In the sprawling agricultural landscape of India, where crops like wheat and rice cover more than 80% of the agricultural land, the accuracy of climate models is paramount. These crops are not just staples for the nation but also significant players in the global energy and carbon cycles. However, the Community Land Model version 5 (CLM5), a widely used tool for simulating terrestrial water, energy, and carbon fluxes, has been plagued by inaccuracies in representing these crops, leading to significant errors in its outputs.
K.N. Reddy, a researcher at the Centre for Atmospheric Sciences, Indian Institute of Technology Delhi, has been at the forefront of addressing these issues. His recent study, published in ‘Geoscientific Model Development’ (translated to ‘Geoscientific Model Development’) aims to improve the representation of wheat and rice crops in CLM5. The challenge? The crop data necessary to calibrate and evaluate the models over the Indian region are not readily available. Reddy and his team had to create a comprehensive dataset by digitizing historical observations, covering 50 years and over 20 sites across tropical regions.
“Our study used novel crop data for India, creating a dataset that is the first of its kind,” Reddy explains. “This dataset covers 50 years and over 20 sites of crop growth data across tropical regions, where data have traditionally been spatially and temporally sparse.”
Reddy and his team used this dataset to calibrate and improve the representation of the sowing dates, growing season, growth parameters, and base temperature in CLM5. The results were striking. The modified CLM5 performed significantly better than the default model in simulating crop phenology, yield, and carbon, water, and energy fluxes compared to site-scale data and remote sensing observations.
For instance, the correlation for monthly leaf area index (LAI) improved from 0.35 to 0.92, and monthly gross primary production (GPP) improved from -0.46 to 0.79 compared to Moderate Resolution Imaging Spectroradiometer (MODIS) monthly data. The correlation value of the monthly sensible and latent heat fluxes improved from 0.76 and 0.52 to 0.9 and 0.88, respectively. Moreover, because of the corrected representation of the growing seasons, the seasonality of the simulated irrigation matched the observations.
This research underscores the importance of using region-specific parameters rather than global parameters for accurately simulating vegetation processes and corresponding land surface processes. The improved CLM5 can be used to investigate changes in growing season lengths, water use efficiency, and climate impacts on crop growth in future scenarios.
The implications for the energy sector are profound. Accurate modeling of crop growth and land surface processes can lead to better predictions of biomass availability for bioenergy, improved water management strategies, and enhanced carbon sequestration estimates. This, in turn, can inform policy decisions, optimize agricultural practices, and mitigate the impacts of climate change.
As Reddy puts it, “The improved CLM5 can help provide estimates of crop productivity and net carbon capture abilities of agroecosystems in future climate scenarios.” This research is a significant step forward in bridging the gap between agricultural practices and climate modeling, paving the way for more accurate and impactful decision-making in the energy and agricultural sectors.