In northern peatlands, near‐saturated surface conditions promote valuable ecosystem services such as carbon storage and drinking water provision. Peat saturated hydraulic conductivity (K_sat) plays an important role in maintaining wet surface conditions by moderating drainage and evapotranspiration. Peat K_sat can exhibit intense spatial variability in three dimensions and can change rapidly in response to disturbance. The development of skillful predictive equations for peat K_sat and other hydraulic properties, akin to mineral soil pedotransfer functions, remains a subject of ongoing research. We report a meta‐analysis of 2,507 northern peat samples, from which we developed linear models that predict peat K_sat from other variables, including depth, dry bulk density, von Post score (degree of humification), and categorical information such as surface microform type and peatland trophic type (e.g., bog and fen). Peat K_sat decreases strongly with increasing depth, dry bulk density, and humification; and increases along the trophic gradient from bog to fen peat. Dry bulk density and humification are particularly important predictors and increase model skill greatly; our best model, which includes these variables, has a cross‐validated r² of 0.75 and little bias. A second model that includes humification but omits dry bulk density, intended for rapid field estimations of K_sat, also performs well (cross‐validated r² = 0.64). Two additional models that omit several predictors perform less well (cross‐validated r² ∼ 0.5), and exhibit greater bias, but allow K_sat to be estimated from less comprehensive data. Our models allow improved estimation of peat K_sat from simpler, cheaper measurements.

Wetlands play an important role in preventing extreme low flows in rivers and groundwater level drawdowns during drought periods. This hydrological function could become increasingly important under a warmer climate. Links between peatlands, aquifers, and rivers remain inadequately understood. The objective of this study was to evaluate the hydrologic functions of the Lanoraie peatland complex in southern Quebec, Canada, under different climate conditions. This peatland complex has developed in the beds of former fluvial channels during the final stages of the last deglaciation. The peatland covers a surface area of ~76 km2 and feeds five rivers. Numerical simulations were performed using a steady-state groundwater flow model. Results show that the peatland contributes on average to 77% of the mean annual river base flow. The peatland receives 52% of its water from the aquifer. Reduced recharge scenarios (−20 and −50% of current conditions) were used as a surrogate of climate change. With these scenarios, the simulated mean head decreases by 0.6 and 1.6 m in the sand. The mean river base flow decreases by 16 and 41% with the two scenarios. These results strongly underline the importance of aquifer-peatland-river interactions at the regional scale. They also point to the necessity of considering the entire hydrosystem in conservation initiatives.

First‐order streams flowing through peatlands receive, carry and transform large amounts of organic carbon, methane (CH₄) and carbon dioxide (CO₂) but remain poorly documented. The objectives of this study were to (a) identify the origins of CO₂ and CH₄ (thereafter C‐GHG for carbon greenhouse gases) in a peatland headwater stream, (b) determine the environmental factors driving C‐GHG export and emissions, and (c) quantify C‐GHG losses from this system and discuss its implications. Data were collected from eight sampling sites along a 3 km boreal peatland headwater stream (Eastern Canada) over the growing seasons 2019 and 2020. The studied stream was oversaturated in pCO₂ [min: 2,044; max: 23,306 μatm] and pCH₄ [14; 17,614 μatm]. A mass balance model showed that ∼81% of in‐stream CO₂ originated from porewater seepage while the remaining 17% and 2% originated from in‐stream productivity and methane oxidation, respectively. Porewater seepage was concluded to be the primary source of CH₄. Seasonal dissolved C‐GHG concentrations were negatively correlated with the peatland water table depth, suggesting an active release of carbon‐rich peat porewater during the base flow. Nevertheless, greater C‐GHG losses occurred during stormflow periods which acted as pulses with most of the C‐GHG being shunted downstream. The sum of C‐GHG export and emissions at our site was 8.08 gC m⁻² y⁻¹ with 86% being released to the atmosphere and 14% being exported downstream. Our study demonstrates that peatland headwater streams act as large sources of C‐GHG and that precipitation events and topography control the magnitude of the fluxes.

Research in hydrological sciences is constantly evolving to provide adequate answers to address various water-related issues. Methodological approaches inspired by mathematical and physical sciences have shaped hydrological sciences from its inceptions to the present day. Nowadays, as a better understanding of the social consequences of extreme meteorological events and of the population’s ability to adapt to these becomes increasingly necessary, hydrological sciences have begun to integrate knowledge from social sciences. Such knowledge allows for the study of complex social-ecological realities surrounding hydrological phenomena, such as citizens’ perception of water resources, as well as individual and collective behaviors related to water management. Using a mixed methods approach to combine quantitative and qualitative approaches has thus become necessary to understand the complexity of hydrological phenomena and propose adequate solutions for their management. In this paper, we detail how mixed methods can be used to research flood hydrology and low-flow conditions, as well as in the management of these hydrological extremes, through the analysis of case studies. We frame our analysis within the three paradigms (positivism, post-positivism, and constructivism) and four research designs (triangulation, complementary, explanatory, and exploratory) that guide research in hydrology. We show that mixed methods can notably contribute to the densification of data on extreme flood events to help reduce forecasting uncertainties, to the production of knowledge on low-flow hydrological states that are insufficiently documented, and to improving participatory decision making in water management and in handling extreme hydrological events.