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1,334 results for “Himalaya”
Supraglacial features of debris covered glaciers in the Himalaya from Landsat-8 spectral umixing and Pleiades
<p>This dataset contains the spectral unmixing output files for the debris covered glacier surfaces based on Landsat-8 OLI imagery and Pleiades imagery of 2015. Files are provided for two domains, the Khumbu reference region of Nepal and the greater Himalaya region (76.3 to 92.6° W and 26.3 to 34.2° N), which covers covering most area from Himachal/Jammu and Kashmir border to Bhutan Himalaya. </p> <ul> <li>Landsat surface reflectance : Himalaya_L8_6S_surface_reflectance_scenes_2015 .zip <ul> <li>Contains surface reflectance images of Landsat-8 OLI scenes mostly from 2015 (two images are from 2014 and 2016 due to clouds in 2015) </li> <li>Collection 1 Level 1 (L1TP)</li> <li>Atmospherically and topographically corrected using the ARCSI routine, supplied in .kea format. These can be converted to GeoTifs using the GDAL command.</li> <li>Naming structure: LS8_yyyymmdd_latYYlongXXXX_rRRpPPP_vmsk_topshad_rad_srefdem_stdsref.kea</li> <li>Projection is UTM (zones depending on the image), from the original Landsat L1TP files</li> <li>The file naming convention, which is a standard output from ARCSI routine, include the image date ("yyyy" = year, mm = "month", "dd" = day), latitude ("YY") and longitude ("XXXX") of the image center, path/row ("PPP" = path, "RRR" = row), and the output products generated by ARCSI ("rad" = radiation, "topshad" = topographic shadows, "srefdem" indicates the use of elevation data, "stdsref" = standardized surface reflectance)</li> </ul> </li> <li>Fractional maps for the Khumbu: LS8_20150930_r41p140_frac_files. zip <ul> <li>Raster format (GeoTiffs) </li> <li>Non-normalized fractional water, light and dark debris and vegetation maps for the Khumbu reference image (Sept 30, 2015, path 140 row 40)</li> <li>Output from the linear mixing model routine used to produce binary maps of surfaces with values ranging from 0 to 1 (0% to 100% pixel coverage)</li> </ul> </li> <li>Binary surface maps for the Himalaya: Himalaya_L8_raw_binary_surface_maps.zip <ul> <li>Vector format (ArcGIS shapefiles)</li> <li>Raw, unprocessed binary maps of ponds, vegetation debris, ice and clouds over the debris covered glacier tongues in the Himalaya around the year 2015 (binary files) </li> <li>Derived from tresholding the fractional maps using a variable threshold (see publication)</li> <li>Maps in this pre-release version have not been manually corrected for misclassified areas due to confusion of classes, and the ice and cloud classes are not highly accurate</li> <li>These are not the final coverages of these surfaces over the domain and should not be used as such</li> <li>The supraglacial pond maps will undergo manual corrections and the datasets will be updated on this page</li> </ul> </li> <li>Dataset for analysis, glacier-by-glacier: Himalaya_SDC_LS_for_analysis_gt1km2_with_frac_and_debris_attributes.txt <ul> <li>original data from the SupraGlacial Debris Cover dataset (Sherler et al 2018)</li> <li>updated with the preliminary fractional cover of each surface (in %) on a glacier-by-glacier basis</li> <li>contains only debris covered tongues >1 km2 </li> <li>debris covered attributes were calculated from the ALOS Global Digital Surface Model (AW3D30 DEM) for each debris covered tongue <ul> <li>DC_area_km2 = recalculated debris covered area</li> <li>DCmin = minimum debris cover elevation (meters)</li> <li>DCmax = maximum debris cover elevation (meters)</li> <li>DCrange = altitudinal range (meters)</li> <li>DCmed = median elevation (meters)</li> <li>SLmean = mean slope (degrees)</li> <li>SLrange = slope range (degrees)</li> <li>SLmin = min slope (degrees)</li> <li>SLmax = max slope (degrees)</li> </ul> </li> </ul> </li> </ul>
Rock glaciers in the Himalaya
