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Published on: October 16, 2018
From GCM grid cell to agricultural plot: scale issues affecting modelling of climate impact
Christian Baron1, Benjamin Sultan, Maud Balme
1CIRAD-Amis/Agronomie/Ecotrop, TA 40/01, Avenue Agropolis 34398 Montpellier cedex 05, France. christian.baron@cirad.fr
Summary
General circulation models (GCM) improve climate predictions but can overestimate crop yields due to scale differences. Disaggregating climate data, especially rainfall, is crucial for accurate crop yield simulations in West Africa.
Area of Science:
- Agricultural Science
- Climate Modeling
- Environmental Science
Background:
- General Circulation Models (GCMs) offer improved climate variability predictions, vital for food security in climate-vulnerable regions like semi-arid West Africa.
- Translating large-scale GCM outputs to plot-level crop yields is challenging due to scale mismatches in hydrological processes (runoff, evaporation, transpiration, storage).
Purpose of the Study:
- To analyze the bias in crop yield simulations caused by spatial and temporal aggregation of climate data from General Circulation Models.
- To evaluate the effectiveness of downscaling models in restoring lost rainfall and solar radiation variability for realistic crop yield predictions.
Main Methods:
- A case study in Senegal used historical weather data with the SARRA-H millet crop model.
- The study extended to a latitudinal climate gradient (10°N-17°N) over 31 years, assessing yield sensitivity to solar radiation and rainfall variability.
- The LGO (Lebel-Guillot-Onibon) downscaling model was employed to generate local rainfall patterns from GCM grid cell means.
Main Results:
- Spatially aggregated rainfall data led to 10-50% yield overestimations in dry regions due to biased water availability for crop transpiration.
- Aggregated solar radiation data introduced significant bias in wetter zones where radiation was a limiting factor for yield.
- Disaggregating GCM data using the LGO model to simulate high-resolution rainfall patterns substantially reduced aggregation bias and improved yield simulation realism.
Conclusions:
- Coupling GCM outputs directly with plot-level crop models can introduce substantial systematic errors stemming from scale incompatibility.
- Transforming GCM outputs, particularly rainfall, to accurately represent plot-level variability is essential to avoid these errors and achieve realistic crop yield simulations.
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