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A novel regional forecastable multiscalar standardized drought index (RFMSDI) for regional drought monitoring and assessment

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  • Batool, Aamina
  • Kartal, Veysi
  • Ali, Zulfiqar
  • Scholz, Miklas
  • Ali, Farman

Abstract

Drought is a complex recurrent natural phenomenon. It is the main outcome of climate change. It has long-term impacts on agriculture, human life as well as the environment. Therefore, quantifying drought at the regional level is essential for developing sustainable policies. This study introduced a new drought index for regional drought forecasting called the Regional Forecastable Multiscalar Standardized Drought Index (RFMSDI). The RFMSDI methodology is based on Forecastable Component Analysis (FCA) and K-Component Gaussian Mixture Distribution (K-CGMD). FCA reduce dimension by focus on components that are inherently more predictable. It ensures that reduced data has a built-in ability to predict future trends by selecting the maximized forecastable components. K-CGMD is utilized to model the multimodel time series data. The study application incorporates eight meteorological stations in Türkiye's Elazig province (Baskil, Agin, Elazig, Karakocan, Keban Maden, Palu and Sivrice). The effectiveness of RFMSDI is evaluated by analyzing precipitation data over these meteorological stations of Türkiye. The comparative assessment of the research signifies the superiority of FCA for regional data aggregation. In this research, the comparative assessment of RFMSDI is evaluated against the Standardized Precipitation Index (SPI) by analyzing Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) metrics across different time scales using various machine learning and traditional time series models. The research findings include the following: 1) K-CGMD is a better fitting approach for standardizing RFMSDI and SPI based on reduced BIC values. 2) RFMSDI has superior performance over SPI based on the lower values of RMSE and MAE. 3) Both machine learning and classical methods reveal that RFMSDI outperforms SPI in predicting droughts. 4) SPI shows localized advantages with the ELM training set at 1- and 6-month time scales but RFMSDI offers a more comprehensive and consistent tool for drought prediction, especially when tested on unseen data. In general, the findings endorse the effectiveness of RFMSDI for monitoring drought on a regional level.

Suggested Citation

  • Batool, Aamina & Kartal, Veysi & Ali, Zulfiqar & Scholz, Miklas & Ali, Farman, 2025. "A novel regional forecastable multiscalar standardized drought index (RFMSDI) for regional drought monitoring and assessment," Agricultural Water Management, Elsevier, vol. 308(C).
    • Handle: RePEc:eee:agiwat:v:308:y:2025:i:c:s0378377425000034
    DOI: 10.1016/j.agwat.2025.109289
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    References listed on IDEAS

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    1. Hyndman, Rob J. & Koehler, Anne B. & Snyder, Ralph D. & Grose, Simone, 2002. "A state space framework for automatic forecasting using exponential smoothing methods," International Journal of Forecasting, Elsevier, vol. 18(3), pages 439-454.
    2. Bostancı, Bülent & Kaynak, Tolga & Çapkurt, Zeynep, 2024. "Location selection for solar power plants by using support vector machines: Adıyaman province, Turkey," Renewable Energy, Elsevier, vol. 229(C).
    3. Kimia Naderi & Mahnoosh Moghaddasi & Ashkan shokri, 2022. "Drought Occurrence Probability Analysis Using Multivariate Standardized Drought Index and Copula Function Under Climate Change," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(8), pages 2865-2888, June.
    4. Neeta Nandgude & T. P. Singh & Sachin Nandgude & Mukesh Tiwari, 2023. "Drought Prediction: A Comprehensive Review of Different Drought Prediction Models and Adopted Technologies," Sustainability, MDPI, vol. 15(15), pages 1-19, July.
    5. Zhenya Li & Zulfiqar Ali & Tong Cui & Sadia Qamar & Muhammad Ismail & Amna Nazeer & Muhammad Faisal, 2022. "A comparative analysis of pre- and post-industrial spatiotemporal drought trends and patterns of Tibet Plateau using Sen slope estimator and steady-state probabilities of Markov Chain," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 113(1), pages 547-576, August.
    6. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    7. Assimakopoulos, V. & Nikolopoulos, K., 2000. "The theta model: a decomposition approach to forecasting," International Journal of Forecasting, Elsevier, vol. 16(4), pages 521-530.
    8. Rahman, Khalil Ur & Ejaz, Nuaman & Shang, Songhao & Balkhair, Khaled S. & Alghamdi, Khalid Mohammad & Zaman, Kifayat & Khan, Mahmood Alam & Hussain, Anwar, 2024. "A robust integrated agricultural drought index under climate and land use variations at the local scale in Pakistan," Agricultural Water Management, Elsevier, vol. 295(C).
    9. Bethuel Sibongiseni Ngcamu & Felix Chari, 2020. "Drought Influences on Food Insecurity in Africa: A Systematic Literature Review," IJERPH, MDPI, vol. 17(16), pages 1-17, August.
    10. J. Kapsomenakis & C. Douvis & A. Poupkou & S. Zerefos & S. Solomos & T. Stavraka & N. S. Melis & E. Kyriakidis & G. Kremlis & C. Zerefos, 2023. "Climate change threats to cultural and natural heritage UNESCO sites in the Mediterranean," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(12), pages 14519-14544, December.
    11. Petris, Giovanni & Petrone, Sonia, 2011. "State Space Models in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 41(i04).
    12. Zuliqar Ali & Ijaz Hussain & Muhammad Faisal & Hafiza Mamona Nazir & Mitwali Abd-el Moemen & Tajammal Hussain & Sadaf Shamsuddin, 2017. "A Novel Multi-Scalar Drought Index for Monitoring Drought: the Standardized Precipitation Temperature Index," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(15), pages 4957-4969, December.
    13. Murat Ozocak & Anil Orhan Akay & Abdullah İlker Esin & Huseyin Yurtseven & Mustafa Akgul, 2024. "A new framework to spatial and temporal drought analysis for 1990–2020 period with Mann–Kendall and innovative trend analysis methods in Turkey," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(2), pages 1463-1517, January.
    Full references (including those not matched with items on IDEAS)

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