Assessing the peatland hummock–hollow classification framework using high-resolution elevation models: implications for appropriate complexity ecosystem modeling
Journal Articles
Overview
Research
Identity
Additional Document Info
View All
Overview
abstract
Abstract. The hummock–hollow classification framework used to categorize peatland
ecosystem microtopography is pervasive throughout peatland experimental
designs and current peatland ecosystem modeling approaches. However,
identifying what constitutes a representative hummock–hollow pair within a
site and characterizing hummock–hollow variability within or between
peatlands remains largely unassessed. Using structure from motion (SfM),
high-resolution digital elevation models (DEMs) of hummock–hollow
microtopography were used to (1) examine how much area needs to be sampled
to characterize site-level microtopographic variation; and (2) examine the
potential role of microtopographic shape/structure on biogeochemical fluxes
using plot-level data from nine northern peatlands. To capture 95 % of
site-level microtopographic variability, on average, an aggregate sampling
area of 32 m2 composed of 10 randomly located plots was required. Both
site- (i.e. transect data) and plot-level (i.e. SfM-derived DEM) results show that
microtopographic variability can be described as a fractal at the submeter
scale, where contributions to total variance are very small below a 0.5 m
length scale. Microtopography at the plot level was often found to be
non-bimodal, as assessed using a Gaussian mixture model (GMM). Our findings
suggest that the non-bimodal distribution of microtopography at the
plot level may result in an undersampling of intermediate topographic
positions. Extended to the modeling domain, an underrepresentation of
intermediate microtopographic positions is shown to lead to potentially
large flux biases over a wide range of water table positions for ecosystem
processes which are non-linearly related to water and energy availability at
the moss surface. Moreover, our simple modeling results suggest that much
of the bias can be eliminated by representing microtopography with several
classes rather than the traditional two (i.e. hummock/hollow). A range of tools
examined herein can be used to easily parameterize peatland models, from
GMMs used as simple transfer functions to spatially explicit fractal
landscapes based on simple power-law relations between microtopographic
variability and scale.
