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Floods in India are recurring natural disasters resulting from extreme precipitation during the summer monsoon season (June–September). The recent flood in North India in July 2023 caused substantial damage to lives, agriculture, and infrastructure. However, what led to the 2023 North India flood and the role of atmospheric and land drivers still need to be examined. Using in situ observations, satellite data, and ERA5 reanalysis combined with hydrological and hydrodynamical modeling, we examine the role of land and atmospheric drivers in flood occurrence and its impacts. Extreme precipitation in a large region during 7–10 July 2023 created favorable conditions for the flood in the hilly terrains and plains of north India. More than 300 mm of precipitation fell in just 4 days, which was eight times higher than the long‐term average (2001–2022). Anomalously high moisture transport over northern India was recorded on 7 July 2023, making atmospheric conditions favorable for intense landfall. Increased column water vapor and specific humidity at different pressure levels confirmed the continuous moisture presence before the extreme rainfall that caused floods in northern India from 7 to 12 July 2023. Atmospheric and land (high antecedent soil moisture) conditions contributed to a more than 200% rise in streamflow at several gauge stations. Satellite‐based flood extent shows a considerable flood inundation that caused damage in the Sutlej and Yamuna River basins. Our findings highlight the crucial role of the favorable land and atmospheric conditions that caused floods and flash floods in north India in July 2023. Plain Language Summary: In July 2023, North India experienced a severe flood that caused significant damage to lives, agriculture, and infrastructure. However, the exact causes of this flood have yet to be examined. Using in situ, satellite, and reanalysis data, we examined the drivers of the flood. Favorable atmospheric and land conditions created a unique situation that led to a significant flood in north India. For instance, extreme precipitation during 7–10 July enhanced antecedent soil moisture conditions in the hilly and plain regions. Anomalously high moisture transport caused intense rainfall, which, combined with high soil moisture, produced high runoff and streamflow conditions. Flood inundation caused damage to the Sutlaj and Yamuna river basins. Our findings show the need to monitor soil moisture and atmospheric processes for early warning of floods in hilly regions. Key Points: The flood in North India in July 2023 caused substantial damage to lives, agriculture, and infrastructureAnomalously high moisture transport over northern India created atmospheric conditions favorable for intense landfallHigh antecedent soil moisture and extreme precipitation caused the north India flood in 2023 [ABSTRACT FROM AUTHOR]

Floods are among India's most frequently occurring natural disasters, which disrupt all aspects of socio-economic well-being. A large population is affected by floods, which leave their footprints through human mortality, migration, and damage to agriculture and infrastructure, during almost every summer monsoon season in India. Despite the massive imprints of floods, sub-basin level flood risk assessment is still in its infancy and requires advancements. Using hydrological and hydrodynamic models, we reconstructed sub-basin level observed floods for the 1901–2020 period. Our modeling framework includes the influence of 51 major reservoirs that affect flow variability and flood inundation. Sub-basins in the Ganga and Brahmaputra river basins witnessed substantial flood inundation extent during the worst flood in the observational record. Major floods in the sub-basins of the Ganga and Brahmaputra occur during the late summer monsoon season (August–September). While the Beas, Brahmani, upper Satluj, upper Godavari, middle and lower Krishna, and Vashishti are among the sub-basins influenced by the presence of dams on downstream flood dynamics, the Beas, Brahmani, Ravi, and lower Satluj sub-basins are the most impacted by floods and the presence of dams. Bhagirathi, Gandak, Kosi, lower Brahmaputra, and Ghaghara are India's sub-basins with the highest flood risk. Our findings have implications for flood risk assessment and mitigation in India. [ABSTRACT FROM AUTHOR]

Floods are among India's most frequently occurring natural disasters, which disrupt all aspects of socio-economic well-being. A large population is affected by floods during almost every summer monsoon season in India, leaving its footprint through human mortality, migration, and damage to agriculture and infrastructure. Despite the massive imprints of floods, sub-basin level flood risk assessment is still in its infancy and needs to be improved. Using hydrological and hydrodynamical models, we reconstructed sub-basin level observed floods for the 1901-2020 period. Our modelling framework includes the influence of 51 major reservoirs that affect flow variability and flood inundation. Sub-basins in the Ganga and Brahmaputra River basins witnessed the greatest flood extent during the worst flood in the observational record. Major floods in the sub-basins of the Ganga and Brahmaputra occur during the late summer monsoon season (August-September). Beas, Brahmani, upper Satluj, Upper Godavari, Middle and Lower Krishna, and Vashishti sub-basins are among the most influenced by the dams, while Beas, Brahmani, Ravi, and Lower Satluj are among the most impacted by floods and the presence of dams. Bhagirathi, Gandak, Kosi, lower Brahmaputra, and Ghaghara are India's sub-basins with the highest flood risk. Our findings have implications for flood mitigation in India. [ABSTRACT FROM AUTHOR]

Developing an ensemble hydrologic prediction system is essential for reservoir operations and flood early warning. However, efforts to build hydrologic ensemble prediction systems considering the influence of reservoirs have been lacking in India. We examine the potential of the Extended Range Forecast System (ERFS, 16 ensemble members) and Global Ensemble Forecast System (GEFS, 21 ensemble members) forecast for streamflow prediction in India using the Narmada River basin as a testbed. We use the Variable Infiltration Capacity (VIC) with reservoir operations (VIC-Res) scheme to simulate the daily river flow at four locations in the Narmada basin. We examined the streamflow forecast skills of the ERFS forecast for the period 2003-2018 at 1-32 day lead. We compared the streamflow forecast skills of raw meteorological forecasts from ERFS and GEFS at a 1-10 day lead for the summer monsoon (June-September) 2019-2020. The ERFS forecast underestimated extreme precipitation against the observations compared to the GEFS during the summer monsoon of 2019-2020. However, both the forecast products showed better skills for minimum and maximum temperatures than precipitation. Ensemble streamflow forecast from the GEFS performed better than the ERFS during 2019-2020. The performance of the GEFS based ensemble streamflow forecast declines after five days lead. Overall, the GEFS ensemble streamflow forecast can provide reliable skills at a 1-5 day lead. Our findings provide directions for developing a flood early warning system based on ensemble streamflow prediction considering the influence of reservoirs in India. [ABSTRACT FROM AUTHOR]