Skip to main content

Advertisement

SpringerLink
Log in

A generalized procedure for joint monitoring and probabilistic quantification of extreme climate events at regional level

  • Research
  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

Abstract

Droughts and heat waves are currently recognized as two of the most serious threats associated with climate changes. Drought is characterized by prolonged dry periods, low precipitation, and high temperature, while heat wave refers to an extended period of exceptionally high temperature, surpassing the region’s average for that time of year. There is a close relationship between droughts and heat waves, as both are often caused by similar weather patterns and can exacerbate each other’s impacts. Therefore, it is crucial to monitor and quantify both droughts and heat waves jointly at a regional level in order to develop sustainable policies and effectively manage water resources. This article develops a new index, the standardized composite index for climate extremes (SCICE), for joint monitoring and probabilistic quantification of extreme climate events at regional level. The procedure of SCICE is mainly based on the joint standardization of standardized precipitation index (SPI) and standardized temperature index (STI). In the application of SCICE, results reveal that the long-term probabilities of the joint occurrence of dry and hot events are significantly greater than those of wet and cold events. Furthermore, the outcomes of the comparative assessment support the validity of using SCICE as a compact statistical approach in regional drought analysis. In summation, the study demonstrates the capability of SCICE to effectively characterize and assess the joint monitoring of drought and heat waves at a regional level, providing a comprehensive approach to understanding the joint impact of climate extremes.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  • Abramowitz, M., & Stegun, I. A. (1965). Handbook of mathematical function: With formulas, graphs, and mathematical tables, chap. 7, Dover, New York.

  • Ali, Z., Hussain, I., Faisal, M., Nazir, H. M., Moemen, M. A. E., Hussain, T., & Shamsuddin, S. (2017). A novel multi-scalar drought index for monitoring drought: The standardized precipitation temperature index. Water resources management, 31, 4957-4969.

  • Ali, Z., Hussain, I., Faisal, M., Grzegorczyk, M. A., Almanjahie, I. M., Nazeer, A., & Ahmad, I. (2019a). Characterization of regional hydrological drought using improved precipitation records under multi-auxiliary information. Theoretical and Applied Climatology, 140(1), 25–36.

    Google Scholar 

  • Ali, Z., Hussain, I., Faisal, M., Shoukry, A. M., Gani, S., & Ahmad, I. (2019b). A framework to identify homogeneous drought characterization regions. Theoretical and Applied Climatology, 137, 3161–3172.

    Article  Google Scholar 

  • Ali, Z., Hussain, I., Grzegorczyk, M. A., Ni, G., Faisal, M., Qamar, S., Shoukry, A. M., Sharkawy, M. A. W., Gani, S., & Al-Deek, F. F. (2020). Bayesian network based procedure for regional drought monitoring: The seasonally combinative regional drought indicator. Journal of Environmental Management, 276, 111296.

    Article  Google Scholar 

  • Ali, Z., Ellahi, A., Hussain, I., Nazeer, A., Qamar, S., Ni, G., & Faisal, M. (2021). Reduction of errors in hydrological drought monitoring–A novel statistical framework for spatio-temporal assessment of drought. Water Resources Management, 35(13), 4363–4380.

    Article  Google Scholar 

  • Ali, F., Li, B. Z., & Ali, Z. (2022). A new weighting scheme for diminishing the effect of extreme values in regional drought analysis. Water Resources Management, 36(11), 4099–4114.

    Article  Google Scholar 

  • Ali, Z., Qamar, S., Khan, N., Faisal, M., & 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, 1–17.

  • Avilés, A., Célleri, R., Solera, A., & Paredes, J. (2016). Probabilistic forecasting of drought events using Markov chain-and Bayesian network-based models: A case study of an Andean regulated river basin. Water, 8(2), 37.

    Article  Google Scholar 

  • Chen, L., Chen, X., Cheng, L., Zhou, P., & Liu, Z. (2019). Compound hot droughts over China: Identification, risk patterns and variations. Atmospheric Research, 227, 210–219. https://doi.org/10.1016/j.atmosres.2019.05.009

    Article  Google Scholar 

  • Dyer, M., & Greenhill, C. (2000). On Markov chains for independent sets. Journal of Algorithms, 35(1), 17–49.

