Meteorological Drought Monitoring Model Based on Multi-source Data and Stacking Ensemble Learning

Abstract

As a complex natural disaster exhibiting marked spatiotemporal heterogeneity, drought threatens socio-economic systems and ecosystem resilience through its frequent occurrence and cumulative destructive impacts. Therefore, accurate monitoring of drought events is of great practical significance. Focusing on Shaanxi Province as the research area and a comprehensive feature variable system was established by integrating vegetation, surface, and climate multi-dimensional drought factors. Using the optimal meteorological drought index as the target variable, the stacked ensemble drought index (SEDI) for Shaanxi Province during 2003—2020 was constructed based on Stacking ensemble learning and multiple machine learning algorithms, and its applicability in meteorological drought monitoring was evaluated. The results demonstrated that the monthly-scale variation trends of the meteorological drought composite index (MCI), standardized precipitation index (SPI), and standardized precipitation evapotranspiration index (SPEI) were generally consistent. However, MCI exhibited high accuracy and sensitivity in identifying drought events, thus it was selected as the target variable for the meteorological drought monitoring model. Among the three ensemble models and five single models, the ensemble model XGB-all, constructed based on XGBoost, demonstrated the best monitoring performance across different regions of Shaanxi Province, with coefficient of determination (R2) ranging from 0.934 to 0.945 and root mean square error (RMSE) ranging from 0.208 to 0.256. During 2003—2020, SEDI and MCI showed drought level matching rates of 87.04%, 83.80%, and 85.65% at Yulin, Qindu, and Shiquan stations respectively, with highly consistent drought trends and simulated R2 values all exceeded 0.91, indicating SEDI’s effectiveness in identifying drought types and variation trends across different stations. Validation through two drought events (spring 2005 and summer 2015) confirmed SEDI’s strong applicability for regional-scale drought monitoring, exhibiting high consistency with MCI in spatial distribution characteristics and similarity in proportions of different drought severity levels, effectively reflecting spatiotemporal evolution patterns of drought processes. Spatial autocorrelation analysis demonstrated significant spatial clustering of meteorological drought in Shaanxi Province, with a global Moran’s I of 0.69 (Z-score=3.58, P<0.001). High-high clusters were predominantly distributed in the southwestern Guanzhong Plain and southern Shaanxi regions, corresponding to areas with relatively lower drought frequency and intensity. Conversely, low-low clusters were concentrated in the northeastern Guanzhong Plain and northern Shaanxi regions, which exhibited high drought occurrence rates and severity. This finding can provide scientific guidance for ecological environment assessment, drought monitoring and early warning systems.

Keywords:drought monitoring;Stacking ensemble learning;machine learning;remote sensing;spatial autocorrelation;Shaanxi Province