@COMMENT This file was generated by bib2html_grace.pl <https://sourceforge.net/projects/bib2html/> version 0.94
@COMMENT written by Patrick Riley <https://sourceforge.net/users/patstg/>
@COMMENT This file was prepared using the NASA Astrophysics Data System (ADS)
@COMMENT https://ui.adsabs.harvard.edu/
@ARTICLE{2026RemS...18.2217Z,
       author = {{Zheng}, Xiaoling and {Yu}, Yilei and {Jiang}, Jiyi and {Yang}, Lihu and {Wang}, Shiqin},
        title = "{Lagged Response of Groundwater Storage to Extreme Precipitation Using Machine-Learning-Downscaled GRACE Data at the Watershed Scale}",
      journal = {Remote Sensing},
     keywords = {GRACE, extreme precipitation events, lagged response, Baiyangdian Watershed},
         year = 2026,
        month = jul,
       volume = {18},
       number = {13},
          eid = {2217},
        pages = {2217},
     abstract = "{What are the main findings? A LightGBM-based framework successfully
        downscaled GRACE-derived groundwater storage anomalies to 1 km
        resolution with high accuracy, enabling watershed-scale analysis
        in the Baiyangdian Watershed. Groundwater storage showed a long-
        term transition from rapid depletion to recovery after 2020,
        while composite analysis of 11 EPEs showed that the groundwater
        storage response was delayed: {\ensuremath{\Delta}}GWSA was weak
        during the event month, became evident one month after the
        event, and reached its maximum approximately two months after
        the event. A LightGBM-based framework successfully downscaled
        GRACE-derived groundwater storage anomalies to 1 km resolution
        with high accuracy, enabling watershed-scale analysis in the
        Baiyangdian Watershed. Groundwater storage showed a long-term
        transition from rapid depletion to recovery after 2020, while
        composite analysis of 11 EPEs showed that the groundwater
        storage response was delayed: {\ensuremath{\Delta}}GWSA was weak
        during the event month, became evident one month after the
        event, and reached its maximum approximately two months after
        the event. What are the implications of the main findings?
        Machine-learning-downscaled GRACE data can provide an effective
        tool for monitoring basin-scale groundwater dynamics where field
        observations are sparse. Extreme precipitation alone is
        insufficient to reverse groundwater depletion; sustained
        recovery depends on cumulative climatic inputs, antecedent
        groundwater conditions, and human water-management
        interventions. Machine-learning-downscaled GRACE data can
        provide an effective tool for monitoring basin-scale groundwater
        dynamics where field observations are sparse. Extreme
        precipitation alone is insufficient to reverse groundwater
        depletion; sustained recovery depends on cumulative climatic
        inputs, antecedent groundwater conditions, and human water-
        management interventions. Understanding how groundwater storage
        responds to extreme precipitation is essential for assessing
        aquifer resilience under climate variability. In this study, we
        developed a 1 km groundwater storage anomaly (GWSA) dataset for
        the Baiyangdian Watershed from 2002 to 2024 by downscaling GRACE
        observations with a Light Gradient Boosting Machine (LightGBM)
        model. The downscaled GWSA showed good consistency with
        independent hydrological datasets, including GLDAS and
        groundwater-level anomalies. Based on the downscaled product, we
        characterized long-term groundwater changes and quantified GWSA
        responses to extreme precipitation events (EPEs). Groundwater
        storage exhibited three distinct phases: rapid depletion before
        2014 ({\ensuremath{-}}1.35 cm/yr), a slower decline during
        2014--2019 ({\ensuremath{-}}1.04 cm/yr), and marked recovery
        after 2020 (+3.45 cm/yr). Spatially, GWSA generally increased
        from the southwest to the northeast of the watershed. Composite
        analysis of 11 EPEs revealed a delayed groundwater response,
        with the strongest signal occurring approximately two months
        after precipitation. Monthly effective precipitation was more
        closely associated with GWSA recovery than short-duration daily
        precipitation extremes, and the response was stronger in plains
        than in mountainous areas. These findings indicate that EPEs
        provide episodic recharge pulses, while sustained groundwater
        recovery depends on cumulative climatic inputs and human water-
        management influences.}",
          doi = {10.3390/rs18132217},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2026RemS...18.2217Z},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}