 Improved gross primary productivity estimation using semi empirical (PRELES) model for moist Indian sal forestDyvavani K. Krishna, Taibanganba Watham, Hitendra Padalia, Ritika Srinet and Subrata Nandy Ecological Modelling, 2023, vol. 475, issue C Abstract: The significant role that forests play in regulating the carbon and water exchange is critical to mitigate climate change. The remote sensing data and models provide good means for estimating gross primary productivity (GPP) and evapotranspiration (ET), although they seldom face issues when implemented without proper calibration. The study compares the performance of empirical (TG model) and semi-empirical (PRELES) model in estimation of GPP and ET of Indian moist sal forest. PRELES-PREdict Light use efficiency, Evapotranspiration and Soil water predicted the GPP and ET adequately; GPP ranged from 1.09 to 19.73 gC m−2 day−1 with RMSE of 1.64 gC m−2 day−1 and ET from 0.25 to 5.31 mm day−1 with RMSE of 0.65 mm day−1. It was found that PRELES estimated GPP with higher accuracy compared to TG model (a reduced RMSE of 0.68 gC m−2 day−1). The study reveals, with site-specific parametrization, semi empirical model can better predict GPP and ET than empirical model. Keywords: Carbon & water cycle; Light-use efficiency; Eddy covariance flux data; Temperature-greenness; Remote sensing (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:475:y:2023:i:c:s0304380022002769 DOI: 10.1016/j.ecolmodel.2022.110175 Access Statistics for this article Ecological Modelling is currently edited by Brian D. Fath More articles in Ecological Modelling from Elsevier |
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