca þMg versus HCO3 þ SO4 diagram, where data plot on a 1:1 equivalence line, indicating the ice chemistry in these two caves is dominated by the dissolution of the karstiﬁed limestone within the cave system (Figure 7). The Mt. Ortles snow pit mean Ca2þ concentration is at least 20 times lower than cave ice mean values, and the mean Mt. Ortles snow pit Mg2þ concentration is 2.5–5 times lower than the cave ice concentrations (Table 2; Gabrielli et al., 2010; Carey et al., 2019). Naþ and Kþ have been determined in previous studies to be derived from both primary precipitation and soil water (Kern et al., 2011b). The very low concentrations of H4SiO4 in our cave ice samples suggest that the primary source of these cations in our samples is

from precipitation. However, in our previous works, we have suggested that at least a portion of the Kþ could be derived from organic matter decomposition (Carey et al., 2019). Citterio et al. (2004) measured what they considered were lower than anticipated NO3 and NH4þ concentrations in cave ice and speculated that the loss of fixed nitrogen may have occurred prior to the freezing of the ice within the cave environment. Clausen et al. (2007) argued that the major source of NO3 in their cave system was derived from precipitation, while NH4þ was derived from bat droppings. Papi and Pipan (2011) reported NO3 concentrations of 24–50 lM in epikarst drip water in Sne zna Cave. Concentrations at five drip sites in different locations within Sne zna Cave had a similar range. This indicates that the source of NO3 þ NO2 , where NO3 is assumed to be the dominant species due to oxic conditions, is from epikarst drip waters that have been leached from within the vadose zone, as also suggested by Kern and Pers oiu (2013) and Carey et al. (2019). The elevated NO3 concentrations measured in drip water combined with diverse (N ¼ 10) and abundant (N ¼ 185) invertebrate taxa measured by Papi and Pipan (2011) support the hypothesis proposed by Citterio et al. (2004) that fixed nitrogen could be lost prior to freezing. The NH4þ concentrations measured in the Sne zna Cave ice cores (,1–4.6 lM) had a greater range than the Iva ci ceva Cave wall ice (,1–1.2 lM). All Sne zna Cave samples contained , 2 lM NH4þ, except for samples at 10–12 cm in core A and 20–24 cm in core B (Supplemental Material Table S1). The three samples with higher NH4þ concentrations may have been derived from biological material and mineralization of organic matter within the vadose zone (Clausen et al., 2007). Differences in Iva ci ceva Cave and Sne zna Cave Ice Chemistry Geochemical data from our previous work in both Sne zna Cave and Iva ci ceva Cave are compared to our newest data in Table 2. We cannot determine the age of the ice in either cave from previous observations or

Table 2. Mean chemical composition for Sne zna Cave ﬂoor ice cores A and B, and Iva ci ceva Caves wall ice values (this study), Iva ci ceva Caves wall ice values from Carey et al. (2020), and Mt. Ortles, Italy, snow pit (Gabrielli et al., 2010). Table is modeled after Table 3 in Carey et al. (2020).

Location Mt. Ortles snow pit Mean Sne zna Cave ice Mean Iva ci ceva Cave ice Mean Sne zna Cave ice Mean Iva ci ceva Cave ice

302

Elevation

SO4

Data Source

(m)

(lM)

Gabrielli et al. (2010) Carey et al. (2019) Carey et al. (2020) This study This study

3,860 1,135 2,457 1,135 2,457

2.4 8.2 1.6 10.6 2.0

2.1 17.8 7 7.8 1.3

1.9 12.8 2.8 10.5 5.0

0.4 11.6 1.6 3.3 1.0

4.2 17.2 10.5 20.8 10.2

7.7 340 128 163 190

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Figure 8. Plot of d18O versus Cl concentrations in ice from Sne zna Cave and Iva ci ceva Cave, showing the best-ﬁt trend line (d18O ¼ 0.24 3 [Cl] 8.92) and the average of d18O values and Cl contents measured for the Mt. Ortles snow pit (Gabrielli et al., 2010). Error bars for Mt. Ortles average show standard deviation (3%), and error bars for Sne zna Cave and Iva ci ceva Cave samples show propagated error.

