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Probabilistic Assessment of Future Drought Characteristics under the Quality Boosted Regional Drought Index Cover

Probabilistic Assessment of Future Drought Characteristics under the Quality Boosted Regional Drought Index

Open Access
|Nov 2025

References

  1. Abbas, S.A., Xuan, Y., Al-Rammahi, A.H. and Addab, H.F. (2022) A comparison study of observed and the CMIP5 modelled precipitation over Iraq 1941–2005. Atmosphere, 13(11): 1869. DOI: 10.3390/atmos13111869
  2. Ahmad, M., Ali, Z., Ilyas, M., Mohsin, M. and Niaz, R. (2023) A common factor analysis based data mining procedure for effective assessment of 21st century drought under multiple global climate models. Water Resources Management, 37: 120. DOI: 10.1007/s11269-023-03581-2
  3. Ali, S., Liu, D., Fu, Q., Cheema, M.J.M., Pal, S.C., Arshad, A. and Zhang, L. (2022) Constructing high-resolution groundwater drought at spatio-temporal scale using GRACE satellite data based on machine learning in the Indus Basin. Journal of Hydrology, 612: 128295. DOI: 10.1016/j.jhydrol.2022.128295
  4. Ali, Z., Almanjahie, I.M., Hussain, I., Ismail, M. and Faisal, M. (2020) A novel generalized combinative procedure for multi-scalar standardized drought indices-The long average weighted joint aggregative criterion. Tellus A: Dynamic Meteorology and Oceanography, 72(1): 123. DOI: 10.1080/16000870.2020.1736248
  5. Ali, Z., Qamar, S., Khan, N., Faisal, M. and Sammen, S.S. (2023) A New Regional Drought Index under X-bar chart based weighting scheme – The Quality Boosted Regional Drought Index (QBRDI). Water Resources Management, 37(5): 18951911. DOI: 10.1007/s11269-023-03461-9
  6. Ali, Z., Hussain, I., Faisal, M., Nazir, H.M., Moemen, M.A.E., Hussain, T. and Shamsuddin, S. (2017) A novel multi-scalar drought index for monitoring drought: the standardized precipitation temperature index. Water resources management, 31(15): 49574969. DOI: 10.1007/s11269-017-1788-1
  7. Batool, A., Ali, Z., Mohsin, M., Masmoudi, A., Kartal, V. and Satti, S. (2024) Assessing the generalization of forecasting ability of machine learning and probabilistic models for complex climate characteristics. Stochastic Environmental Research and Risk Assessment, 38(8): 29272947. DOI: 10.1007/s00477-024-02721-3
  8. Batool, A., Ali, Z., Mohsin, M. and Shakeel, M. (2023) A generalized procedure for joint monitoring and probabilistic quantification of extreme climate events at regional level. Environmental Monitoring and Assessment, 195(10): 1223. DOI: 10.1007/s10661-023-11717-5
  9. Batool, A., Kartal, V., Ali, Z., Scholz, M. and Ali, F. (2025) A novel regional forecastable multiscalar standardized drought index (RFMSDI) for regional drought monitoring and assessment. Agricultural Water Management, 308: 109289. DOI: 10.1016/j.agwat.2025.109289
  10. Citakoglu, H. and Coşkun, Ö. (2022) Comparison of hybrid machine learning methods for the prediction of short-term meteorological droughts of Sakarya Meteorological Station in Turkey. Environmental Science and Pollution Research, 29(50): 7548775511. DOI: 10.1007/s11356-022-21083-3
  11. Cook, B.I., Mankin, J.S., Marvel, K., Williams, A.P., Smerdon, J.E. and Anchukaitis, K.J. (2020) Twenty-First Century Drought Projections in the CMIP6 Forcing Scenarios. Earth’s Future, 8: e2019EF001461. DOI: 10.1029/2019EF001461
  12. Dost, R. and Kasiviswanathan, K.S. (2023) Quantification of water resource sustainability in response to drought risk assessment for Afghanistan river basins. Natural Resources Research, 32(1): 235256. DOI: 10.1007/s11053-022-10129-5
  13. Erhardt, T.M. and Czado, C. (2018) Standardized drought indices: A novel univariate and multivariate approach. Journal of the Royal Statistical Society Series C: Applied Statistics, 67(3): 643664. DOI: 10.1111/rssc.12242