<p><span>This dataset consists in rock glacier inventoried over the Himalayas, described briefly in Jones et al. (2021) and in more detail in the associated paper Harrison et al. (2024). </span></p> <p> </p> <p><span>Data description:</span></p> <p> </p> <p><span>1) Point-based inventory for the Greater Himalayas (24,968 landforms)</span></p> <p><span> Rock glacier identification for the Greater Himalaya was done in Google Earth Pro based on geomorphic indicator</span></p> <p><span> (surface flow structure, rock glacier body and frontal slope). For consistency, each pin point was digitised at the elevation at which the base of the frontal slope met the slope downstream. </span></p> <p> </p> <p><span>2) Sampled rock glacier points</span></p> <p><span> Randomly selected sample (5%, n= 2070 landforms) from the Western, Central, and Eastern Himalaya regions, provided as point shapefile.</span></p> <p> </p> <p><span>3) Sampled rock glacier polygons </span></p> <p><span><em> </em></span><span>Randomly selected sample (5%, n= 2070 landforms) from the Western, Central, and Eastern Himalaya regions digitised on Google Earth. The extended geomorphological footprint was used, in agreement with RGIK (2022) guidelines. </span><span><em>In the GH rock glacier inventory both intact and relict rock glaciers were digitised. Data are provided as polygon shape file and excel sheet.</em></span></p> <p><span><em>Data are provided in<span> </span>Geographic Coordinate System: GCS_WGS_1984</em></span></p> <p><span><em>Datum: D_WGS_1984</em></span></p> <p> </p>
Atmospheric River Database for the Himalayas
<p>Atmospheric Rivers (ARs) are long and narrow regions of intense moisture transport in the lower troposphere. The dataset comprises of Atmospheric Rivers that have happened over the Himalayan Basins from 1982 to 2018. It includes the dates and times, duration, intensity/magnitude, tracks, and categories of the ARs.</p> <p> </p> <p><strong>File Names and description:</strong></p> <p><strong>1. </strong><strong>ERA5_Persistant_Database2000km:</strong> This file includes the date, times, average Integrated Water Vapor Transport (IVT) magnitude (kg.m^-1s^-1), starting IVT, maximum IVT, and duration of ARs. These terms are explained below in greater details.</p> <p><strong>Column “Date”:</strong></p> <p>Gives the date and time (in Coordinated Universal Time UTC) of each AR timestep. The IVT data used to identify ARs is 6-hourly (00UTC, 06UTC, 12UTC and 18UTC).</p> <p><strong>Column “AR_ID”:</strong></p> <p>Each identified persistent AR, lasting for at least 18 hours, is given a unique ID, which remains same for all timesteps of the AR. This column gives the ID of ARs. The ID of an AR is based on the year in which the AR occurred, the letters “AR”, and the occurrence serial of the AR in the year. For example, the first AR in 1990 has ID 1980AR1. If the AR lasted for 10 timesteps, all 10 timesteps will have the same ID.</p> <p><strong>Column “Ind”:</strong></p> <p>This column gives the python index of IVT data in 6-hour yearly data, giving the date and time of each AR timestep. This column can be ignored since the same information is more directly available in “Date” column.</p> <p><strong>Column “AvgIVT”:</strong></p> <p>This column gives the average IVT magnitude (kg.m^-1s^-1) along the AR major axis, i.e., the gridcells that have maximum IVT along the AR track. For example, the first value corresponds to the average of all values from column <em>“0”</em> to column <em>“88”,</em> which give the IVT magnitude at each gridcell of the major axis of the first timestep.</p> <p> </p> <p><strong>Column “StartIVT”:</strong></p> <p>This column gives the IVT magnitude (kg.m^-1s^-1) at the initial gridcell on the first timestep when AR condition was identified.</p> <p><strong>Column “ARDuration”:</strong></p> <p>This column gives duration of the AR in hours; for example, an AR lasting for three timesteps will have the duration of 18 hours, an AR lasting for four timesteps will have duration of 24 hours.