observations in this study. Our original work in Sne zna Cave (Carey et al., 2019) was done by sampling a wall of ice close to the major entrance of the cave that had been formed by a large doline collapse into a large subsurface gallery. The data presented herein are from ice cores obtained by auguring into the massive ﬂoor ice of Sne zna Cave in the large collapsed region, which has a mean annual temperature of 5°C (Mihevc, 2018). Both locations are within close proximity and »75 m from the entrance (Mihevc, 2018). The mean Cl and Naþ concentrations are similar in both sample sets (Table 2). If these ions are considered as conservative tracers, then the concentration differences in other ions may suggest variation in dissolution of the karst system (Ca2þ), organic material decomposition in the vadose zone (Kþ), and/or variation in atmospheric aerosols (SO42 ). The Iva ci ceva Cave wall ice samples have lower mean Cl , SO42 , Naþ, Mg2þ, and Kþ concentrations than the lower elevation Sne zna Cave ice (Table 2). In addition, the Iva ci ceva Cave ice is more similar in composition to higher elevation snow and ice from the Italian Alps, as previously noted in Carey et al. (2020). Even though Iva ci ceva Cave ice samples have Cl , SO42 , Naþ, and Kþ concentrations that are similar to high-elevation precipitation (snow from Mt. Ortles; Table 2), Iva ci ceva Cave d18O signatures resemble those of precipitation from lower elevations (Figure 6). Figure 8 shows the similarity in Mt. Ortles snow pit and Iva ci ceva Cave ice ion chemistry, using Cl as a conservative tracer, but it also shows a difference in d18O signature. The isotopically enriched d18O values of the Iva ci ceva Cave wall

ice and most Sne zna Cave floor ice samples in comparison to the Mt. Ortles average snow pit may be explained by a mixture of snow and rainfall contributing to epikarst drip waters at lower elevations, which results in 2H- and 18 O-enriched signatures when compared to the average at Mt. Ortles. The 3% standard deviation for Mt. Ortles d18O values is caused by the seasonality captured in the ice core, particularly from warm years that caused ablation and melt percolation (Gabrielli et al., 2010). Another explanation may be incomplete freezing of epikarst drip waters where non-equilibrium fractionation resulted in the preferential incorporation of heavier isotopes into ice. Sne zna Cave ice had greater ion concentrations compared to Iva ci ceva Cave ice and Mt. Ortles snow pit (Table 2 and Figure 8), but the mean Sne zna Cave d18O signatures plot between the two locations (Figure 8). These differences in ice chemistry and isotopic composition between Sne zna Cave and Iva ci ceva Cave drive the significant inverse relationships between stable water isotopes and ions that we observed in the entire data set (Table 1). However, the individual cave d2H and d18O relationships with ions are not as robust (Supplemental Material Tables S2 and S3). Figure 8 shows an example of this visually. The apparent correlation coefficient (r) for d18O and Cl in the entire data set is 0.82 (Table 1); however, Iva ci ceva Cave ice samples have an insignificant d18O and Cl relationship (r ¼ 0.22; Supplemental Material Table S3), and Sne zna Cave samples demonstrate a significant inverse relationship for d18O and Cl (r ¼ 0.34; Supplemental Material Table S2). Sne zna Cave ice d2H and d18O values also have significant inverse

