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Development of Ridge Ensemble Standardized Drought Index (RESDI) for improving drought characterization and future assessment

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Abstract

Global warming upsets the environmental balance and leads to more frequent and severe climatic events. These extreme events include floods, droughts, and heatwaves. These widespread extreme events disrupt various sectors of ecosystems directly. However, among all these events, drought is one of the most prolonged climatic events that significantly destroys the ecosystem. Therefore, accurate and efficient assessment of droughts is necessary to mitigate their detrimental impacts. In recent years, several drought indices based on global climate models (GCMs) of Coupled Model Intercomparison Project Phase 6 (CMIP6) have been proposed to quantify and monitor droughts. However, each index has its advantages and limitations. As each index ensembles different models by using different statistical approaches, it is well known that the margin of error is always a part of statistics. Therefore, this study proposed a new drought index to reduce the uncertainty involved in the assessment of droughts. The proposed index named the Ridge Ensemble Standardized Drought Index (RESDI) is based on the innovative ensemble approach termed ridge parameters and distance-based weighting (RDW) scheme. And the development of this RDW scheme is based on two types of methods i.e., ridge regression and divergence-based method. In this research, we ensemble 18 different GCMs of CMIP6 using the RDW scheme. A comparative analysis of the RDW scheme is performed against the simple model average (SMA) and Bayesian model averaging (BMA) schemes at 32 locations on the Tibetan plateau. The comparison revealed that RDW has less mean absolute error (MAE) and root-mean-square error (RMSE). Therefore, the developed RESDI based on RDW is used to project drought properties under three distinct shared socioeconomic pathway (SSP) scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5, across seven different time scales (1, 3, 7, 9, 12, 24, and 48). The projected data is then standardized by using the K-components Gaussian mixture model (K-CGMM). In addition, the study employs steady-state probabilities (SSPs) to determine the long-term behavior of drought. The outcome of this research shows that “normal drought (ND)” has the highest probability of occurrence under all scenarios and time scales.

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Data Availability

The data and codes used for the preparation of the manuscript are available with the corresponding author and can be provided upon request.

References

  • Afan, H. A., El-Shafie, A., Yaseen, Z. M., Hameed, M. M., Wan Mohtar, W. H. M., & Hussain, A. (2015). ANN based sediment prediction model utilizing different input scenarios. Water Resources Management, 29, 1231–1245.

    Article  Google Scholar 

  • Agrawal, A. K., Murthy, V. M. S. R., Chattopadhyaya, S., & Raina, A. K. (2022). Prediction of TBM disc cutter wear and penetration rate in tunneling through hard and abrasive rock using multi-layer shallow neural network and response surface methods. Rock Mechanics and Rock Engineering, 55(6), 3489–3506.

    Article  Google Scholar 

  • Ahmad, M., Ali, Z., Ilyas, M., Mohsin, M., & 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, 1-20.

  • Ali, Z., Almanjahie, I. M., Hussain, I., Ismail, M., & 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), 1–23.

    Article  Google Scholar 

  • Alimonti, G., Mariani, L., Prodi, F., & Ricci, R. A. (2022). A critical assessment of extreme events trends in times of global warming. The European Physical Journal Plus, 137(1), 1–20.

    Article  Google Scholar 

  • Allerbo, O., & Jörnsten, R. (2023). Solving kernel ridge regression with gradient-based optimization methods. ar**v preprint ar**v:2306.16838.

  • Amundrud, S. L., Clay-Smith, S. A., Flynn, B. L., Higgins, K. E., Reich, M. S., Wiens, D. R., & Srivastava, D. S. (2019). Drought alters the trophic role of an opportunistic generalist in an aquatic ecosystem. Oecologia, 189, 733–744.

    Article  Google Scholar 

  • Ban, N., Caillaud, C., Coppola, E., Pichelli, E., Sobolowski, S., Adinolfi, M., et al. (2021). The first multi-model ensemble of regional climate simulations at kilometer-scale resolution, part I: evaluation of precipitation. Climate Dynamics, 57, 275–302.

    Article  Google Scholar 

  • Baseer, A., Ali, Z., Ilyas, M., & Yousaf, M. (2023). A new Monte Carlo feature selection (MCFS) algorithm-based weighting scheme for multi-model ensemble of precipitation. Theoretical and Applied Climatology, 1-12.

  • Batool, A., Ali, Z., Mohsin, M., & 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.

