Drought prediction across Vietnam using a hybrid approach
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https://doi.org/10.15625/2615-9783/22194Keywords:
Drought, Seasonal Prediction, Vietnam, ANN, Hybrid approachAbstract
Drought is one of the most pervasive and complex natural hazards, significantly impacting ecosystems, agriculture, and communities, particularly in Vietnam. The study constructed a hybrid model to explore the sensitivity of drought forecast over Vietnam, utilizing bias-corrected precipitation and temperature data from regional climate models, RegCM, and clWRF. The resulting 6-month scale Standardized Precipitation Evapotranspiration Index (SPEI-6), is then processed through two different multi-model ensemble approaches: a simple averaging method (ENS) and a more complex artificial neural network (CTL), forming the basis of our two experimental setups. CTL consistently outperformed ENS, demonstrating more substantial drought-predictive skills. CTL effectively captured the spatio-temporal distribution of SPEI-6, showing high accuracy at a 1-month lead time. Its performance is promising, particularly in regions with complex climate patterns like the Central of Vietnam (R4 and R5), though discrepancies in predicting SPEI-6 amplitudes become slightly evident at a 5-month lead time. The geographic extent analysis further supports CTL's strengths in short-term forecasting, highlighting its utility in early warning systems and immediate drought response planning. Nonetheless, the decrease in accuracy at extended lead times underscores the need for model refinement. The study contributes to the growing body of literature on ANN-based drought forecasting, emphasizing the potential and limitations of these models in the context of Vietnam.
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