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.

Drought is among the costliest natural disasters that affect the economy, food and water security, and socioeconomic well‐being of about 1.4 billion people in India. Despite the profound implications of droughts, the propagation of meteorological to hydrological droughts in India is not examined. Here, we use observations and simulations from a well‐calibrated and evaluated Variable Infiltration Capacity (VIC) model to estimate drought propagation in India. Standardized Precipitation Index (SPI), Standardized Soil Moisture Index (SSMI), and Standardized Streamflow Index (SSI) were estimated for 223 catchments in India to represent meteorological, agricultural, and hydrological droughts, respectively. We estimated drought propagation time for these catchments located in 18 major Indian subcontinental river basins. Internal propagation of hydrological drought was estimated using optimal hydrological Instantaneous Development Speed (IDS) and Instantaneous Recovery Speed (IRS) from onset to the termination. Indus, Sabarmati, and Godavari river basins have higher propagation time of meteorological to hydrological droughts. The high (low) development rate of hydrological drought is followed by the high (low) recovery rate for most of the locations. We find significant influence of Seasonality Index (SI) and Base Flow Index (BFI) on propagation time of meteorological to hydrological droughts in the Indian subcontinental river basins. Overall, understanding of drought propagation, development/recovery speed, and their deriving factors can assist in the management and planning of water resources in India.

Despite the implications of meteorological drought propagation to agricultural, hydrological, and groundwater droughts, the focus of previous studies has been primarily on meteorological droughts in India. We use the well‐calibrated and evaluated Variable Infiltration Capacity‐ Simple Groundwater Model (VIC‐SIMGM) to simulate soil moisture, runoff, and groundwater storage variability in India for the 1951–2016 period. The Integrated Drought Index (IDI) that combines meteorological, agricultural, hydrological, and groundwater droughts was developed for the 1951–2016 period for India. Using a spatial clustering algorithm based on the traditional interpoint distance metric, eight homogeneous clusters based on IDI were identified. The majority of clusters in India experience the onset and termination of droughts during the summer monsoon season (June–September). The analysis of moisture back trajectories using the Hybrid Single‐Particle Lagrangian Integrated Trajectory (HYSPLIT) model showed that the Arabian Sea and Bay of Bengal are the two major moisture sources for the identified clusters in India. We performed the Empirical Orthogonal Function (EOF) and Maximum Covariance Analysis (MCA) using monthly IDI and Sea Surface Temperature (SST) to evaluate the influence of long‐term climate variability on droughts in India. Droughts based on 1‐month IDI that affect a majority of drought clusters are associated with the positive phase of El Nino Southern Oscillations (ENSO) and Indian Ocean Dipole (IOD). On the other hand, drought clusters in the Gangetic Plain and peninsular India are affected by the SST warming over the Indian Ocean. Overall, drought clusters based on IDI, their moisture source, and large‐scale teleconnection can assist in drought management and assessment in India.

Although reservoirs serve as a lifeline for drought mitigation and adaptation, reservoir‐based droughts (RBD) have received limited attention. Especially RBD that incorporate reservoir evaporation have remained overlooked. Here, we develop an Integrated Reservoir Drought Index (IRDI) using a Frank copula by combining the effects of reservoir storage and evaporation rate for 164 global reservoirs. Results show that Africa, South America, and Australia have experienced relatively longer RBD, whereas Asia and Africa have experienced more intense RBD. Furthermore, when significant (p‐value < 0.05) trends in inflow and IRDI have shown opposite signs at a given reservoir, it is implied that human influence was the dominant driver of the long‐term changes in RBD. We found that RBD was driven by climate in 71% of the 38 reservoirs examined and by human activities in the remaining 29% of reservoirs. Our study provides reservoir specific drought outlook that has significant environmental and sustainability implications.

Rapid groundwater depletion due to anthropogenic pumping for irrigation poses enormous challenges on socio‐economic, agricultural, and financial spheres of India. However, the role of groundwater depletion on flood potential in India remains unexplored. We examine the role of changes in terrestrial water and groundwater storage on flood potential using in situ and Gravity Recovery and Climate Experiment (GRACE) satellites observations and the simulations from the Variable Infiltration Capacity (VIC) model. Terrestrial Water Storage (TWS) has declined significantly (p value <0.05) with a rate of −0.63 cm yr⁻¹ during 2002–2016 in the Indian subcontinent. Ganga, Brahmaputra, Indus, Brahmani, and Subernarekha river basins experienced a significant decline while Godavari, Mahi, Tapi, and Narmada basins witnessed a substantial increase in TWS during 2002–2016. The relative contribution of TWS on flood potential is higher than precipitation for the majority of the subcontinental river basins. The significant increase/decline in TWS in the subcontinental river basins is driven by the changes in groundwater storage either due to groundwater pumping or natural variability in climate. A significant decrease (increase) in the flood potential in Ganga, Brahmaputra, and Indus (Godavari, Narmada, and Tapi) is linked to the changes in groundwater storage, which shows the influence of groundwater storage variability beyond water and food security in the region.

Global reservoir information can not only benefit local water management but can also improve our understanding of the hydrological cycle. This information includes water area, elevation, and storage; evaporation rate and volume values; and other characteristics. However, operational wall-to-wall reservoir storage and evaporation monitoring information is lacking on a global scale. Here we introduce NASA’s new MODIS/VIIRS Global Water Reservoir product suite based on moderate resolution remote sensing data—the Moderate Resolution Imaging Spectroradiometer (MODIS), and the Visible Infrared Imaging Radiometer Suite (VIIRS). This product consists of 8-day (MxD28C2 and VNP28C2) and monthly (MxD28C3 and VNP28C3) measurements for 164 large reservoirs (MxD stands for the product from both Terra (MOD) or Aqua (MYD) satellites). The 8-day product provides area, elevation, and storage values, which were generated by first extracting water areas from surface reflectance data and then applying the area estimations to the pre-established Area–Elevation (A–E) relationships. These values were then further aggregated to monthly, with the evaporation rate and volume information added. The evaporation rate and volume values were calculated after the Lake Temperature and Evaporation Model (LTEM) using MODIS/VIIRS land surface temperature product and meteorological data from the Global Land Data Assimilation System (GLDAS). Validation results show that the 250 m area classifications from MODIS agree well with the high-resolution classifications from Landsat (R² = 0.99). Validation of elevation and storage products for twelve Indian reservoirs show good agreement in terms of R² values (0.71–0.96 for elevation, and 0.79–0.96 for storage) and normalized root-mean-square error (NRMSE) values (5.08–19.34% for elevation, and 6.39–18.77% for storage). The evaporation rate results for two reservoirs (Lake Nasser and Lake Mead) agree well with in situ measurements (R² values of 0.61 and 0.66, and NRMSE values of 16.25% and 21.76%). Furthermore, preliminary results from the VIIRS reservoir product have shown good consistency with the MODIS based product, confirming the continuity of this 20-year product suite. This new global water reservoir product suite can provide valuable information with regard to water-sources-related studies, applications, management, and hydrological modeling and change analysis such as drought monitoring.