Publications related to the GRACE Missions (no abstracts)

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Reconstructing Long-Term, High-Resolution Groundwater Storage Changes in the Songhua River Basin Using Supplemented GRACE and GRACE-FO Data

Liu, Chuanqi, Zhang, Zhijie, Xu, Chi, and Zhang, Wanchang, 2024. Reconstructing Long-Term, High-Resolution Groundwater Storage Changes in the Songhua River Basin Using Supplemented GRACE and GRACE-FO Data. Remote Sensing, 16(23):4566, doi:10.3390/rs16234566.

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@ARTICLE{2024RemS...16.4566L,
       author = {{Liu}, Chuanqi and {Zhang}, Zhijie and {Xu}, Chi and {Zhang}, Wanchang},
        title = "{Reconstructing Long-Term, High-Resolution Groundwater Storage Changes in the Songhua River Basin Using Supplemented GRACE and GRACE-FO Data}",
      journal = {Remote Sensing},
     keywords = {GRACE, GWSA, downscaling method, Songhua River Basin, ESSI-3 model},
         year = 2024,
        month = dec,
       volume = {16},
       number = {23},
          eid = {4566},
        pages = {4566},
     abstract = "{The Gravity Recovery and Climate Experiment (GRACE) enables large-scale
        monitoring of terrestrial water storage changes, significantly
        contributing to hydrology and related fields. However, the
        coarse resolution of groundwater storage anomaly (GWSA) data
        limits local-scale research utilizing GRACE and GRACE-FO
        missions. In this study, we develop a regional downscaling model
        based on the linear regression relationship between GWSA and
        environmental variables, reducing the grid resolution of GWSA
        obtained from GRACE from approximately 25 km to 1 km. First, we
        estimate the missing values of monthly continuous terrestrial
        water storage anomaly (TWSA) for the period from 2003 to 2020
        using interpolated multi-channel singular spectrum analysis
        (IMSSA). Next, we apply the water balance equation to separate
        GWSA from TWSA, which is provided jointly by the Global Land
        Data Assimilation System (GLDAS) and the distributed
        ecohydrological model ESSI-3. We then employ a partial least
        squares regression (PLSR) model to identify the most significant
        environmental variables related to GWSA. Precipitation (Prec),
        normalized difference vegetation index (NDVI), and actual
        evapotranspiration (AET), with variable importance in projection
        (VIP) values greater than 1.0, are recognized as effective
        variables for reconstructing long-term, high-resolution
        groundwater storage changes. Finally, we downscale and
        reconstruct the long-term (2003{\textendash}2020), high-
        resolution (1 km {\texttimes} 1 km) monthly GWSA in the Songhua
        River Basin using fused and supplemented GRACE/GRACE-FO data,
        employing either geographically weighted regression (GWR) or
        random forest (RF) models. The results demonstrate superior
        performance of the GWR model (CC = 0.995, NSE = 0.989, RMSE =
        2.505 mm) compared to the RF model in downscaling. The
        downscaled GWSA in the Songhua River Basin not only achieves
        high spatial resolution but also exhibits improved accuracy when
        compared to in situ groundwater observation records. This
        research enhances understanding of spatiotemporal variations in
        regional groundwater due to local agricultural and industrial
        water use, providing a scientific basis for regional water
        resource management.}",
          doi = {10.3390/rs16234566},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2024RemS...16.4566L},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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