# Avery Lake, Illinois 3,100 Year Multiproxy Sediment Data #----------------------------------------------------------------------- # World Data Service for Paleoclimatology, Boulder # and # NOAA Paleoclimatology Program # National Centers for Environmental Information (NCEI) #----------------------------------------------------------------------- # Template Version 3.0 # Encoding: UTF-8 # NOTE: Please cite Publication, and Online_Resource and date accessed when using these data. # If there is no publication information, please cite Investigators, Title, and Online_Resource and date accessed. # # Online_Resource: https://www.ncdc.noaa.gov/paleo/study/27071 # Description: NOAA Landing Page # Online_Resource: https://www1.ncdc.noaa.gov/pub/data/paleo/paleolimnology/northamerica/usa/illinois/avery2019pollen.txt # Description: NOAA location of the template # # Original_Source_URL: # Description: # # Description/Documentation lines begin with # # Data lines have no # # # Archive: Paleolimnology # # Dataset DOI: # # Parameter_Keywords: geochemistry, carbon isotopes, physical properties, population abundance #-------------------- # Contribution_Date # Date: 2019-09-19 #-------------------- # File_Last_Modified_Date # Date: 2019-09-19 #-------------------- # Title # Study_Name: Avery Lake, Illinois 3,100 Year Multiproxy Sediment Data #-------------------- # Investigators # Investigators: Bird, B.W.; Barr, R.C.; Commerford, J.; Gilhooly III, W.P.; Wilson, J.J.; Finney, B.; McLauchlan, K.; Monaghan, G.W. #-------------------- # Description_Notes_and_Keywords # Description: 3,100 year multiproxy sediment record of floodplain development, land-use, and climate flood relationships on the lower Ohio River from Avery Lake, Illinois. # Provided Keywords: Paleoclimate, Fluvial Dynamics, Floodplain Lakes, Anthropogenic Environmental Impacts, Sedimentology, Geochemistry, Pacific North American Mode, Pacific Decadal Oscillation #-------------------- # Publication # Authors: Broxton W. Bird, Robert C. Barr, Julie Commerford, William P. Gilhooly III, Jeremy J. Wilson, Bruce Finney, Kendra McLauchlan, and G. William Monaghan # Published_Date_or_Year: 2019-12-01 # Published_Title: Late-Holocene floodplain development, land-use, and hydroclimate-flood relationships on the lower Ohio River, US # Journal_Name: The Holocene # Volume: 29 # Edition: # Issue: 12 # Pages: 1856-1870 # Report_Number: # DOI: 10.1177/0959683619865598 # Online_Resource: https://journals.sagepub.com/doi/full/10.1177/0959683619865598 # Full_Citation: # Abstract: Floodplain development, land-use, and flooding on the lower Ohio River are investigated with a 3100-year-long sediment archive from Avery Lake, a swale lake on the Black Bottom floodplain in southern Illinois, US. In all, 12 radiocarbon dates show that Avery Lake formed at 1130 BCE (3100 cal. yr BP), almost 3000 years later than previously thought, indicating that the Black Bottom floodplain is younger and more dynamic than previously estimated. Three subsequent periods of extensive land clearance were identified by changes in pollen composition, corresponding to Native American occupations before 1500 CE and the current Euro-American occupation beginning in the 18th century. Sedimentation rates prior to 1820 CE changed independently of