Advancing Drought Monitoring in India through Land Data Assimilation with CLM5-DART

Drought, a recurring extreme climate event caused by prolonged below-average precipitation, results in significant water deficits and poses a substantial threat to India's economy, which is heavily reliant on agriculture. Despite notable monsoon rainfall, drought remains a persistent annual phenomenon, underscoring the need for accurate estimation and continuous monitoring to mitigate its adverse socio-economic impacts. Real-time drought monitoring, including spatial and temporal characterization, is critical for guiding policymakers and water resource managers in revising strategies, facilitating timely drought assistance programs, and distributing relief funds to affected areas and farmers. In India, drought monitoring faces challenges due to limited in-situ data for critical parameters such as evapotranspiration, soil moisture, runoff, and streamflow. Although satellites offer regular surface observations, their data is limited in spatial and temporal coverage due to orbital revisit cycles. Land Surface Models (LSMs), on the other hand, while offering uniform spatiotemporal estimates, are often hindered by uncertainties from atmospheric forcing and initial conditions. To address these limitations, integrating observations (in-situ/satellite) with LSMs through a Land Data Assimilation System (LDAS) has emerged as a promising solution to improve model accuracy, reduce uncertainties, and increase drought monitoring and forecasting skills. This study integrates the Community Land Model version 5.0 (CLM5) with the Data Assimilation Research Testbed (DART) to establish a robust Land Data Assimilation System (LDAS) framework. Specifically, soil moisture data from the European Space Agency’s (ESA) Climate Change Initiative (CCI) were assimilated to enhance soil moisture (SM) estimation.  The performance and efficacy of soil moisture (SM) estimates derived from the CLM5-DART LDAS were evaluated across India. Results indicate that CLM5 - DART reanalysis outputs significantly improved the representation of SM compared to standalone CLM5 simulations. These improvements were further analyzed for their impacts on key hydrological components, including evapotranspiration, runoff, and drought monitoring capabilities. The findings demonstrate that data assimilation integration substantially enhances the accuracy and resolution of SM estimates, advancing the reliability of real-time drought monitoring and risk management. This research provides a robust framework for improving drought resilience in India, offering valuable insights to support better-informed water resource management strategies and policy decisions.