# Searles Valley, CA Plant Wax Carbon, Hydrogen Isotopes, GDGTs, and Pollen Data from the Latest Pleistocene and Modern Taxa #----------------------------------------------------------------------- # World Data Service for Paleoclimatology, Boulder # and # NOAA Paleoclimatology Program #----------------------------------------------------------------------- # Template Version 4.0 # Encoding: UTF-8 # NOTE: Please cite original publication, NOAA Landing Page URL, dataset and publication DOIs (where available), and date accessed when using downloaded data. # If there is no publication information, please cite investigator, study title, NOAA Landing Page URL, and date accessed. # # Description/Documentation lines begin with '#' followed by a space # Data lines have no '#' # # NOAA_Landing_Page: https://www.ncei.noaa.gov/access/paleo-search/study/36393 # Landing_Page_Description: NOAA Landing Page of this file's parent study, which includes all study metadata. # # Study_Level_JSON_Metadata: https://www.ncei.noaa.gov/pub/data/metadata/published/paleo/json/noaa-lake-36393.json # Study_Level_JSON_Description: JSON metadata of this data file's parent study, which includes all study metadata. # # Data_Type: Paleolimnology # # Dataset_DOI: 10.25921/nmse-3986 # # Science_Keywords: air temperature reconstruction, glacial, interglacial, last glacial maximum, precipitation reconstruction #--------------------------------------- # Resource_Links # # Data_Download_Resource: https://www.ncei.noaa.gov/pub/data/paleo/paleolimnology/northamerica/usa/california/peaple2022/peaple2022-biomarker.txt # Data_Download_Description: NOAA Template File; SLAPP-SRLS17-1A/B Biomarker Data # #--------------------------------------- # Contribution_Date # Date: 2022-04-28 #--------------------------------------- # File_Last_Modified_Date # Date: 2023-03-24 #--------------------------------------- # Title # Study_Name: Searles Valley, CA Plant Wax Carbon, Hydrogen Isotopes, GDGTs, and Pollen Data from the Latest Pleistocene and Modern Taxa #--------------------------------------- # Investigators # Investigators: Peaple, Mark; Bhattacharya, Tripti; Lowenstein, Tim; McGee, David; Olson, Kristian; Stroup, Justin; Tierney, Jessica; Feakins, Sarah #--------------------------------------- # Description_Notes_and_Keywords # Description: Provided Keywords: biomarker, plant wax, carbon isotopes, hydrogen isotopes, pollen, GDGT #--------------------------------------- # Publication # Authors: Peaple, Mark D., Tripti Bhattacharya, Tim K. Lowenstein, David McGee, Kristian J. Olson, Justin S. Stroup, Jessica E. Tierney, Sarah J. Feakins # Published_Date_or_Year: 2022-10-01 # Published_Title: Biomarker and pollen evidence for late Pleistocene pluvials in the Mojave Desert # Journal_Name: Paleoceanography and Paleoclimatology # Volume: 37 # Edition: # Issue: 10 # Pages: # Report_Number: e2022PA004471 # DOI: 10.1029/2022PA004471 # Online_Resource: # Full_Citation: # Abstract: The climate of the southwestern North America has experienced profound changes between wet and dry phases over the past 200 kyr. To better constrain the timing, magnitude and paleoenvironmental impacts of these changes in hydroclimate, we conducted a multiproxy biomarker study from samples collected from a new 76 m sediment core (SLAPP-SRLS17) drilled in Searles Lake, California. Here, we use biomarkers and pollen to reconstruct vegetation, lake conditions and climate. We find that dD values of long chain n-alkanes are dominated by glacial to interglacial changes that match nearby Devils Hole calcite d18O variability, suggesting both archives predominantly reflect precipitation isotopes. However, precipitation isotopes do not simply covary with evidence for wet-dry changes