Assessing model performance via the most limiting environmental driver in two differently stressed pine stands.

Climate change will impact forest productivity worldwide. Forecasting the magnitude of such impact, with multiple environmental stressors changing simultaneously, is only possible with the help of process-based models. In order to assess their performance, such models require careful evaluation against measurements. However, direct comparison of model outputs against observational data is often not reliable, as models may provide the right answers due to the wrong reasons. This would severely hinder forecasting abilities under unprecedented climate conditions. Here, we present a methodology for model assessment, which supplements the traditional output-to-observation model validation. It evaluates model performance through its ability to reproduce observed seasonal changes of the most limiting environmental driver (MLED) for a given process, here daily gross primary productivity (GPP). We analyzed seasonal changes of the MLED for GPP in two contrasting pine forests, the Mediterranean Pinus halepensis Mill. Yatir (Israel) and the boreal Pinus sylvestris L. Hyytiala (Finland) from three years of eddy-covariance flux data. Then, we simulated the same period with a state-of-the-art process-based simulation model (LandscapeDNDC). Finally, we assessed if the model was able to reproduce both GPP observations and MLED seasonality. We found that the model reproduced the seasonality of GPP in both stands, but it was slightly overestimated without site-specific fine-tuning. Interestingly, although LandscapeDNDC properly captured the main MLED in Hyytiala (temperature) and in Yatir (soil water availability), it failed to reproduce high-temperature and high-vapor pressure limitations of GPP in Yatir during spring and summer. We deduced that the most likely reason for this divergence is an incomplete description of stomatal behavior. In summary, this study validates the MLED approach as a model evaluation tool, and opens up new possibilities for model improvement.