Systematic modelling errors undermine the application of land data assimilation systems for hydrological and weather forecasting

Authors: Crow, Wade; KIM, HYUNGLOK - Oak Ridge Institute For Science And Education (ORISE); KUMAR, SUJAY - Goddard Space Flight Center

Submitted to: Journal of Hydrometeorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/11/2023
Publication Date: 12/14/2023
Citation: Crow, W.T., Kim, H., Kumar, S. 2023. Systematic modelling errors undermine the application of land data assimilation systems for hydrological and weather forecasting. Journal of Hydrometeorology. 25, 3-26. https://doi.org/10.1175/JHM-D-23-0069.1.
DOI: https://doi.org/10.1175/JHM-D-23-0069.1

Interpretive Summary: Land data assimilation is the process by which land surface model estimates of water states (e.g., soil moisture) and water fluxes (e.g., runoff and evapotranspiration) are improved via the incorporation of state observations. Over the past decade, large improvements have been made in the precision of land surface model state estimates via the assimilation of satellite-based soil moisture information. However, to date, these improvements have not yet been extended into water flux estimates like runoff and evapotranspiration. This is a critical shortcoming for agricultural applications since numerical weather prediction and water-resource forecasting are generally dependent on the improved estimation of such fluxes. Here we demonstrate that this shortcoming is linked to the inability of existing land surface models to accurately describe the relationship between water states and water fluxes and propose strategies for correcting this issue in future land data assimilation systems. This work will eventually lead to the more efficient use of existing satellite-based soil moisture products in agricultural applications.

Technical Abstract: Due to recent advances in the development of land data assimilation systems (LDAS) and the availability of high-quality, satellite-based surface soil moisture (SSM) retrieval products, we now have clear evidence that the assimilation of SSM retrievals, or their proxy, can improve the precision of surface state estimates provided by a land surface model (LSM). However, this clarity does not yet extend to the estimation of LSM surface water fluxes that are key to hydrologic and numerical weather forecasting applications. Here, we hypothesize that a key obstacle to extrapolating realized improvements in water state precision into comparable improvements in water flux accuracy is the presence of water state/water flux coupling strength biases existing in LSMs. To this endTo test this hypothesis, we conduct a series of fraternal-twin data assimilation system experiments where realistic levels of state/flux coupling strength biases - involving both evapotranspiration and runoff - are systematically introduced into an assimilation LSM. Results show that the accuracy of the resulting water flux analysis is sharply reduced by the presence of such bias – even in cases where the precision of water state estimates remain robustly improved. The re-scaling of SSM observations prior to their assimilation (i.e., the most common existing approach for addressing systematic differences between LSMs and assimilated observations) is not always a robust strategy for addressing these errors and can, in certain circumstances, actually degrade water flux accuracy. Overall, results underscore the critical need to assess, and correct for, LSM water state/water flux coupling strength biases in LSMs prior to their application in an LDAS.