in vegetation and lake conditions, indicating a partial disconnect between large scale atmospheric circulation tracked by precipitation isotopes and landscape moisture availability. Increased crenarchaeol production and decreased evidence for methane cycling reveal a 10 kyr interval of a fresh, productive and well-mixed lake during Termination II, corroborating evidence for a paleolake highstand from shorelines and spillover deposits in downstream Panamint Basin during the end of the penultimate (Tahoe) glacial (140–130 ka). At the same time brGDGTs yield the lowest temperature estimates (mean months above freezing = 9 ± 3°C) of the 200 kyr record. These limnological conditions are not replicated elsewhere in the 200 kyr record, suggesting that the Heinrich stadial 11 highstand was wetter than that during the last glacial maximum and Heinrich 1 (18–15 ka). #--------------------------------------- # Publication # Authors: Peaple, Mark D., Jessica E. Tierney, David McGee, Tim K. Lowenstein, Tripti Bhattacharya, Sarah J. Feakins # Published_Date_or_Year: 2021-03-23 # Published_Title: Identifying plant wax inputs in lake sediments using machine learning # Journal_Name: Organic Geochemistry # Volume: 156 # Edition: # Issue: # Pages: # Report_Number: 104222 # DOI: 10.1016/j.orggeochem.2021.104222 # Online_Resource: # Full_Citation: # Abstract: This study aims to evaluate whether machine learning techniques can be successfully applied to process the complex information contained within the molecular abundance distributions of plant wax n-alkane and n-alkanoic acid homologous series. We trained five vegetation identification models using plant wax chain length distributions from modern plants in the Mojave Desert (hyperarid) and the San Bernardino Mountains (conifer forest) and previously published data for macrophytes from Blood Pond (USA) and Mt Kenya (Kenya). All vegetation identification models proved accurate (mean classification accuracy = 0.81) at classifying the modern plant wax chain length distributions into desert plants, conifer and macrophyte categories. We then applied the models to fossil waxes extracted from a 76 m lacustrine sediment core drilled in Searles Valley, CA with an approximate age range of 10 to 150 kyrs (SLAPP-SRLS17) to reconstruct the proportion of desert plants, conifer woodland and lake vegetation. We compared our machine learning models with a previously published linear mixing model and validated our modelled plant type distributions by comparing the results with the archaeol caldarchaeol ecometric (ACE), a proxy for lake salinity, measured in the same core. We found a moderate positive correlation (r = 0.40) between the modelled desert plant proportion and high lake salinity in our models as well as a negative correlation (r = –0.45) between modelled macrophyte plants and ACE, validating the ability of the machine learning techniques to detect both xeric and macrophyte plant communities. Our results suggest that machine learning of plant wax molecular abundance distributions has potential to reconstruct past plant communities, given information from two compound classes and highly differentiated vegetation types. #--------------------------------------- # Funding_Agency # Funding_Agency_Name: US National Science Foundation # Grant: 1903665, 1903659 #--------------------------------------- # Funding_Agency # Funding_Agency_Name: David and Lucile Packard Foundation # Grant: #--------------------------------------- # Funding_Agency # Funding_Agency_Name: Comer Family Foundation # Grant: #--------------------------------------- # Site_Information # Site_Name: Searles Valley # Location: California # Northernmost_Latitude: 35.707579 # Southernmost_Latitude: 35.707579 # Easternmost_Longitude: -117.312931 # Westernmost_Longitude: -117.312931 # Elevation_m: 493 #--------------------------------------- # Data_Collection # Collection_Name: SLAPP-SRLS17-1A/B Biomarker Peaple2022 # First_Year: 200000 # Last_Year: 10000 # Time_Unit: calendar year before present # Core_Length_m: 76.7 # Parameter_Keywords: geochemistry, hydrogen isotopes, carbon isotopes # Notes: #--------------------------------------- # Chronology_Information # Chronology: #--------------------------------------- # Variables # PaST_Thesaurus_Download_Resource: https://www.ncei.noaa.gov/access/paleo-search/skos/past-thesaurus.rdf # PaST_Thesaurus_Download_Description: Paleoenvironmental Standard Terms (PaST) Thesaurus terms, definitions, and relationships in SKOS format. # # Data variables follow that are preceded by "##" in columns one and two. # Variables list, one per line, shortname-tab-var components: what, material, error, units, seasonality, data type, detail, method, C or N for Character or Numeric data) # ## depth_m depth,,,meter,,paleolimnology; climate reconstructions,,,N,spliced depth ## age_calkyrBP age,,,calendar kiloyear before present,,paleolimnology; climate reconstructions,,,N,Age model from Stroup et al. pers. comm. ## ID sample identification,,,,,paleolimnology,,,C,Core-year-hole-section ## kACL average chain length,n-alkane,,dimensionless,,paleolimnology,,gas chromatography - flame ionization detection,N,n-alkane Average Chain Length (C24-35) ## kCPI carbon preference index,n-alkane,,dimensionless,,paleolimnology,,gas chromatography - flame ionization detection,N,n-alkane Carbon Preference Index (C24-35) ## aACL average chain length,n-alkanoic acid,,dimensionless,,paleolimnology,,gas chromatography - flame ionization detection,N,n-alkanoic acid Average Chain Length (C24-34) ## aCPI carbon preference index,n-alkanoic acid,,dimensionless,,paleolimnology,,gas chromatography - flame ionization detection,N,n-alkanoic Carbon Preference Index (C23-34) ## d13Calk27 delta 13C,C27 n-alkane,,per mil VPDB,,paleolimnology,,isotope ratio mass spectrometry,N, ## d13Calk29 delta 13C,C29 n-alkane,,per mil VPDB,,paleolimnology,,isotope ratio mass spectrometry,N, ## d13Calk31 delta 13C,C31 n-alkane,,per mil VPDB,,paleolimnology,,isotope ratio mass spectrometry,N, ## d13Calk27err delta 13C,C27 n-alkane,one standard deviation,per mil VPDB,,paleolimnology,,isotope ratio mass spectrometry,N, ## d13Calk29err delta 13C,C29 n-alkane,one standard deviation,per mil VPDB,,paleolimnology,,isotope ratio mass spectrometry,N, ## d13Calk31err delta 13C,C31 n-alkane,one standard deviation,per mil VPDB,,paleolimnology,,isotope ratio mass spectrometry,N, ## d13Calkn number of samples,n-alkane,,count,,paleolimnology,,,N,number of carbon isotope replicates ## dDalk27 delta 2H,C27 n-alkane,,per mil VPDB,,paleolimnology,,isotope ratio mass spectrometry,N, ## dDalk29 delta 2H,C29 n-alkane,,per mil VPDB,,paleolimnology,,isotope ratio mass spectrometry,N, ## dDalk31 delta 2H,C31 n-alkane,,per mil VPDB,,paleolimnology,,isotope ratio mass spectrometry,N, ## dDalk27err delta 2H,C27 n-alkane,one standard deviation,per mil VPDB,,paleolimnology,,isotope ratio mass spectrometry,N, ## dDalk29err delta 2H,C29 n-alkane,one standard deviation,per mil VPDB,,paleolimnology,,isotope ratio mass spectrometry,N, ## dDalk31err delta 2H,C31 n-alkane,one standard deviation,per mil VPDB,,paleolimnology,,isotope ratio mass spectrometry,N, ## dDalkn number of samples,n-alkane,,count,,paleolimnology,,,N,number of hydrogen isotope replicates ## d13Cacid22 delta 13C,C22 n-alkanoic acid,,per mil VPDB,,paleolimnology,,isotope ratio mass spectrometry,N, ## d13Cacid24 delta 13C,C24 n-alkanoic acid,,per mil VPDB,,paleolimnology,,isotope ratio mass