Ecohydrological modelling in a deciduous boreal forest: Model evaluation for application in non‐stationary climates
Ecohydrological modelling in a deciduous boreal forest: Model evaluation for application in non‐stationary climates
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DOI：
10.1002/hyp.14251
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发表时间：
2021-05
影响因子：
3.2
通讯作者：
A. Marshall;T. Link;G. Flerchinger;D. Nicolsky;M. Lucash
中科院分区：
文献类型：
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作者：
A. Marshall;T. Link;G. Flerchinger;D. Nicolsky;M. Lucash
土壤湿度是阿拉斯加北部地区生长的重要驱动因素，但在这个数据稀疏的地区，估算土壤水力参数可能具有挑战性。在气候快速变暖的地区，参数估计更加复杂，需要尽量减少模式误差对年际气候变化的依赖。为了更好地识别土壤水力参数，量化能量和水分平衡以及土壤水分动态，我们应用了基于物理的一维生态水文同步热水（SHAW）模型，并与地球物理永久冻土研究所（GIPL）模型松散耦合，对阿拉斯加内陆的一个山地落叶林分进行了13年的研究。利用广义似然不确定性估计参数化，SHAW很好地再现了5年验证期内土壤水分的年际和垂直空间变异，0.5 m处体积含水量的均方根误差（RMSE）低至0.020 cm3/cm3。许多参数集再现了合理的土壤水分动态，表明相当大的均衡性。在8年的验证期内，模型性能普遍下降，这表明存在一些过拟合，并证明了年际变异性在模型评估中的重要性。我们比较了基于传统性能度量选择的参数集的性能，例如在土壤湿度模拟中最小化误差的RMSE，与使用我们称为CSMP的新诊断方法来最小化模型误差对年际气候变率的依赖的参数集，CSMP代表模型性能的气候敏感性。使用CSMP方法会适度降低传统模式的性能，但可能更适合气候变化应用，因为重要的是模式误差与气候变率无关。这些发现表明：(1)SHAW模型与GIPL相结合可以充分模拟该北方落叶地区的土壤水分动态；(2)年际变率在模型参数化中的重要性；(3)为参数选择提供了一个新的目标函数，以提高在非平稳气候中的适用性。
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.