GRACE and GRACE-FO Related Publications (no abstracts)

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Remote Sensing Estimation of Shallow and Deep Aquifer Response to Precipitation-Based Recharge Through Downscaling

Kalu, Ikechukwu, Ndehedehe, Christopher E., Ferreira, Vagner G., Janardhanan, Sreekanth, Currell, Matthew, Crosbie, Russell S., and Kennard, Mark J., 2024. Remote Sensing Estimation of Shallow and Deep Aquifer Response to Precipitation-Based Recharge Through Downscaling. Water Resources Research, 60(12):2024WR037360, doi:10.1029/2024WR037360.

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BibTeX

@ARTICLE{2024WRR....6037360K,
       author = {{Kalu}, Ikechukwu and {Ndehedehe}, Christopher E. and {Ferreira}, Vagner G. and {Janardhanan}, Sreekanth and {Currell}, Matthew and {Crosbie}, Russell S. and {Kennard}, Mark J.},
        title = "{Remote Sensing Estimation of Shallow and Deep Aquifer Response to Precipitation-Based Recharge Through Downscaling}",
      journal = {Water Resources Research},
     keywords = {groundwater, downscaling, GRACE, Perth basin, Gnangara basin, recharge},
         year = 2024,
        month = dec,
       volume = {60},
       number = {12},
        pages = {2024WR037360},
     abstract = "{The Gnangara groundwater system is a highly productive water resource in
        southwestern Australia. However, it is considered one of the
        most vulnerable groundwater systems to climate change, due to
        consistent declines in precipitation and recharge, and regional
        climate models project further declines into the future. This
        study introduces a new framework underpinned by machine learning
        techniques to provide reliable estimates of precipitation-based
        recharge over the whole Perth Basin (including the Gnangara
        system). By combining estimates of baseflow, groundwater
        evaporation, and extraction, groundwater recharge was estimated
        over the Perth (testing site) and Gnangara (calibration site)
        systems using downscaled Groundwater Storage Anomalies (GWSA)
        from the Gravity Recovery and Climate Experiment (GRACE)
        mission. The random forest regression (RFR) model was used to
        downscale the spatial resolution of GRACE to 0.05{\textdegree}
        (approx. 5 km), providing estimable signals over the relatively
        small calibration site ({\ensuremath{\sim}}2,200 km$^{2}$) in
        order to discern any meaningful signals from the original GRACE
        resolution. Our study reveals that downscaled signals from GRACE
        can be used to provide precipitation-based recharge estimates
        for groundwater systems accurately. However, the growing impacts
        of climate change, which has led to sporadic precipitation
        patterns over Western Australia, can limit the efficiency of
        satellite remote sensing methods in estimating recharge,
        especially in deep and complex aquifers.}",
          doi = {10.1029/2024WR037360},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2024WRR....6037360K},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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