spectrometry,N, ## d13Cacid26 delta 13C,C26 n-alkanoic acid,,per mil VPDB,,paleolimnology,,isotope ratio mass spectrometry,N, ## d13Cacid28 delta 13C,C28 n-alkanoic acid,,per mil VPDB,,paleolimnology,,isotope ratio mass spectrometry,N, ## d13Cacid22err delta 13C,C22 n-alkanoic acid,one standard deviation,per mil VPDB,,paleolimnology,,isotope ratio mass spectrometry,N, ## d13Cacid24err delta 13C,C24 n-alkanoic acid,one standard deviation,per mil VPDB,,paleolimnology,,isotope ratio mass spectrometry,N, ## d13Cacid26err delta 13C,C26 n-alkanoic acid,one standard deviation,per mil VPDB,,paleolimnology,,isotope ratio mass spectrometry,N, ## d13Cacid28err delta 13C,C28 n-alkanoic acid,one standard deviation,per mil VPDB,,paleolimnology,,isotope ratio mass spectrometry,N, ## d13Cacidn number of samples,n-alkanoic acid,,count,,paleolimnology,,,N,number of carbon isotope replicates ## dDacid22 delta 2H,C22 n-alkanoic acid,,per mil VSMOW,,paleolimnology,,isotope ratio mass spectrometry,N, ## dDacid24 delta 2H,C24 n-alkanoic acid,,per mil VSMOW,,paleolimnology,,isotope ratio mass spectrometry,N, ## dDacid26 delta 2H,C26 n-alkanoic acid,,per mil VSMOW,,paleolimnology,,isotope ratio mass spectrometry,N, ## dDacid28 delta 2H,C28 n-alkanoic acid,,per mil VSMOW,,paleolimnology,,isotope ratio mass spectrometry,N, ## dDacid22err delta 2H,C22 n-alkanoic acid,one standard deviation,per mil VSMOW,,paleolimnology,,isotope ratio mass spectrometry,N, ## dDacid24err delta 2H,C24 n-alkanoic acid,one standard deviation,per mil VSMOW,,paleolimnology,,isotope ratio mass spectrometry,N, ## dDacid26err delta 2H,C26 n-alkanoic acid,one standard deviation,per mil VSMOW,,paleolimnology,,isotope ratio mass spectrometry,N, ## dDacid28err delta 2H,C28 n-alkanoic acid,one standard deviation,per mil VSMOW,,paleolimnology,,isotope ratio mass spectrometry,N, ## dDacidn number of samples,n-alkanoic acid,,count,,paleolimnology,,,N,number of hydrogen isotope replicates ## archaeol archaeol,sediment,,parts per billion,,paleolimnology,,mass spectrometry,N, ## isoGDGT-0 isoprenoid glycerol dialkyl glycerol tetraether,sediment,,parts per billion,,paleolimnology,,mass spectrometry,N,GDGT-0 ## isoGDGT-1 isoprenoid glycerol dialkyl glycerol tetraether,sediment,,parts per billion,,paleolimnology,,mass spectrometry,N,GDGT-1 ## isoGDGT-2 isoprenoid glycerol dialkyl glycerol tetraether,sediment,,parts per billion,,paleolimnology,,mass spectrometry,N,GDGT-2 ## isoGDGT-3 isoprenoid glycerol dialkyl glycerol tetraether,sediment,,parts per billion,,paleolimnology,,mass spectrometry,N,GDGT-3 ## isoGDGT-5 isoprenoid glycerol dialkyl glycerol tetraether,sediment,,parts per billion,,paleolimnology,,mass spectrometry,N,crenarchaeol ## isoGDGT-5_prime isoprenoid glycerol dialkyl glycerol tetraether,sediment,,parts per billion,,paleolimnology,,mass spectrometry,N,crenarchaeol_prime ## b-IIIa branched glycerol dialkyl glycerol tetraether,sediment,,parts per billion,,paleolimnology,,mass spectrometry,N,brGDGT IIIa ## b-IIIa_prime branched glycerol dialkyl glycerol tetraether,sediment,,parts per billion,,paleolimnology,,mass spectrometry,N,brGDGT IIIa_prime ## b-IIIa_prime_prime branched glycerol dialkyl glycerol tetraether,sediment,,parts per billion,,paleolimnology,,mass spectrometry,N,brGDGT IIIa_prime_prime ## b-IIIb branched glycerol dialkyl glycerol tetraether,sediment,,parts per billion,,paleolimnology,,mass spectrometry,N,brGDGT IIIb ## b-IIIb_prime branched glycerol dialkyl glycerol tetraether,sediment,,parts per billion,,paleolimnology,,mass spectrometry,N,brGDGT IIIb_prime ## b-IIIc branched glycerol dialkyl glycerol tetraether,sediment,,parts per billion,,paleolimnology,,mass spectrometry,N,brGDGT IIIc ## bIIIc_prime branched glycerol dialkyl glycerol