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1,069 results for “Karst”
Predicted locations and nitrate pollution of groundwater discharge from D3pl karst aquifer in Latvia
<p><strong>Description</strong></p> <p>A georeferenced raster data layer [5_predicted_D3pl_GW_discharge_zone.tif] showing predicted likelihood that groundwater polluted with nitrate (NO<sub>3</sub><sup>-</sup>) is discharging as springs or diffuse seepage from the Upper Devonian Pļaviņas (<em>D<sub>3</sub>pl</em>) dolomite karst aquifer in Latvia is presented. The cell value indicates the likelihood (0 – low, 1 - high) that groundwater with nitrate contamination is discharging from the <em>D<sub>3</sub>pl</em> aquifer at this location. Value of 0 means that no groundwater is discharging there. The BalticTM 93 (EPSG:25884) references system is used.</p> <p>The rationale and methodology for elaborating the map of groundwater discharge as springs or diffuse seepage from the <em>D<sub>3</sub>pl</em> dolomite karst aquifer is described in the main article (Kalvāns et al. under review). In short, a 3D regional geological model (Popovs et al. 2015), land surface elevation model and bedrock surface elevation model (Popovs et al. under review) were combined to identify locations where aquitard at the base of <em>D<sub>3</sub>pl</em> aquifer is outcropping at bedrock surface and in the nearby depressions (in a distance up to 0.25 km) the land surface was below the surface of this aquitard. The likely contamination with NO<sub>3</sub><sup>-</sup> was estimated from proportion of arable land (European Environment Agency 2018) within 4.75 km window. It is assumed that the NO<sub>3</sub><sup>-</sup> contamination in the <em>D<sub>3</sub>pl</em> karst aquifer is likely only close to its distribution margins, where groundwater table is deeper than the top of the aquifer.</p> <p>This work was supported by the EU Interreg Est–Lat program project GroundEco No. Est-Lat62, and base funding grant from the Latvian Ministry of Education and Science to the University of Latvia, No. ZD2016/AZ03.</p> <p><strong>References</strong></p> <p>European Environment Agency (2018) Corine Land Cover 2018. https://land.copernicus.eu/pan-european/corine-land-cover/clc2018?tab=download (CLC). Accessed 1 Jun 2020</p> <p>Popovs K, Kalvāns A, Jemeljanova M, et al (under review) Bedrock surface topography map of Latvia. J Maps</p> <p>Popovs K, Saks T, Jātnieks J (2015) A comprehensive approach to the 3D geological modelling of sedimentary basins: example of Latvia, the central part of the Baltic Basin. Est J Earth Sci 64:173–188. https://doi.org/10.3176/earth.2015.25</p> <p> </p>
Short-Term Synchronous and Asynchronous Ambient Noise Tomography in Urban Areas: Application to Karst Investigation
<p>We used DSurfTomo (<a href="https://github.com/HongjianFang/DSurfTomo">HongjianFang/DSurfTomo: Direct inversion of surface dispersion data based on ray tracing (github.com)</a>) for the tomography.</p> <p>ABC2_2023.dat is the travel time of C1 and C2 cross-correlation functions, used in our tomography.</p> <p>ManualDSurfTomoV1.3.pdf is the manual of DSurfTomo, including the data format description for ABC2_2023.dat.</p> <p>yunqiVs3D.txt is the interpolated 3D Vs model, including longitude, latitude, depth (meter), Vs (m/s).</p> <p>Previous version error: I forgot to write the Vs value.</p>
Data set | Water- and land-borne geophysical surveys before and after the sudden water-level decrease of two large karst lakes in southern Mexico (v1.1)
<p>This repository contains raw and processed data along with the Matlab scripts used to prepare the visualizations presented in the manuscript</p> <p>Bücker, M., Flores Orozco, A., Gallistl, J., Steiner, M., Aigner, L., Hoppenbrock, J., Glebe, R., Morales Barrera, W., Pita de la Paz, C., García García, E., Razo Pérez, J.A., Buckel, J., Hördt, A., Schwalb, A., and Perez, L. (2020). <strong><em>Water- and land-borne geophysical surveys before and after the sudden water-level decrease of two large karst lakes in southern Mexico</em></strong>. Submitted to Solid Earth.</p> <p>If you find this data useful in your own research, please mention this data set and/or the manuscript.</p>
Fig. 3 in New species of karst-dwelling Pselaphinae from southwestern China (Coleoptera: Staphylinidae)
Fig. 3. Zopherobatrus lusciosus sp. nov., male. A – habitus; B – head dorsum; C – pronotum; D – apex of left mesotibia; E – sternite IX; F–G aedeagus, ventral (F) and lateral (G). Scale bars: 1.0 mm in A; 0.2 mm in B–E; 0.1 mm in F–G.
