From Hydrological Feedbacks in Northern Peatlands 2: Peat Depth as a Control on Peatland Resilience (Furukawa et al. 2025)

This paper was made possible through the contributions in the data collection, conceptualization and writing phases, of members (past and present) of the Mac Ecohydrology Lab as reflected in the authorship. In particular, I would like to acknowledge Drs. Owen Sutton and Mike Waddington for their pivotal roles in writing and supervision.

This paper is a decade-later follow up to Waddington et al. (2015)’s Hydrological Feedbacks in Northern Peatlands that synthesized seven hydrological feedbacks that operate in northern peatlands, including their strength and direction. That is, whether they are negative, stabilizing feedbacks that attenuate shifts in water table, or positive, amplifying feedbacks that enhance them. It also expands upon Sutton et al. (2025) which posed critical research questions concerning the feedbacks and resilience of shallow peatlands, which are hypothesized to be climate sentinels that are more sensitive to the enhanced drying predicted with climatic change. In Furukawa et al. (2025), we review structural differences between shallow and deep peatlands relevant to ecohydrological feedbacks and hypothesize the resulting effects on their feedback direction, strength, interactions and tipping points. This post will briefly discuss the role played by remotely sensed data in this analysis, which was based on peatlands found on the Boreal Shield in Central Ontario.

Firstly, in order to characterize peatlands as deep or shallow, along a continuum or based on a threshold of 0.4 m average peat depth set out by the Canadian Wetland Classification System (National Wetlands Working Group, 1997), we had to measure peat depth. While this is largely a physical task of inserting a depth probe to the bedrock or mineral soil at enough representative points in a peatland, we wanted these measurements to be spatially explicit. This entailed the use of ArcGIS Field Maps for data collection (Figure 1), paired with a Juniper Geode GNSS receiver to allow us to collect points at sub-meter accuracy, because some of the smallest shallow peatlands were only several meters across. Ordinary smartphone GPS accuracy of a 3-5 meters would have been insufficient for this purpose.

Figure 1: Peat depths were measured and recorded using ArcGIS Field Maps, with at least one transect (points in pink) spaced at 1-2 m to characterize each peatland (pink outline).

Feedback A: Water table depth – afforestation/shrubification is what we have categorized as a slow feedback that operates on a scale of months to decades. It is a positive, amplifying feedback whereby drops in the water table are amplified by the growth of vascular vegetation such as trees and shrubs which can draw water from deeper in the peat profile, allowing for continued water losses via evapotranspiration. We hypothesized that the net effect of this feedback would be stronger in shallow peatlands due to their more frequent and severe water table fluctuations. Then, we explored how this may express itself by differences in tree cover between shallow and deep peatlands. LiDAR surveys of our study peatlands allowed us to rapidly estimate this across dozens (n=43) of sites. In particular, we used the R package treetop developed by Silva et al. (2022) to detect individual trees and their heights from the LiDAR dataset to obtain measures of tree density (trees per square meter) and tree height (meters per square meter) as a proxy for biomass. Greater tree densities and biomass would suggest Feedback A has acted more strongly and there is greater potential for increased evapotranspiration if the water table drops by virtue of greater tree cover per unit area. Both LiDAR-estimated tree density (Figure 2a-b) and biomass (Figure 2d-e) were significantly negatively correlated with peatland depth. Categorically (based on a 0.4 m depth threshold) shallow peatlands had significantly higher mean tree densities (Figure 2c) and biomass (Figure 2f).

Figure 2: Tree density (trees m−2) as a function of (a) maximum peatland depth (Spearman’s ρ = −0.5218, p = 0.0033), (b) average peatland depth (Spearman’s ρ = −0.6277, p = 6.6 × 10−6) and (c) peatland class (n = 13 for shallow, n = 30 for deep peatlands). Cumulative tree height was normalized by peatland area (m m−2) and presented as a function of (d) maximum (Spearman’s ρ = −0.5495, p = 1.4 × 10−4) and (e) mean (Spearman’s ρ = −0.6448, p = 3.1 × 10−6) peatland depth and (f) peatland class. Mean tree density was significantly greater (p = 0.0017, Kruskal–Wallis test) among shallow peatlands. Tree height was significantly greater in shallow peatlands compared to deep peatlands (p = 0.0014, Kruskal–Wallis test).

