On the Utility of Soil Moisture for Monitoring and Prediction of Compound Hot and Dry Extremes in India

Abstract

The increasing frequency of compound hot and dry extremes (CHDEs) across India poses serious risks to agriculture and ecosystems. However, real-time monitoring and prediction of CHDEs in India have been lacking. Here, we examine the effectiveness of soil moisture (SM) and evaporative stress ratio (ESR) in capturing dry extremes during the summer monsoon (June–September) and non-monsoon (October–May) seasons. Indicators solely based on soil moisture or ESR fail to capture dry extremes across the seasons and regions in India. However, a joint indicator based on soil moisture and (ESR) effectively captures dry extremes during the monsoon and non-monsoon seasons. We developed a catalog of CHDEs in India using the Standardized Hot and Drought Index (SHDI) for the 1979–2020 period. Most CHDEs in India occur during the summer monsoon season, driven by precipitation deficit and increased vapor pressure deficit (VPD). CHDEs cause vegetation stress as captured by solar-induced fluorescence (SIF) and the normalized difference vegetation index (NDVI). However, SIF responds more quickly to CHDEs than NDVI during the summer monsoon and non-monsoon seasons. We used SHDI and NDVI to examine their utility for near-real-time monitoring and predicting CHDEs in India. CHDEs in India can be predicted 2 weeks in advance using soil moisture, ESR, and temperature based on the Extended Range Forecasting System (ERFS), highlighting the potential of integrating observations and forecasts for monitoring and prediction.