Chronology of a Kettle-Hole Peat Bog, Cherryfield, Maine

1972 ◽  
Vol 83 (3) ◽  
pp. 827 ◽  
Author(s):  
DAVID M. MICKELSON ◽  
HAROLD W. BORNS
Keyword(s):  
Peat Bog ◽  
Kettle Hole

scholarly journals The ratio of methanogens to methanotrophs and water-level dynamics drive methane transfer velocity in a temperate kettle-hole peat bog

2019 ◽  
Vol 16 (16) ◽  
pp. 3207-3231 ◽  
Author(s):  
Camilo Rey-Sanchez ◽  
Gil Bohrer ◽  
Julie Slater ◽  
Yueh-Fen Li ◽  
Roger Grau-Andrés ◽  
...  
Keyword(s):  
Water Level ◽  
Pore Water ◽  
Relative Abundance ◽  
Water Concentration ◽  
Open Water ◽  
Peat Bog ◽  
Transfer Velocity ◽  
Ch4 Flux ◽  
Kettle Hole ◽  
Vegetation Zones

Abstract. Peatlands are a large source of methane (CH4) to the atmosphere, yet the uncertainty around the estimates of CH4 flux from peatlands is large. To better understand the spatial heterogeneity in temperate peatland CH4 emissions and their response to physical and biological drivers, we studied CH4 dynamics throughout the growing seasons of 2017 and 2018 in Flatiron Lake Bog, a kettle-hole peat bog in Ohio. The site is composed of six different hydro-biological zones: an open water zone, four concentric vegetation zones surrounding the open water, and a restored zone connected to the main bog by a narrow channel. At each of these locations, we monitored water level (WL), CH4 pore-water concentration at different peat depths, CH4 fluxes from the ground and from representative plant species using chambers, and microbial community composition with a focus here on known methanogens and methanotrophs. Integrated CH4 emissions for the growing season were estimated as 315.4±166 mgCH4m-2d-1 in 2017 and 362.3±687 mgCH4m-2d-1 in 2018. Median CH4 emission was highest in the open water, then it decreased and became more variable through the concentric vegetation zones as the WL dropped, with extreme emission hotspots observed in the tamarack mixed woodlands (Tamarack) and low emissions in the restored zone (18.8–30.3 mgCH4m-2d-1). Generally, CH4 flux from above-ground vegetation was negligible compared to ground flux (<0.4 %), although blueberry plants were a small CH4 sink. Pore-water CH4 concentrations varied significantly among zones, with the highest values in the Tamarack zone, close to saturation, and the lowest values in the restored zone. While the CH4 fluxes and pore-water concentrations were not correlated with methanogen relative abundance, the ratio of methanogens to methanotrophs in the upper portion of the peat was significantly correlated to CH4 transfer velocity (the CH4 flux divided by the difference in CH4 pore-water concentration between the top of the peat profile and the concentration in equilibrium with the atmosphere). Since ebullition and plant-mediated transport were not important sources of CH4 and the peat structure and porosity were similar across the different zones in the bog, we conclude that the differences in CH4 transfer velocities, and thus the flux, are driven by the ratio of methanogen to methanotroph relative abundance close to the surface. This study illustrates the importance of the interactions between water-level and microbial composition to better understand CH4 fluxes from bogs and wetlands in general.

scholarly journals The ratio of methanogens to methanotrophs and water-level dynamics drive methane exchange velocity in a temperate kettle-hole peat bog

2019 ◽  
Author(s):  
Camilo Rey-Sanchez ◽  
Gil Bohrer ◽  
Julie Slater ◽  
Yueh-Fen Li ◽  
Roger Grau-Andrés ◽  
...  
Keyword(s):  
Water Level ◽  
Microbial Community Composition ◽  
Water Concentration ◽  
Open Water ◽  
Ground Vegetation ◽  
Peat Bog ◽  
Ch4 Flux ◽  
Ch4 Emissions ◽  
Kettle Hole ◽  
Vegetation Zones

Abstract. Peatlands are a large source of methane (CH4) to the atmosphere, yet the uncertainty around the estimates of CH4 flux from peatlands is large. To better understand the spatial heterogeneity in temperate peatland CH4 emissions and their response to physical and biological drivers, we studied CH4 dynamics throughout the growing seasons of 2017 and 2018 in Flatiron Lake Bog, a kettle-hole peat bog in Ohio. The site is composed of six different hydro-biological zones: an open water zone, four concentric vegetation zones surrounding the open water, and a restored zone connected to the main bog by a narrow channel. At each of these locations, we monitored water level (WL), CH4 pore-water concentration at different peat depths, CH4 fluxes from the ground and from representative plant species using chambers, and microbial community composition with focus here on known methanogens and methanotrophs. Integrated CH4 emissions for the growing season were estimated as 315.4 ± 166 mg CH4 m−2 d−1 in 2017, and 362.3 ± 687 mg CH4 m−2 d−1 in 2018. Median CH4 emission was highest in the open water, then decreased and became more variable through the concentric vegetation zones as the WL dropped, with extreme emission hotspots observed in the Tamarack mixed woodlands (TMW), and low emissions in the restored zone (18.8–30.3 mg CH4 m−2 d−1). Generally, CH4 flux from above-ground vegetation was negligible compared to ground flux (

scholarly journals Mapping the Groundwater Level and Soil Moisture of a Montane Peat Bog Using UAV Monitoring and Machine Learning