land clearance events, suggesting natural as opposed to land-use controls. Comparison with high-resolution paleoclimate data from Martin Lake, IN, indicates that lower Ohio River flooding was frequent when cold-season precipitation originating from the Pacific/Arctic predominated when atmospheric circulation resembled positive Pacific North American (PNA) conditions and the Pacific Decadal Oscillation (PDO) was in a positive mean state (1130 BCE to 350 CE and 1150-1820 CE). Conversely, Ohio River flooding was less frequent when warm-season precipitation from the Gulf of Mexico prevailed during negative PDO- and PNA-like mean states (350 and 1150 CE). This flood dynamic appears to have been fundamentally altered after 1820 CE. We suggest that extensive land clearance in the Ohio River watershed increased runoff and landscape erosion by reducing interception, infiltration, and evapotranspiration, thereby increasing flooding despite a shift to negative PDO- and PNA-like mean states. Predicted increases in average precipitation and extreme rainfall events across the mid-continental US are likely to perpetuate current trends toward more frequent flood events, because anthropogenic modifications have made the landscape less resilient to changing hydroclimatic conditions. #------------------ # Funding_Agency # Funding_Agency_Name: US National Science Foundation - REU - Social, Behavioral & Economic Sciences # Grant: 1262530 #------------------ # Funding_Agency # Funding_Agency_Name: Indiana University # Grant: Collaborative Research Grant #------------------ # Funding_Agency # Funding_Agency_Name: Indiana University-Purdue University, Indianapolis # Grant: Multidisciplinary Undergraduate Research Initiative #------------------ # Site_Information # Site_Name: Avery Lake # Location: North America>United States Of America>Illinois # Country: United States Of America # Northernmost_Latitude: 37.081 # Southernmost_Latitude: 37.081 # Easternmost_Longitude: -88.486 # Westernmost_Longitude: -88.486 # Elevation: 102 m #------------------ # Data_Collection # Collection_Name: Avery2019pollen # Earliest_Year: 3232 # Most_Recent_Year: -63 # Time_Unit: Cal. Year BP # Core_Length: 10.0 # Notes: #------------------ # Chronology_Information # Chronology: # # 14C data # Labcode UCI # # comp_depth Composite depth (cm) # mat.dated Material dated # 14C.raw conventional radiocarbon age, years before 1950AD # 14C.raw_err radiocarbon age, standard error # datemeth Dating method # calib.14C Median calibrated 14C age # calib.14C_1sig_lo Calibrated age, 1-sigma lower confidence bound # calib.14C_1sig_up Calibrated age, 1-sigma upper confidence bound # calib_method Calibration database # notes Notes # # Labcode comp_depth mat.dated 14C.raw 14C.raw_err datemeth calib.14C calib.14C_2sig_lo calib.14C_2sig_up calib_method # 145745 404 Charcoal 135 20 AMS 14C 130 116 72 intcal13.14c # 145746 505 Charcoal 315 20 AMS 14C 390 429 375 intcal13.14c # 145747 656.5 Charcoal 805 25 AMS 14C 720 730 693 intcal13.14c # 145748 724.5 Charcoal 950 20 AMS 14C 850 864 827 intcal13.14c # 182487 727 Wood 1160 15 AMS 14C 1070 1085 1054 intcal13.14c # 180591 731.5 Charcoal 1285 15 AMS 14C 1240 1267 1239 intcal13.14c # 180592 742.25 Charcoal 1685 15 AMS 14C 1590 1604 1578 intcal13.14c # 180593 774 