Publications related to the GRACE Missions (no abstracts)

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A non-stationary downscaling and gap-filling approach for GRACE/GRACE-FO data under climatic and anthropogenic influences

Mousavimehr, Seyed Mojtaba and Kavianpour, Mohammad Reza, 2025. A non-stationary downscaling and gap-filling approach for GRACE/GRACE-FO data under climatic and anthropogenic influences. Applied Water Science, 15(5):91, doi:10.1007/s13201-025-02427-z.

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@ARTICLE{2025ApWS...15...91M,
       author = {{Mousavimehr}, Seyed Mojtaba and {Kavianpour}, Mohammad Reza},
        title = "{A non-stationary downscaling and gap-filling approach for GRACE/GRACE-FO data under climatic and anthropogenic influences}",
      journal = {Applied Water Science},
     keywords = {Groundwater level, GRACE, Machine learning, Non-stationary time series, Downscaling, Hodrick{\textendash}Prescott filter, Earth Sciences, Physical Geography and Environmental Geoscience},
         year = 2025,
        month = may,
       volume = {15},
       number = {5},
          eid = {91},
        pages = {91},
     abstract = "{Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On
        (GRACE-FO) are being increasingly used as valuable data sources
        for hydrological monitoring. However, their coarse spatial
        resolution is considered as a limitation for regional studies,
        especially in areas with remarkable hydroclimate variability. In
        this study, a novel approach is presented for downscaling, and
        gap filling of terrestrial water storage (TWS) in Tehran
        province, Iran. Non-stationarity in the GRACE/GRACE-FO derived
        TWS is a significant challenge for predictive models. In this
        regard, the Hodrick{\textendash}Prescott filter was adopted to
        detrend the TWS data. Afterward, several machine learning and
        deep learning techniques are employed for TWS prediction using
        Global Land Data Assimilation System and the fifth-generation
        ECMWF reanalysis (ERA5) datasets. The methodology is employed
        for bridging the gap between GRACE and GRACE-FO as well.
        Subsequently, the models are trained with different combinations
        of input variables and their performance is evaluated against
        the actual values. In parallel, a separate regression model
        based on the temporal index of the sample is developed for trend
        estimation and highlighting the role of anthropogenic
        activities. The proposed methodology is employed for bridging
        the gap between GRACE and GRACE-FO as well. The models with the
        highest accuracy are fed by input data with a spatial resolution
        of 0.25{\textdegree} {\texttimes} 0.25{\textdegree} to obtain
        fine-resolution TWS. Finally, the downscaled TWS derived from
        the predictive model is applied to calculate groundwater storage
        (GWS). The monthly TWS prediction results exhibit a strong
        correlation (CC = 0.93) and a low error (RMSE = 4.75 cm),
        underscoring the effectiveness of the proposed approach. TWS and
        GWS computations reveal rapid declines in groundwater-level
        prevailing by anthropogenic factors which exacerbate water
        crisis issues and environmental problems in the study area.}",
          doi = {10.1007/s13201-025-02427-z},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2025ApWS...15...91M},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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