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

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

Agricultural Water Management, 2025, vol. 308, issue C

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.

Keywords: Drought; Forecastable component analysis; Machine learning methods; Classical methods; Türkiye (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:agiwat:v:308:y:2025:i:c:s0378377425000034

DOI: 10.1016/j.agwat.2025.109289

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