Drought forecasting across multiple temporal scales using CMIP6 projections and hybrid deep learning models for Central Anatolia, Türkiye

Abstract

This study proposed a multi-temporal drought forecasting framework that integrates CMIP6 climate projections with hybrid deep learning models to enhance forecasting accuracy under climate change. For the Kayseri, Nevşehir, and Kırşehir stations located in the Central Anatolia Region of Türkiye, climate uncertainty was reduced by adopting a multi-model ensemble mean derived from 19 CMIP6 global climate models. Subsequently, monthly precipitation outputs under three socio-economic pathways (SSP1–2.6, SSP2–4.5, and SSP5–8.5) were used to compute the Standardized Precipitation Index at 1-, 3-, and 6-month time scales (SPI-1, SPI-3, and SPI-6). The SPI time series were decomposed using the Tunable Q-factor Wavelet Transform (TQWT), Maximal Overlap Discrete Wavelet Transform (MODWT), Variational Mode Decomposition (VMD), and Intrinsic Time-Scale Decomposition (ITD). The resulting components were combined with Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Gated Recurrent Units (GRU), and Long Short-Term Memory (LSTM) architectures to develop sixteen hybrid models. Model training was conducted using a chronological data partitioning strategy, while future projections were exclusively employed for performance evaluation. The results indicate that the TQWT-GRU model consistently outperforms other approaches at short-term (1-month) and long-term (6-month) time scales, whereas the MODWT-RNN model exhibits superior performance at the medium-term (3-month) scale across all stations and scenarios. For these optimal models, the coefficients of determination (R2) and Nash–Sutcliffe efficiency (NSE) values predominantly exceed 0.99, while the mean absolute error (MAE) and root mean square error (RMSE) remain within a low-error range of approximately 0.01–0.05. As a result, the findings underscore the necessity of scale- and region-specific hybrid model selection in drought forecasting and provide a robust framework for climate risk assessment and sustainable water resources management.