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Analysing agricultural drought resilience over Indian mainland at sub-basin level using long-term (2002-23) precipitation, soil moisture and ancillary datasets

Author

Listed:
  • Prabir Kumar Das

    (Regional Remote Sensing Centre-East, National Remote Sensing Centre, Newtown
    TERI School of Advanced Studies)

  • Koyena Das

    (TERI School of Advanced Studies)

  • Sharmistha B. Pandey

    (Regional Remote Sensing Centre-East, National Remote Sensing Centre, Newtown)

  • Rituparna Das

    (Regional Remote Sensing Centre-East, National Remote Sensing Centre, Newtown)

  • Arindam Guha

    (Regional Remote Sensing Centre-East, National Remote Sensing Centre, Newtown)

  • Suparn Pathak

    (Regional Remote Sensing Centre-East, National Remote Sensing Centre, Newtown)

Abstract

Drought resilience information is essential for effective drought management and mitigation planning under changing climate scenarios to ensure food security. The present study proposed a novel approach for generating agricultural drought resilience map over Indian mainland using meteorological and agricultural drought occurrence information during long-time period (2002-23). The gridded rainfall data from the India Meteorological Department (IMD) and root-zone soil moisture data from Global Land Data Assimilation System (GLDAS) were utilized to compute standardized precipitation index (SPI) and standardized soil moisture index (SSMI) at different timescales, respectively. The SPI and SSMI thresholds approach were used to identify meteorological and agricultural drought events, respectively, along with severity categories. Relative behavior of temporal profiles of SPI and SSMI were analyzed at individual grid level and two parameters, i.e., conditional probability (Cp) and buffering index (Bi), were computed. The Cp represents the rate of conversion from meteorological droughts to agricultural drought, whereas Bi is a metric used to assess the buffering capacity of soil during drought conditions. Both the parameters were re-scaled into 0–1 scale and were coupled through normalization to generate drought resilience information, i.e., normalized index (NI), for different drought categories. Further, the varying weightages were applied on NI for addressing the differential impacts of drought severity classes and finally the agricultural drought resilience maps were generated at sub-basin level. An attempt was also directed to find the causative factors, in terms of soil water holding capacity and percentage available command area, towards spatial variation in drought resilience across Indian mainland.

Suggested Citation

  • Prabir Kumar Das & Koyena Das & Sharmistha B. Pandey & Rituparna Das & Arindam Guha & Suparn Pathak, 2025. "Analysing agricultural drought resilience over Indian mainland at sub-basin level using long-term (2002-23) precipitation, soil moisture and ancillary datasets," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 121(15), pages 18113-18140, August.
    • Handle: RePEc:spr:nathaz:v:121:y:2025:i:15:d:10.1007_s11069-025-07508-5
    DOI: 10.1007/s11069-025-07508-5
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    1. J. V. N. S. Prasad & N. Loganandhan & P. R. Ramesh & C. A. Rama Rao & B. M. K. Raju & K. V. Rao & A. V. M. Subba Rao & R. Rejani & Sumanta Kundu & Prabhat Kumar Pankaj & C. M. Pradeep & B. V. S. Kiran, 2024. "Assessment of Resilience Due to Adoption of Technologies in Frequently Drought-Prone Regions of India," Sustainability, MDPI, vol. 16(17), pages 1-23, August.
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