tetraether,sediment,,parts per billion,,paleolimnology,,mass spectrometry,N,brGDGT IIIc_prime ## b-IIa branched glycerol dialkyl glycerol tetraether,sediment,,parts per billion,,paleolimnology,,mass spectrometry,N,brGDGT IIa ## b-IIa_prime branched glycerol dialkyl glycerol tetraether,sediment,,parts per billion,,paleolimnology,,mass spectrometry,N,brGDGT IIa_prime ## b-IIa_prime_prime branched glycerol dialkyl glycerol tetraether,sediment,,parts per billion,,paleolimnology,,mass spectrometry,N,brGDGT IIa_prime_prime ## b-IIb branched glycerol dialkyl glycerol tetraether,sediment,,parts per billion,,paleolimnology,,mass spectrometry,N,brGDGT IIb ## b-IIb_prime branched glycerol dialkyl glycerol tetraether,sediment,,parts per billion,,paleolimnology,,mass spectrometry,N,brGDGT IIb_prime ## b-IIc branched glycerol dialkyl glycerol tetraether,sediment,,parts per billion,,paleolimnology,,mass spectrometry,N,brGDGT IIc ## b-IIc_prime branched glycerol dialkyl glycerol tetraether,sediment,,parts per billion,,paleolimnology,,mass spectrometry,N,brGDGT IIc_prime ## b-Ia branched glycerol dialkyl glycerol tetraether,sediment,,parts per billion,,paleolimnology,,mass spectrometry,N,brGDGT Ia ## b-Ib branched glycerol dialkyl glycerol tetraether,sediment,,parts per billion,,paleolimnology,,mass spectrometry,N,brGDGT Ib ## b-Ic branched glycerol dialkyl glycerol tetraether,sediment,,parts per billion,,paleolimnology,,mass spectrometry,N,brGDGT Ic ## isoGDGTs_ngg isoprenoid glycerol dialkyl glycerol tetraether,sediment,,parts per billion,,paleolimnology,,mass spectrometry,N,concentration of summed isoprenoid GDGTs ## brGDGTs_ngg branched glycerol dialkyl glycerol tetraether,sediment,,parts per billion,,paleolimnology,,mass spectrometry,N,concentration of summed branched GDGTs ## IIIa_IIa organic compound index,sediment,,dimensionless,,paleolimnology,,mass spectrometry,N,Index expressing relative proportion of brGDGT IIIa to brGDGT IIa: IIIa/IIa = (IIIa+IIIa')/(IIa+IIa') ## %isoGDGT-0 isoprenoid glycerol dialkyl glycerol tetraether,sediment,,dimensionless,,paleolimnology,,mass spectrometry,N,Index expressing relative proportion of isoGDGT-0 to chrenarchaeol: %isoGDGT-0 = 100*[GDGT-0]/([GDGT-0]+[Chrenarchaeol]) ## BIT branched and isoprenoid tetraether index,sediment,,dimensionless,,paleolimnology,,mass spectrometry,N,BIT index ## deltaRI ring index,sediment,,dimensionless,,paleolimnology,,mass spectrometry,N,delta ring index ## MBT5Me methylation index of branched tetraethers,sediment,,dimensionless,,paleolimnology,,mass spectrometry,N,pentamethylated brGDGT index MBTprime5Me; brGDGT index: MBT’5Me = (Ia + Ib + Ic) / (Ia + Ib + Ic + IIa + IIb + IIc + IIIa); De Jonge et al.(2014). https://doi.org/10.1016/j.gca.2014.06.013 ## MAT air temperature,glycerol dialkyl glycerol tetraether index,,degree Celsius,,paleolimnology; climate reconstructions,,mass spectrometry,N,mean months above freezing; mean annual air temperature using BayMBT0; Martínez-Sosa et al. (2021) https://doi.org/10.31223/X5PS3P ## MAT_5th air temperature,glycerol dialkyl glycerol tetraether index,,degree Celsius,,paleolimnology; climate reconstructions,,mass spectrometry,N,5th percentile months above freezing; mean annual air temperature using BayMBT0 ## MAT_95th air temperature,glycerol dialkyl glycerol tetraether index,,degree Celsius,,paleolimnology; climate reconstructions,,mass spectrometry,N,95th percentile months above freezing; mean annual air temperature using BayMBT0 ## CBTprime cyclization index of branched tetraethers,sediment,,dimensionless,,paleolimnology,,mass spectrometry,N,cyclization of hexamethyl branched tetraethers index; CBTprime ## ACE archaeol caldarchaeol ecometric,sediment,,dimensionless,,paleolimnology,,mass spectrometry,N,Archeol