Tellus: Series A, Dynamic Meteorology and Oceanography (Jan 2021)

A new propagation-based framework to enhance competency in regional drought monitoring

  • Rizwan Niaz,
  • Xiang Zhang,
  • Zulfiqar Ali,
  • Ijaz Hussain,
  • Muhammad Faisal,
  • Elsayed Elsherbini Elashkar,
  • Jameel Ahmad Khader,
  • Sadaf Shamshoddin Soudagar,
  • Alaa Mohamd Shoukry,
  • Fares Fawzi Al-Deek

DOI
https://doi.org/10.1080/16000870.2021.1975404
Journal volume & issue
Vol. 73, no. 1
pp. 1 – 12

Abstract

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Drought is considered a regional phenomenon that usually covers large territorial extensions. It can occur anywhere in the world with severe impacts on water resources and socioeconomic activities. Therefore, it is compulsory to develop reliable tools and execute national plans based on the preeminent information and characterization of drought. There are numerous drought monitoring tools available in the literature to handle spatial and temporal behavior of the drought for regional forecasting and early warning mitigation policies. Standardized Drought Indices (SDI) are frequently used for drought characterization and comparing climatic characteristics of the regions. However, analyzing the spatiotemporal dynamics of the region requires more reliable methods and procedures for drought monitoring. In this perspective, the present study proposes a novel procedure for monitoring drought at a regional level: The Regional Propagation Spatially Weighted Accumulated Drought Index (RPSWADI). The first phase of the proposed procedure is intended to accumulate information from various meteorological stations placed in the homogenous region. In the second phase, accumulated information is used to propagate a new drought index. The proposed procedure is validated on six homogenous meteorological stations of the Northern areas of Pakistan. Furthermore, the commonly used standardized drought indices are used to observe the performance of the proposed procedure. The choice of the indices depends on the climatic conditions of the specific region and will be quantified accordingly. Results show that the RPSWADI can incorporate the spatiotemporal structure of various time series in various stations.

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