Charcoal 1945 15 AMS 14C 1890 1900 1875 intcal13.14c # 180589 849.5 Charcoal 2260 15 AMS 14C 2310 2335 2308 intcal13.14c # 180590 872 Charcoal 2425 15 AMS 14C 2430 2465 2376 intcal13.14c # 180594 946 Charcoal 2790 15 AMS 14C 2890 2890 2859 intcal13.14c # 145749 972.5 Charcoal 2930 20 AMS 14C 3080 3144 3091 intcal13.14c # #---------------- # Variables # # Data variables follow are preceded by "##" in columns one and two. # Data line variables format: one per line, shortname-tab-variable components (what, material, error, units, seasonality, data type,detail, method, C or N for Character or Numeric data, free text) # ## depth_cm depth, , , cm, , , , ,N, composite depth ## age_calBP age, , , calendar years before present, , , , ,N, ## Asteraceae_Undiff identified pollen, , , count, ,pollen, ,,N, Asteraceae_Undiff ## Asteraceae_Liguliflorae identified pollen, , , count, ,pollen, ,,N, Asteraceae_Liguliflorae ## Asteraceae_Tubuliflorae identified pollen, , , count, ,pollen, ,,N, Asteraceae_Tubuliflorae ## Ambrosia identified pollen, , , count, ,pollen, ,,N, Ambrosia ## Artemisia identified pollen, , , count, ,pollen, ,,N, Artemisia ## Achillea identified pollen, , , count, ,pollen, ,,N, Achillea ## Iva identified pollen, , , count, ,pollen, ,,N, Iva ## Xanthium identified pollen, , , count, ,pollen, ,,N, Xanthium ## Brassicaceae identified pollen, , , count, ,pollen, ,,N, Brassicaceae ## Cephalanthus identified pollen, , , count, ,pollen, ,,N, Cephalanthus ## Amaranthaceae identified pollen, , , count, ,pollen, ,,N, Amaranthaceae ## Circaea identified pollen, , , count, ,pollen, ,,N, Circaea ## Dalea identified pollen, , , count, ,pollen, ,,N, Dalea ## Ericaceae identified pollen, , , count, ,pollen, ,,N, Ericaceae ## Fabaceae_Undiff identified pollen, , , count, ,pollen, ,,N, Fabaceae_Undiff ## Gallium identified pollen, , , count, ,pollen, ,,N, Gallium ## Zea mays identified pollen, , , count, ,pollen, ,,N, Zea mays ## Malvaceae identified pollen, , , count, ,pollen, ,,N, Malvaceae ## Gleditsia identified pollen, , , count, ,pollen, ,,N, Gleditsia ## Poaceae identified pollen, , , count, ,pollen, ,,N, Poaceae ## Polygonum-type identified pollen, , , count, ,pollen, ,,N, Polygonum-type ## Plantago identified pollen, , , count, ,pollen, ,,N, Plantago ## Rosaceae identified pollen, , , count, ,pollen, ,,N, Rosaceae ## Rubus identified pollen, , , count, ,pollen, ,,N, Rubus ## Rumex identified pollen, , , count, ,pollen, ,,N, Rumex ## Stellaria identified pollen, , , count, ,pollen, ,,N, Stellaria ## Urtica identified pollen, , , count, ,pollen, ,,N, Urtica ## Verbenaceae identified pollen, , , count, ,pollen, ,,N, Verbenaceae ## Viburnum identified pollen, , , count, ,pollen, ,,N, Viburnum ## Vitis identified pollen, , , count, ,pollen, ,,N, Vitis ## Abies identified pollen, , , count, ,pollen, ,,N, Abies ## Acer identified pollen, , , count, ,pollen, ,,N, Acer ## Alnus identified pollen, , , count, ,pollen, ,,N, Alnus ## Betula identified pollen, , , count, ,pollen, ,,N, Betula ## Carya identified pollen, , , count, ,pollen, ,,N, Carya ## Castanea identified pollen, , , count, ,pollen, ,,N, Castanea ## Celtis identified pollen, , , count, ,pollen, ,,N, Celtis ## Cornus_Undiff identified pollen, , , count, ,pollen, ,,N, Cornus_Undiff ## Corylus