Soil moisture and evapotranspiration (ET) are important components of boreal forest hydrology that affect ecological processes and land-atmosphere feedbacks. Future trends in soil moisture in particular are uncertain, therefore accurate modeling of these fluxes and understanding of concomitant sources of uncertainty are critical. Here, we conduct a global sensitivity analysis, Monte Carlo parameterization, and analysis of parameter uncertainty and its contributions to future soil moisture and ET uncertainty using a physically-based ecohydrologic model in multiple boreal forest types. Soil and plant hydraulic parameters and LAI have the largest effects on summer soil moisture at two contrasting sites. We report best estimates and uncertainty of these parameters via a multi-site Generalized Likelihood Uncertainty Estimation approach. In future scenario simulations, parameter and global climate model (GCM) choice influence projected changes in soil moisture and evapotranspiration as much as the projected effects of climate change in a late-century, high-emissions scenario, though the relative effect of parameters, GCM, and climate vary between objective and study site. Saturated water content, as well as the sensitivity of stomatal conductance to vapor pressure deficit, have the most statistically significant effects on change in evapotranspiration and soil moisture, though there is considerable variability between sites and GCMs. In concert, the results of this study provide estimates of: (1) parameter importance and statistical significance for soil moisture modeling, (2) parameter values for physically-based soil-vegetation-atmosphere transfer models in multiple boreal forest types, and (3) the contributions of uncertainty in these parameters to soil moisture and evapotranspiration uncertainty in future climates.

Soil moisture is an important driver of growth in boreal Alaska, but estimating soil hydraulic parameters can be challenging in this data-sparse region. To better identify soil hydraulic parameters and quantify energy and water balance and soil moisture dynamics, we applied the physically-based, one-dimensional ecohydrologic Simultaneous Heat and Water (SHAW) model, loosely coupled with the Geophysical Institute of Permafrost Laboratory (GIPL) model, to an upland deciduous forest stand in interior Alaska over a 13-year period. Using a Generalized Likelihood Uncertainty Estimation (GLUE) parameterization, SHAW reproduced interannual and vertical spatial variability of soil moisture during a five-year validation period quite well, with root mean squared error (RMSE) of volumetric water content at 0.5 m as low as 0.020. Many parameter sets reproduced reasonable soil moisture dynamics, suggesting considerable equifinality. Model performance generally declined in the eight-year validation period, indicating some overfitting and demonstrating the importance of interannual variability in model evaluation. We compared the performance of parameter sets selected based on traditional performance measures (RMSE) that minimize error in soil moisture simulation, with those that were designed to minimize the dependence of model performance on interannual climate variability. The latter case moderately decreases traditional model performance but is likely more suitable for climate change applications, for which it is important that model error is independent from climate variability. These findings illustrate (1) that the SHAW model, coupled with GIPL, can adequately simulate soil moisture dynamics in this boreal deciduous region, (2) the importance of interannual variability in model parameterization, and (3) a novel objective function for parameter selection to improve applicability in non-stationary climates.

Forests reduce snow accumulation on the ground through canopy interception and subsequent evaporative losses. To understand snow interception and associated hydrological processes, studies have typically relied on resource-intensive point scale measurements derived from weighed trees or indirect measurements that compared snow accumulation between forested sites and nearby clearings. Weighed trees are limited to small or medium sized trees and indirect comparisons can be confounded by wind redistribution of snow, branch unloading, and clearing size. A potential alternative method could use terrestrial lidar (light detection and ranging) because three-dimensional lidar point clouds can be generated for any size tree and can be utilized to calculate volume of the intercepted snow. The primary objective of this study was to provide a feasibility assessment for estimating snow interception mass with terrestrial laser scanning (TLS), providing information on challenges and opportunities for future research. During the winters of 2017 and 2018, intercepted snow masses were continuously measured for two model trees suspended from load-cells. Simultaneously, autonomous terrestrial lidar scanning (ATLS) was used to develop volumetric estimates of intercepted snow. Multiplying ATLS volume estimates by snow density estimates (derived from empirical models based on air temperature) enabled comparison of predicted vs. measured snow mass. Results indicate agreement between predicted and measured values (R2 ≥ 0.69, RMSE ≥ 0.91 kg, slope ≥ 0.97, intercept ≥ -1.39) when multiplying TLS snow interception volume with a constant snow density estimate. These results suggest that TLS might be a viable alternative to traditional approaches for mapping snow interception, potentially useful for estimating snow loads on large trees, collecting data from hazardous or remote terrain, and calibrating snow interception models to new forest types around the globe.