2021 ◽  
Vol 13 (5) ◽  
pp. 907
Author(s):  
Theodora Lendzioch ◽  
Jakub Langhammer ◽  
Lukáš Vlček ◽  
Robert Minařík
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Spatial Data ◽  
Groundwater Level ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Sampling Strategy ◽  
Peat Bog ◽  
Strong Impact ◽  
Ground Truth Data

One of the best preconditions for the sufficient monitoring of peat bog ecosystems is the collection, processing, and analysis of unique spatial data to understand peat bog dynamics. Over two seasons, we sampled groundwater level (GWL) and soil moisture (SM) ground truth data at two diverse locations at the Rokytka Peat bog within the Sumava Mountains, Czechia. These data served as reference data and were modeled with a suite of potential variables derived from digital surface models (DSMs) and RGB, multispectral, and thermal orthoimages reflecting topomorphometry, vegetation, and surface temperature information generated from drone mapping. We used 34 predictors to feed the random forest (RF) algorithm. The predictor selection, hyperparameter tuning, and performance assessment were performed with the target-oriented leave-location-out (LLO) spatial cross-validation (CV) strategy combined with forward feature selection (FFS) to avoid overfitting and to predict on unknown locations. The spatial CV performance statistics showed low (R2 = 0.12) to high (R2 = 0.78) model predictions. The predictor importance was used for model interpretation, where temperature had strong impact on GWL and SM, and we found significant contributions of other predictors, such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Index (NDI), Enhanced Red-Green-Blue Vegetation Index (ERGBVE), Shape Index (SHP), Green Leaf Index (GLI), Brightness Index (BI), Coloration Index (CI), Redness Index (RI), Primary Colours Hue Index (HI), Overall Hue Index (HUE), SAGA Wetness Index (TWI), Plan Curvature (PlnCurv), Topographic Position Index (TPI), and Vector Ruggedness Measure (VRM). Additionally, we estimated the area of applicability (AOA) by presenting maps where the prediction model yielded high-quality results and where predictions were highly uncertain because machine learning (ML) models make predictions far beyond sampling locations without sampling data with no knowledge about these environments. The AOA method is well suited and unique for planning and decision-making about the best sampling strategy, most notably with limited data.

scholarly journals Interpreting 14C Measurements on 3rd–4th Century AD Iron Artifacts from Nydam, Denmark

Radiocarbon ◽  
2019 ◽  
Vol 61 (5) ◽  
pp. 1517-1529
Author(s):  
C Matthias Hüls ◽  
John Meadows ◽  
Andreas Rau
Keyword(s):  
Isotopic Composition ◽  
Combustion Temperature ◽  
Peat Bog ◽  
Iron Production ◽  
Aging Effects ◽  
Carbon Dissolution ◽  
Fossil Carbon ◽  
Common Effect ◽  
Iron Artifacts ◽  
Good Agreement

ABSTRACTRadiocarbon (14C) ages were determined for 10 iron samples from the war booty offering site in the Nydam peat bog (SE Denmark), and compared to archaeologically inferred periods of deposition. Additional 14C measurements were carried out for modern iron standards made with charcoal of known isotopic composition to evaluate possible effects of handling. Modern iron standards give depleted 14C concentrations, compared to the initial charcoal 14C composition, and may indicate carbon fractionation effects during carbon dissolution in the iron lattice. Further studies are needed to verify if this is a common effect during iron production. 14C dating of two swords and one ax head are in comparatively good agreement with expected deposition times and indicate only small old-wood effects. In contrast, 14C dating of iron rivets from the Nydam (B) oak boat proved difficult due to corrosion with siderite (FeCO3) and conservation with wax. A step-combustion procedure was applied, using a low (∼570–600°C) temperature prior to the high (∼970–1000°C) combustion temperature for carbon extraction, aiming to remove siderite and wax before collecting the original carbon dissolved in the iron lattice. Nevertheless, measured 14C ages of the iron rivets differ by about 200–300 years from the dendro-date of the Nydam (B) oak boat they belong to, indicating persisting aging effects (e.g. old-wood, contamination with fossil carbon added during iron making and/or handling prior 14C dating). Also, a possible recycling of older iron cannot be excluded.

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