Abstract
This study simulates annual net primary production (NPP) of forests over peninsular Spain during the years 2005–2012. The modeling strategy consists of a linked production efficiency model based on the Monteith approach and the bio-geochemical model Biome-BGC. Recently produced databases and data layers over the study area including meteorological daily series, ecophysiological parameters, and maps containing information about forest type, rooting depth, and growing stock volume (GSV), were employed. The models, which simulate forest processes assuming equilibrium conditions, were previously optimized for the study area. The production efficiency model was used to estimate daily gross primary production (GPP), while Biome-BGC was used to simulate growth (RG) and maintenance (RM) respirations. To account for actual forest conditions, GPP, RG, and RM were corrected using the ratio of the remotely-sensed derived actual to potential GSV as an indicator of the actual state of forests. The obtained results were evaluated against current annual increment observations from the Third Spanish Forest Inventory. Coefficients of determination ranged from 0.46 to 0.74 depending on the forest type. A simplified dataset was produced by applying regular increments in air temperature and reductions in precipitation to the original 2005–2012 daily series with the goal of covering the range of variation of the climate projections corresponding to the different climate change scenarios reported in the literature. The modified meteorological series were used to simulate new GPP, RG, and RM through Biome-BGC and corrected using GSV. Precipitation was confirmed as the main limiting factor in the study area. In the regions where precipitation was already a limiting factor during 2005–2012, both the increment in air temperature and the reduction in precipitation contributed to a reduction of NPP. In the regions where precipitation was not a limiting factor during 2005–2012, the increment in air temperature led to an increment of NPP. This study is therefore relevant to characterize the growth of Spanish forests both in current and expected climate conditions.
Highlights
The signature of the Kyoto Protocol has renewed interest in the study of forests
The largest NPPNFI3 explained variance amongst forest types was obtained for evergreen broadleaved forest (EBF) with almost 70% (R2 = 0.69); ~60% (R2 = 0.60, R2 = 0.62, and R2 = 0.59 respectively) was obtained for low-altitude deciduous broadleaved forest (LDBF), high-altitude deciduous broadleaved forest (HDBF), and low-altitude evergreen needleleaved forest (LENF); and slightly less than 50% (R2 = 0.46) for high-altitude evergreen needleleaved forest (HENF)
The present study focuses on the use of a modeling strategy for the simulation of annual forest net primary production (NPP) over peninsular Spain through the combination of a production efficiency model, which was used for the estimation of annual forest gross primary production (GPP), and the bio-geochemical model Biome-BGC, which was used to simulate GPP, RG, and RM
Summary
Forests act as potential carbon sinks for the mitigation of climate change impacts, but in turn, they are affected by climate. According to [1], an increment of annual mean air temperature of 3–4 ◦C and a reduction of annual precipitation of 20% would lead to a decrease in photosynthesis and a decline of biomass accumulation during the current century. In this context, the net primary production (NPP) of terrestrial ecosystems is a variable that can reflect these changes, and its monitoring becomes key to apply the needed adaptation and mitigation actions. NPP quantifies the flux of carbon stored by plants in their structure
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