Predicting Droughts: A Comparative Study of ARIMAX, LSTM, XGBoost, and Random Forest Models

Droughts pose significant challenges to global water resources, agriculture, and ecosystems, necessitating accurate prediction tools. This study evaluates four advanced modeling techniques-ARIMAX, LSTM, XGBoost, and Random Forest-using a novel dataset from the Gunnison River Basin (1979-2023). Integrating meteorological, hydrological, and oceanic predictors, augmented with lagged variables and seasonal encodings, the framework predicts streamflow six months in advance, a key proxy for drought conditions. LSTM outperformed other models, achieving the lowest RMSE (317.71) and MAE (164.83), excelling in capturing nonlinear and temporal dependencies. ARIMAX provided interpretability but struggled with linearity and overfitting, while XGBoost and Random Forest balanced feature interactions and overfitting reduction. Crossvalidation highlighted dataset limitations, with future spatially resolved data offering opportunities for improved robustness. Key features like precipitation, temperature, and reference evapotranspiration emerged as critical, emphasizing lagged effects and ocean-atmosphere teleconnections, such as those captured by the Multivariate ENSO Index (MEI). This research advances drought prediction by integrating diverse predictors, addressing dataset challenges, and offering actionable insights for resource management and policy development to mitigate the impacts of hydrological extremes.