identified pollen, , , count, ,pollen, ,,N, Corylus ## Fagus identified pollen, , , count, ,pollen, ,,N, Fagus ## Fraxinus_Undiff identified pollen, , , count, ,pollen, ,,N, Fraxinus_Undiff ## Ilex identified pollen, , , count, ,pollen, ,,N, Ilex ## Juglans_Undiff identified pollen, , , count, ,pollen, ,,N, Juglans_Undiff ## Cupressaceae identified pollen, , , count, ,pollen, ,,N, Cupressaceae ## Larix identified pollen, , , count, ,pollen, ,,N, Larix ## Liriodendron identified pollen, , , count, ,pollen, ,,N, Liriodendron ## Liquidambar identified pollen, , , count, ,pollen, ,,N, Liquidambar ## Morus identified pollen, , , count, ,pollen, ,,N, Morus ## Ostrya identified pollen, , , count, ,pollen, ,,N, Ostrya ## Picea identified pollen, , , count, ,pollen, ,,N, Picea ## Pinus identified pollen, , , count, ,pollen, ,,N, Pinus ## Platanus identified pollen, , , count, ,pollen, ,,N, Platanus ## Populus identified pollen, , , count, ,pollen, ,,N, Populus ## Quercus identified pollen, , , count, ,pollen, ,,N, Quercus ## Rhamnus identified pollen, , , count, ,pollen, ,,N, Rhamnus ## Salix identified pollen, , , count, ,pollen, ,,N, Salix ## Tilia identified pollen, , , count, ,pollen, ,,N, Tilia ## Tsuga identified pollen, , , count, ,pollen, ,,N, Tsuga ## Ulmus identified pollen, , , count, ,pollen, ,,N, Ulmus ## Unknowns identified pollen, , , count, ,pollen, ,,N, Unknowns ## Cyperaceae identified pollen, , , count, ,pollen, ,,N, Cyperaceae ## Marker identified pollen, , , count, ,pollen, ,,N, Foreign marker raw count # #---------------- # Data: # Data lines follow (have no #) # Data line format - tab-delimited text, variable short name as header # Missing Values: NAN # depth_cm age_calBP Asteraceae_Undiff Asteraceae_Liguliflorae Asteraceae_Tubuliflorae Ambrosia Artemisia Achillea Iva Xanthium Brassicaceae Cephalanthus Amaranthaceae Circaea Dalea Ericaceae Fabaceae_Undiff Gallium Zea mays Malvaceae Gleditsia Poaceae Polygonum-type Plantago Rosaceae Rubus Rumex Stellaria Urtica Verbenaceae Viburnum Vitis Abies Acer Alnus Betula Carya Castanea Celtis Cornus_Undiff Corylus Fagus Fraxinus_Undiff Ilex Juglans_Undiff Cupressaceae Larix Liriodendron Liquidambar Morus Ostrya Picea Pinus Platanus Populus Quercus Rhamnus Salix Tilia Tsuga Ulmus Unknowns Cyperaceae Marker 2.5 -62.77 11 0 0 36 1 0 4 0 0 0 4 0 0 0 0 0 0 0 0 12 0 1 0 0 0 0 12 0 0 0 0 4 2 4 18 1 0 0 1 7 9 0 2 4 0 7 0 0 21 0 32 1 0 88 0 0 3 0 12 4 0 124 82.5 -23.36 6 0 0 83 1 0 1 1 2 0 7 0 0 0 2 1 1 0 0 18 0 3 1 0 2 0 12 0 0 1 8.5 4 1 0 13 4 0 0 0 1 8 0 3 16 0 0 4 0 0 0 12 6 1 61 0 1 0 0 24 5 0 330 162.5 16.04 0 1 0 4 1 0 0 0 0 0 4 0 0 0 0 0 4 0 0 29 2 0 0 0 0 0 6 0 1 1 4 2 0 0 39 0 0 0 0 6 3 0 14 2 0 0 16 0 4 0 35 0 0 79 0 2 0 0 36 2 1 0 222.5 45.60 2 0 0 78 1 0 0 1 0 0 4 0 0 0 0 1 3 0 0 14 0 1 0 1 1 0 6 0 0 1 0 1 0 6 7 1 0 0 0 15 10 0 2 3 0 0 11 0 17 0 14.5 7 0 79 0 2 0 0 19 0 3 442 282.5 75.15 9 1 0 66 4 1 0 5 3 0 8 0 0 0 0 0 14 0 0 47 2 0 0 0 0 0 4 0 0 0 0 1 0 4 12 0 0 0 0 1 2 0 3 2 0 3 3 0 2 1 46 0 1 38 0 0 1 2 26 4 2 67 322.5 94.86 0 0 0 76 2 1 0 0 0 0 2 0 0 0 0 0 0 0 0 8 0 0 0 0 1 0 13 0 0 0 0 2 0 6 14 0 0 0 0 12 2 0 3 4 1 0 33 0 8 0 11 4 0 79 0 0 0 0 18 0 3 442 432.5 206.96 2 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 14 0 0 0 0 0 0 9 0 0 0 3 2 0 0 60 0 0 0 0 9 22 0 10 29 0 0 16 