    Article  Google Scholar 

  • Chen, H., Li, C., Mafarja, M., Heidari, A. A., Chen, Y., & Cai, Z. (2023). Slime mould algorithm: A comprehensive review of recent variants and applications. International Journal of Systems Science, 54(1), 204–235.

    Article  Google Scholar 

  • Chicco, D., Warrens, M. J., & Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science, 7, e623.

    Article  Google Scholar 

  • Dhurmea, K. R., Boojhawon, R., & Rughooputh, S. D. D. V. (2019). A drought climatology for Mauritius using the standardized precipitation index. Hydrological Sciences Journal, 64(2), 227–240.

    Article  Google Scholar 

  • Ding, L., Kapp, P., Cai, F., Garzione, C. N., **ong, Z., Wang, H., & Wang, C. (2022). Timing and mechanisms of Tibetan Plateau uplift. Nature Reviews Earth and Environment, 3(10), 652–667.

    Article  Google Scholar 

  • Dorugade, A. V. (2014). New ridge parameters for ridge regression. Journal of the Association of Arab Universities for Basic and Applied Sciences, 15, 94–99.

    Article  Google Scholar 

  • Farzin, S., Singh, V. P., Karami, H., Farahani, N., Ehteram, M., Kisi, O., et al. (2018). Flood routing in river reaches using a three-parameter Muskingum model coupled with an improved bat algorithm. Water, 10(9), 1130.

    Article  Google Scholar 

  • Feng, X., Li, Y., Yu, E., Yang, J., Wang, S., & Yuan, W. (2023). Spatiotemporal evolution pattern and simulation of the coupling of carbon productivity and land development in the Yangtze River Delta, China. Ecological Informatics, 77, 102186.

    Article  Google Scholar 

  • Hamed, M. M., Nashwan, M. S., & Shahid, S. (2022). A novel selection method of CMIP6 GCMs for robust climate projection. International Journal of Climatology, 42(8), 4258–4272.

    Article  Google Scholar 

  • Hari, V., Rakovec, O., Markonis, Y., Hanel, M., & Kumar, R. (2020). Increased future occurrences of the exceptional 2018–2019 Central European drought under global warming. Scientific Reports, 10(1), 12207.

    Article  CAS  Google Scholar 

  • Harrison, L. M., Noble, D. W., & Jennions, M. D. (2022). A meta-analysis of sex differences in animal personality: no evidence for the greater male variability hypothesis. Biological Reviews, 97(2), 679–707.

    Article  Google Scholar 

  • Hessami, M., Gachon, P., Ouarda, T. B., & St-Hilaire, A. (2008). Automated regression-based statistical downscaling tool. Environmental Modelling & Software, 23(6), 813–834.

    Article  Google Scholar 

  • Jafarzadeh, A., Khashei-Siuki, A., & Pourreza-Bilondi, M. (2022). Performance assessment of model averaging techniques to reduce structural uncertainty of groundwater modeling. Water Resources Management, 36(1), 353–377.

    Article  Google Scholar 

  • Jain, A., Rao, A. C. S., Jain, P. K., & Hu, Y. C. (2023). Optimized levy flight model for heart disease prediction using CNN framework in big data application. Expert Systems with Applications, 223, 119859.

    Article  Google Scholar 

  • Jobst, L. J., Bader, M., & Moshagen, M. (2023). A tutorial on assessing statistical power and determining sample size for structural equation models. Psychological Methods, 28(1), 207.

    Article  Google Scholar 

  • Kraaijenbrink, P. D., Bierkens, M. F., Lutz, A. F., & Immerzeel, W. W. (2017). Impact of a global temperature rise of 1.5 degrees celsius on Asia’s glaciers. Nature, 549(7671), 257–260.

    Article  CAS  Google Scholar 

  • Kuwayama, Y., Thompson, A., Bernknopf, R., Zaitchik, B., & Vail, P. (2019). Estimating the impact of drought on agriculture using the US Drought Monitor. American Journal of Agricultural Economics, 101(1), 193–210.

    Article  Google Scholar 

  • Kyriazos, T., & Poga, M. (2023). Dealing with multicollinearity in factor analysis: The problem, detections, and solutions. Open Journal of Statistics, 13(3), 404–424.