</p> <p><strong>Column “MaxIVT”:</strong></p> <p>This column gives the maximum of all IVT values (kg.m^-1s^-1) at the starting gridcells on each timestep of an AR.</p> <p><strong>Column “ARCat”:</strong></p> <p>This column gives category of the AR, based on IVT magnitude and duration of the ARs. Six categories have been defined, Cat0 denoting the weakest AR and Cat5 denoting the strongest AR. More details on this can be found in the accompanying paper.</p> <p><strong>Column “0” to the end.</strong></p> <p>These columns give the IVT magnitude (kg.m^-1s^-1) at each gridcell of the major axis of each AR timestep.</p> <p> </p> <p><em>Note that the cyclone dates were not available before 1982, so AR dates for 1979 to 1981 includes cyclonic IVT structures.</em></p> <p><strong>2. </strong><strong>ERA5_Persistant_Database_lats_2000km:</strong> The file gives the latitudes of grid points of maximum IVT, i.e., the latitude of major axes of ARs throughout their duration.</p> <p><strong><em>Columns “Date”, “AR_ID”, “Ind”, “AvgIVT”, “StartIVT”, “ARDuration”, “MaxIVT”, “ARCat” are the same as given above for “ERA5_Persistant_Database2000km.csv” file.</em></strong></p> <p><strong>Column “0” to end.</strong></p> <p>These columns give the latitude ( in degrees North) at each gridcell of the major axis of each AR timestep.</p> <p><strong>3. </strong><strong>ERA5_Persistant_Database_lons_2000km:</strong> The file gives the longitudes of grid points of maximum IVT, i.e., the longitudes of major axes of ARs throughout their duration</p> <p>Columns “Date”, “AR_ID”, “Ind”, “AvgIVT”, “StartIVT”, “ARDuration”, “MaxIVT”, “ARCat” are the same as given above for “ERA5_Persistant_Database2000km.csv” file.</p> <p><strong>Column “0” to end.</strong></p> <p>These columns give the longitude (in degrees East) at each gridcell of the major axis of each AR timestep</p>
Data to Support Predictive Models for Detrital Titanite Provenance with application to the Nanga Parbat syntaxial massif, western Himalaya."
<p>The files published here are metadata that are being used to support a manuscript currently (Mar, 2024) undergoing final reviews in Journal of Geophysical Research: Earth Surface.</p> <p>The intention of these data and code is to support a publication that is about generating a predictive categorisation scheme for the mineral titanite.</p> <p>The code to generate the titanite classification schemes was created in Python3, using Jupyter Notebook. The files also provide more motivation for why a predictive categorisation scheme for the mineral titanite is desirable, and other similar context. Chiefly, the dataset and random forest models published here will allow us to trace titanite in detritus.</p> <p>For info on running Jupyter Notebook, please visit (<a href="https://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/execute.html">https://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/execute.html</a>) to seek instructions. We also provide a readme file with some instructions. If you get really stuck, just email the authors.</p> <p>Our Model can be compared to similar previously published works (e.g. <a href="https://doi.org/10.1111/ter.12574">https://doi.org/10.1111/ter.12574</a>). Model was trained using skikit-learn v1.41.</p> <p>The supplementary file "Table_S4_Merged.csv" was used to train and generate the model.</p> <p>Your unknowns must contain the correct elements and labelling for the code to successfully run, these details are provided in the code (Titanite_Random_Forest_Model1_Mar24.ipynb). A template is also provided for you to paste your unknown data into (titanite_data_template.csv)</p> <p>Any new published data are titanite compositional or isotopic data collected by LA-ICP-MS. Description of how those data were collected is given in "OSullivan_et_al_Supp..." file.</p> <p>Some of the data, information and code in this submission has been subject to change after journal review, this is a second version of this content.</p> <p>References for the dataset compilation are provided in File S3.</p> <p>If you have any queries contact:<br>Gary O'Sullivan, Trinity College Dublin</p>