    Article  Google Scholar 

  • Fahad, S., & Wang, J. (2020). Climate change, vulnerability, and its impacts in rural Pakistan: A review. Environmental Science and Pollution Research, 27, 1334–1338.

    Article  Google Scholar 

  • Feng, S., Hao, Z., Zhang, X., & Hao, F. (2019). Probabilistic evaluation of the impact of compound dry-hot events on global maize yields. Science of the Total Environment, 689, 1228–1234.

    Article  CAS  Google Scholar 

  • García-Herrera, R., Díaz, J., Trigo, R. M., Luterbacher, J., & Fischer, E. M. (2010). A review of the European summer heat wave of 2003. Critical Reviews in Environmental Science and Technology, 40(4), 267–306.

    Article  Google Scholar 

  • Gazol, A., & Camarero, J. J. (2022). Compound climate events increase tree drought mortality across European forests. Science of the Total Environment, 816, 151604.

    Article  CAS  Google Scholar 

  • Haldar, I. (2011). Global warming: The causes and consequences. Readworthy.

  • Hao, Y., Hao, Z., Feng, S., Zhang, X., & Hao, F. (2020). Response of vegetation to El Niño-southern oscillation (ENSO) via compound dry and hot events in southern Africa. Global and Planetary Change, 195, 103358.

    Article  Google Scholar 

  • Hao, Z., Hao, F., Singh, V. P., & Zhang, X. (2018). Quantifying the relationship between compound dry and hot events and El Niño–southern oscillation (ENSO) at the global scale. Journal of Hydrology, 567, 332–338.

    Article  Google Scholar 

  • Hao, Z., Hao, F., Xia, Y., Singh, V. P., & Zhang, X. (2019). A monitoring and prediction system for compound dry and hot events. Environmental Research Letters, 14(11), 114034.

    Article  CAS  Google Scholar 

  • Jamro, S., Dars, G. H., Ansari, K., & Krakauer, N. Y. (2019). Spatio-temporal variability of drought in Pakistan using standardized precipitation evapotranspiration index. Applied Sciences, 9(21), 4588.

    Article  Google Scholar 

  • Ji, G., Lai, Z., Yan, D., Wu, L., & Wang, Z. (2021). Spatiotemporal patterns of future meteorological drought in the Yellow River Basin based on SPEI under RCP scenarios. International Journal of Climate Change Strategies and Management.

  • Jiang, T., Su, X., Singh, V. P., & Zhang, G. (2022). Spatio-temporal pattern of ecological droughts and their impacts on health of vegetation in northwestern China. Journal of Environmental Management, 305, 114356.

    Article  Google Scholar 

  • Jiang, H., Khan, M. A., Li, Z., Ali, Z., Ali, F., & Gul, S. (2020). Regional drought assessment using improved precipitation records under auxiliary information. Tellus a: Dynamic Meteorology and Oceanography, 72(1), 1–26.

    Article  Google Scholar 

  • Khan, M., Jiang, H., Ali, Z., Nazeer, A., Ni, G., & Qamar, S. (2020). On the reduction of inaccuracies in drought monitoring-A novel blended procedure for standardized type drought indicators.

  • Kang, Y., Guo, E., Wang, Y., Bao, Y., & Zhao, S. (2022). Spatiotemporal variation in compound dry and hot events and its effects on NDVI in Inner Mongolia. China. Remote Sensing, 14(16), 3977.

    Article  Google Scholar 

  • Kousar, S., Khan, A. R., Ul Hassan, M., Noreen, Z., & Bhatti, S. H. (2020). Some best-fit probability distributions for at-site flood frequency analysis of the Ume River. Journal of Flood Risk Management, 13(3), e12640.

    Article  Google Scholar 

  • Leng, G., Zhang, X., Huang, M., Asrar, G. R., & Leung, L. R. (2016). The role of climate covariability on crop yields in the conterminous United States. Scientific Reports, 6(1), 1–11.

