Improving the representation of major Indian crops in the Community Land Model version 5.0 (CLM5) using site-scale crop data

Accurate representation of croplands is essential for simulating terrestrial water, energy, and carbon fluxes over India because croplands constitute more than 50 % of the Indian land mass. Wheat and rice are the two major crops grown in India, covering more than 80 % of the agricultural land. The Community Land Model version 5 (CLM5) has significant errors in simulating the crop phenology, yield, and growing season lengths due to errors in the parameterizations of the crop module, leading to errors in carbon, water, and energy fluxes over these croplands. Our study aimed to improve the representation of wheat and rice crops in CLM5. Unfortunately, the crop data necessary to calibrate and evaluate the models over the Indian region are not readily available. This study used comprehensive wheat and rice novel crop data for India created by digitizing historical observations. This dataset is the first of its kind, covering 50 years and over 20 sites of crop growth data across tropical regions, where data have traditionally been spatially and temporally sparse. We used eight wheat sites and eight rice sites from the recent decades. Many sites have multiple growing seasons, taking the total up to nearly 20 growing seasons for each crop. We used these data to calibrate and improve the representation of the sowing dates, growing season, growth parameters, and base temperature in CLM5. The modified CLM5 performed much better than the default model in simulating the crop phenology, yield, and carbon, water, and energy fluxes compared to site-scale data and remote sensing observations. For instance, Pearson's r 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 r 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 study demonstrates that global land models must use 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 the changes in growing season lengths, water use efficiency, and climate impacting crop growth of Indian crops in future scenarios. The model can also help provide estimates of crop productivity and net carbon capture abilities of agroecosystems in future climate.