Modeling micro-topographic controls on boreal peatland hydrology and methane fluxes

Aleina, Fabio Cresto; Runkle, Benjamin R. K.; Kleinen, Thomas; Kutzbach, Lars; Schneider, J.; Brovkin, Victor

doi:10.5194/bg-12-5689-2015

Cited by 38 publications

(44 citation statements)

References 45 publications

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“…The input anoxic respiration was created from two components: simulated NPP and temperature-dependent anoxic peat decomposition V pR (mol m −2 s −1 ). As methanogens seem to be keen on fresh, newly fixed carbon (Couwenberg and Fritz, 2012), such as the root exudates, many models relate the CH 4 production rate directly with the NPP of the wetland vegetation (Wania et al, 2010;Walter and Heimann, 2000;Zhuang et al, 2004). We simply simulated the NPP time series for the sites, allocated the NPP vertically along the root distribution (Eq.…”

Section: Appendix Amentioning

confidence: 99%

“…Some of the models also simulate the O 2 transport and the simulated O 2 concentrations affect the CH 4 processes. These models have been used in multiple studies (e.g., van Huissteden, 2009, 2011;Khvorostianov et al, 2008;Ringeval et al, 2011;Melton et al, 2013;Budishchev et al, 2014;Cresto Aleina et al, 2015;Grant et al, 2015), and some are referred to in the assessment report of the Intergovernmental Panel on Climate Change (IPCC; Ciais et al, 2013). These models have different approaches in simulating the production of CH 4 , ranging from separating distinct heterotrophic microbial communities (Grant and Roulet, 2002) to taking a constant fraction of the simulated heterotrophic soil respiration (Riley et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

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HIMMELI v1.0: HelsinkI Model of MEthane buiLd-up and emIssion for peatlands

et al. 2017

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Abstract. Wetlands are one of the most significant natural sources of methane (CH 4 ) to the atmosphere. They emit CH 4 because decomposition of soil organic matter in waterlogged anoxic conditions produces CH 4 , in addition to carbon dioxide (CO 2 ). Production of CH 4 and how much of it escapes to the atmosphere depend on a multitude of environmental drivers. Models simulating the processes leading to CH 4 emissions are thus needed for upscaling observations to estimate present CH 4 emissions and for producing scenarios of future atmospheric CH 4 concentrations. Aiming at a CH 4 model that can be added to models describing peatland carbon cycling, we composed a model called HIMMELI that describes CH 4 build-up in and emissions from peatland soils. It is not a full peatland carbon cycle model but it requires the rate of anoxic soil respiration as input. Driven by soil temperature, leaf area index (LAI) of aerenchymatous peatland vegetation, and water table depth (WTD), it simulates the concentrations and transport of CH 4 , CO 2 , and oxygen (O 2 ) in a layered one-dimensional peat column. Here, we present the HIMMELI model structure and results of tests on the model sensitivity to the input data and to the description of the peat column (peat depth and layer thickness), and demonstrate that HIMMELI outputs realistic fluxes by comparing modeled and measured fluxes at two peatland sites. As HIMMELI describes only the CH 4 -related processes, not the full carbon cycle, our analysis revealed mechanisms and dependencies that may remain hidden when testing CH 4 models connected to complete peatland carbon models, which is usually the case. Our results indicated that (1) the model is flexible and robust and thus suitable for different environments; (2) the simulated CH 4 emissions largely depend on the prescribed rate of anoxic respiration; (3) the sensitivity of Published by Copernicus Publications on behalf of the European Geosciences Union. 4666M. Raivonen et al.: HelsinkI Model of MEthane buiLd-up and emIssion for peatlands the total CH 4 emission to other input variables is mainly mediated via the concentrations of dissolved gases, in particular, the O 2 concentrations that affect the CH 4 production and oxidation rates; (4) with given input respiration, the peat column description does not significantly affect the simulated CH 4 emissions in this model version.