0 6 0 3.5 3 1 127 0 0 0 1 34 4 4 116 482.5 333.19 1 0 0 11 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 1 0 0 0 1 0 11 2 0 0 0 2 0 3 50 1 1 0 1 0 14 0 22 24 0 0 7 0 1 0 13.5 6 0 110 0 0 0 1 27 6 2 118 532.5 448.99 4 0 0 4 1 0 0 0 2 0 0 0 0 0 0 0 0 0 0 16 0 0 0 0 0 0 1 0 1 0 0 11 0 0 67 0 0 0 0 8 2 0 6 0 0 0 9 0 0 0 37 0 0 89 0 0 1 0 8 0 47 70 592.5 577.71 5 0 0 45 1 0 11 2 0 1 0 0 1 0 0 0 0 3 0 10 3 0 0 0 1 0 2 1 0 0 6 5 0 7 23 0 0 0 0 1 1 0 4 5 0 0 2 0 1 0 23.5 1 1 137 0 0 2 3 10 5 1 279 657.5 716.99 2 0 0 113 1 0 4 1 0 0 8 0 0 0 0 0 0 0 0 6 8 0 0 0 0 0 2 0 0 0 2 2 0 9 26 0 0 0 1 2 3 0 4 1 1 0 2 0 0 1 10 2 0 99 0 0 2 0 11 2 2 209 672 745.77 2 0 2 41 6 0 9 0 0 0 6 0 0 0 0 1 3 0 0 9 2 0 0 0 0 0 1 0 0 0 0 1 0 3 46 0 0 0 2 6 3 0 5 4 0 0 2 0 0 0 20 4 1 123 0 1 0 0 9 4 0 479 687.5 776.54 1 0 0 50 4 0 7 0 0 0 7 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 1 0 0 0 0 2 2 10 45 1 0 0 0 1 4 0 1 7 0 0 2 0 9 0 13 0 0 115 0 1 1 0 3 9 4 277 700.5 802.35 4 0 0 41 3 0 6 0 0 0 2 0 0 0 0 0 2 0 0 6 1 0 0 0 2 0 3 0 0 1 4 2 0 8 32 1 0 0 0 6 9 0 10 14 0 0 7 0 2 1 7.5 5 1 131 0 0 0 0 22 5 2 197 712.5 826.18 0 0 0 44 3 0 2 0 0 0 0 0 0 1 0 0 1 0 0 3 0 0 0 0 0 0 3 0 0 3 0 3 0 7 67 0 0 0 0 1 5 0 15 6 0 0 1 0 0 1 5.5 4 0 140 0 0 0 0 14 1 1 153 717.5 836.10 1 0 0 53 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 5 1 0 0 0 0 0 3 0 0 0 0 3 0 10 57 3 0 0 0 2 3 1 8 8 0 0 5 1 1 0 8.5 7 0 123 0 2 0 0 20 7 1 193 720.5 842.06 3 0 0 44 2 0 0 1 0 0 1 0 0 0 0 3 2 0 0 8 1 0 0 0 0 0 6 0 0 1 0 1 0 9 87 0 0 0 0 1 8 0 12 7 0 0 1 0 0 0 10 5 0 114 0 1 0 0 15 4 0 126 725.5 938.00 0 0 0 27 3 0 0 0 0 0 4 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 2 0 0 0 0 0 0 14 93 1 0 0 0 0 6 0 9 6 0 0 3 0 1 0 5 0 0 128 0 0 0 0 19 0 1 123 730.5 1202.22 2 0 0 23 2 0 1 1 0 0 1 0 0 0 0 1 0 0 0 3 1 0 0 0 0 0 6 0 0 2 4.5 7 0 3 87 0 0 0 0 0 4 0 38 15 0 0 5 0 0 0 6 10 0 100 0 0 0 0 40 1 1 122 742.5 1593.02 0 1 0 17 1 0 0 0 0 0 2 0 0 1 0 0 0 0 0 3 3 0 0 0 0 0 5 0 0 0 0 2 0 7 70 2 0 0 0 0 4 0 20 3 0 1 4 0 1 0 13 5 0 112 0 0 1 0 35 10 1 64 760.5 1760.31 1 0 0 78 0 0 3 0 0 0 1 0 0 0 1 0 0 0 0 12 0 0 0 0 0 1 0 0 0 0 0 4 0 13 67 4 0 0 0 1 1 0 10 8 0 0 5 0 0 1 6 5 0 116 0 0 0 0 5 4 0 87 805.5 2065.23 3 1 0 108 0 0 1 3 0 0 1 0 1 0 0 0 0 0 0 8 1 0 0 0 0 0 3 0 0 0 0 1 0 2 31 0 0 0 0 3 4 0 4 3 0 0 4 0 4 0 11 1 0 78 0 2 1 2 21 3 0 301 846.5 2293.31 0 0 0 110 0 0 0 0 0 0 15 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 3 4 1 37 0 0 0 0 1 8 0 7 7 0 0 3 0 1 0 24.5 1 0 119 0 0 0 1 14 1 1 125 886.5 2524.16 3 0 0 12 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 4 0 1 0 0 0 6 0 0 0 1 1 0 5 148 6 0 0 0 0 12 0 19 7 1 0 5 0 2 0 2.5 1 0 87 0 0 0 0 30 2 1 114 909.5 2665.57 0 1 0 9 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 1 2 4 1 0 145 0 0 3 0 0 1 0 20 7 0 3 4 0 0 1 9 0 0 62 0 0 1 0 34 2 1 105 935.5 2825.44 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 5 0 0 0 1.5 1 1 0 147 1 1 0 0 0 0 0 12 2 0 0 11 0 1 0 29 0 1 84 0 0 0 0 19 3 2 7 960.5 2996.70 2 0 0 56 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 2 1 0 0 0 0 0 6 0 0 1 9 1 0 5 91 0 0 0 0 0 7 0 6 5 0 0 2 0 0 1 6.5 6 0 89 0 0 0 0 23 3 2 147 985.5 3180.66 1 1 0 11 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 4 3 0 0 0 0 0 1 0 0 0 0 1 0 4 30 0 0 0 2 0 6 0 5 3 0 0 4 0 1 0 20 4 0 220 1 0 1 0 7 0 0 102