1. Bayesian network based procedure for regional drought monitoring: The Seasonally Combinative Regional Drought Indicator.
- Author
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Ali, Zulfiqar, Hussain, Ijaz, Grzegorczyk, Marco Andreas, Ni, Guangheng, Faisal, Muhammad, Qamar, Sadia, Shoukry, Alaa Mohamd, Wahab Sharkawy, Mohammed Abdel, Gani, Showkat, and Al-Deek, Fares Fawzi
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DROUGHT management , *DROUGHT forecasting , *CLIMATE change , *ENVIRONMENTAL engineering , *DROUGHTS , *WATER management , *RAINFALL - 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. • 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. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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