The DigiBog peatland development model 1: rationale, conceptual model, and hydrological basis

Baird, Andy J.; Morris, Paul J.; Belyea, Lisa R.

doi:10.1002/eco.230

Cited by 74 publications

(97 citation statements)

References 71 publications

(98 reference statements)

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“…The most recent of these point models computes water table depth from monthly rainfall, using a site-specific model (16). Meanwhile, numerical models have been used to simulate peat accumulation under constant rainfall (17,18). Although these subsequent works simulate the dynamics of peat production and decomposition in increasing detail, a strength of Ingram's model was that it provided quantitative intuition for how peat dome morphology depends on peat hydrologic properties and average rainfall.…”

Section: Significancementioning

confidence: 99%

How temporal patterns in rainfall determine the geomorphology and carbon fluxes of tropical peatlands

Cobb

Hoyt

Gandois

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

111

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Tropical peatlands now emit hundreds of megatons of carbon dioxide per year because of human disruption of the feedbacks that link peat accumulation and groundwater hydrology. However, no quantitative theory has existed for how patterns of carbon storage and release accompanying growth and subsidence of tropical peatlands are affected by climate and disturbance. Using comprehensive data from a pristine peatland in Brunei Darussalam, we show how rainfall and groundwater flow determine a shape parameter (the Laplacian of the peat surface elevation) that specifies, under a given rainfall regime, the ultimate, stable morphology, and hence carbon storage, of a tropical peatland within a network of rivers or canals. We find that peatlands reach their ultimate shape first at the edges of peat domes where they are bounded by rivers, so that the rate of carbon uptake accompanying their growth is proportional to the area of the still-growing dome interior. We use this model to study how tropical peatland carbon storage and fluxes are controlled by changes in climate, sea level, and drainage networks. We find that fluctuations in net precipitation on timescales from hours to years can reduce long-term peat accumulation. Our mathematical and numerical models can be used to predict long-term effects of changes in temporal rainfall patterns and drainage networks on tropical peatland geomorphology and carbon storage.tropical peatlands | peatland geomorphology | peatland hydrology | peatland carbon storage | climate-carbon cycle feedbacks T ropical peatlands store gigatons of carbon in peat domes, gently mounded land forms kilometers across and 10 or more meters high (1). The carbon stored as peat in these domes has been sequestered by photosynthesis of peat swamp trees (2) and preserved for thousands of years by waterlogging, which suppresses decomposition. Human disturbance of tropical peatlands by fire and drainage for agriculture is now causing reemission of that carbon at rates of hundreds of megatons per year (2-5): Emissions from Southeast Asian peatlands alone are equivalent to about 2% of global fossil fuel emissions or 20% of global land use and land cover change emissions (6, 7). Because peat is mostly organic carbon, a description of the growth and subsidence of tropical peatlands also quantifies fluxes of carbon dioxide (1,4,8). Evidence from a range of studies establishes that accumulation and loss of tropical peat are controlled by water table dynamics (4, 9). When the water table is low, aerobic decomposition occurs, releasing carbon dioxide; when the water table is high, aerobic decomposition is inhibited by lack of oxygen, production of peat exceeds its decay, and peat accumulates. In this way, the rate of peat accumulation is determined by the fraction of time that peat is exposed by a low water table (Fig. 1).The water table rises and falls in a peatland according to the balance between rainfall, evapotranspiration, and groundwater flow. Water flows downslope toward the edge of each peat dome, where it is bo...

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Section: Significancementioning

confidence: 99%

How temporal patterns in rainfall determine the geomorphology and carbon fluxes of tropical peatlands

Cobb

Hoyt

Gandois

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

111

View full text Add to dashboard Cite

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“…Peatland water table response to changes in climate is complex and dependant on a number of variables including, but not limited to; hydraulic conductivity, peat mass, porosity, dry bulk density, water table height, bog surface height, decay rate, underlying substrate and lateral extent (Baird et al, 2008;Baird et al, 2011;Frolking et al, 2010;Holden, 2005;Morris et al, 2011). There has recently been a concerted effort to improve the understanding of raised bog hydrological response to climatic factors through manipulation of a peatland development model (Swindles et al, submitted …”

Section: Improving Analoguesmentioning

confidence: 99%

Comparing regional and supra-regional transfer functions for palaeohydrological reconstruction from Holocene peatlands

