Publications related to the GRACE Missions (no abstracts)

Sorted by DateSorted by Last Name of First Author

Developing a Long Short-Term Memory (LSTM)-Based Model for Reconstructing Terrestrial Water Storage Variations from 1982 to 2016 in the Tarim River Basin, Northwest China

Wang, Fei, Chen, Yaning, Li, Zhi, Fang, Gonghuan, Li, Yupeng, Wang, Xuanxuan, Zhang, Xueqi, and Kayumba, Patient Mindje, 2021. Developing a Long Short-Term Memory (LSTM)-Based Model for Reconstructing Terrestrial Water Storage Variations from 1982 to 2016 in the Tarim River Basin, Northwest China. Remote Sensing, 13(5):889, doi:10.3390/rs13050889.

Downloads

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

BibTeX

@ARTICLE{2021RemS...13..889W,
       author = {{Wang}, Fei and {Chen}, Yaning and {Li}, Zhi and {Fang}, Gonghuan and {Li}, Yupeng and {Wang}, Xuanxuan and {Zhang}, Xueqi and {Kayumba}, Patient Mindje},
        title = "{Developing a Long Short-Term Memory (LSTM)-Based Model for Reconstructing Terrestrial Water Storage Variations from 1982 to 2016 in the Tarim River Basin, Northwest China}",
      journal = {Remote Sensing},
     keywords = {terrestrial water storage, Tarim River Basin, LSTM model, climate change},
         year = 2021,
        month = feb,
       volume = {13},
       number = {5},
          eid = {889},
        pages = {889},
     abstract = "{Estimating Terrestrial Water Storage (TWS) not only helps to provide a
        comprehensive insight into water resource variability and the
        hydrological cycle but also for better water resource
        management. In the current research, Gravity Recovery And
        Climate Experiment (GRACE) data are combined with the available
        hydrological data to reconstruct a longer record of Terrestrial
        Water Storage Anomalies (TWSA) prior to 2003 of the Tarim River
        Basin (TRB), based on a Long Short-Term Memory (LSTM) model. We
        found that the TWSA generated by LSTM using soil moisture,
        evapotranspiration, precipitation, and temperature best matches
        the GRACE-derived TWSA, with a high correlation coefficient (r)
        of 0.922 and a Normalized Root Mean Square Error (NRMSE) of
        0.107 during the period 2003-2012. These results show that the
        LSTM model is an available and feasible method to generate TWSA.
        Further, the TWSA reveals a significant fluctuating downward
        trend (p < 0.001), with an average decline rate of 0.03 mm/month
        during the period 1982-2016 in the TRB. Moreover, the TWSA
        amount in the north of the TRB was less than that in the south
        of the basin. Overall, our findings unveiled that the LSTM model
        and GRACE data can be combined effectively to analyze the long-
        term TWSA in large-scale basins with limited hydrological data.}",
          doi = {10.3390/rs13050889},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2021RemS...13..889W},
      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 Thu Aug 14, 2025 17:55:11

GRACE-FO

Thu Aug 14, F. Flechtner