Evaluating Climate Change Impacts and Adaptation Options for Paddy Yield Using Data-Curated Modelling in Goa, India

This study investigates the coastal-region impacts of climate change on rice yield in Goa, India, a monsoon-driven agroecosystem highly dependent on paddy cultivation and vulnerable to rainfall variability, salinity intrusion, and rising temperatures. The study aims to (i) estimate future crop evapotranspiration (ET_c) and rice yield projections under different Shared Socioeconomic Pathways (SSP 2.6, SSP 4.5, and SSP 8.5), and (ii) assess the effectiveness of adjusting planting dates, along with the integration of drought-resilient cultivars, alternate wetting and drying (AWD) irrigation, and soil management practices, as adaptation strategies to mitigate yield reductions. To achieve these objectives, the CropWat and AquaCrop models were employed, using statistically downscaled CMIP6 CESM2 climate data.

The AquaCrop model was calibrated using data from 1994 to 2004 and validated for the period 2005–2014, demonstrating strong performance metrics (Nash–Sutcliffe Efficiency = 0.86, RMSE = 278.5, r² = 0.93). Our findings indicate that projected climatic changes pose a significant threat to rice yield stability in the region. Rising temperatures and shifting monsoon patterns are expected to elevate evapotranspiration demand by 10–14%, thereby intensifying irrigation requirements even in high-rainfall areas.

In response, adjusting planting dates emerged as a promising adaptation strategy. Specifically, delaying planting by 5 days until 2070 and by 10 days from 2071 to 2099 significantly mitigated yield declines across all SSP scenarios. An optimum 10-day delay in planting was found to recover up to 17% of yield losses under SSP 2.6 and SSP 4.5. Furthermore, compound strategies—including drought-tolerant rice cultivars, AWD irrigation, and improved soil management—provided up to 25% additional yield gains. These integrated approaches not only improved crop water productivity but also stabilized yields under moderate emission pathways. However, under the high-emission SSP 8.5 scenario, yield reductions remained substantial (up to 20%) due to increased temperature stress and shortened grain-filling duration, underscoring the limits of adaptation under extreme climate conditions.

The results highlight the importance of temporally optimized sowing schedules, integrated irrigation management, and improved soil practices for enhancing the resilience of coastal rice systems. This study further demonstrates that reliable data curation, model calibration, and parameter selection are essential to improving predictive accuracy in agro-hydrologic modelling. The findings emphasize the need for consistent methodological frameworks that couple climate projections with process-based crop models to assess adaptation effectiveness under uncertain future conditions.

Overall, the study provides actionable insights for strengthening the accuracy and reliability of water- and climate-based agricultural modelling frameworks. The outcomes contribute to developing climate-resilient strategies for paddy cultivation in coastal India, reinforcing the broader understanding of model validation, uncertainty reduction, and data-driven adaptation in hydrologic and agricultural research.