Showing 2,081 - 2,100 results of 18,313 for search 'significant ((((((teer decrease) OR (a decrease))) OR (greatest decrease))) OR (mean decrease))', query time: 0.56s Refine Results
  1. 2081

    Water level changes in the foresight scenarios. by Zihao Duan (17403792)

    Published 2025
    “…Compared to 1930, the water level decreased by 2.6 m by 2023, with 1935 marking the onset of a significant downward trend. …”
  2. 2082

    Water level changes in the Caspian Sea. by Zihao Duan (17403792)

    Published 2025
    “…Compared to 1930, the water level decreased by 2.6 m by 2023, with 1935 marking the onset of a significant downward trend. …”
  3. 2083

    Methods used to extract shoreline changes. by Zihao Duan (17403792)

    Published 2025
    “…Compared to 1930, the water level decreased by 2.6 m by 2023, with 1935 marking the onset of a significant downward trend. …”
  4. 2084

    Morlet wavelet power spectrum of the sea level. by Zihao Duan (17403792)

    Published 2025
    “…Compared to 1930, the water level decreased by 2.6 m by 2023, with 1935 marking the onset of a significant downward trend. …”
  5. 2085

    Coastal area changes during the period 1985–2023. by Zihao Duan (17403792)

    Published 2025
    “…Compared to 1930, the water level decreased by 2.6 m by 2023, with 1935 marking the onset of a significant downward trend. …”
  6. 2086

    Detailed information of the observation datasets. by Weidong Ji (129916)

    Published 2025
    “…On longer time scales (6–24 hours), the score and correlation between ERA5 and observations further increased, while the centered root-mean-square error (CRMSE) and standard deviation decrease. 4) Hourly wind data with a regular spatial distribution in ERA5 reanalysis provides valuable information for further detailed research on meteorology or renewable energy perspectives, but some inherent shortcomings should be considered.…”
  7. 2087

    General technical specification for GW154/6700. by Weidong Ji (129916)

    Published 2025
    “…On longer time scales (6–24 hours), the score and correlation between ERA5 and observations further increased, while the centered root-mean-square error (CRMSE) and standard deviation decrease. 4) Hourly wind data with a regular spatial distribution in ERA5 reanalysis provides valuable information for further detailed research on meteorology or renewable energy perspectives, but some inherent shortcomings should be considered.…”
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