资源环境科技发展态势分析平台

Drought monitoring and declaration in India are challenging due to the requirement of multiple drought indices representing meteorological, hydrological, and agricultural droughts that are often not available in near real-time. In addition, the current drought monitoring efforts do not consider groundwater storage variability. To overcome this, we develop an Integrated Drought Index (IDI) that combines the response of meteorological, hydrological, and agricultural droughts and accounts for groundwater storage. We use the Gaussian copula to integrate the 12-month Standardized Precipitation Index (SPI), 4-month Standardized Runoff Index (SRI), 1-month Standardized Soil moisture Index (SSI), and 1-month Standardized Groundwater Index (SGI) to develop IDI. Hydrologic variables (total runoff, soil moisture, and groundwater) required in IDI were simulated using the Variable Infiltration Capacity (VIC) with SIMple Groundwater Model (VIC-SIMGM). We evaluated IDI against the Drought Severity Index (DSI), terrestrial and groundwater storage anomalies from the Gravity Recovery and Climate Experiment (GRACE) satellites, groundwater well, and streamflow anomalies. Moreover, we identify the three major droughts with the highest severity (based on IDI) that occurred in 1965, 1987, and 2002 in the Sabarmati river basin. The three most severe droughts occurred in 1966, 1979, and 2010 in the Brahmani basin. Notwithstanding the large intermodel uncertainty, which arises primarily from precipitation projections, the drought frequency based on IDI is projected to decline in Sabarmati while it increases in Brahmani basin under the warming climate. Our results show that IDI can be effectively used for drought monitoring and assessment under retrospective and future climate in India.