Ecohydrological modelling in a deciduous boreal forest: Model evaluation for application in non‐stationary climates

Abstract

Soil moisture is an important driver of growth in boreal Alaska, but estimating soil hydraulic parameters can be challenging in this data‐sparse region. Parameter estimation is further complicated in regions with rapidly warming climate, where there is a need to minimize model error dependence on interannual climate variations. To better identify soil hydraulic parameters and quantify energy and water balance and soil moisture dynamics, we applied the physically based, one‐dimensional ecohydrological 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 parameterisation, 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 cm3/cm3. 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 such as the RMSE that minimize error in soil moisture simulation, with one that is designed to minimize the dependence of model error on interannual climate variability using a new diagnostic approach we call CSMP, which stands for Climate Sensitivity of Model Performance. Use of the CSMP approach moderately decreases traditional model performance but may be 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 parameterisation, and (3) a novel objective function for parameter selection to improve applicability in non‐stationary climates.

Long‐Term Simulation of Snow Cover and Its Potential Impacts on Seasonal Frost Dynamics in Croplands Across Southern Canada

“…Similar speculation was mentioned by Marshall et al. (2021), where soil temperature was underestimated using the SHAW model for a boreal forest in the United States, and the SHAW assigned soil thermal conductivities may be larger than reality.…”

Importance of Parameter and Climate Data Uncertainty for Future Changes in Boreal Hydrology

“…Leaf area index (LAI) is a time‐varying input. At the deciduous sites, leafout timing was estimated based on an empirical relationship with snow disappearance date (Marshall, Link, Flerchinger, Nicolsky, & Lucash, 2021), and LAI was set to increase linearly over a 30‐day period. Maximum LAI was used as a calibration parameter.…”

“…Input climate data sources varied between sites depending on availability. Data for the birch-dominated UP1A were obtained from the Bonanza Creek Long-Term Ecological Research site and described in detail in Marshall, Link, Flerchinger, Nicolsky, and Lucash (2021). Briefly, collocated hourly air temperature, relative humidity, and wind speed data were obtained and gap filled using correlations with data from nearby weather stations.…”

“…While this development was based in temperate regions, this feature and the physical simulation basis make it particularly attractive for modeling in discontinuous permafrost regions. SHAW has previously been applied successfully in boreal and permafrost sites (Marshall, Link, Flerchinger, Nicolsky, & Lucash, 2021;Zhang et al, 2010Zhang et al, , 2013, including a coupling with a groundwater flow model for which SHAW provided ground temperature and surface water balance boundary conditions (Langford et al, 2020).…”

Evaluating Ecohydrological Model Sensitivity to Input Variability with an Information-Theory-Based Approach

“…Many ecohydrologic modeling studies have focused on model sensitivity to the modeling hypotheses (e.g., parameter specification [ 15 , 16 , 17 ] and model structures [ 11 , 18 ]). Here, we consider a model that is constant in terms of structure, processes, and parameters such that the information provided by these modeling hypotheses is conditional on the nature of the input forcing data and their associated uncertainties [ 19 , 20 , 21 ].…”