Data for "Age effect on tree structure and biomass allocation in Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies [L.] Karst.)"
<p>VAPU dataset for tree biomass was collected from southern Finland in 1988-1990 by the Finnish Forest Research Institute (Metla, now Natural Resources Institute Finland, Luke) (VAPU data set).</p> <p>Those sample trees (162 Scots pine and 163 Norway spruce) are originated from the whole VAPU data set. The sheet 'Pine' and 'Spruce' data have been matched between 'sample branch measurements' and the 'biomass' information (by cluster X, Y, and plot, tree number).</p> <p>Biomass estimation for foliage and branches has been described here: https://doi.org/10.1016/j.ecolmodel.2004.04.024 and https://doi.org/10.1093/treephys/25.7.803<br> </p>
Fig. 5 in Nemaspela borkoae sp. nov. (Opiliones: Nemastomatidae), the second species of the genus from the Dinaric Karst
Fig. 5. Nemaspela borkoae sp. nov., ♀, allotype (PMSL-Opiliones-PK&TN 7/2019). A. Ovipositor, ventral view. B. Receptacula seminis, lateral (left) and ventral (right) views. C. Pseudoarticles in femur II, and distal tarsomerae I̅IV with claws, lateral views. D. Female (PMSL-Opiliones-PK&TN 8/2019) in the natural habitat in Vodna jama u Dragalju Cave. Photograph by T. Delić.
Fig. 3 in Nemaspela borkoae sp. nov. (Opiliones: Nemastomatidae), the second species of the genus from the Dinaric Karst
Fig. 3. Nemaspela borkoae sp. nov., ♂ holotype (PMSL-Opiliones-PK&TN 4/2019). A. Right pedipalp, lateral (left) and medial (right) views. B. Penis, ventral, lateral and dorsal view. C. Penis protruding through genital opening. D. Pseudoarticles in femur II, and distal tarsomerae I̅IV with claws, lateral views.
Figure 1 in First evidence of parasitation of a Bosmina (Cladocera) by a water mite larva in a karst sinkhole, in Quintana Roo (Yucatán Peninsula, México)
Figure 1 Water mite larva (Unionicola sp.) attached to a water flea (Bosmina tubicen). The scale bar indicates 50 μm.
Figure 2 A in First evidence of parasitation of a Bosmina (Cladocera) by a water mite larva in a karst sinkhole, in Quintana Roo (Yucatán Peninsula, México)
Figure 2 A – Lateral view of the Unionicola larva, frontal view on the Bosmina. B – Close up of perforations made by pedipalps and chelicerae of the water mite in the valve of the water flea. Scale bars indicate 50 μm.
Differential associations between nucleotide polymorphisms and physiological traits in Norway spruce (Picea abies Karst.) provenances under contrasting water regimes
<p>Three datasets are provided here, yielded by a study on drought-stressed and control (well-watered) seedlings of Norway spruce (Picea abies Karst.), coming from 5 provenances distributed along a steep altitudinal gradient from 550 to 1,280 m a.s.l. in central Slovakia:</p> <p>1. physiological traits</p> <p>2. double-digest restriction-site associated sequencing data (ddRAD)</p> <p>3. nuclear microsatellite (nSSR) genotypes</p>
Fig. 5 in Another new species of karst-associated pitviper (Serpentes, Viperidae: Trimeresurus) from the Isthmus of Kra, Peninsular Thailand
Fig. 5. Comparison of body colouration between members of the Trimeresurus kanburiensis species complex (males). A. Trimeresurus ciliaris Idiiatullina et al., 2023 from Trang Province, Thailand. B. T. kanburiensis Smith, 1943 from Kanchanaburi Province, Thailand. C. Trimeresurus kraensis sp. nov. from Chumphon Province, Thailand. D. T. cf. venustus Vogel, 1991 from Langkawi Island, Kedah State, Malaysia. E. T. kuiburi Sumontha et al., 2021 from Prachuap Khiri Khan Province, Thailand. F. T. venustus from Krabi Province, Thailand. Photographs by P. Pawangkhanant (A–C, F), T. Chalton (D), and T. Woranuch (E).
