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Improved gross primary productivity estimation using semi empirical (PRELES) model for moist Indian sal forest

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  • Krishna, Dyvavani K.
  • Watham, Taibanganba
  • Padalia, Hitendra
  • Srinet, Ritika
  • Nandy, Subrata

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.

Suggested Citation

  • Krishna, Dyvavani K. & Watham, Taibanganba & Padalia, Hitendra & Srinet, Ritika & Nandy, Subrata, 2023. "Improved gross primary productivity estimation using semi empirical (PRELES) model for moist Indian sal forest," Ecological Modelling, Elsevier, vol. 475(C).
    • Handle: RePEc:eee:ecomod:v:475:y:2023:i:c:s0304380022002769
    DOI: 10.1016/j.ecolmodel.2022.110175
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