Developing a sequential cropping capability in the JULESvn5.2 land–surface model - INRAE - Institut national de recherche pour l’agriculture, l’alimentation et l’environnement Access content directly
Journal Articles Geoscientific Model Development Discussions Year : 2019

Developing a sequential cropping capability in the JULESvn5.2 land–surface model

Abstract

Sequential cropping (also known as multiple or double cropping) is a common feature, particularly for tropical regions, where the crop seasons are largely dictated by the main wet season such as the Asian summer monsoon (ASM). The ASM provides the water resources for crops grown for the whole year, thereby influencing crop production outside the ASM period. Land surface models (LSMs) typically simulate a single crop per year, however, in order to understand how sequential cropping influences demand for resources, we need to simulate all of the crops grown within a year in a seamless way. In this paper we implement sequential cropping in a branch of the Joint UK Land Environment Simulator (JULES) and demonstrate its use at Avignon, a site that uses the sequential cropping system and provides over 15-years of continuous flux observations which we use to evaluate JULES with sequential cropping. In order to implement the method in future regional simulations where there may be large variations in growing conditions, we apply the same method to four locations in the North Indian states of Uttar Pradesh and Bihar to simulate the rice--wheat rotation and compare model yields to observations at these locations. JULES is able to simulate sequential cropping at Avignon and the four India locations, representing both crops within one growing season in each of the crop rotations presented. At Avignon the maxima of LAI, above ground biomass and canopy height occur at approximately the correct time for both crops. The magnitudes of biomass, especially for winter wheat, are underestimated and the leaf area index is overestimated. The JULES fluxes are a good fit to observations (r-values greater than 0.7), either using grasses to represent crops or the crop model, implying that both approaches represent the surface coverage correctly. For the India simulations, JULES successfully reproduces observed yields for the eastern locations, however yields are under estimated for the western locations. This development is a step forward in the ability of JULES to simulate crops in tropical regions, where this cropping system is already prevalent, while also providing the opportunity to assess the potential for other regions to implement it as an adaptation to climate change.

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Global Changes
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hal-02618243 , version 1 (25-05-2020)

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Camilla Mathison, Andrew J. Challinor, Chetan Deva, Pete Falloon, Sébastien Garrigues, et al.. Developing a sequential cropping capability in the JULESvn5.2 land–surface model. Geoscientific Model Development Discussions, 2019, pp.1-50. ⟨10.5194/gmd-2019-85⟩. ⟨hal-02618243⟩
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