Integrating Remote Sensing Data with Hydrological Modeling for Drought Analysis

Traditional drought monitoring primarily relies on ground observations, which are often limited in coverage. Consequently, many earlier drought analysis studies were limited to a narrow geographic area and a single drought index. This study aims to use multiple remote sensing parameters to provide a more comprehensive analysis of droughts in the Padiyathalawa catchment area, a dry zone river basin in Sri Lanka. Three satellite-derived indices, namely the Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and Standardized Precipitation (SP), were integrated using the Principal Component Analysis (PCA) technique to derive a Combined Drought Index (CDI). The impact of spatially distributed drought on variations in river flow was further evaluated by employing the Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) rainfall-runoff model, configured as a semi-distributed model with five (5) sub-catchments. The CDI analysis shows dry conditions from June to August and wetter periods influenced by the northeast monsoon. The developed CDI displayed a correlation with streamflow but requires refinement to reflect streamflow dynamics accurately during high rainfall events. While the HEC-HMS model effectively simulated streamflow (NSE = 0.78-0.92), limitations emerged in low-flow simulation, particularly when the discharge was below $0.1 \mathrm{~m}^{3} / \mathrm{sec}$. The drought-prone areas identified by the CDI were further analyzed using the HEC-HMS with a set of hypothetical drought scenarios. It was found that river flow reduces with increased drought severity and its effect reduces when the sub-catchment is further from the catchment outlet. This study highlights the potential of integrating remote sensing data, PCA, and hydrological modeling for drought assessment. The methodology and the results of this study can be used in formulating adaptation measures to reduce drought impacts.