    Article  Google Scholar 

  • Lai, J., Zou, Y., Zhang, J., & Peres-Neto, P. R. (2022). Generalizing hierarchical and variation partitioning in multiple regression and canonical analyses using the rdacca. hp R package. Methods in Ecology and Evolution, 13(4), 782–788.

    Article  Google Scholar 

  • Li, Z., Chen, T., Wu, Q., **a, G., & Chi, D. (2020). Application of penalized linear regression and ensemble methods for drought forecasting in Northeast China. Meteorology and Atmospheric Physics, 132, 113–130.

    Article  Google Scholar 

  • Marcy, C., Goforth, T., Nock, D., & Brown, M. (2022). Comparison of temporal resolution selection approaches in energy systems models. Energy, 251, 123969.

    Article  Google Scholar 

  • Mohsin, M., & Adnan, S. (2023). Probabilistic modeling of interarrival time of drought for different operational drought indices used in Pakistan. International Journal of Climatology, 43, 6851–6865.

    Article  Google Scholar 

  • Mukhtar, A., Ali, Z., Nazeer, A., Dhahbi, S., Kartal, V., & Deebani, W. (2024). A novel semi data dimension reduction type weighting scheme of the multi-model ensemble for accurate assessment of twenty-first century drought. Stochastic Environmental Research and Risk Assessment, 1–25.

  • Myhre, G., Alterskjær, K., Stjern, C. W., Hodnebrog, Ø., Marelle, L., Samset, B. H., et al. (2019). Frequency of extreme precipitation increases extensively with event rareness under global warming. Scientific Reports, 9(1), 16063.

    Article  CAS  Google Scholar 

  • Naumann, G., Cammalleri, C., Mentaschi, L., & Feyen, L. (2021). Increased economic drought impacts in Europe with anthropogenic warming. Nature Climate Change, 11(6), 485–491.

    Article  Google Scholar 

  • Navarro, M. M., Young, M. N., Prasetyo, Y. T., & Taylar, J. V. (2023). Stock market optimization amidst the COVID-19 pandemic: Technical analysis, K-means algorithm, and mean-variance model (TAKMV) approach. Heliyon.

    Google Scholar 

  • Ombadi, M., Nguyen, P., Sorooshian, S., & Hsu, K. L. (2021). Retrospective analysis and Bayesian model averaging of CMIP6 precipitation in the Nile River Basin. Journal of Hydrometeorology, 22(1), 217–229.

    Article  Google Scholar 

  • Patil, J., Sharma, P., & Mhatre, K. (2021). Global warming induced stress and its impact on biodiversity. Science and Technology, 6, 21–29.

    Google Scholar 

  • Razak Hasach Albasri, N. A., Shakir, H. S., & Al-Jawari, S. M. (2023). Monitoring and prediction functional change of land uses toward urban sustainability. International Journal of Sustainable Development and Planning, 18(7).

  • Saklani, N., & Khurana, A. (2019). Global warming: Effect on living organisms, causes and its solutions. International Journal of Engineering and Management Research.

  • Shakeel, M., & Ali, Z. (2024). Integration of exponential weighted moving average chart in ensemble of precipitation of multiple global climate models (GCMs). Water Resources Management, 38(3), 935–949.

    Article  Google Scholar 

  • Sharma, U., Gupta, N., & Verma, M. (2023). Prediction of compressive strength of GGBFS and Flyash-based geopolymer composite by linear regression, lasso regression, and ridge regression. Asian Journal of Civil Engineering, 24(8), 3399–3411.

    Article  Google Scholar 

  • Shiru, M. S., Shahid, S., Dewan, A., Chung, E. S., Alias, N., Ahmed, K., & Hassan, Q. K. (2020). Projection of meteorological droughts in Nigeria during growing seasons under climate change scenarios. Scientific Reports, 10(1), 10107.

    Article  CAS  Google Scholar 

  • Singh, P., Shamseldin, A. Y., Melville, B. W., & Wotherspoon, L. (2023). Development of statistical downscaling model based on Volterra series realization, principal components and ridge regression. Modeling Earth Systems and Environment, 9(3), 3361–3380.

    Article  Google Scholar 

  • Susanty, A., Akshinta, P. Y., Ulkhaq, M. M., & Puspitasari, N. B. (2022). Analysis of the tendency of transition between segments of green consumer behavior with a Markov chain approach. Journal of Modelling in Management, 17(4), 1177–1212.

