Fusion-based framework for meteorological drought modeling using remotely sensed datasets under climate change scenarios: Resilience, vulnerability, and frequency analysis

Severe drought events in recent decades and their catastrophic effects have called for drought prediction and monitoring needed for developing drought readiness plans and mitigation measures. This study used a fusionbased framework for meteorological drought modeling for the historical (1983-2016) and future (2020-2050) periods using remotely sensed datasets versus ground-based observations and climate change scenarios. To this aim, high-resolution remotely sensed precipitation datasets, including PERSIANN-CDR and CHIRPS (multisource products), ERA5 (reanalysis datasets), and GPCC (gauge-interpolated datasets), were employed to estimate non-parametric SPI (nSPI) as a meteorological drought index against local observations. For more accurate drought evaluation, all stations were classified into different clusters using the K-means clustering algorithm based on ground-based nSPI. Then, four Individual Artificial Intelligence (IAI) models, including Adaptive NeuroFuzzy Inference System (ANFIS), Group Method of Data Handling (GMDH), Multi-Layer Perceptron (MLP), and General Regression Neural Network (GRNN), were developed for drought modeling within each cluster. Finally, two advanced fusion-based methods, including Multi-Model Super Ensemble (MMSE) as a linear weighted model and a nonlinear model called machine learning Random Forest (RF), combined results by IAI models using different remotely sensed datasets. The proposed framework was implemented to simulate each remotely sensed precipitation data for the future based on CORDEX regional climate models (RCMs) under RCP4.5 and RCP8.5 scenarios for drought projection. The efficiency of IAI and fusion models was evaluated using statistical error metrics, including the coefficient of determination (R2), Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE). The proposed methodology was employed in the Gavkhooni basin of Iran, and results showed that the RF model with the lowest estimation error (RMSE of 0.391 and R-2 of 0.810) had performed well compared to all other models. Finally, the resilience, vulnerability, and frequency of probability metrics indicated that the 12-month time scale of drought affected the basin more severely than other time scales.

Automated summary

This study used a fusion-based framework for meteorological drought modeling, combining remote sensing data and climate change scenarios. Through clustering and artificial intelligence models, a more accurate evaluation of drought was achieved. Results showed that the Random Forest model performed the best in predicting drought in the Gavkhooni basin in Iran.