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Abstract Water table depth in peatlands is strongly linked to physical properties of the peat, such as density (ρ dry ), peat composition and humification, hydraulic conductivity (K), and specific yield (S y ). Dry bulk density and peat depth are commonly used as indicators of K in ecohydrological models. However, no mathematical relationship exists to quantify S y based on K and ρ dry . As a result, ecohydrological models cannot explicitly reproduce the strong buffering capacity of peatlands. The objectives of this study were to analyse the literature‐reported mathematical link between all the physical properties to develop new mathematical relationships between these parameters and to evaluate whether variations in the physical properties of the peat control water table depth in peatlands. Seven peatlands located in the St. Lawrence Lowlands (Québec, Canada) were sampled, and 1 m long peat cores were collected from up‐gradient, mid‐gradient, and down‐gradient zones. All cores were used to measure ρ dry , K, S y , and to estimate peat composition and humification. Statistically significant correlations were found between (a) K and S y (log–log model), (b) K and depth (log–log model), (c) S y and depth (log–log model), (d) ρ dry and S y (log model), and (e) ρ dry and K (log model). No significant difference was found in either K or S y between sites. However, significant differences were found in water table depths. Because they provide a fuller description of the peat properties that control water table depths, these newly developed functions have the potential to improve the capacity of ecohydrological models to simulate time‐varying hydrological conditions.
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Peatlands are relatively common in the province of Quebec (Canada) where they occupy about 12% of the surface. The hydrology of peatlands remains insufficiently documented, more specifically during the spring period where data are currently lacking in many regions, including in the Quebec boreal territory. The paucity of spring data are due to snowmelt that causes flooding in peatlands and along rivers, which makes hydrometry complicated during this period of the year. In this paper, the Peatland Hydrological Impact Model (PHIM) was coupled with a snowmelt module (CemaNeige) to simulate spring flows in an ombrotrophic peatland located in the Romaine River watershed (Quebec). Discharge data from two summer seasons (2019 and 2020) were used to calibrate the hydrological model. Despite the relatively short time series, the results show a good performance. The simulated spring flows resulting from the PHIM + CemaNeige combination are of the right order of magnitude.