PRELES (Mäkelä et al. 2008, Peltoniemi et al. 2015) is a semi-empirical Light Use Efficiecy (LUE) based ecosystem flux model that predicts daily GPP (P, g C m⁻² d-1), ET (E, mm d-1) and soil water (mm). The requirements of site-specific inputs include the soil depth exploited by the roots (mm), field capacity (mm) and wilting point (mm) of the soil, fAPAR, and daily meteorological observations that include the PPFD (mol m-2 d-1) above the canopy, air temperature (°C), VPD (kPa) and precipitation (mm d-1). A detailed description of PRELES can be found in Peltoniemi et al. (2015). The code is provided in the GitHub repository.
The semi‐empirical ecosystem flux model PRELES（PREdict Light‐use efficiency, Evapotranspiration and Soil water）was calibrated and evaluated for various forest types and climate conditions (Minunno et al. 2016, Tian et al. 2020). The variations among sites within one plant functional type (PFT) could be effectively simulated by simply adjusting the parameter of potential light‐use efficiency, implying significant convergence of simulated vegetation processes within PFT.
There is evidence that the potential LUE is also influenced by foliar nitrogen content, but this has not yet been included in PRELES formulation (Peltoniemi et al. 2012, Tian et al. 2021).
The model has been applied to regional estimation of forest gas exchange (Peltoniemi et al. 2015b, Climate guide), estimation of uncertainties in climate projections (Kalliokoski et al. 2018, Mäkelä et al. 2020), and to ecosystem case studies (Vernay et al. 2020, Tian et al. 2021).
Mäkelä, A., Pulkkinen, M., Kolari, P., Lagergren, F., Berbigier, P., Lindroth, A., Loustau, D., Nikinmaa, E., Vesala, T. & Hari, P.2008. Developing an empirical model of stand GPP with the LUE approach: analysis of eddy covariance data at five contrasting conifer sites in Europe. Global Change Biology 14(1):92–108.
Peltoniemi M., Pulkkinen M., Kolari, P., Duursma, R., Montagnani, L., Wharton, S., Lagergren, F., Takagi, K., Verbeeck, H., Christensen, T., Vesala, T., Falk, M., Loustau, D., Mäkelä, A. 2012. Does canopy mean N concentration explain differences in light use efficiencies of canopies in 14 contrasting forest sites? Tree Physiology, 32(2): 200-218
Peltoniemi, M., Pulkkinen, M., Aurela M., Pumpanen, J., Kolari, P., Mäkelä, A. 2015b. A semi-empirical model of boreal forest gross primary production, evapotranspiration, and soil water – calibration and sensitivity analysis. Boreal Environment Research, 20: 151–171.
Kalliokoski T., Mäkelä A., Fronzek T., Minunno F., Peltoniemi M. 2018. Decomposing sources of uncertainty in climate change projections of boreal forest primary production. Agricultural and Forest Meteorology, 262: 192-205
Minunno F., Peltoniemi M., Launiainen S., Aurela M., Lindroth A., Lohila A., Mammarella I., Minkkinen K., Mäkelä A., 2016. Calibration and validation of a semi-empirical flux ecosystem model for coniferous forests in the Boreal region. Ecological Modelling, 341: 37-52.
Tian, X, Minunno, F, Cao, T, Peltoniemi, M, Kalliokoski, T, Mäkelä, A. 2020. Extending the range of applicability of the semi-empirical ecosystem flux model PRELES for varying forest types and climate. Global Change Biology, 26: 2923–2943.
Tian X, Minunno F, Schiestl-Aalto P, Chi J, Zhao P, Peichl M, Marshall J, Näsholm T, Lim H, Peltoniemi M, Linder S, Mäkelä A 2021. Disaggregating the effects of nitrogen addition on gross primary production in a boreal Scots pine forest. Agricultural and Forest Meteorology, 301–302: 108337.
Vernay A., Tian X., Chi J., Linder S., Mäkelä A., Oren R., Peichl M., Stangle ZR, Tor-Ngern P, Marshall JD. 2020. Estimating canopy gross primary production by combining phloem stable isotopes with canopy and mesophyll conductances. Plant Cell and Environment. 43: 2124-2142
Mäkelä J, Minunno F, Aalto T, Mäkelä A, Markkanen T, Peltoniemi M. 2020. Sensitivity of 21st century simulated ecosystem indicators to model parameters, prescribed climate drivers, RCP scenarios and forest management actions for two Finnish boreal forest sites. Biogeosciences 17: 2681-2700.