    Article  Google Scholar 

  • Li, J., Wang, Z., Wu, X., Zscheischler, J., Guo, S., & Chen, X. (2021). A standardized index for assessing sub-monthly compound dry and hot conditions with application in China. Hydrology and Earth System Sciences, 25(3), 1587–1601.

    Article  Google Scholar 

  • Matiu, M., Ankerst, D. P., & Menzel, A. (2017). Interactions between temperature and drought in global and regional crop yield variability during 1961–2014. PLoS ONE, 12(5), e0178339.

    Article  Google Scholar 

  • McPhillips, L. E., Chang, H., Chester, M. V., Depietri, Y., Friedman, E., Grimm, N. B., ... & Shafiei Shiva, J. (2018). Defining extreme events: A cross‐disciplinary review. Earth's Future, 6(3), 441–455.

  • Pishro-Nik, H. (2016). Introduction to probability, statistics, and random processes.

  • Qamar, S., Khalique, A., & Grzegorczyk, M. A. (2021). On the Bayesian network based data mining framework for the choice of appropriate time scale for regional analysis of drought Hazard. Theoretical and Applied Climatology, 143(3), 1677–1695.

    Article  Google Scholar 

  • Qazlbash, S. K., Zubair, M., Manzoor, S. A., Haq, A., & Baloch, M. S. (2021). Socioeconomic determinants of climate change adaptations in the flood-prone rural community of Indus Basin. Pakistan. Environmental Development, 37, 100603.

    Article  Google Scholar 

  • Ray, D. K., Gerber, J. S., MacDonald, G. K., & West, P. C. (2015). Climate variation explains a third of global crop yield variability. Nature Communications, 6(1), 5989.

    Article  CAS  Google Scholar 

  • Raza, A., Hussain, I., Ali, Z., Faisal, M., Elashkar, E. E., Shoukry, A. M., Al-Deek, F. F., & Gani, S. (2021). A seasonally blended and regionally integrated drought index using Bayesian network theory. Meteorological Applications, 28(3), e1992.

    Article  Google Scholar 

  • Salma, S., Rehman, S., & Shah, M. A. (2012). Rainfall trends in different climate zones of Pakistan. Pakistan Journal of Meteorology, 9(17).

  • Spate, O. H. K., & Learmonth, A. T. A. (2017). India and Pakistan: A general and regional geography. Routledge.

  • Rehman, A., Jingdong, L., Shahzad, B., Chandio, A. A., Hussain, I., Nabi, G., & Iqbal, M. S. (2015). Economic perspectives of major field crops of Pakistan: An empirical study. Pacific Science Review B: Humanities and Social Sciences, 1(3), 145–158.

    Google Scholar 

  • Sharma, T. C., & Panu, U. S. (2012). Prediction of hydrological drought durations based on Markov chains: Case of the Canadian prairies. Hydrological Sciences Journal, 57(4), 705–722.

    Article  Google Scholar 

  • Shaw, R. (2015). Hazard, vulnerability and risk: The Pakistan context. Disaster Risk Reduction Approaches in Pakistan, 31–52.

  • Shiau, J. T. (2020). Effects of gamma-distribution variations on SPI-based stationary and nonstationary drought analyses. Water Resources Management, 34, 2081–2095.

    Article  Google Scholar 

  • Stagge, J. H., Tallaksen, L. M., Gudmundsson, L., Van Loon, A. F., & Stahl, K. (2015). Candidate distributions for climatological drought indices (SPI and SPEI). International Journal of Climatology, 35(13), 4027–4040.

    Article  Google Scholar 

  • Watterson, I. G. (2005). Simulated changes due to global warming in the variability of precipitation, and their interpretation using a gamma-distributed stochastic model. Advances in Water Resources, 28(12), 1368–1381.

