ORCHIDEE‐STICS, a process‐based model of sugarcane biomass production: calibration of model parameters governing phenology

Aude Valade,Françoise Ruget,Philippe Ciais,Nicolas Viovy,Benoît Gabrielle,Neil Huth,Jean‐François Martiné,Nicolas Vuichard

doi:10.1111/gcbb.12074

Abstract

AbstractAgro‐Land Surface Models (agro‐LSM) combine detailed crop models and large‐scale vegetation models (DGVMs) to model the spatial and temporal distribution of energy, water, and carbon fluxes within the soil–vegetation–atmosphere continuum worldwide. In this study, we identify and optimize parameters controlling leaf area index (LAI) in the agro‐LSM ORCHIDEE‐STICSdeveloped for sugarcane. Using the Morris method to identify the key parameters impactingLAI, at eight different sugarcane field trial sites, in Australia and La Reunion island, we determined that the three most important parameters for simulatingLAIare (i) the maximum predefined rate ofLAIincrease during the early crop development phase, a parameter that defines a plant density threshold below which individual plants do not compete for growing theirLAI, and a parameter defining a threshold for nitrogen stress onLAI. A multisite calibration of these three parameters is performed using three different scoring functions. The impact of the choice of a particular scoring function on the optimized parameter values is investigated by testing scoring functions defined from the model‐dataRMSE, the figure of merit and a Bayesian quadratic model‐data misfit function. The robustness of the calibration is evaluated for each of the three scoring functions with a systematic cross‐validation method to find the most satisfactory one. Our results show that the figure of merit scoring function is the most robust metric for establishing the best parameter values controlling theLAI. The multisite average figure of merit scoring function is improved from 67% of agreement to 79%. The residual error inLAIsimulation after the calibration is discussed.

Highlights

Ethanol produced from crop biomass has emerged as a potential contributor to a more renewable transportation energy mix
The land surface modules of Earth System Models (ESM), which operate at larger scales, are based on a restricted set of generic vegetation types and are unable to take into account the specificities of any given crop
Because the goal of this study is to obtain a multisite calibration, our selection of the most important parameters retained for calibration from the Morris sensitivity analysis is constrained by two criteria: the importance of a parameter at all the sites, and the limited amount of nonlinearities and/or interactions associated with this parameter

Summary

Introduction

Ethanol produced from crop biomass has emerged as a potential contributor to a more renewable transportation energy mix. Agronomical plot-scale models generally simulate the growth and biomass yield of sugarcane (both from a quantitative and qualitative standpoint) with good accuracy under different types of conditions (Keating et al, 1999; Singels & Bezuidenhout, 2002) They may be used to study the interactions between crops and their environment, for instance soil carbon dynamics (Galdos et al, 2009) and water use (Inman-Bamber et al, 1993). A highly simplified sugarcane new crop functional type was added in LPJml (Lapola et al, 2009) and sugarcane has been included to the Agro-IBIS and JULES models with a different approach, by adding a new module with specific parameters and allocation rules (Black et al, 2012; Cuadra et al, 2012). STICS drives ORCHIDEE mainly through its crop-specific phenology component (Gervois et al, 2004), while other ecosystem state variables (biomass, fluxes) are produced by ORCHIDEE (Fig. 1)

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