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

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http://hdl.handle.net/10138/331694

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Nadal-Sala , D , Grote , R , Birami , B , Lintunen , A , Mammarella , I , Preisler , Y , Rotenberg , E , Salmon , Y , Tatarinov , F , Yakir , D & Ruehr , N K 2021 , ' Assessing model performance via the most limiting environmental driver in two differently stressed pine stands ' , Ecological Applications , vol. 31 , no. 4 , 02312 . https://doi.org/10.1002/eap.2312

Title: Assessing model performance via the most limiting environmental driver in two differently stressed pine stands
Author: Nadal-Sala, Daniel; Grote, Ruediger; Birami, Benjamin; Lintunen, Anna; Mammarella, Ivan; Preisler, Yakir; Rotenberg, Eyal; Salmon, Yann; Tatarinov, Fedor; Yakir, Dan; Ruehr, Nadine K.
Contributor: University of Helsinki, Department of Forest Sciences
University of Helsinki, Institute for Atmospheric and Earth System Research (INAR)
University of Helsinki, Department of Forest Sciences
Date: 2021-06
Language: eng
Number of pages: 16
Belongs to series: Ecological Applications
ISSN: 1051-0761
URI: http://hdl.handle.net/10138/331694
Abstract: 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.
Subject: Aleppo pine
gross primary productivity
model evaluation
most limiting environmental driver
productivity seasonality
random forest
Scots pine
NET PRIMARY PRODUCTIVITY
SOIL-WATER
SCOTS PINE
VEGETATION MODELS
EDDY COVARIANCE
BOREAL FORESTS
CLIMATE-CHANGE
CO2 EXCHANGE
ALEPPO PINE
CARBON
4112 Forestry
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