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relationships with H4SiO4 (Supplemental Material Table S2). This may be an indication of changing H4SiO4 concentrations in precipitation by season or year; however, this relationship seems to be driven by a few samples. Iva ci ceva Cave ice d2H and d18O values have significant inverse relationships with Ca2þ concentrations, and d18O values are inversely related with Kþ concentrations (Supplemental Material Table S3). These significant relationships, where depleted stable isotopic values correlate with higher ion concentrations, suggest cold-season precipitation had an elongated residence time in the vadose zone and epikarst. However, Iva ci ceva Cave sits above the tree line with scarce patches of soil and grasses, so the soil depth and vegetation density are low relative to the location of Sne zna Cave, which is below the tree line. Infiltrating waters derived from local meteoric precipitation likely have an overall shorter residence time in the shallow vadose zone above the tree line. This may limit weathering and the significance of vegetation decomposition, which results in lower ion concentrations in epikarst drip waters in Iva ci ceva Cave compared to Sne zna Cave, except for Ca2þ and HCO3 . Ice samples in Sne zna Cave had higher ion concentrations, which is indicative of longer water residence times and weathering/dissolution within the vadose zone and supports our hypothesis. Given the strong chemical signal of karst water input (i.e., high concentrations of Ca2þ and HCO3 ), it is clear that these ice samples include the major components derived from CaCO3 dissolution. The complex topography at these locations in a karst landscape has previously allowed the preservation of snow and ice through the warmer portions of the year (Securo et al., 2022). However, these glaciokarst environments in the Dinaric and southeastern Alps, with their collapsed dolines, “schachtdolines,” and shafts, have prompted high rates of chemical weathering of the underlying limestones and dolomites (Securo et al., 2022). Clearly, primary processes such as direct precipitation input as well as hydrologically associated processes related to infiltration and water transport within the triple-porosity karst system control the chemical character of cave ice. Secondary processes such as melting, mixing, and re-freezing also play a role, depending upon the elevation and aspect of the cave location (Citterio et al., 2004). In Slovenia, most ice caves are located where the mean annual temperature is above 0°C (Mihevc, 2018). Because of this, conditions within caves have to be favorable for the ice to survive (Mihevc, 2018). Thus, the freezing of epikarst waters must play a role in permanent ice formation and therefore influences ice chemistry. Cave Ice as a Paleo-Climate Proxy Previous work on the chemistry of cave ice in Central Europe has explored the use of ice cave profiles as a 304

potential proxy for understanding paleo-climate variations and past air-pollution history (Clausen et al., 2007; Kern et al., 2011b). In order to do this, a knowledge of the depth-age relationship is needed, and the assumption is made that the ice profiles represent unperturbed primary precipitation, and that melting and mixing of water within the profile have not occurred to a great extent. The horizontal variations in the Sne zna Cave ice cores, especially between the Cl and SO42 concentrations at the same depths, suggest either a different age-depth relationship between the two cores or a potential pattern of differential melting and re-freezing within the floor ice at Sne zna Cave, paralleling the interpretations made from stable water isotope data. The age of these cores is not known, and based on the geochemical data, it is probable that these ice profiles do not represent a true historic record of the chemical composition of precipitation. It is highly likely that layers of different precipitation ages have been melted, mixed together, and re-frozen to produce chemical and isotopic profiles that integrate periods of time. The depth to which this may occur is unknown at this time. The difference in fast inflow and slow inflow water delivery to the cave can also affect the chemical composition of ice. Previous work in a Romanian ice cave has demonstrated that annual ice accumulation can vary from 0.84 cm over the long term to 2 cm over the short term (Fórizs et al., 2004). Yet, many of these ice caves in Central Europe have rather thick deposits of ice associated with them, and thus many contain longer records that could reflect pre–late 20th century warming (Perșoiu, 2018). CONCLUSION The chemistry of ice formations of two caves in Slovenia reflects their precipitation source waters that have interacted with epikarst and have been altered by a set of complex processes (e.g., melting, re-freezing, mixing, sublimation, evaporation) that probably varied temporally. The d2H and d18O values of Sne zna Cave and Iva ci ceva Cave ice samples plot above the Slovenia RMWL and GMWL in isospace, but within the range of MWLs from higher elevations (Vre ca et al., 2022). The analysis of samples with MWLs in a d2H-d18O biplot shows that precipitation from lower elevations is not appropriate in relating cave ice to source water and deducing the environmental mechanisms that cause isotopic fractionation (i.e., elevation, temperature). The Iva ci ceva Cave ice regression line demonstrates the relationship between ice and weighted mean source water isotopic composition. This relationship is likely derived from (1) fractionation caused by freezing of thin layers of ice in an open system where ice is formed in cool months, (2) seasonal freeze-thaw activity that drives mixing and partial freezing, and (3) partial evaporation of meltwater, which is caused by fluctuating cave temperatures

