Publications related to the GRACE Missions (no abstracts)

• Sorted by Date • Sorted by Last Name of First Author •

A systematic review of the NASA Land Information System (LIS): Two decades of advancements in hydrological modeling, data assimilation, and operational earth system applications

Marshall, Sebastian R. O., Tran, Thanh–Nhan–Duc, and Lakshmi, Venkataraman, 2026. A systematic review of the NASA Land Information System (LIS): Two decades of advancements in hydrological modeling, data assimilation, and operational earth system applications. Journal of Hydrology, 666:134784, doi:10.1016/j.jhydrol.2025.134784.

Downloads

• from the NASA Astrophysics Data System  • by the DOI System  •

@ARTICLE{2026JHyd..66634784M,
       author = {{Marshall}, Sebastian R.~O. and {Tran}, Thanh-Nhan-Duc and {Lakshmi}, Venkataraman},
        title = "{A systematic review of the NASA Land Information System (LIS): Two decades of advancements in hydrological modeling, data assimilation, and operational earth system applications}",
      journal = {Journal of Hydrology},
     keywords = {Systematic review, NASA LIS, Land Data Assimilation (LDA), Land Surface Modeling (LSM), Hydrological modeling, Earth system science, SMAP, Data assimilation},
         year = 2026,
        month = feb,
       volume = {666},
          eid = {134784},
        pages = {134784},
     abstract = "{The NASA Land Information System (LIS) is a high-performance, open-
        source software framework integrating diverse land surface
        models (LSMs), observational datasets, and advanced data
        assimilation (DA) techniques. This systematic literature review
        (SLR) synthesizes and critically evaluates two decades of
        scientific advancements enabled by LIS, quantifying its impact
        on Earth system science. Key findings reveal: (1) LIS-DA
        consistently generates enhanced estimates of land surface
        conditions (e.g., soil moisture, snow, TWS) by systematically
        assimilating satellite observations from missions including
        SMAP, GRACE, and ASO. This process provides quantifiable error
        reductions, with studies showing, for example, over 60\%
        reduction in RMSE for snow estimates and KGE skill scores for
        streamflow improving from 0.04 to 0.44. (2) Coupling LIS with
        atmospheric models (e.g., WRF) and advanced hydrological routing
        models (e.g., HyMAP) demonstrably improves the skill of regional
        weather and flood forecasts by providing physically consistent,
        observationally constrained initial conditions. (3) LIS has
        successfully transitioned from a research tool to a proven
        operational asset (R2O), becoming the backbone for critical
        decision-support systems like the Famine Early Warning Systems
        Network (FLDAS) and the U.S. Air Force's global snow analysis.
        The review documents a clear methodological trend toward
        multivariate DA (MVDA) to address complex human-natural system
        interactions, such as flash droughts and irrigation impacts. By
        correcting prior mischaracterizations and providing a structured
        synthesis, this review identifies persistent limitations and
        outlines key future research directions, establishing LIS as a
        cornerstone of modern hydrological modeling.}",
          doi = {10.1016/j.jhydrol.2025.134784},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2026JHyd..66634784M},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

Generated by bib2html_grace.pl (written by Patrick Riley modified for this page by Volker Klemann) on Mon Feb 16, 2026 23:51:58

---

Mon Feb 16, F. Flechtner