Turner

Swindles

Charman

et al. 2013

Palaeogeography, Palaeoclimatology, Palaeoecology

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Testate amoebae-based transfer functions are commonly used in peatland palaeoclimate studies. These models have been developed in several regions of the world and are sometimes used for palaeohydrological reconstruction from fossil data in locations where no transfer functions exist. Limitations of this approach may include missing modern analogues and problems associated with site-specific or regional factors in testate amoebae ecology and biogeography. This study presents new testate amoebae-hydrology transfer functions based on data from six peatlands in Northern England. Transfer functions were generated for water table depth and moisture content using weighted averaging tolerance downweighted regression with inverse deshrinking and model performance was assessed using leave-one-out (jacknifing) cross-validation.To examine the robustness of applying transfer functions extra-regionally, we performed a number of spatially independent cross-validation (SICV) tests using contemporary testate amoebae and environmental data from established Northern Ireland and European transfer functions. Inferred water table depths were consistent with observed values in terms of position along the hydrological gradient, however magnitudes varied. We then applied the three independent transfer functions to fossil data from a peatland in Northern England to compare the reconstructions. The results show that the direction of the reconstructions is consistent in terms of wet/dry shifts.However, variation in the magnitudes of the reconstructed water tables is apparent.This probably reflects the sampling regime, including temporal/seasonal effects, and differences in testate amoebae ecology between regions and individual sites.

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“…In the current study we deactivated DigiBog's peat accumulation, decomposition and hydrophysical subroutines described by Morris et al (2012). We refer the reader to Baird et al (2012) for a comprehensive description of DigiBog's governing equations and numerical implementation, although a few points are pertinent here. DigiBog represents a peatland as a grid of vertical peat columns; in plan each column is equivalent to one square grid cell in the SGCJ models, although DigiBog also allows for vertical variation in peat hydraulic properties.…”

Section: Hydrological Submodelmentioning

confidence: 99%

“…Both the peat surface and the impermeable base had a constant slope of 1 : 50; the permeable upper peat had a uniform thickness of 0.2 m. In Models 3 and 4 we assumed the thick, lower peat layer is also permeable with its own hydraulic conductivity, K deep , meaning that K for each cell is depth-averaged to account for the vertical transition in K between the upper and lower layers. Baird et al (2012) provide a full description of DigiBog's calculation of K ave and inter-cell T . We used the groundwater mound equation (Ingram, 1982) to calculate the dimensions of a deep peat layer that is hemi-elliptical in cross section, has a uniform K of 1.25 × 10 −5 m s −1 , is 200 m from central axis to the margin, is underlain by a flat impermeable base, and receives a net water input (from an implied upper peat layer) of 155 mm yr −1 .…”

Section: Model Spatial and Temporal Domains; Boundary Conditionsmentioning

confidence: 99%

“…As such, our models neglect some of the structural complexity that peatlands exhibit in three spatial dimensions (e.g. Barber, 1981). Our simulations with nonconservative hydrological steady-state criteria (particularly ∆t e = 50 and 100 h) implicitly contain a type of ecological memory effect, because the influence of previous patterns of surface vegetation and shallow soil properties are expressed in the model's hydrological behaviour; this memory effect decreases in strength with increasingly stringent hydrological steady-state criteria.…”

Section: Hydrological Transience and Ecological Memorymentioning

confidence: 99%

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The role of hydrological transience in peatland pattern formation

2013

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Abstract. The sloping flanks of peatlands are commonly patterned with non-random, contour-parallel stripes of distinct micro-habitats such as hummocks, lawns and hollows. Patterning seems to be governed by feedbacks among peatland hydrological processes, plant micro-succession, plant litter production and peat decomposition. An improved understanding of peatland patterning may provide important insights into broader aspects of the long-term development of peatlands and their likely response to future climate change.We recreated a cellular simulation model from the literature, as well as three subtle variants of the model, to explore the controls on peatland patterning. Our models each consist of three submodels, which simulate: peatland water tables in a gridded landscape, micro-habitat dynamics in response to water-table depths, and changes in peat hydraulic properties.We found that the strength and nature of simulated patterning was highly dependent on the degree to which water tables had reached a steady state in response to hydrological inputs. Contrary to previous studies, we found that under a true steady state the models predict largely unpatterned landscapes that cycle rapidly between contrasting dry and wet states, dominated by hummocks and hollows, respectively. Realistic patterning only developed when simulated water tables were still transient.Literal interpretation of the degree of hydrological transience required for patterning suggests that the model should be discarded; however, the transient water tables appear to have inadvertently replicated an ecological memory effect that may be important to peatland patterning. Recently buried peat layers may remain hydrologically active despite no longer reflecting current vegetation patterns, thereby highlighting the potential importance of three-dimensional structural complexity in peatlands to understanding the two-dimensional surface-patterning phenomenon.The models were highly sensitive to the assumed values of peat hydraulic properties, which we take to indicate that the models are missing an important negative feedback between peat decomposition and changes in peat hydraulic properties. Understanding peatland patterning likely requires the unification of cellular landscape models such as ours with cohort-based models of long-term peatland development.

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The DigiBog peatland development model 1: rationale, conceptual model, and hydrological basis

Cited by 74 publications

References 71 publications

How temporal patterns in rainfall determine the geomorphology and carbon fluxes of tropical peatlands

How temporal patterns in rainfall determine the geomorphology and carbon fluxes of tropical peatlands

Comparing regional and supra-regional transfer functions for palaeohydrological reconstruction from Holocene peatlands

The role of hydrological transience in peatland pattern formation

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