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Browsing by Author Raj, R.

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  • BB

  • Bayesian integration of flux tower data into a process-based simulator for quantifying uncertainty in simulated output

    • Authors: Tol, C.V.D.; Raj, R.;  Advisor: -;  Participants: - (2017)

  • In this study, we present a Bayesian framework to calibrate the widely used process-based simulator Biome-BGC against estimates of gross primary production (GPP) data. We used GPP partitioned from flux tower measurements of a net ecosystem exchange over a 55-year-old Douglas fir stand as an example. The uncertainties of both the Biome-BGC parameters and the simulated GPP values were estimated. The calibrated parameters leaf and fine root turnover (LFRT), ratio of fine root carbon to leaf carbon (FRC : LC), ratio of carbon to nitrogen in leaf (C : Nleaf), canopy water interception coefficient (Wint), fraction of leaf nitrogen in RuBisCO (FLNR), and effective soil rooting depth (SD) cha...
  • LA

  • Quality assured estimates of forest gross primary production :Integration of flux tower data and a process-based simulator

    • Authors: Stein, A. ; Raj, R.;  Advisor: -;  Participants: - (2016)

  • Thesis provides the detailed method for quantifying the prior distribution of each BIOME-BGC parameter based on literature review and field inventory data; provides procedures for partitioning of GPP with the associated uncertainty from flux tower measurements of net ecosystem exchange; provides the quantification of posterior uncertainty in BIOME-BGC input parameters and simulated GPP by integrating flux tower GPP into BIOME-BGC using a Bayesian statistical method.