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Abstract. This study investigates and compares soil moisture andhydrology projections of broadly used land models with permafrost processesand highlights the causes and impacts of permafrost zone soil moistureprojections. Climate models project warmer temperatures and increases inprecipitation (P) which will intensify evapotranspiration (ET) and runoff inland models. However, this study shows that most models project a long-termdrying of the surface soil (0–20 cm) for the permafrost region despiteincreases in the net air–surface water flux (P-ET). Drying is generallyexplained by infiltration of moisture to deeper soil layers as the activelayer deepens or permafrost thaws completely. Although most models agree ondrying, the projections vary strongly in magnitude and spatial pattern.Land models tend to agree with decadal runoff trends but underestimaterunoff volume when compared to gauge data across the major Arctic riverbasins, potentially indicating model structural limitations. Coordinatedefforts to address the ongoing challenges presented in this study will helpreduce uncertainty in our capability to predict the future Arctichydrological state and associated land–atmosphere biogeochemical processesacross spatial and temporal scales. 

Peatlands store substantial amounts of carbon and are vulnerable to climate change. We present a modified version of the Organising Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE) land surface model for simulating the hydrology, surface energy, and CO₂ fluxes of peatlands on daily to annual timescales. The model includes a separate soil tile in each 0.5° grid cell, defined from a global peatland map and identified with peat-specific soil hydraulic properties. Runoff from non-peat vegetation within a grid cell containing a fraction of peat is routed to this peat soil tile, which maintains shallow water tables. The water table position separates oxic from anoxic decomposition. The model was evaluated against eddy-covariance (EC) observations from 30 northern peatland sites, with the maximum rate of carboxylation (V_cmax) being optimized at each site. Regarding short-term day-to-day variations, the model performance was good for gross primary production (GPP) (r² =  0.76; Nash–Sutcliffe modeling efficiency, MEF  =  0.76) and ecosystem respiration (ER, r² =  0.78, MEF  =  0.75), with lesser accuracy for latent heat fluxes (LE, r² =  0.42, MEF  =  0.14) and and net ecosystem CO₂ exchange (NEE, r² =  0.38, MEF  =  0.26). Seasonal variations in GPP, ER, NEE, and energy fluxes on monthly scales showed moderate to high r² values (0.57–0.86). For spatial across-site gradients of annual mean GPP, ER, NEE, and LE, r² values of 0.93, 0.89, 0.27, and 0.71 were achieved, respectively. Water table (WT) variation was not well predicted (r² < 0.1), likely due to the uncertain water input to the peat from surrounding areas. However, the poor performance of WT simulation did not greatly affect predictions of ER and NEE. We found a significant relationship between optimized V_cmax and latitude (temperature), which better reflects the spatial gradients of annual NEE than using an average V_cmax value.