  14. Fu, G., Rojas, R. and Gonzalez, D. (2022) Trends in groundwater levels in alluvial aquifers of the Murray–Darling basin and their attributions. Water, 14(11): 1808. DOI: 10.3390/w14111808
  15. Hao, Z. and AghaKouchak, A. (2014) A nonparametric multivariate multi-index drought monitoring framework. Journal of Hydrometeorology, 15(1): 89101. DOI: 10.1175/JHM-D-12-0160.1
  16. Jehanzaib, M., Sattar, M.N., Lee, J.H. and Kim, T.W. (2020) Investigating effect of climate change on drought propagation from meteorological to hydrological drought using multi-model ensemble projections. Stochastic Environmental Research and Risk Assessment, 34: 721. DOI: 10.1007/s00477-019-01760-5
  17. Jiang, T., Su, X., Zhang, G., Zhang, T. and Wu, H. (2023) Estimating propagation probability from meteorological to ecological droughts using a hybrid machine learning copula method. Hydrology and Earth System Sciences, 27(2): 559576. DOI: 10.5194/hess-27-559-2023
  18. Jose, D.M., Vincent, A.M. and Dwarakish, G.S. (2022) Improving multiple model ensemble predictions of daily precipitation and temperature through machine learning techniques. Scientific Reports, 12(1): 125. DOI: 10.1038/s41598-022-08786-w
  19. Karanja, J., Svoma, B.M., Walter, J. and Georgescu, M. (2023) Southwest US winter precipitation variability: Reviewing the role of oceanic teleconnections. Environmental Research Letters, 18(5): 053003. DOI: 10.1088/1748-9326/accd84
  20. Katipoğlu, O.M. (2023) Revealing the trend and change point in Hargreaves equation based on potential evapotranspiration values with various statistical approaches. Environmental Science and Pollution Research, 30: 117. DOI: 10.1007/s11356-023-27417-z
  21. Keilson, J. and Syski, R. (1974) Compensation measures in the theory of Markov chains. Stochastic Processes and Their Applications, 2(1): 5972. DOI: 10.1016/0304-4149(74)90012-X
  22. Kendall, M.G. (1975) Rank correlation methods. London: Charles Griffin.
  23. Kheyruri, Y., Sharafati, A. and Shahid, S. (2023) Evaluation of the impact of large-scale atmospheric indicators and meteorological variables on drought in different regions of Iran. Environmental Earth Sciences, 82(12): 317. DOI: 10.1007/s12665-023-11015-w
  24. Lorente, D.B., Mohammed, K.S., Cifuentes-Faura, J. and Shahzad, U. (2023) Dynamic connectedness among climate change index, green financial assets and renewable energy markets: Novel evidence from sustainable development perspective. Renewable Energy, 204: 94105. DOI: 10.1016/j.renene.2022.12.085
  25. Mann, H.B. (1945) Nonparametric tests against trend. Econometrica: Journal of the Econometric Society, 13: 245259. DOI: 10.2307/1907187
  26. Markov, A.A. (1906) Generalization of the law of large numbers to dependent quantities. Russian. Izvestiia Fiz. Mat. Obschestva Kazan Univ., 2nd. Ser, 15(135): 339.
  27. McLachlan, G.J. and Peel, D. (2000) Finite mixture models. John Wiley & Sons. DOI: 10.1002/0471721182
  28. Mukherjee, S., Mishra, A. and Trenberth, K.E. (2018) Climate change and drought: A perspective on drought indices. Current Climate Change Reports, 4(2): 145163. DOI: 10.1007/s40641-018-0098-x
  29. Niaz, R., Almazah, M., Hussain, I., Al-Ansari, N. and Sammen, S.S. (2022) Assessing the probability of drought severity in a homogeneous region. Complexity, 2022: Article ID 3139870, 8 pages. DOI: 10.1155/2022/3139870
  30. Ombadi, M., Nguyen, P., Sorooshian, S. and Hsu, K.L. (2021) Retrospective analysis and Bayesian model averaging of CMIP6 precipitation in the Nile River Basin. Journal of Hydrometeorology, 22(1): 217229. DOI: 10.1175/JHM-D-20-0157.1
  31. Rajak, J. (2021) A preliminary review on impact of climate change and our environment with reference to global warming. International Journal of Environmental Sciences, 10: 1114.