Major ions in Trambau ice core, Nepal Himalaya
<p>An 81.2-m-long ice core was drilled in November 2019 at 5862m a.s.l. of Trambau Glacier, Rolwaling region, Nepal Himalaya (27.919° N, 86.545° E). This data set contains the concentrations of major ions and tritium concentrations in the ice core.</p> <p>The data set contains Depth in snow/ice (m), Depth in water equivalent (m w.e.), Date (digit year), Na (ppb), Cl (ppb), NH4 (ppb), K (ppb), Mg (ppb), Ca (ppb), NO3 (ppb), SO4 (ppb), T_rough (TU), T_fine (TU)</p>
Understanding monsoon controls on the energy and mass balance of glaciers in the Central and Eastern Himalaya (Data Sets and Codes)
<p>This repository contains AWS datasets for the modelling periods considered in the analysis presented in the research paper, together with ablation measurements, pre-processed forcing data, T&C model codes, outputs and scripts for analysing outputs. When previously published elsewhere, references and links to the full, original datasets are provided under References.</p> <p>Matlab scripts for executing the T&C model are provided and should work stand-alone on any machine with a Matlab version 2019b or later installed.</p>
Stress regimes in the Himalaya-Karakoram-Tibet, the western part of India-Eurasia collision: stress field implications based on focal mechanism solution data
<p>This dataset contains valuable information on earthquake events, including their location, magnitude, depth, and focal mechanism solutions. This README file provides detailed explanations of each header in the dataset, as well as information about the files included in the repository.<br><br><em>"Stress regimes in the Himalaya-Karakoram-Tibet, the western part of India-Eurasia collision: stress field implications based on focal mechanism solution data"</em> <strong>(Under Review)</strong><br> </p>
Figure 32 in The genus Chaerilus Simon, 1877 (Scorpiones, Chaerilidae) in the Himalayas and description of a new species
Figure 32. Map of Nepal showing the type locality of the new species (black asterisk). The largest asterisk indicates the site where the holotype was collected.
Figs 5, 6 in Contributions to the knowledge of the "Staphylinus-complex" of China. Part 26. The genus Ocychinus S 2003, section 4. Two new species of Ocychinus, one of them the first representative of the genus in the Himalaya (Coleoptera: Staphylinidae: Staphylininae: Staphylinini)
Figs 5, 6: Habitus. (5) Ocychinus schneideri nov.sp.; (6) Ocychinus caeruleatus nov.sp. Actual size of O. schneideri nov.sp. 13.0 mm, that of O. caeruleatus nov.sp. 12.0 mm.
Figs 1-4 in Contributions to the knowledge of the "Staphylinus-complex" of China. Part 26. The genus Ocychinus S 2003, section 4. Two new species of Ocychinus, one of them the first representative of the genus in the Himalaya (Coleoptera: Staphylinidae: Staphylininae: Staphylinini)
Figs 1-4: Tergite 10 of female genital segment. (1) Ocychinus schneideri nov.sp.; (2) Ocychinus caeruleatus nov.sp. 3, 4: detail of dorsal surface of head. (3) Ocychinus schneideri nov.sp.; (4) Ocychinus caeruleatus nov.sp.
Figs 31–36 in First record of the genus Alainites Waltz & McCafferty, 1994 (Ephemeroptera, Baetidae) from India with the description of a new species from the North-western Himalayas
Figs 31–36. Alainites neeru sp. nov. 31–32. Paratype (AMC). 33–36. Holotype (AMC). 31. Tergalius I. 32. Tergalius IV. 33. Posterior margin of tergum IV denticulation. 34–35. Paraproct. 36. Closer view of paraproct.
Figs 27–30 in First record of the genus Alainites Waltz & McCafferty, 1994 (Ephemeroptera, Baetidae) from India with the description of a new species from the North-western Himalayas
Figs 27–30. Alainites neeru sp. nov., paratype (AMC), thorax and abdomen of larva. 27. Foreclaw. 28. Hindwing pad (pointed by arrow). 29. Tergal segments III–X. 30. Tergal segments VIII–X.
