Crop-water-climate modelling for resource quantification and its optimization

The research demonstrates the robustness and applicability of crop-water-climate modelling frameworks in enhancing water resource management and agricultural planning under climate uncertainty;

• Dey et al. (2022, International Journal of Climatology) introduced a multimodel ensemble machine learning approach leveraging CMIP6 projections in the Damodar River Basin, India. The study showed improved reliability in downscaling climate variables, which are crucial inputs for long-term water resource and crop planning. This method significantly reduced the uncertainty of climate model outputs, offering a more confident basis for water demand forecasting.  
• Complementing this, Dey and Remesan (2022, Journal of Hydrology) presented a detailed quantification of green and blue water components using multiple model structures. Their findings highlight how parameter uncertainty and input data choice affect the accuracy of hydrological assessments critical for crop-water optimization.
• Sahoo et al. (2024, Remote Sensing Applications: Society and Environment) developed a multi-mission virtual monitoring station framework for streamflow estimation, providing high-resolution hydrodynamic data. This innovation supports the calibration and validation of crop-water-climate models in data-sparse regions, enhancing the reliability of simulation outcomes.  
• Additionally, Panda et al. (2022, Journal of Hydrology) proposed the RGL-MARS downscaling algorithm for improved seasonal rainfall prediction. By accurately capturing both dry and wet season dynamics, the method enhances the temporal precision of water availability assessments, directly impacting irrigation scheduling and water-use planning.

Collectively, these studies validate the effectiveness of advanced modelling techniques in quantifying water resources, reducing uncertainty, and strengthening agricultural resilience. They also provide a foundation for scaling up these approaches to other river basins and agro-ecological zones, with demonstrated utility in both research and operational contexts.