    Article  Google Scholar 

  • Tapiador, F. J., Navarro, A., Levizzani, V., García-Ortega, E., Huffman, G. J., Kidd, C., et al. (2017). Global precipitation measurements for validating climate models. Atmospheric Research, 197, 1–20.

    Article  Google Scholar 

  • Tebaldi, C., Ranasinghe, R., Vousdoukas, M., Rasmussen, D. J., Vega-Westhoff, B., Kirezci, E., et al. (2021). Extreme sea levels at different global warming levels. Nature Climate Change, 11(9), 746–751.

    Article  Google Scholar 

  • Thackeray, C. W., Hall, A., Norris, J., & Chen, D. (2022). Constraining the increased frequency of global precipitation extremes under warming. Nature Climate Change, 12(5), 441–448.

    Article  Google Scholar 

  • Wu, B., Yan, J., & Cao, K. (2023). l0-Norm variable adaptive selection for geographically weighted regression model. Annals of the American Association of Geographers, 113(5), 1190–1206.

    Article  Google Scholar 

  • Wu, J., Chen, X., Yuan, X., Yao, H., Zhao, Y., & AghaKouchak, A. (2021). The interactions between hydrological drought evolution and precipitation-streamflow relationship. Journal of Hydrology, 597, 126210.

    Article  Google Scholar 

  • Xu, L., Chen, N., Zhang, X., & Chen, Z. (2020). A data-driven multi-model ensemble for deterministic and probabilistic precipitation forecasting at seasonal scale. Climate Dynamics, 54, 3355–3374.

    Article  Google Scholar 

  • Yao, N., Li, L., Feng, P., Feng, H., Li Liu, D., Liu, Y., et al. (2020). Projections of drought characteristics in China based on a standardized precipitation and evapotranspiration index and multiple GCMs. Science of the Total Environment, 704, 135245.

    Article  CAS  Google Scholar 

  • Yousaf, M., Ali, Z., Mohsin, M., Ilyas, M., & Shakeel, M. (2023). Development of a new hybrid ensemble method for accurate characterization of future drought using multiple global climate models. Stochastic Environmental Research and Risk Assessment, 1–21.

  • 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 SERIES A-DYNAMIC METEOROLOGY AND OCEANOGRAPHY, 74, 236–249.

    Article  Google Scholar 

  • Zhai, W., Li, C., Cheng, Q., Mao, B., Li, Z., Li, Y., et al. (2023). Enhancing wheat above-ground biomass estimation using UAV RGB images and machine learning: Multi-feature combinations, flight height, and algorithm implications. Remote Sensing, 15(14), 3653.

    Article  Google Scholar 

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Acknowledgements

The authors extend their appreciation to Taif University, Saudi Arabia, for supporting this work through project number (TU-DSPP-2024-94).

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Authors and Affiliations

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

    Mahrukh Yousaf, Abdul Baseer & Zulfiqar Ali

  2. Department of Statistics, Faculty of Science, University of Tabuk, Tabuk, Saudi Arabia

    Olayan Albalawi

  3. Department of Statistics, University of Sargodha, Sargodha, Pakistan

    Sadia Qamar

  4. Department of Mathematics and Statistics, Collage of Science, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia

    Emad E. Mahmoud

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  1. Mahrukh Yousaf
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  2. Abdul Baseer
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  4. Olayan Albalawi
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  5. Sadia Qamar
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  6. Emad E. Mahmoud
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Contributions

Mahrukh Yousaf and Zulfiqar Ali conceived the idea. Abdul Baseer provided technical assistance in programming. Zulfiqar Ali supervised the writing, grammar, and overall restructuring of the paper. Olayan Albalawi and Emad E. Mahmoud, as a subject expert, significantly contributed by providing detailed consultations to address reviewer comments pertaining to statistical aspects, thereby improving the technical content. Sadia Qamar undertook the responsibility of overseeing and enhancing the language, grammar, and statistical aspects. These contributions collectively strengthened the quality and comprehensibility of the manuscript, justifying the inclusion of these authors in the revised authorship. All authors made equal contributions to the study.

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Correspondence to Zulfiqar Ali.

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Yousaf, M., Baseer, A., Ali, Z. et al. Development of Ridge Ensemble Standardized Drought Index (RESDI) for improving drought characterization and future assessment. Environ Monit Assess 196, 614 (2024). https://doi.org/10.1007/s10661-024-12796-8

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