V. B. Virani

The study aims to assess the climate change projections in South Gujarat region using bias-corrected General Circulation Model (GCM) projections under SSP245 and SSP585 scenarios. For maximum temperature, the chosen models were ACCESS-CM2, CMCC-ESM2, GFDL-CM4, KIOST-ESM, and TaiESM1. For minimum temperature, ACCESS-ESM1-5, CNRM-ESM2-1, EC-Earth3, and INM-CM5-0 were selected. The models identified for rainfall simulation included ACCESS-CM2, KACE-1-0-G, MPI-ESM1-2-LR, MRI-ESM2-0, and TaiESM1. These models were selected based on their accuracy in representing historical climate data and their applicability for future climate projections in the study region. Under SSP585, maximum temperature is projected to rise by 2.4 °C and minimum temperature by over 5.6 °C by the end of the century. Rainfall projections suggest a potential increase of up to 14.50% by 2090. An evaluation of GCM bias correction methods revealed that Quantile Mapping (QM) significantly outperformed Linear Scaling (LS) in reducing Root Mean Square Error (RMSE). While LS struggled with complex deviations, QM effectively corrected distributional biases and extreme outliers across temperature and precipitation datasets, proving essential for reliable climate modeling.

This study investigates the sensitivity of the APSIM and CANEGRO crop models to key climatic parameters and genetic coefficients in simulating sugarcane growth and yield. Sensitivity analyses to genetic coefficient identified critical genetic parameters influencing crop performance. In the CANEGRO model, MaxPARCE, APFMX, and STKPFMAX were the most influential for yield, biomass, and sucrose content, while LFMAX and SER0 significantly impacted LAI and stalk height. These findings suggest that calibration efforts should prioritize phenological, growth, and yield-related parameters. In contrast, global sensitivity analysis using the APSIM model highlighted RUE4, MSS, and GLN as the most impactful parameters affecting cane yield, commercial cane sugar (CCS), and sucrose accumulation, with RUE and MSS emerging as key contributors to both biomass production and sugar content. Additionally, both models exhibited sensitivity to climatic variables. A rise in temperature resulted in a decline in cane yield, more pronounced in CANEGRO (31.35% reduction at +6 °C) compared to APSIM (15.81%). Increases in solar radiation enhanced cane yield and sucrose dry mass, while reduced radiation had adverse effects. Overall, the findings provide actionable insights for improving model calibration and support cultivar selection and management practices under projected climate change scenarios.

Land surface heat fluxes encompass net radiation flux (Rn), soil heat flux (G), sensible heat flux (H), and latent heat flux (LE), all of which play a crucial role in understanding energy transfer within earth–atmosphere interactions. This study utilized Landsat 8 data to estimate land surface heat fluxes over the Navsari district of South Gujarat, India using the SEBAL (Surface Energy Balance Algorithm for Land) model. Rn followed a seasonal trend of summer > autumn > spring > winter, with median values ranging from 607.7 W/m2 in summer to 459.9 W/m² in winter. G exhibited a similar pattern, while H varied as summer > winter > spring > autumn. LE showed the opposite trend, peaking in autumn (427.3 W/m2) and decreasing through spring, winter, and summer. Notably, the LE remained higher than the H across all seasons. Rn was primarily allocated to LE across most LULC types, except in water bodies, where it was nearly evenly distributed between LE and G. In the absence of ground-based instruments, SEBAL outputs were validated using EEFlux METRIC, a cloud-based evapotranspiration (ET) estimation tool. The validation showed strong agreement for land surface temperature (LST) (R2 = 0.976, RMSE = 5.63 K) and moderate agreement for ET (R2 = 0.632, RMSE = 1.40 mm/day), albedo (R2 = 0.532, RMSE = 0.06), and crop coefficient Kc (R2 = 0.452, RMSE = 0.18). The SEBAL model was also applied to estimate seasonal ET and determine the total water requirement for sugarcane.

The study aims to assess the impact of climate change on sugarcane yield attributes in South Gujarat region using bias-corrected General Circulation Model (GCM) projections under SSP245 and SSP585 scenarios. These models were selected based on their accuracy in representing historical climate data and their applicability for future climate projections in the study region. Under SSP585, maximum temperature is projected to rise by 2.4 °C and minimum temperature by over 5.6 °C by the end of the century. Rainfall projections suggest a potential increase of up to 14.50% by 2090. Yield simulations using CANEGRO indicate moderate yield declines (-1% to -2.1%) under SSP245 but substantial reductions (-14% to -15%) under SSP585 due to heat and water stress. Sucrose content also exhibited sharper declines, underscoring the adverse effects of high-emission scenarios. These findings highlight the necessity for climate adaptation and mitigation strategies in sugarcane cultivation.