Developing a Composite Hydrological Drought Index Using the VIC Model: Case Study in Northern Thailand

Hydrological drought indices, while critical for monitoring, are often limited by their reliance on single variables, failing to capture the multidimensional complexity of water scarcity, particularly in data-scarce and climate-sensitive regions. This study addresses this critical gap by introducing a Composite Hydrological Drought Index (CHDI) for a northern watershed in Thailand, a region where drought risk is intensified by climatic shifts and intensive land use. The proposed methodology integrates multiple outputs from the Variable Infiltration Capacity (VIC) hydrological model, including precipitation, runoff, evapotranspiration, baseflow, and soil moisture layers, and employs Principal Component Analysis (PCA) to synthesize the dominant drivers of water-level variability. The first principal component (PC1), which accounted for over 50% of the total variance, served as the basis for the CHDI, and was strongly correlated with precipitation, surface runoff, and surface soil moisture. The performance of CHDI was rigorously evaluated against observed data from eight hydrological stations. The index demonstrated significant predictive skill, with Pearson’s correlation coefficients (R) ranging from 0.49 to 0.79 (p < 0.05), a maximum Nash–Sutcliffe Efficiency (NSE) of 0.63, and F1-scores for drought detection as high as 0.92. It effectively captured seasonal and interannual variability, including the accurate identification of low-flow events reported by the National Hydro Informatics Data Center (NHC). While the CHDI showed robust performance, particularly under high-flow conditions and in drought classification, some limitations were observed in complex or anthropogenically influenced sub-catchments. These findings highlight the potential of CHDI as a reliable and integrative tool for hydrological drought monitoring and for supporting water resource management in data-scarce and climate-sensitive regions.