  32. Rajwar, K., Deep, K. and Das, S. (2023) An exhaustive review of the metaheuristic algorithms for search and optimization: Taxonomy, applications, and open challenges. Artificial Intelligence Review, 56: 171. DOI: 10.1007/s10462-023-10470-y
  33. Reddy, N.M. and Saravanan, S. (2023) Extreme precipitation indices over India using CMIP6: A special emphasis on the SSP585 scenario. Environmental Science and Pollution Research, 30(16): 4711947143. DOI: 10.1007/s11356-023-25649-7
  34. Rhymee, H., Shams, S., Ratnayake, U. and Rahman, E.K.A. (2022) Comparing statistical downscaling and arithmetic mean in simulating CMIP6 multi-model ensemble over Brunei. Hydrology, 9(9): 161. DOI: 10.3390/hydrology9090161
  35. Saha, S., Kundu, B., Saha, A., Mukherjee, K. and Pradhan, B. (2023) Manifesting deep learning algorithms for developing drought vulnerability index in monsoon climate dominant region of West Bengal, India. Theoretical and Applied Climatology, 151(1–2): 891913. DOI: 10.1007/s00704-022-04300-4
  36. Sen, P.K. (1968) Estimates of the regression coefficient based on Kendall’s tau. Journal of the American Statistical Association, 63(324): 13791389. DOI: 10.1080/01621459.1968.10480934
  37. Soylu Pekpostalci, D., Tur, R., Danandeh Mehr, A., Vazifekhah Ghaffari, M.A., Dąbrowska, D. and Nourani, V. (2023) Drought monitoring and forecasting across Turkey: A contemporary review. Sustainability, 15(7): 6080. DOI: 10.3390/su15076080
  38. Spiess, A.N. (2018) Package ‘propagate’. Available at https://mirror.las.iastate.edu/CRAN/web/packages/propagate/propagate.pdf
  39. Svoboda, M.D. and Fuchs, B.A. (2016) Handbook of drought indicators and indices, vol. 2. Geneva, Switzerland: World Meteorological Organization. DOI: 10.1201/b22009-11
  40. Swain, S., Sahoo, S., Taloor, A.K., Mishra, S.K. and Pandey, A. (2022) Exploring recent groundwater level changes using innovative trend analysis (ITA) technique over three districts of Jharkhand, India. Groundwater for Sustainable Development, 18: 100783. DOI: 10.1016/j.gsd.2022.100783
  41. Tian, Q., Wang, F., Tian, Y., Jiang, Y., Weng, P. and Li, J. (2023) Copula-based comprehensive drought identification and evaluation over the Xijiang River Basin in South China. Ecological Indicators, 154: 110503. DOI: 10.1016/j.ecolind.2023.110503
  42. Wang, W., Ertsen, M.W., Svoboda, M.D. and Hafeez, M. (2016) Propagation of drought: from meteorological drought to agricultural and hydrological drought. Advances in Meteorology, 2016, Article ID 6547209, 5 pages. DOI: 10.1155/2016/6547209
  43. Wang, X., Chen, H., Heidari, A.A., Zhang, X., Xu, J., Xu, Y. and Huang, H. (2020) Multi-population following behavior-driven fruit fly optimization: A Markov chain convergence proof and comprehensive analysis. Knowledge-Based Systems, 210: 106437. DOI: 10.1016/j.knosys.2020.106437
  44. Won, J., Choi, J., Lee, O. and Kim, S. (2020) Copula-based Joint Drought Index using SPI and EDDI and its application to climate change. Science of the Total Environment, 744: 140701. DOI: 10.1016/j.scitotenv.2020.140701
  45. Yihdego, Y., Vaheddoost, B. and Al-Weshah, R.A. (2019) Drought indices and indicators revisited. Arabian Journal of Geosciences, 12: 112. DOI: 10.1007/s12517-019-4237-z
Language: English
Page range: 240 - 253
Submitted on: Feb 21, 2024
Accepted on: Mar 19, 2024
Published on: Nov 14, 2025
Published by: Stockholm University Press
In partnership with: Paradigm Publishing Services

© 2025 Veysi Kartal, Muhammad Ahmad, Olayan Albalawi, Zulfiqar Ali, Saad Sh. Sammen, Miklas Scholz, Aamina Batool, published by Stockholm University Press
This work is licensed under the Creative Commons Attribution 4.0 License.