Feedback G: Water table depth – moss productivity is another slow feedback but one we have classified as negative, or stabilizing to attenuate water losses. It operates on the basis that lowered water tables will favour hummock-forming mosses such as Sphagnum fuscum that have have enhanced moisture retention properties that facilitate continued productivity and of their litter and resulting peat. We hypothesized that deep peatlands, with more stable water tables and favourable surface moisture conditions will allow for more diverse, self-organized moss communities that maintain moss productivity across a range of water tables. Similar to Feedback A, rather than extensive on-the-ground vegetation surveys across dozens of peatlands, we opted to use the LiDAR data collected across our Boreal Shield peatlands to explore how this differs with peatland depth. We looked at peatland microtopography as a proxy for moss diversity, whereby more varied topography from the LiDAR-derived digital terrain model (DTM) would suggest stronger Feedback G; greater ability to maintain productivity in the face of water table drawdown. First, we calculated the mean topographic roughness index (TRI) (Riley et al. 1999) for each peatland using the Terrain Ruggedness Index tool in Arc Hydro. Across 24 peatlands (n=11 shallow, n=13 deep), there was no significant correlation with peatland depth (Figure 3a-b), or significant difference between depth classes (Figure 3c). As an alternative metric, we calculated the variability (as Zonal standard deviation in ArcGIS Pro) of the DTM for each peatland, which was significantly correlated with peatland depth (Figure 3d-e) and significantly higher in deep peatlands (Figure 3f). While this was more mixed compared to Feedback A, it provided some evidence in support of there being structural differences with peatland depth that would lead to the water table – moss productivity feedback being stronger in deep peatlands.

Figure 3: Topographic roughness index (TRI) as a function of (a) maximum peatland depth (no significant Spearman correlation, p = 0.8686), (b) mean peatland depth (no significant Spearman correlation, p = 0.2631) and (c) peatland depth classes, and variability (standard deviation) in peatland surface elevation as a function of (d) maximum peatland depth (Spearman’s ρ = 0.5578, p = 0.0046), (e) mean peatland depth (Spearman’s ρ = 0.5102, p = 0.0109), (f) and peatland class. There was no significant difference in mean TRI between shallow and deep peatland classes (p = 0.3263, Kruskal–Wallis test). However, the variability (standard deviation) in ground elevation was significantly different (p = 0.0243, Kruskal–Wallis test) between shallow and deep peatlands.

While the remaining hydrological feedbacks discussed in Furukawa et al. (2025) are explored using more traditional field-collected data, in the form of profiles of hydrophysical properties from cores extracted from peatlands, LiDAR gave us the opportunity to quickly characterize the tree cover and topography of dozens of peatlands that may have taken days to weeks to measure in the field. Depending on the availability of LiDAR data, we can potentially expand these analyses to a much larger sample size of peatlands beyond those of Central Ontario Boreal Shield to see how the relationships between feedback indicators and peatland depth compare in other hydroclimates.

References

Furukawa, A.K., Sutton, O.F., Simone, K.L., Verkaik, G.J., Moore, P.A., Clark, A., Fallas, R., Moore, M., Sherwood, E., Broyd, R.C., Van Huizen, B., Morris, P.J., Waddington, J.M., (2025). Hydrological Feedbacks in Northern Peatlands 2: Peat Depth as a Control on Peatland Resilience. Ecohydrology 18, e70158. https://doi.org/10.1002/eco.70158

National Wetlands Working Group. (1997). The Canadian Wetland Classification System, 2nd Edition. Warner, B.G. and C.D.A. Rubec (eds.), Wetlands Research Centre, University of Waterloo, Waterloo, ON, Canada. 68 p.

Riley, S. J., DeGloria, S. D., & Elliot, R. (1999). Index that quantifies topographic heterogeneity. intermountain Journal of sciences, 5(1-4), 23-27.

Silva, C. A., Hudak, A. T., Vierling, L. A., Valbuena, R., Cardil, A., Mohan, M., deAlmeida, D. R., Broadbent, E. N., Almeyda Zambrano, A. M., Wilkinson, B., Sharma, A., Drake, J. B., Medley, P. B., Vogel, J. G., Prata, G. A., Atkins, J. W., Hamamura, C., Johnson, D. J. & Klauberg, C. (2022). treetop: A Shiny-based application and R package for extracting forest information from LiDAR data for ecologists and conservationists. Methods in Ecology and Evolution, 13, 1164–1176. https://doi.org/10.1111/2041-210X.13830

Sutton, O.F., Furukawa, A.K., Moore, P.A., Morris, P.J., Waddington, J.M., (2025). Shallow peatlands as sentinels of climate change. Environ. Res. Lett. 20, 061001. https://doi.org/10.1088/1748-9326/add179

Waddington, J.M., Morris, P.J., Kettridge, N., Granath, G., Thompson, D.K., Moore, P.A., (2015). Hydrological feedbacks in northern peatlands. Ecohydrology 8, 113–127. https://doi.org/10.1002/eco.1493