Bayesian network based procedure for regional drought monitoring: The Seasonally Combinative Regional Drought Indicator. (15th December 2020)

• Record Type: Journal Article
• Authors: Ali, Zulfiqar; Hussain, Ijaz; Grzegorczyk, Marco Andreas; Ni, Guangheng; Faisal, Muhammad; Qamar, Sadia; Shoukry, Alaa Mohamd; Wahab Sharkawy, Mohammed Abdel; Gani, Showkat; Al-Deek, Fares Fawzi
• Is Part Of: Journal of environmental management. Volume 276(2020)
• Publication Date: 2020-12-15
• Subjects: Drought -- Standardized precipitation temperature index (SPTI) -- Bayesian networks -- Geospatial variation; Environmental policy -- Periodicals; Environmental management -- Periodicals; Environment -- Periodicals; Ecology -- Periodicals; 363.705
• DOI: 10.1016/j.jenvman.2020.111296
• Languages: English
• ISSNs: 0301-4797

Abstract

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. Highlights: Comprehensive drought monitoring. Save time and resources in the forecasting of future drought. Systematic way to combine SDI by using a Bayesian network approach. Easy in the calculation, save time and resources.