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and relative humidities. Compounding changes in air temperature and hydroclimate are needed to extract patterns of ice accumulation and melt in ice caves in the region (Racine et al., 2022b). Localized differences in freeze-thaw activity were observed on a small (»5 m) spatial scale, as ice core profiles in Sne zna Cave had the same isotopic composition at the top of the core but did not follow the same isotopic pattern with depth. Ice chemistry data support meltwater or precipitation mixing based on stable isotopic character, in that Cl and SO42 concentrations are also inconsistent at similar depths in Sne zna Cave core A and core B. Our data show that the chemical profiles of ice in caves differ between caves and within a cave, despite similar (or identical) source waters. Ice chemistry is dominated by high Ca2þ and HCO3 concentrations from carbonate material dissolution in the epikarst domain above both caves. Variations in concentrations of NO3 þ NO2 suggested biological utilization of nitrogen prior to freezing, and Naþ and Kþ in ice were derived from vadose zone input. While we cannot determine the age of the ice, we do conclude that the ice reflects a precipitation signature that has been modified isotopically due to freeze-thaw processes that induce fractionation, and chemically from interaction with the vadose zone and epikarst before entering the cave. Regardless of these modifications in Sne zna Cave and Iva ci ceva Cave, previous studies have demonstrated that the ice in caves can be used to reconstruct climate (Fórizs et al., 2004; Pers oiu et al., 2011, 2017). However, as noted above, cryosphere loss in Central Europe, and particularly in Slovenia, is rapidly occurring, and it has been predicted that increased warming through the 21st century will lead to the demise of these features over the next decades (Čekada et al., 2016; Zorn et al., 2020; and Racine et al., 2022a). Ice caves can be important repositories of historical climate information and atmospheric chemistry despite ice formation and freeze-thaw processes that can alter the initial chemistry. Furthermore, cave ice masses are currently being lost due to warming conditions. Warming may also increase the degree of diagenesis, further altering the original chemistry recorded during ice formation. To extract historical climate data from these ice caves, the ice formation and freeze-thaw patterns must be examined at a small scale. Further chemical research and age dating of cave ice cores and ice wall profiles in the southeastern Alps will enhance the utility of these caves as climatic proxies. ACKNOWLEDGMENTS We would like to thank The Ohio State University’s Slovene Research Initiative for partial support to A.E.C. and W.B.L. to visit Slovenia and collect samples. We are extremely grateful to Professor Lonnie Thompson at Ohio State University for the use of his SIPRE auger. Additional

support for ﬁeldwork came from research program no. P6-0101, research project no. J6-3141 and J6-50214, and bilateral project BI-US/22-24-023 of the Slovenian Research and Innovation Agency. Support for laboratory instrumentation used for geochemical analyses came from National Science Foundation Directorate for Geosciences (GEO) grants EAR-0744166, EAR-0930016, and EAR1342632. We would also like to thank Russell Harmon for the suggestion that we to participate in this special issue. We acknowledge that The Ohio State University has long served as a site of meeting and exchange of Indigenous peoples, including the Shawnee, Miami, Wyandot, and Delaware Nations. We honor and respect the diverse Indigenous peoples connected to the territory where data analysis and writing of this work took place. SUPPLEMENTAL MATERIAL Supplemental Material associated with this article can be found online at https://doi.org/10.2113/EEG-D-2300001 and https://www.aegweb.org/e-eg-supplements. REFERENCES ARSO (SLOVENIAN ENVIRONMENT AGENCY), 2023, Climate Statistics: Slovenian Environment Agency, Ljubljana, Slovenia. Electronic document, available at https://meteo.arso.gov.si/met/en/climate/ tables/ BǍDǍLUTǍ, C. A.; PERȘOIU, A.; IONITA, M.; AND PIOTROWSKA, N., 2020, Stable isotopes in cave ice suggest summer temperatures in eastcentral Europe are linked to Atlantic Multidecadal Oscillation variability: Climate of the Past, Vol. 16, No. 6, pp. 2445–2458. BLATNIK, M.; OBU, J.; KOŠUTNIK, J.; AND GABROVŠEK, F., 2023, Use of terrestrial LiDAR scanner for monitoring of ice thickness in ice caves: Examples from Slovenia. In Andreo, B.; Barberá, J. A.; Durán-Valsero, J. J.; Gil-Márquez, J. M.; and Mudarra, M. (Editors), EuroKarst 2022: Springer, Málaga, Spain, pp. 185–192. CAREY, A.; SMITH, D.; WELCH, S.; ZORN, M.; TIČAR, J.; LIPAR, M.; KOMAC, B.; AND LYONS, B., 2020, The geochemistry of ice in the southeastern Alps, Slovenia: Acta Geographica Slovenica, Vol. 60, No. 2, pp. 141–153. CAREY, A. E.; ZORN, M.; TIČAR, J.; LIPAR, M.; KOMAC, B.; WELCH, S. A.; SMITH, D. F.; AND LYONS, W. B., 2019, Glaciochemistry of cave ice: Paradana and Sne zna Caves, Slovenia: Geosciences (Switzerland), Vol. 9, No. 2, pp. 1–12. ČEKADA, M. T.; ZORN, M.; AND COLUCCI, R. R., 2016, Monitoring glacier changes with the use of archive images: The example of the Julian Alps (NW Slovenia, NE Italy). In Koulov, B. and Zhelezov, G. (Editors), Sustainable Mountain Regions: Challenges and Perspectives in Southeastern Europe: Springer, Cham, Switzerland, pp. 233–242. CITTERIO, M.; TURRI, S.; BINI, A.; AND MAGGI, V., 2004, Observed trends in the chemical composition, d18O and crystal sizes vs. depth in the ﬁrst ice core from the LoLc 1650 “Abisso sul Margine dell’Alto Bregai” ice cave (Lecco, Italy): Theoretical and Applied Karstology, Vol. 17, pp. 45–50. CLARK, I. AND FRITZ P., 1997, Environmental Isotopes in Hydrogeology, 1st ed.: Lewis Publishers, Boca Raton, FL, 22 p. CLAUSEN, H. B.; VRANA, K.; HANSEN, S. B.; LARSEN, L. B.; BAKER, J.; SIGGAARD-ANDERSEN, M.-L.; SJOLTE, J.; AND LUNDHOLM, S. C., 2007, Continental ice body in Dobšiná Ice Cave (Slovakia)—Part