Fig. 4 in Another new species of karst-associated pitviper (Serpentes, Viperidae: Trimeresurus) from the Isthmus of Kra, Peninsular Thailand
Fig. 4. Habitat of Trimeresurus kraensis sp. nov. A. Macrohabitat of the new species near the Wat Tham Sanook, Chumphon Province, Thailand. B. Photos in life in situ, adult male (uncollected). C. Subadult female (paratype, ZMMU Re-17665). Photographs by P. Pawangkhanant (A), Rupert Grassby-Lewis (B), and N.A. Poyarkov (C).
Fig. 3 in Another new species of karst-associated pitviper (Serpentes, Viperidae: Trimeresurus) from the Isthmus of Kra, Peninsular Thailand
Fig. 3. The holotype of Trimeresurus kraensis sp. nov. in life (AUP-02036, adult female) from Wat Tham Sanook, Chumphon Province, Thailand. A. Dorsolateral view. B. Ventrolateral view. C. Close-up of dorsal scales. D. Left side of the head. E. Dorsal view of the head. F. Ventral view of the head. Photographs by P. Pawangkhanant.
Fig. 2 in Another new species of karst-associated pitviper (Serpentes, Viperidae: Trimeresurus) from the Isthmus of Kra, Peninsular Thailand
Fig. 2. Maximum Likelihood (ML) tree of the genus Trimeresurus Lacépède, 1804 derived from the analysis of 2427 bp of cyt b, ND4, and 16S rRNA mitochondrial DNA gene sequences. For voucher specimen information and GenBank accession numbers see Table 1. Numbers at tree nodes correspond to ML UFBS/BI PP support values, respectively. Colours of clades and locality numbers correspond to those on the map in Fig. 1. Photograph showing the new species Trimeresurus kraensis sp. nov. by P. Pawangkhanant.
Fig. 1 in Another new species of karst-associated pitviper (Serpentes, Viperidae: Trimeresurus) from the Isthmus of Kra, Peninsular Thailand
Fig. 1. Distribution of members of the Trimeresurus kanburiensis species complex in Thai-Malay Peninsula. Localities: Thailand: T. kanburiensis Smith, 1943 (yellow): 1 = Kanchanaburi Prov., Sai Yok Dist., Wat Tham, Phom Lo Khao Yai; T. kuiburi Sumontha et al., 2021 (blue): 2 = Prachuap Khiri Khan Prov., Kuiburi Dist., Wat Khao Daeng; 3 = Prachuap Khiri Khan Prov., Kuiburi Dist., Khao Daeng Beach; 4 = Prachuap Khiri Khan Prov., Kuiburi Dist., Khao Daeng, near Ban Thung Noi; Trimeresurus kraensis sp. nov. (green): 5 = Chumphon Prov., Wat Tham Sanook; T. venustus Vogel, 1991 (pink): 6 = Krabi Prov., Mueang Krabi Dist., Tiger Cave viewpoint; 7 = Nakhon Si Thammarat Prov., Khao Luang; 8 = Nakhon Si Thammarat Prov., Thung Song; 9 = Surat Thani Prov.; Trimeresurus ciliaris Idiiatullina et al., 2023 (red): 10 = Trang Prov., Pa Lian Dist., Thum Khao Ting; 11 = Tha Le Ban NP., Khuan Don Dist., Satun Prov.; Malaysia: 12 = Perlis State National Park, Perlis State; T. cf. venustus (purple): 13 = Langkawi Island, Kedah State. Stars denote type localities (except for T. kanburiensis for which the type locality was not sampled). Abbreviations: MY = Myanmar; MA = Malaysia; Prov. = Province; Dist. = District.
