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Multiscale computation of solar radiation for predictive vegetation modelling

Calcul multi-échelle du rayonnement solaire pour la modélisation prédictive de la végétation

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Abstract

The recent development of large environmental databases allow the analysis of the ecological behaviour of species or communities over large territories. Solar radiation is a fundamental component of ecological processes, but is poorly used at this scale due to the lack of available data. Here we present a GIS program allowing to calculate solar radiation as well locally as at large scale, taking into account both topographical (slope, aspect, altitude, shadowing) and global (cloudiness and latitude) parameters. This model was applied to the whole of France (540 000 km2) for each month of the year, using only a 50-m digital elevation model (DEM), latitude values and cloudiness data. Solar radiation measured from 88 meteorological stations used for validation indicated a R 2 of 0.78 between measured and predicted annual radiation with better predictions for winter than for summer. Radiation values increase with altitude, and with slope for southern exposure, excepted in summer. They decrease with latitude, nebulosity, and slope for north, east, and west exposures. The effect of cloudiness is important, and reduces radiation by around 20% in winter and 10% in summer. Models of plant distribution were calculated for Abies alba, Acer pseudoplatanus, and Quercus pubescens, for France. The use of solar radiation improved modelling for the three species models directly or through the water balance variable. We conclude that models which incorporates both topographical and global variability of solar radiation can improve efficiency of large-scale models of plant distribution.

Résumé

Le développement récent d’importantes bases de données phytoécologiques permet l’analyse du comportement des espèces ou des communautés sur de larges territoires. Le rayonnement solaire est une composante essentielle du fonctionnement des écosystèmes, mais il est peu utilisé à cette échelle du fait du manque de données disponibles. Nous présentons un programme élaboré sous SIG permettant de calculer le rayonnement aussi bien localement que sur de vastes espaces, prenant à la fois en compte des paramètres locaux (pente, exposition, altitude, effet de masque) et globaux (latitude, nébulosité). Ce modèle a permis de calculer le rayonnement solaire sur l’ensemble de la France (540 000 km2), pour chaque mois de l’année, en utilisant seulement un Modèle Numérique de Terrain (MNT) de 50 m de résolution, des valeurs de latitude et des données de nébulosité. Les radiations solaires de 88 postes météorologiques ont été utilisées pour la validation, le R 2 entre le rayonnement annuel prédit par le modèle et celui mesuré sur les postes météorologiques s’établissant à 0,78, avec de meilleures prédictions pour l’hiver que pour l’été. Les valeurs de radiations augmentent avec l’altitude, et la pente pour les expositions sud, hormis en été. Elles diminuent avec la latitude, la nébulosité, et la pente pour les expositions nord, est et ouest. L’effet de la nébulosité est important et réduit le rayonnement d’environ 20 % en hiver et 10 % en été. Des modèles de distribution ont été calculés pour trois essences, Abies alba, Acer pseudoplatanus, et Quercus pubescens, pour la France. L’utilisation du rayonnement solaire améliore les trois modèles, directement ou à travers la variable de bilan hydrique. Nous concluons qu’un modèle de rayonnement solaire qui inclut à la fois la variabilité topographique et des facteurs plus globaux, est approprié pour améliorer l’efficacité des modèles de distribution des plantes réalisés à large échelle.

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  1. LERFoB UMR INRA-ENGREF 1092 - Équipe Écologie Forestière, AgroparisTech-ENGREF, 14 rue Girardet, 54042, Nancy Cedex, France

    Christian Piedallu & Jean-Claude Gégout

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  1. Christian Piedallu
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  2. Jean-Claude Gégout
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Correspondence to Christian Piedallu.

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Piedallu, C., Gégout, JC. Multiscale computation of solar radiation for predictive vegetation modelling. Ann. For. Sci. 64, 899–909 (2007). https://doi.org/10.1051/forest:2007072

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