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Book Review Introduction to Waste Management: A Textbook

(Syed E. Hasan) Review by: Thomas Oommen Department of Geology and Geological Engineering, University of Mississippi, University, MS 38677

The United Nations (UN) Human Settlements Program noted, “Managing solid waste well and affordably is one of the key challenges of the 21st century, and one of the key responsibilities of a city government” (UNHabitat, 2010, p. xix). Improper waste management can lead to environmental degradation, soil and water pollution, and wildlife harm. Mismanagement of hazardous waste can pose significant health risks to communities and the environment. Waste management helps conserve natural resources by reducing waste sent to landfills and recovering materials that can be recycled or reused. Landfills are a significant source of methane emissions, a potent greenhouse gas contributing to climate change. Landfill space is finite and expensive, and as the amount of waste generated continues to increase, finding new landfill sites becomes more difficult. Therefore, proper waste management practices and policies are essential to protect public health and the environment and conserve resources. Hasan (2022), an experienced educator, has come forth with a college textbook in waste management, distilling information from 35 years of a teaching career. The book is written for students majoring in environmental science, environmental engineering, environmental studies, environmental geology, resource management, and sustainability programs. It covers all major types of waste: municipal solid waste, hazardous waste, medical/healthcare waste, nuclear/ radioactive waste, and electronic waste, besides a chapter on waste minimization and pharmaceuticals and personal care products (PPCPs). This book by Professor Hasan provides an overview of the waste management industry, including its history, current practices, and prospects. It covers key topics such as waste generation and characterization, collection and transportation, treatment and disposal, and environmental impacts from its mismanagement. The author presents the material clearly and concisely, making the book understandable to readers with little to no prior knowledge of the subject. The book is well organized, with each chapter building on the previous one to provide a comprehensive understanding of the topic at hand. One of the book’s strengths is its use of real-world

examples and case studies to illustrate the concepts discussed. The content is illustrated with 85 figures (nearly all in color) and 87 tables, which make the material easier to understand. The book includes discussion of contemporary topics, such as coronavirus 2019 (COVID19) waste, its proper management, and how to deal with medical waste (MW) from future pandemics/epidemics, which are projected to increase in frequency and intensity in the wake of climate change. The book is organized into ten chapters that can be utilized as the content for a one-semester course to introduce students to waste management. The ten chapters provide students with a fundamental understanding of waste management. Each chapter begins with the chapter’s learning objectives and concludes with a chapter summary, study questions, related supplemental readings, web resources, and detailed references. Solved numerical problems on calculation of landfill life and area, waste quantities, groundwater flow, etc., enhance the value of the book. A textbook flavor permeates throughout the book, and this style makes it easy for faculty to adopt for an introductory course in waste management. Chapter 1 discusses the evolution of waste management practices and the development of modern waste management systems. The chapter also defines waste and its classification into different categories. Overall, this chapter provides a solid introduction to the subject, providing a comprehensive overview of the field of waste management and its history. Chapter 2 discusses the essentials of geology, its relevance to waste management, the various geotechnical aspects of earth materials, and their significance in the selection of wastedisposal facilities and remediation of contaminated sites. The chapter also explains the principles of toxicology and relates them to human and ecological health issues caused by waste mismanagement. Chapter 3 describes the history and evolution of environmental laws in the United States and explains laws dealing with waste management and pollution control. The chapter also discusses the European Union’s environmental laws, along with UN guidelines and recommendations. Chapter 4

