Bayesian network based procedure for regional drought monitoring: The Seasonally Combinative Regional Drought Indicator

J Environ Manage. 2020 Dec 15:276:111296. doi: 10.1016/j.jenvman.2020.111296. Epub 2020 Sep 6.

Abstract

Drought is a complex natural hazard. It occurs due to a prolonged period of deficient in rainfall amount in a certain region. Unlike other natural hazards, drought hazard has a recurrent occurrence. Therefore, comprehensive drought monitoring is essential for regional climate control and water management authorities. In this paper, we have proposed a new drought indicator: the Seasonally Combinative Regional Drought Indicator (SCRDI). The SCRDI integrates Bayesian networking theory with Standardized Precipitation Temperature Index (SPTI) at varying gauge stations in various month/seasons. Application of SCRDI is based on five gauging stations of Northern Area of Pakistan. We have found that the proposed indicator accounts the effect of climate variation within a specified territory, accurately characterizes drought by capturing seasonal dependencies in geospatial variation scenario, and reduces the large/complex data for future drought monitoring. In summary, the proposed indicator can be used for comprehensive characterization and assessment of drought at a certain region.

Keywords: Bayesian networks; Drought; Geospatial variation; Standardized precipitation temperature index (SPTI).

MeSH terms

  • Bayes Theorem
  • Droughts*
  • Pakistan
  • Seasons
  • Temperature