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The Middle East and North Africa (MENA) region has experienced more frequent and severe drought events in recent decades, leading to increasingly pressing concerns over already strained food and water security. An effective drought monitoring and early warning system is thus critical to support risk mitigation and management by countries in the region. Here we investigate the potential for assimilation of leaf area index (LAI) and soil moisture observations to improve representation of the overall hydrological and carbon cycles and drought by an advanced land surface model. The results reveal that assimilating soil moisture does not meaningfully improve model representation of the hydrological and biospheric processes for this region, but rather it degrades simulation of interannual variation of evapotranspiration (ET) and carbon fluxes, mainly due to model weaknesses in representing dynamic phenology. However, assimilating LAI leads to greater improvement, especially for transpiration and carbon fluxes, by constraining the timing of simulated vegetation growth response to evolving climate conditions. LAI assimilation also helps to correct for the erroneous interaction between the dynamic phenology and irrigation during summertime, effectively reducing a large positive bias in ET and carbon fluxes. Independently assimilating LAI or soil moisture alters the categorization of drought, with the differences being greater for more severe drought categories. We highlight the vegetation representation in response to changing land use and hydroclimate as one of the key processes to be captured for building a successful drought early warning system for the MENA region. [ABSTRACT FROM AUTHOR]

Though coarse in spatial resolution, the nearly all weather measurements from passive microwave sensors can help in improving the spatio‐temporal coverage of optical and thermal infrared sensors for monitoring vegetation changes on the land surface. This study demonstrates the use of vegetation optical depth (VOD) retrievals from the Soil Moisture Active Passive mission for capturing the vegetation alterations from the recent 2019 to 2020 Australian bushfires and drought. The impact of vegetation disturbances on terrestrial water budget is examined by assimilating the VOD retrievals into a dynamic phenology model. The results demonstrate that assimilating VOD observations lead to improved simulation of evapotranspiration, runoff, and soil moisture states. The study also demonstrates that the vegetation changes from the 2019 to 2020 Australian drought and fires led to significant modifications in the partitioning of evaporative and runoff fluxes, resulting in increased bare soil evaporation, reduced transpiration, and higher runoff. Plain Language Summary: Satellite remote sensing, provides the ability to obtain integrated assessments of vegetation changes on the land surface. In this study, we employ the vegetation optical depth (VOD) retrievals from the NASA SMAP mission for characterizing the recent 2019‐2020 Australian drought and bushfires. The results demonstrate that SMAP VOD retrievals are effective in capturing the vegetation alterations from the unprecedented fires and drought. The study also examined the associated impact of these changes on the hydrological cycle. The removal of vegetation impacted the components of evaporation, by reducing the transpiration and increasing the bare soil evaporation. The results show that the reduction in transpiration likely amplified the amount of precipitation that is converted to runoff. Key Points: Soil Moisture Active Passive vegetation optical depth (VOD) detects features of the 2019–2020 Australian bushfires and droughtVOD assimilation improves land surface states and fluxesThe vegetation changes from the 2019 to 2020 Australian fires and drought causes significant changes in the water budget partitioning [ABSTRACT FROM AUTHOR]