Hydrological Research Letters
Online ISSN : 1882-3416
ISSN-L : 1882-3416
17 巻, 1 号
選択された号の論文の2件中1~2を表示しています
  • Stanley N. Chapasa, Andrew C. Whitaker
    2023 年 17 巻 1 号 p. 1-8
    発行日: 2023年
    公開日: 2023/02/28
    ジャーナル オープンアクセス
    電子付録

    Baseflow is the portion of streamflow derived from delayed subsurface pathways. The baseflow index (BFI) is a ratio of baseflow to total streamflow, and is an important hydrological variable when linking watershed characteristics to baseflow. The ‘smoothed minima’ procedure of baseflow separation was applied to streamflow data (29–67 years) from twenty-six gauges of watersheds in eastern Japan. The Mann-Kendall statistical test and Sen’s slope estimator were used to identify trends and estimate the rate of change in annual and seasonal runoff and BFI per decade at 0.01 and 0.05 significance levels. To the best of our knowledge, this is the first study to investigate long-term trends in runoff and BFI for watersheds in the large-scale region of eastern Japan. Results showed significant trends in annual runoff and BFI, with a concentration of significant seasonal trends occurring in winter with five gauges showing trends in runoff and nine gauges showing trends in BFI. The results suggest that the response of annual and seasonal runoff and BFI to climate change can already be seen, which implies that policymakers need more information on the impacts of climate change and human activities to manage water resources sustainably.

  • Orie Sasaki, Yugo Tsumura, Masafumi Yamada, Yukiko Hirabayashi
    2023 年 17 巻 1 号 p. 9-14
    発行日: 2023年
    公開日: 2023/03/28
    ジャーナル オープンアクセス

    Increasing needs for real-time flood forecasting, proactive disaster prevention using hazard maps, and adaptation to flood risks associated with climate change require more detailed and accurate inundation information. Although river levees are an important factor in defining the extent and depth of flood water, data on the height and location of levees that can be introduced into global river models are not well developed. Therefore, in this study, an algorithm was developed to automatically determine the presence of levees when multiple river levee conditions are met from high-resolution Digital Elevation Models (DEMs). In the case study on the Kinu river, the 5-meter resolution DEM data was used to properly extract the location and height of levees including discontinuous levees, and the average error in levee height was in the range of 0.7 m. When a 10‑meter, 20-meter, and 30-meter resolution DEM was used, the levee location was detected reasonably while erroneous determinations increased as the resolution became coarser, suggesting that the automatic detection method requires a resolution of at least 10-meter.

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