Monthly drought prediction based on ensemble models

Muhammad Haroon Shaukat; Ijaz Hussain; Muhammad Faisal; Ahmad Al-Dousari; Muhammad Ismail; Alaa Mohamd Shoukry; Elsayed Elsherbini Elashkar; Showkat Gani

doi:10.7717/peerj.9853

Monthly drought prediction based on ensemble models

PeerJ. 2020 Sep 8:8:e9853. doi: 10.7717/peerj.9853. eCollection 2020.

Authors

Muhammad Haroon Shaukat¹, Ijaz Hussain¹, Muhammad Faisal^{2

3}, Ahmad Al-Dousari⁴, Muhammad Ismail⁵, Alaa Mohamd Shoukry^{6

7}, Elsayed Elsherbini Elashkar^{8

9}, Showkat Gani¹⁰

Affiliations

¹ Department of Statistics, Quaid-i-Azam University, Islamabad, Pakistan.
² Faculty of Health Studies, University of Bradford, Bradford, United Kingdom.
³ Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, United Kingdom.
⁴ Department of Geography, Kuwait University, Kuwait, Kuwait.
⁵ Department of Statistics, COMSATS University Islamabad, Lahore Campus, Lahore, Pakistan.
⁶ Arriyadh Community College, King Saud University, Riyadh, Saudi Arabia.
⁷ KSA, Workers University Egypt, Nasr, Egypt.
⁸ Administrative Sciences Department, Community College, King Saud University, Riyadh, Saudi Arabia.
⁹ Applied Statistics Department, Faculty of Commerce, Mansoura University, Mansoura, Egypt.
¹⁰ College of Business Administration, King Saud University, Muzahimiyah, Saudi Arabia.

Abstract

Drought is a natural hazard, which is a result of a prolonged shortage of precipitation, high temperature and change in the weather pattern. Drought harms society, the economy and the natural environment, but it is difficult to identify and characterize. Many areas of Pakistan have suffered severe droughts during the last three decades due to changes in the weather pattern. A drought analysis with the incorporation of climate information has not yet been undertaken in this study region. Here, we propose an ensemble approach for monthly drought prediction and to define and examine wet/dry events. Initially, the drought events were identified by the short term Standardized Precipitation Index (SPI-3). Drought is predicted based on three ensemble models i.e., Equal Ensemble Drought Prediction (EEDP), Weighted Ensemble Drought Prediction (WEDP) and the Conditional Ensemble Drought Prediction (CEDP) model. Besides, two weighting procedures are used for distributing weights in the WEDP model, such as Traditional Weighting (TW) and the Weighted Bootstrap Resampling (WBR) procedure. Four copula families (i.e., Frank, Clayton, Gumbel and Joe) are used to explain the dependency relation between climate indices and precipitation in the CEDP model. Among all four copula families, the Joe copula has been found suitable for most of the times. The CEDP model provides better results in terms of accuracy and uncertainty as compared to other ensemble models for all meteorological stations. The performance of the CEDP model indicates that the climate indices are correlated with a weather pattern of four meteorological stations. Moreover, the percentage occurrence of extreme drought events that have appeared in the Multan, Bahawalpur, Barkhan and Khanpur are 1.44%, 0.57%, 2.59% and 1.71%, respectively, whereas the percentage occurrence of extremely wet events are 2.3%, 1.72%, 0.86% and 2.86%, respectively. The understanding of drought pattern by including climate information can contribute to the knowledge of future agriculture and water resource management.

Keywords: Conditional ensemble drought prediction; Copulas; Equal ensemble drought prediction; Standardized precipitation index; Weighted ensemble drought prediction.

Grants and funding

The authors received funding for this work from the Deanship of Scientific Research at King Saud University, through research group no RG-1439-015. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.