Caldarcheol Ecometric; ACE = [archaeol]/[archaeol + caldarchaeol] x 100 where caldarchaeol = isoGDGT-0. Turich et al. (2011). https://doi.org/10.1016/j.orggeochem.2011.06.002 ## IR67me branched glycerol dialkyl glycerol tetraether,sediment,,dimensionless,,paleolimnology,,mass spectrometry,N,IR6+7Me = [(IIa'+IIb'+IIc'+IIIa'+IIIb'+IIIc')/(IIa+IIb+IIc+IIIa+IIIb+IIIc+IIa'+IIb'+IIc'+IIIa'+IIIb'+IIIc')+(IIIa'''+IIa''')/(IIIa+IIIa'+IIIa'''+IIa+IIa'+IIa''')]*0.5. Wang et al. (2021) https://doi.org/10.1016/j.gca.2021.05.004 ## TEX86 tetraether index of 86 carbon atoms,sediment,,dimensionless,,paleolimnology,,mass spectrometry,N,Tex86=([GDGT-2]+[GDGT-3]+[cren']) / ([GDGT-1]+[GDGT-2]+[GDGT-3]+ [cren']). Schouten et al.(2002) doi: 10.1016/S0012-821X(02)00979-2 ## LST Lake surface temperature,glycerol dialkyl glycerol tetraether index,,degree Celsius,,paleolimnology; climate reconstructions,,mass spectrometry,N,calculated where BIT<0.4 using the calibration from Tierney et al.(2010) https://doi.org/10.1038/ngeo865 ## LST_5th Lake surface temperature,glycerol dialkyl glycerol tetraether index,,degree Celsius,,paleolimnology; climate reconstructions,,mass spectrometry,N,5th percentile lake surface temperature; using the calibration from Tierney et al.(2010) https://doi.org/10.1038/ngeo865 ## LST_95th Lake surface temperature,glycerol dialkyl glycerol tetraether index,,degree Celsius,,paleolimnology; climate reconstructions,,mass spectrometry,N,95th percentile lake surface temperature; using the calibration from Tierney et al.(2010) https://doi.org/10.1038/ngeo865 #------------------------ # Data: # Data lines follow (have no #) # Data line format - tab-delimited text, variable short name as header # Missing_Values: -999999 depth_m age_calkyrBP ID kACL kCPI aACL aCPI d13Calk27 d13Calk29 d13Calk31 d13Calk27err d13Calk29err d13Calk31err d13Calkn dDalk27 dDalk29 dDalk31 dDalk27err dDalk29err dDalk31err dDalkn d13Cacid22 d13Cacid24 d13Cacid26 d13Cacid28 d13Cacid22err d13Cacid24err d13Cacid26err d13Cacid28err d13Cacidn dDacid22 dDacid24 dDacid26 dDacid28 dDacid22err dDacid24err dDacid26err dDacid28err dDacidn archaeol isoGDGT-0 isoGDGT-1 isoGDGT-2 isoGDGT-3 isoGDGT-5 isoGDGT-5_prime b-IIIa b-IIIa_prime b-IIIa_prime_prime b-IIIb b-IIIb_prime b-IIIc bIIIc_prime b-IIa b-IIa_prime b-IIa_prime_prime b-IIb b-IIb_prime b-IIc b-IIc_prime b-Ia b-Ib b-Ic isoGDGTs_ngg brGDGTs_ngg IIIa_IIa %isoGDGT-0 BIT deltaRI MBT5Me MAT MAT_5th MAT_95th CBTprime ACE IR67me TEX86 LST LST_5th LST_95th 5.33 3.62 SLAPP-SRLS17-1A-1V-1 30.4 6.0 30.4 7.6 -25.37 -27.37 -28.00 0.1 0.0 0.0 2.0 -999999 -999999 -999999 -999999 -999999 -999999 -999999 -999999 -22.00 -26.13 -25.20 -999999 0.20 0.28 0.04 2 -999999 -999999 -999999 -185.2 -999999 -999999 -999999 1.6 2 10.344 10.997 1.543 0.267 0.043 0.018 0.001 0.008 0.034 0.240 0 0 0 0 0.041 0.099 0.184 0.012 0.020 0.000 0.003 0.184 0.013 0.003 12.869 0.842 1.036 99.833 0.963 15.280 0.765 23.014 16.917 28.227 -0.167 27.339 0.70811539 0.167560658 -999999 -999999 -999999 10.23 7.07 SLAPP-SRLS17-1A-4V-1 29.7 7.8 29.7 5.7 -27.72 -29.17 -29.99 0.2 0.2 0.3 2.0 -999999 -999999 -999999 -999999 -999999 -999999 -999999 -999999 -999999 -999999 -26.36 -999999 -999999 -999999 0.02 2 -999999 -999999 -999999 -182.8 -999999 -999999 -999999 0.9 2 50.082 13.311 3.391 0.653 0.119 0.039 0.003 0.021 0.077 0.554 0 0 0 0 0.103 0.149 0.446 0.024 0.033 0.000 0.004 0.219 0.021 0.005 17.515 1.655 0.387 99.710 0.936 10.581 0.622 18.229 12.622 24.386 -0.108 60.080 0.68956258 0.18597812 -999999 -999999 -999999 11.78 8.22 SLAPP-SRLS17-1A-6V-1 29.8 6.0 29.8 7.3 -26.70 -27.62 -30.03 0.0 1.7 0.0 2.0 -999999 -999999 -999999 -999999 -999999 -999999 -999999 -28.82 -28.06 -30.08 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