Figs 14–18 in First record of the genus Alainites Waltz & McCafferty, 1994 (Ephemeroptera, Baetidae) from India with the description of a new species from the North-western Himalayas
Figs 14–18. Alainites neeru sp. nov., paratype (AMC), mouthparts of larva. 14. Labium. 15. Glossae and paraglossae. 16. Closer view of labial palp segment III. 17. Maxilla. 18. Closer view of crown of maxilla.
Figs 19–21 in First record of the genus Alainites Waltz & McCafferty, 1994 (Ephemeroptera, Baetidae) from India with the description of a new species from the North-western Himalayas
Figs 19–21. Alainites neeru sp. nov., paratype (AMC), legs of larva. 19. Forefemur. 20. Middle femur. 21. Hind femur.
Figs 22–26 in First record of the genus Alainites Waltz & McCafferty, 1994 (Ephemeroptera, Baetidae) from India with the description of a new species from the North-western Himalayas
Figs 22–26. Alainites neeru sp. nov., paratype (AMC), legs of larva. 22. Foretibia and foretarsus. 23. Outer marginal setation of foretibia. 24. Inner marginal setation of foretibia. 25. Middle tibia and middle tarsus. 26. Hind tibia and hind tarsus.
Figs 7–13 in First record of the genus Alainites Waltz & McCafferty, 1994 (Ephemeroptera, Baetidae) from India with the description of a new species from the North-western Himalayas
Figs 7–13. Alainites neeru sp. nov. paratypes (AMC), mouthparts of larva. 7. Left mandible. 8. Right mandible. 9. Prostheca and incisor of left mandible. 10. Closer view of left mandible. 11–12. Prostheca and incisor of right mandible. 13. Prostheca of right mandible.
Figs 1–5 in First record of the genus Alainites Waltz & McCafferty, 1994 (Ephemeroptera, Baetidae) from India with the description of a new species from the North-western Himalayas
Figs 1–5. Alainites neeru sp. nov. 1. Holotype, ♀ (AMC), mature larva. 2–6. Paratype (AMC). 2. Immature larva. 3. Antenna. 4. Labrum. 5. Labrum, sub-marginal setae (arrows indicate 1+2 long, simple setae). 6. Setae of the ventral surface of the labrum.
Figures 1 in Biogeographical affinities and evolution of terrestrial fauna in the Qinghai-Tibetan Plateau and the Himalayas: a case study of Aphidomorpha
Figures 1. Generalized tracks and nodes based on the global distributions of aphid species in the QTPH. Generalized tracks are indicated by colored lines, and nodes are indicated with orange dots. A generalized track is a summary of replicated distribution patterns of different taxa (species); a node is a distribution area where two or more generalized tracks intersect. Generalized tracks and nodes together indicate biogeographical affinities between aphid faunas in the QTPH and other regions.
Basin-scale spatio-temporal development of glacial lakes in the Hindukush-Karakoram-Himalayas
<p>This dataset offers detailed inventories of glacial lakes for the years 1990, 2000, 2010, and 2020 in the Hindu Kush Himalaya (HKH) region. Landsat satellite imagery was utilised to map glacial lakes, encompassing all lakes that are equal to or exceed 0.0036 km² in size.</p> <p>In the latest inventory from 2020, we identified a total of 19,284 glacial lakes, encompassing a cumulative area of 1191.81 ± 209.21 km². The investigation encompasses comprehensive mapping at the sub-basin level over the whole study area, facilitating regional-scale evaluations of glacial lake distribution and expansion. The findings reveal a significant rise in glacial lakes over the last thirty years, with a 9.31% increase in the number of lakes and a 10.09% increase in total lake area within the HKH region.</p>
FIG. 3 in Foraminiferal biostratigraphy, facies and sequence stratigraphy analysis across the K-Pg Boundary in Hazara, Lesser Himalayas (Dhudial Section)
FIG. 3. — Lithostratigraphic column showing the lithology, constituents and facies of the Dhudial Section.
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