Developing a Composite Drought Indicator Using PCA Integration of CHIRPS Rainfall, Temperature, and Vegetation Health Products for Agricultural Drought Monitoring in New Mexico

Drought indices are important resources for monitoring and warning of drought impacts. However, regions like New Mexico, which are highly vulnerable to drought, as identified by the United States Drought Monitor (USDM), lack a comprehensive drought monitoring system that integrates multiple agrometeorological variables into a single indicator. The purpose of this study is to create a Combined Drought Indicator for New Mexico (CDI-NM) as an indicator tool for use in monitoring historical drought events and measuring its extent across the New Mexico. The CDI-NM was constructed using four key variables: the Vegetation Condition Index (VCI), temperature, Smoothed Normalized Difference Vegetation Index (SMN), and gridded rainfall data. A quantitative approach was used to assign weights to these variables employing Principal Component Analysis (PCA) to produce the CDI-NM. Unlike conventional indices, CDI-NM assigns weights to each variable based on their statistical contributions, allowing the index to adapt to local spatial and temporal drought dynamics. The performance of CDI-NM was evaluated against gridded rainfall data using the 3-month Standardized Precipitation Index (SPI3) over a 17-year period (2003–2019). The results revealed that CDI-NM reliably detected moderate and severe droughts with a strong correlation (R2 > 0.8 and RMSE = 0.10) between both indices for the entire period of analysis. CDI-NM showed negative correlation (r < 0) with crop yield. While promising, the method assumes linear relationships among variables and consistent spatial resolution in the input datasets, which may affect its accuracy under certain local conditions. Based on the results, the CDI-NM stands out as a promising instrument that brings us closer to improved decision-making by stakeholders in the fight against agricultural droughts throughout New Mexico.