A review of current best practices and future directions in assimilating GRACE/-FO terrestrial water storage data into numerical models

Le résumé fourni par la source

Water cycle reanalyses, generated by integrating observations into hydrological and land surface models, provide long-term and consistent estimates of key water cycle components. Reanalyses are essential to understand hydrological variability, extreme events such as droughts and floods, and to improve water resource management. Over the past two decades, the assimilation of terrestrial water storage anomaly data from the GRACE and GRACE Follow-On (GRACE/-FO) missions has significantly enhanced these reanalyses, as GRACE/-FO observations uniquely constrain total water storage variability across all terrestrial compartments. Incorporating GRACE/-FO data has led to major advances in representing trends in key hydrological variables, climate-driven changes in the water cycle, and anthropogenic influences such as irrigation-induced groundwater depletion – factors often poorly captured in models. With processing pipelines now being developed for low-latency short-term data products from the upcoming next-generation gravity missions, we expect that low-latency periodically updated reanalyses and analyses from assimilation will become more relevant. However, challenges remain, particularly in resolving mismatches in spatial and temporal resolution between GRACE/-FO observations and high-resolution models, and there is no consensus yet on the optimal approach for assimilating GRACE/-FO data. In light of the upcoming launches of next-generation gravity missions and the development of increasingly sophisticated Earth system modeling frameworks, this review synthesizes insights from approximately 60 GRACE/-FO data assimilation studies in an attempt to converge to best practices. The review reveals that the most effective assimilation strategies leverage (robust modifications of) the classical ensemble Kalman filter and localization techniques, explicitly account for correlated observation errors, and address biases contained in the observations as well as those arising from model perturbations. Unmodeled processes must be carefully handled through signal separation, multi-source assimilation, or removal prior to assimilation. Future directions include developing low-latency products for near-real-time assimilation, integrating enhanced and combined satellite observations, and employing machine-learning approaches for downscaling and hybrid assimilation. Collectively, these strategies provide a pathway toward more accurate, physically consistent, and operationally useful water cycle reanalyses.

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