# Umbanhowar fire data from Doroo Nuur, western Mongolia - IMPD MNDRN001 #----------------------------------------------------------------------- # World Data Center for Paleoclimatology, Boulder # and # NOAA Paleoclimatology Program #----------------------------------------------------------------------- # NOTE: Please cite original publication, online resource and date accessed when using this data. # If there is no publication information, please cite Investigator, title, online resource and date accessed. # # Description/Documentation lines begin with # # Data lines have no # # # Online_Resource: http://www.ncdc.noaa.gov/paleo/study/20425 # Online_Resource: http://www1.ncdc.noaa.gov/pub/data/paleo/firehistory/charcoal/asia/mndrn001.txt # # Archive: Fire History # # Parameter_Keywords: #--------------------------------------- # Contribution_Date # Date: 2016-08-06 #--------------------------------------- # Title # Study_Name: Umbanhowar fire data from Doroo Nuur, western Mongolia - IMPD MNDRN001 #--------------------------------------- # Investigators # Investigators: Umbanhowar, Charles #--------------------------------------- # Description and Notes # Description: # Provided Keywords: fire, paleoclimate, climate, charcoal #--------------------------------------- # Publication # Authors: Umbanhowar, C.E., Shinneman, A.L.C., Tserenkhand, G., Hammon, E.R., Lor, P., and Nail, K. # Published_Date_or_Year: 2009 # Published_Title: Regional fire history based on charcoal analysis of sediments from nine lakes in western Mongolia. # Journal_Name: The Holocene # Volume: 19 # Edition: # Issue: # Pages: 611-624 # Report Number: # DOI: # Online_Resource: # Full_Citation: # Abstract: Fires are common in grassland regions of the world, and the frequency and severity of fire is linked to climate-driven changes in fuel loads. Western Mongolia is dominated by grasslands but the fire history of this region is largely unknown. We reconstructed modern fire (48 lakes) and historical fires (9 lakes) using sediment charcoal. Modern fuel loads were estimated using a combination of clipped plots, satellite-based estimates of annual aboveground net primary productivity (NPP) and NPP modeled from annual temperature and precipitation. Loss-on-ignition and environmental magnetics of lake sediments were analyzed as proxies for climate. We found little evidence for modern or historical fire in the landscape, as charcoal was absent from the surface sediments of 34 of 48 lakes. Charcoal influxes were uniformly low, averaging from 0.002 to 0.028 mm2/cm2 per yr, over the past 1200 years at nine lakes, and the past 6000-5000 years at two of the lakes with longer sediment records. In the modern landscape, livestock grazing has eliminated most of the fuels necessary to carry a fire, as measured fuel loads (27.3+-4.9 g/m2) were only ~20% of aboveground annual NPP estimated using MODIS Imagery or modeled from climate data. The historical absence of fire may indicate a longer history of intensive grazing than sometimes assumed, and cultural prohibitions against burning may also play a role. Regional summary indicated a >50% decrease in charcoal influxes since AD 1600 at most sites which may be related to lower temperatures or greater aridity during the 'Little Ice Age'. Alternatively this decrease in charcoal influxes may reflect increases in livestock numbers or increased local concentrations because of restrictions on the movement of animals coincident with the establishment of Manchu rule in the late seventeenth century. #--------------------------------------- # Funding_Agency # Funding_Agency_Name: # Grant: #--------------------------------------- # Site Information # Site_Name: Doroo Nuur # Location: Asia # Country: Mongolia # Northernmost_Latitude: 48.2459 # Southernmost_Latitude: 48.2459 # Easternmost_Longitude: 90.6639 # Westernmost_Longitude: 90.6639 # Elevation: 2394 #--------------------------------------- # Data_Collection # Collection_Name: MNDRN001-CHAR # First_Year: 6994 # Last_Year: -46 # Time_Unit: cal yr BP # Core_Length: # Notes: #--------------------------------------- # Species # Species_Name: # Common_Name: # Tree_Species_Code: #--------------------------------------- # Chronology_Information # Chronology: # # Short name What Units Data type # ID_DATE_INFO NaN NaN # ID_SITE Site ID NaN C # CORE_NAME Name of core NaN C # AVG_DEPTH (cm) Average depth of aged sample cm N # THICKNESS Thickness of sample cm N # ID_DATE_TYPE NaN NaN C # AGE_CALIB Calibrated age NaN N # AGE_C14 Carbon-14 Age NaN N # ERROR positive/negative error NaN N # LAB_NUM Lab number NaN N # ID_MAT_DATED NaN NaN NaN # ID_COMMENTS NaN NaN C # AGE_BASIS Used for age model? NaN C # # ID_DATE_INFO ID_SITE CORE_NAME AVG_DEPTH (m) THICKNESS ID_DATE_TYPE AGE_CALIB AGE_C14 ERROR LAB_NUM ID_MAT_DATED ID_COMMENTS AGE_BASIS # Doroo 911.00 2.00 Pb -54 # Doroo 912.00 2.00 Pb -46 # Doroo 913.00 2.00 Pb -34 # Doroo 914.00 2.00 Pb -21 # Doroo 934.5 5 Corrected radiocarbon date 1329 1425 70 132980 charcoal YES # Doroo 995.5 3 Corrected radiocarbon date 4658 4120 80 113011 charcoal YES # Doroo 1022.5 1 Corrected radiocarbon date 7443 6220 340 113010 charcoal YES #--------------------------------------- # Variables # Data variables follow that are preceded by "##" in columns one and two. # Variables list, one per line, shortname-tab-longname components (9 components: what, material, error, units, seasonality, archive, detail, method, C or N for Character or Numeric data) ## depth-cm-top Depth top,,,cm ,,,,,N ## depth-cm-bottom Depth bottom,,,cm ,,,,,N ## age-calBP Age,,,calendar years BP,,,,,N ## char>180um Charcoal ,wet sediment,,count,,fire history,> 180 um,,N ## vol-wetsed Volume,wet sediment,,cm3,,fire history,used for charcoal counts,,N #------------------------ # Data # Data lines follow (have no #) # Data line format - tab-delimited text, variable short name as header # Missing Value: depth-cm-top depth-cm-bottom age-calBP char>180um vol-wetsed 910 911 -46 1 1.0 911 912 -24 0 1.0 912 913 -2 1 1.0 913 914 20 0 1.0 914 915 30 1 1.0 915 916 70 11 1.0 916 917 133 12 1.0 917 918 196 1 1.0 918 919 259 0 1.0 919 920 322 4 1.0 920 921 385 2 1.0 921 922 448 0 1.0 922 923 510 0 1.0 923 924 573 2 1.0 924 925 636 1 1.0 925 926 699 0 1.0 926 927 762 1 1.0 927 928 825 1 1.0 928 929 888 1 1.0 929 930 951 0 1.0 930 931 1014 0 1.0 931 932 1077 0 1.0 932 933 1140 2 1.0 933 934 1203 2 1.0 934 935 1266 1 1.0 935 936 1329 2 1.0 936 937 1392 1 1.0 937 938 1455 0 1.0 938 939 1518 0 1.0 939 940 1581 2 1.0 940 941 1643 0 1.0 941 942 1706 0 1.0 942 943 1769 7 1.0 943 944 1832 1 1.0 944 945 1895 1 1.0 945 946 1958 0 1.0 946 947 2021 0 1.0 947 948 2084 1 1.0 948 949 2147 1 1.0 949 950 2210 1 1.0 950 951 2273 2 1.0 951 952 2336 1 1.0 952 953 2399 0 1.0 953 954 2462 0 1.0 954 955 2525 3 1.0 955 956 2588 1 1.0 956 957 2651 0 1.0 957 958 2714 1 1.0 958 959 2776 0 1.0 959 960 2839 1 1.0 960 961 2902 2 1.0 961 962 2965 3 1.0 962 963 3028 3 1.0 963 964 3091 1 1.0 964 965 3154 0 1.0 965 966 3217 1 1.0 966 967 3280 0 1.0 967 968 3343 4 1.0 968 969 3406 7 1.0 969 970 3469 1 1.0 970 971 3532 0 1.0 970 971 3595 0 1.0 971 972 3658 3 1.0 972 973 3721 3 1.0 973 974 3784 0 1.0 974 975 3847 2 1.0 975 976 3909 1 1.0 976 977 3972 1 1.0 977 978 4035 3 1.0 978 979 4098 2 1.0 979 980 4161 1 1.0 980 981 4224 2 1.0 981 982 4287 1 1.0 982 983 4350 0 1.0 983 984 4413 0 1.0 984 985 4476 15 1.0 985 986 4539 11 1.0 986 987 4602 1 1.0 987 988 4665 4 1.0 988 989 4728 1 1.0 989 990 4791 0 1.0 990 991 4854 4 1.0 991 992 4917 3 1.0 992 993 4980 7 1.0 993 994 5043 9 1.0 994 995 5105 18 1.0 995 996 5168 11 1.0 996 997 5231 6 1.0 997 998 5357 12 1.0 998 999 5420 3 1.0 999 1000 5483 3 1.0 1000 1001 5546 2 1.0 1001 1002 5609 3 1.0 1002 1003 5672 4 1.0 1003 1004 5735 2 1.0 1004 1005 5798 3 1.0 1005 1006 5861 3 1.0 1006 1007 5924 6 1.0 1007 1008 5987 1 1.0 1008 1009 6050 0 1.0 1009 1010 6113 2 1.0 1010 1011 6176 4 1.0 1011 1012 6238 4 1.0 1012 1013 6301 9 1.0 1013 1014 6364 3 1.0 1014 1015 6427 2 1.0 1015 1016 6490 11 1.0 1016 1017 6553 11 1.0 1017 1018 6616 12 1.0 1018 1019 6679 2 1.0 1019 1020 6742 6 1.0 1020 1021 6805 18 1.0 1021 1022 6868 10 1.0 1022 1023 6931 15 1.0 1023 1024 6994 11 1.0