GRACE/-FO Data Assimilation for Hydrological Applications: From Model-Specific Solutions Towards Broader Use

Abstract

For 20 years now, GRACE and GRACE-FO data have been integrated into hydrological and land-surface models using data assimilation (DA) approaches. Numerous studies have demonstrated the value of GRACE/-FO DA systems for improving estimates of groundwater evolution, enhancing drought monitoring, and providing more accurate flood potential estimates. However, unlike other observation types such as remotely sensed soil moisture, GRACE/-FO observations are rarely assimilated into hydrological and land-surface models on an operational basis. With the prospect of near-real-time observations from upcoming satellite gravimetry missions, the question of how to make GRACE/-FO DA more broadly applicable is becoming increasingly relevant.Currently, there is no single established method for assimilating GRACE/-FO data into hydrological and land-surface models. While some settings have already reached a certain degree of consensus, for example suitable ensemble-based DA methods and the treatment of GRACE/-FO error covariance matrices, other aspects remain less well established. A key challenge is that GRACE/-FO observes integrated terrestrial water storage, whereas hydrological and land-surface models represent this storage through different components, such as soil moisture, snow, groundwater, and surface water. Assimilating GRACE/-FO data therefore requires determining how the observed TWS information is distributed among the model states. The formulation of the state vector and observation operator plays an important role in this process: the state vector determines which model variables are included in the assimilation, while the observation operator determines how these states are combined to represent the integrated TWS observed by GRACE/-FO. Different formulations of the state vector and observation operator can therefore change how information from the observations is distributed among storage components. Additionally, the representation of model uncertainty can strongly influence the assimilation results. We illustrate these challenges using two different DA systems: one based on the global model WaterGap Global Hydrological Model (WGHM) and one based on the regional high-resolution European land-surface model enCore Community Land Model (eCLM).Our results from eCLM-DA show that the choice of the state vector and the representation of model uncertainty strongly influence the assimilation results. Experiences with WGHM further indicate that some of these challenges are common across modelling systems, while others depend on model structure and resolution. We therefore distinguish between general methodological challenges, model-specific requirements, and computational and operational constraints, and identify areas where further methodological development is needed, including for coupled and multivariate DA systems. These considerations highlight challenges that must be overcome for the broader application of GRACE/-FO DA across modelling systems and regions, including its integration into regional monitoring and early warning systems.