    Article  CAS  Google Scholar 

  • Wang, H., Zhang, G., Zhang, S., Shi, L., Su, X., Song, S., … & Fu, X. (2023). Development of a novel daily-scale compound dry and hot index and its application across seven climatic regions of China. Atmospheric Research, 287, 106700.Williams, A. P., Allen, C. D., Millar, C. I., Swetnam, T. W., Michaelsen, J., Still, C. J., & Leavitt, S. W. (2010). Forest responses to increasing aridity and warmth in the southwestern United States. Proceedings of the National Academy of Sciences, 107(50), 21289–21294.

    Article  Google Scholar 

  • Williams, N. M., Crone, E. E., T’ai, H. R., Minckley, R. L., Packer, L., & Potts, S. G. (2010). Ecological and life-history traits predict bee species responses to environmental disturbances. Biological Conservation, 143(10), 2280–2291.

    Article  Google Scholar 

  • Wolski, P., Conradie, S., Jack, C., & Tadross, M. (2021). Spatio-temporal patterns of rainfall trends and the 2015–2017 drought over the winter rainfall region of South Africa. International Journal of Climatology, 41, E1303–E1319.

    Article  Google Scholar 

  • Wu, X., Hao, Z., Zhang, X., Li, C., & Hao, F. (2020). Evaluation of severity changes of compound dry and hot events in China based on a multivariate multi-index approach. Journal of Hydrology, 583, 124580.

    Article  Google Scholar 

  • Xiao, R., Guo, Y., Zhang, Z., & Li, Y. (2022). A hidden Markov Model based unscented Kalman filtering framework for ecosystem health prediction: A case study in Shanghai-Hangzhou Bay urban agglomeration. Ecological Indicators, 138, 108854.

    Article  Google Scholar 

  • Ye, N. (2000, June). A markov chain model of temporal behavior for anomaly detection. In Proceedings of the 2000 IEEE Systems, Man, and Cybernetics Information Assurance and Security Workshop, 166, 169.

  • Zakaria, N. N., Othman, M., Sokkalingam, R., Daud, H., Abdullah, L., & Abdul Kadir, E. (2019). Markov chain model development for forecasting air pollution index of Miri, Sarawak. Sustainability, 11(19), 5190.Zhang, Y., Hao, Z., Feng, S., Zhang, X., & Hao, F. (2022). Changes and driving factors of compound agricultural droughts and hot events in eastern China. Agricultural Water Management, 263, 107485.

  • Yuanbin, S., Qamar, S., Ali, Z., Yang, T., Nazeer, A., & Fayyaz, R. (2022). A new ensemble index for extracting predictable drought features from multiple historical simulations of climate. Tellus A: Dynamic Meteorology and Oceanography, 74(1).

  • Zhang, Y., Hao, Z., Feng, S., Zhang, X., & Hao, F. (2022). Comparisons of changes in compound dry and hot events in China based on different drought indicators. International Journal of Climatology, 42(16), 8133–8145.

    Article  Google Scholar 

Download references

Funding

The current research is a part of a funded research project awarded by the University of the Punjab Lahore, Pakistan (2022). Therefore, the authors are thankful to the project awarding institution.

Author information

Authors and Affiliations

  1. College of Statistical Sciences, University of the Punjab, Lahore, Pakistan

    Aamina Batool, Zulfiqar Ali, Muhammad Mohsin & Muhammad Shakeel

Authors
  1. Aamina Batool
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zulfiqar Ali
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Muhammad Mohsin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Muhammad Shakeel
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Amina Batool and Zulfiqar Ali conceived of the presented idea. Amina Batool developed the theory and performed the computations. Muhammad Mohsin and Muhammad Shakeel verified the analytical methods. All authors discussed the results and contributed to the final manuscript.

Corresponding author

Correspondence to Zulfiqar Ali.

Ethics declarations

Ethical approval

The manuscript is prepared in accordance with the ethical standards of the responsible committee on human experimentation and with the latest (2008) version of the Helsinki Declaration of 1975.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Batool, A., Ali, Z., Mohsin, M. et al. A generalized procedure for joint monitoring and probabilistic quantification of extreme climate events at regional level. Environ Monit Assess 195, 1223 (2023). https://doi.org/10.1007/s10661-023-11717-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-023-11717-5

Keywords

Search

Navigation