Fig. 6 in Another new species of karst-associated pitviper (Serpentes, Viperidae: Trimeresurus) from the Isthmus of Kra, Peninsular Thailand
Fig. 6. Comparison of head colouration (left profile and dorsal view of the head) between members of the Trimeresurus kanburiensis species complex (males). A–B. Trimeresurus kraensis sp. nov. C–D. T. ciliaris Idiiatullina et al., 2023. E–F. T. kanburiensis Smith, 1943. G–H. T. kuiburi Sumontha et al., 2021. I–J. T. venustus Vogel, 1991. Photographs by P. Pawangkhanant (A–F), A. Kaosung (G–H, J) and M. Naiduangchan (I).
Dataset: Osmoregulation and hypoxia tolerance in the cenote isopod Creaseriella anops: Insights into its distribution in Karst Subterranean Estuaries
<p>This data set contains the information supporting the research article "Osmoregulation and hypoxia tolerance in the cenote isopod Creaseriella anops: Insights into its distribution in Karst Subterranean Estuaries" </p> <p>It contains Respirometry, indicators of cellular damage and Antioxidant system, critical temperatures, and Temperature induced metabolic rates of the isopod Creaseriella anops, and endemic species of the Karst Subterranean Estuaries from the Yucatan Peninsula Mexico.</p>
An Image Dataset for Training Deep Learning Segmentation Models to Identify Karst Sinkholes
<p>The image dataset was prepared for training deep learning image segmentation models to identify karst sinkholes. Information about the work can be found at (https://github.com/mvrl/sink-seg/). The dataset consists of a DEM image, an aerial image, and a binary sinkhole label image in an area in central Kentucky, USA. It also includes four images derived from the DEM image. The image dataset is sourced from publicly available data from Kentucky's Elevation Data & Aerial Photography Program (https://kyfromabove.ky.gov/) and Kentucky LiDAR-derived sinkholes (https://kgs.uky.edu/geomap).</p> <p> </p>
Karst diagram from PhD thesis of Laia Comas-Bru
<p>Schematic illustration showing the formation of speleothems</p> <p>Formats: editable pdf and jpeg.</p> <p>Versions: one with the parameters modifying stable oxygen isotopes and one without.</p> <p>Original image: Fig 1.3 of "Schematic illustration showing the formation of speleothems" PhD thesis of Laia Comas-Bru. University College Dublin, Ireland.2015.</p>
Geoelectrical and hydro-chemical monitoring of karst formation at the laboratory scale
<p>We propose a dataset of two Estaillades limestone core samples subjected to acid percolation at two different rates.</p> <p>We monitored:</p> <ul> <li><strong>Permeability </strong>from high-frequency pressure gradient acquisition. Facing noise, we filtered this permeability with a Savitsky-Golay filter. Then, we resampled permeability values to compare them at specific times when we performed other measurements.</li> <li><strong>pH</strong> at the inlet and outlet.</li> <li><strong>Calcium </strong>concentration at the outlet.</li> <li><strong>Porosity</strong> was initially measured with petrophysical measurements and then calculated from calcium concentration during percolation experiments. We resampled the values to compare them at specific times when we performed other measurements.</li> <li><strong>Water electrical conductivity</strong> at the inlet and outlet.</li> <li><strong>Rock sample electrical conductivity</strong>, from which we calculated the formation factor. Rock sample electrical conductivity was high-frequency acquired. We thus resampled the values to compare them at specific times when we performed other measurements.</li> </ul> <p>We also present the pore size distribution of both samples before and after the percolation experiments. The distribution comes from the mean <strong>chord lengths distribution</strong> generated from tomography imaging.</p> <p>In addition to this dataset, we display a picture of the mesh of the sample and the electrodes generated with EIDORS software and with which we computed the geometric factor.</p>
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