Abstract
Abstract. The representation of snow processes in forest growth models is necessary to accurately predict the hydrological cycle in boreal ecosystems and the isotopic signature of soil water extracted by trees, photosynthates and tree-ring cellulose. Yet, most process-based models do not include a snow module; consequently, their simulations may be biased in cold environments. Here, we modified the MAIDENiso model to incorporate a new snow module that simulates snow accumulation, melting and sublimation, as well as thermal exchanges driving freezing and thawing of the snow and the soil. We tested these implementations in two sites in eastern and western Canada for black spruce (Picea mariana (Mill.) B.S.P.) and white spruce (Picea glauca (Moench) Voss) forests, respectively. The new snow module improves the skills of the model to predict components of the hydrological cycle. The MAIDENiso model is now able to reproduce the spring discharge peak and to simulate stable oxygen isotopes in tree-ring cellulose more realistically than in the original snow-free version of the model. The new implementation also results in simulations with a higher contribution from the source water on the oxygen isotopic composition of the simulated cellulose, leading to more accurate estimates of cellulose isotopic composition. Future work may include the development of inverse modelling with this new version of MAIDENiso to produce robust reconstructions of the hydrological cycle and isotope processes in cold environments.
Highlights
In boreal regions of Canada and Alaska, snow represents about 30 %–50 % of total precipitation (Mesinger et al, 2006)
The MAIDENiso simulations reproduced the temporal change of the snow pile and showed a similar pattern to the real snow water equivalent (SWE) observations collected at the Caniapiscau site (Fig. 3b)
Our SWE simulations made by MAIDENiso using as inputs NARR meteorological data were in better agreement with the direct observations of SWE at the Caniapiscau site than the SWE data obtained directly from the NARR dataset
Summary
In boreal regions of Canada and Alaska, snow represents about 30 %–50 % of total precipitation (Mesinger et al, 2006). This feature has a notable influence on hydrological and ecological system functioning in these cold environments (Beria et al, 2018). Snowpack dynamics greatly influence water infiltration in soils, groundwater and aquifer replenishment, runoff production, and water supplies to both natural and artificial water bodies during spring flood (Li et al, 2017; Barnhart et al, 2016; Berghuijs et al, 2014). Snowpack dynamics have the potential to alter heat fluxes, temperature and depth of freezing in soils, all of which can impact the timing of critical ecophysiological processes that drive growth in high-latitude forest stands
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