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Section: Appendix Amentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

HIMMELI v1.0: HelsinkI Model of MEthane buiLd-up and emIssion for peatlands

et al. 2017

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“…Linear function for moisture impacts (e.g., CLASS use linear function for moisture impact on methanotrophy) (Curry, 2007).…”

Section: Environmental Controls On Ch 4 Processesmentioning

confidence: 99%

Reviews and syntheses: Four decades of modeling methane cycling in terrestrial ecosystems

et al. 2016

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Abstract. Over the past 4 decades, a number of numerical models have been developed to quantify the magnitude, investigate the spatial and temporal variations, and understand the underlying mechanisms and environmental controls of methane (CH 4 ) fluxes within terrestrial ecosystems. These CH 4 models are also used for integrating multi-scale CH 4 data, such as laboratory-based incubation and molecular analysis, field observational experiments, remote sensing, and aircraft-based measurements across a variety of terrestrial ecosystems. Here we summarize 40 terrestrial CH 4 models to characterize their strengths and weaknesses and to suggest a roadmap for future model improvement and application. Our key findings are that (1) the focus of CH 4 models has shifted from theoretical to site-and regional-level applications over the past 4 decades, (2) large discrepancies exist among models in terms of representing CH 4 processes and their environmental controls, and (3) significant datamodel and model-model mismatches are partially attributed to different representations of landscape characterization and inundation dynamics. Three areas for future improvements and applications of terrestrial CH 4 models are that (1) CH 4 models should more explicitly represent the mechanisms underlying land-atmosphere CH 4 exchange, with an emphasis on improving and validating individual CH 4 processes over depth and horizontal space, (2) models should be developed that are capable of simulating CH 4 emissions across highly heterogeneous spatial and temporal scales, particularly hot moments and hotspots, and (3) efforts should be invested to develop model benchmarking frameworks that can easily be used for model improvement, evaluation, and integration with data from molecular to global scales. These improvements in CH 4 models would be beneficial for the Earth system models and further simulation of climate-carbon cycle feedbacks.

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“…Fine-scale variability in elevation, commonly referred to as microtopography, is an important factor in the distribution of greenhouse gas (GHG) flux across peatland ecosystems [1][2][3][4][5][6][7]. Consequently, including microtopographic information in carbon-balance models is often recommended as a means of improving model accuracies [6,[8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, including microtopographic information in carbon-balance models is often recommended as a means of improving model accuracies [6,[8][9][10][11]. Most commonly, microtopographic data are acquired via terrestrial surveys using real-time kinematic global navigation satellite system (RTK GNSS) equipment, which are capable of centimeter accuracies [12,13].…”

Section: Introductionmentioning

confidence: 99%

Assessing the Value of UAV Photogrammetry for Characterizing Terrain in Complex Peatlands

2017

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Microtopographic variability in peatlands has a strong influence on greenhouse gas fluxes, but we lack the ability to characterize terrain in these environments efficiently over large areas. To address this, we assessed the capacity of photogrammetric data acquired from an unmanned aerial vehicle (UAV or drone) to reproduce ground elevations measured in the field. In particular, we set out to evaluate the role of (i) vegetation/surface complexity and (ii) supplementary LiDAR data on results. We compared remote-sensing observations to reference measurements acquired with survey grade GPS equipment at 678 sample points, distributed across a 61-hectare treed bog in northwestern Alberta, Canada. UAV photogrammetric data were found to capture elevation with accuracies, by root mean squares error, ranging from 14-42 cm, depending on the state of vegetation/surface complexity. We judge the technology to perform well under all but the most-complex conditions, where ground visibility is hindered by thick vegetation. Supplementary LiDAR data did not improve results significantly, nor did it perform well as a stand-alone technology at the low densities typically available to researchers.

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Modeling micro-topographic controls on boreal peatland hydrology and methane fluxes

Cited by 38 publications

References 45 publications

HIMMELI v1.0: HelsinkI Model of MEthane buiLd-up and emIssion for peatlands

HIMMELI v1.0: HelsinkI Model of MEthane buiLd-up and emIssion for peatlands

Reviews and syntheses: Four decades of modeling methane cycling in terrestrial ecosystems

Assessing the Value of UAV Photogrammetry for Characterizing Terrain in Complex Peatlands

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