Publications related to the GRACE Missions (no abstracts)

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

Skills in sub–seasonal to seasonal terrestrial water storage forecasting: insights from the FEWS NET land data assimilation system

Li, Bailing, Hazra, Abheera, McNally, Amy, Slinski, Kimberly, Shukla, Shraddhanand, and Anderson, Weston, 2026. Skills in sub–seasonal to seasonal terrestrial water storage forecasting: insights from the FEWS NET land data assimilation system. Hydrology and Earth System Sciences Discussions, 30(4):1097–1115, doi:10.5194/hess-30-1097-2026.

Downloads

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

@ARTICLE{2026HESSD..30.1097L,
       author = {{Li}, Bailing and {Hazra}, Abheera and {McNally}, Amy and {Slinski}, Kimberly and {Shukla}, Shraddhanand and {Anderson}, Weston},
        title = "{Skills in sub-seasonal to seasonal terrestrial water storage forecasting: insights from the FEWS NET land data assimilation system}",
      journal = {Hydrology and Earth System Sciences Discussions},
         year = 2026,
        month = feb,
       volume = {30},
       number = {4},
        pages = {1097-1115},
     abstract = "{Accurate prediction of terrestrial water storage (TWS), the sum of soil
        moisture, groundwater, snow/ice, and surface water, is critical
        for informing disaster responses. Here we evaluated subseasonal
        to seasonal (S2S) TWS forecasts produced by the Famine Early
        Warning Systems Network (FEWS NET) land data assimilation system
        (FLDAS) over Africa using observations from the Gravity Recovery
        and Climate Experiment (GRACE) and its Follow-On (GRACE/FO)
        mission. FLDAS consists of two advanced land surface models,
        Noah-MP and the NASA Catchment Land Surface Model (CLSM), both
        of which simulate key TWS components including groundwater.
        Results show that CLSM generally outperformed Noah-MP, with
        relative operating characteristics scores exceeding 0.6 (the
        threshold for predictive skill) for tercile forecasts over >50
        \% of the study domain across the 1─6 months lead times, and
        stronger correlations with GRACE/FO data. The superior
        performance of CLSM is largely attributed to its reanalysis-
        based initial conditions, which more accurately captured
        interannual variability observed in GRACE/FO observations
        (correlation of 0.72 vs 0.56 for Noah-MP for domain averaged
        TWS). CLSM also simulates strong TWS temporal variability and
        thus temporal persistence, enabling skillful initial conditions
        to propagate across forecast lead times. Accurate representation
        of interannual variability is essential for S2S forecasts
        because TWS is a long memory process, and interannual
        variability also directly affects climatology used to determine
        anomalies. Although persistence provides a source of
        predictability, this study shows that inaccurate persistence,
        such as that associated with anthropogenic trends and
        misrepresented precipitation variability, can degrade forecast
        skill. TWS forecasts from both models are also highly sensitive
        to precipitation interannual variability, achieving higher
        forecast skill when driven by precipitation forecasts with lower
        interannual variability. These findings underscore strong
        impacts of model physics and the critical role of independent
        observations such as GRACE/FO for evaluating and improving TWS
        forecasts.}",
          doi = {10.5194/hess-30-1097-2026},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2026HESSD..30.1097L},
      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 Fri Apr 10, 2026 11:13:49

---

Fri Apr 10, F. Flechtner