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reviews the historical evolution of municipal solid waste (MSW) management in the United States and provides detailed discussions of common waste-disposal methods, such as landfills and incinerators, and environmental and health impacts associated with these methods. Chapter 5 begins with differentiation between hazardous substances and hazardous waste and explains health problems caused by hazardous waste mismanagement. The chapter also covers sources and generators of hazardous waste and methods for its storage, transportation, treatment, and disposal. Chapter 6 describes the history and need for regulating MW and describes the types, sources, and hazards associated with MW and its safe management. The chapter discusses waste types generated in hospitals, best management practices, and the benefits of segregating common waste from regulated medical waste (RMW); the chapter also summarizes methods of treatment and disposal of RMW and reviews severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and other emerging diseases and their impacts on MW management. Chapter 7 describes the history of radioactivity, nuclear fission, atomic physics, and nuclear technology. The chapter also provides a sound knowledge of nuclear waste management and the global status of high-level waste disposal. Chapter 8 describes the nature and source of electronic waste (e-waste) and the laws governing e-waste management. The chapter also summarizes the problem of increasing ewaste quantities, and the health and environmental impacts associated with its informal recycling, highlighting the

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urgent need for dealing with its management in a sustainable way. Chapter 9 discusses the historical development of waste-minimization approaches and compares and contrasts integrated waste management and source reduction. The chapter also explains the zero-waste concept, identifies U.S. laws related to waste minimization and recycling, and describes innovative waste-minimization trends. Chapter 10 defines pharmaceuticals and personal care products (PPCPs), explains their nature and threat to human and ecological health, and identifies the source, occurrence, and hazards of PPCPs. Introduction to Waste Management: A Textbook is the only book available in the market that offers a comprehensive coverage of all five major types of waste: solid, hazardous, medical, nuclear, and electronic. Overall, this book is an excellent resource for students, professionals, and anyone interested in learning about waste management. The author’s expertise in the field is evident, and the book provides a thorough and up-to-date overview of the subject. If you are looking for a comprehensive guide to waste management, this book is a must-read. REFERENCES HASAN, S. E., 2022, Introduction to Waste Management: A Textbook: John Wiley & Sons, Hoboken, NJ, 436 p. UN-HABITAT (UNITED NATIONS HUMAN SETTLEMENT PROGRAM), 2010, Solid Waste Management in the World’s Cities: Water and Sanitation in World’s Cities 2010: Earthscan, London, 257 p.

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THIS PUBLICATION IS PRINTED ON ACID-FREE PAPER EDITORS ABDUL SHAKOOR Kent State University Kent, OH 44242 ashakoor@kent.edu

ERIC PETERSON Department of Geography, Geology, and the Environment Illinois State University Normal, IL 61790 309-438-5669 ewpeter@ilstu.edu

KAREN E. SMITH, Editorial Assistant, kesmith6@kent.edu

OOMMEN, THOMAS Board Chair, Michigan Technological University SASOWSKY, IRA D. University of Akron

Environmental & Engineering Geoscience NOVEMBER 2023

VOLUME XXIX, NUMBER 4

Cover photo View of the landslide that destroyed the Munnar College building, Kerala, India (August 20th, 2018). Photo credit: Thomas Oommen. See article on page 245.

Volume XXIX, Number 4, November 2023

ADVISORY BOARD WATTS, CHESTER “SKIP” F. Radford University HASAN, SYED University of Missouri, Kansas City NANDI, ARPITA East Tennessee State University

ENVIRONMENTAL & ENGINEERING GEOSCIENCE