論文

筆頭著者 責任著者 本文へのリンクあり
2021年7月

Dem-based river cross-section extraction and 1-d streamflow simulation for eco-hydrological modeling: A case study in upstream hiikawa river, japan

Hydrological Research Letters
  • Tomohiro Tanaka
  • ,
  • Hidekazu Yoshioka
  • ,
  • Yumi Yoshioka

15
3
開始ページ
71
終了ページ
76
記述言語
掲載種別
研究論文(学術雑誌)
DOI
10.3178/hrl.15.71

Simulating streamflow under both high-and low-flows is required for versatile eco-hydrological modeling. Typical streamflow simulators require hydrological data such as river geometry and observed river discharge/water level as upstream/downstream boundary conditions. However, these are not always available in data-sparse regions. Furthermore, because of the potential inaccuracy of digital elevation model (DEM) data around water surfaces, this data has not generally been utilized in streamflow simulations. Therefore, this study explores the potential applicability of DEM data to extract river cross-sections, focusing on the upstream Hiikawa River, Japan. A 1-D streamflow simulation was performed using river cross-sections extracted from a 5 m LiDAR DEM and the observed dam discharge from 2018 to 2020 as the upstream boundary condition. The simulated water depths with Manning’s roughness coefficients of 0.03 to 0.05 m–1/3 s reproduce the observation results with Nash-Sutcliffe coefficients of 0.91–0.97 for the whole period and 0.60–0.97 for a flood event. The accurate results for both low and high flows were considered to reflect the reasonable representations of the river cross-section. Finally, the velocity-based suitability index for Ayu (P. altivelis) was evaluated. We demonstrate applicability and several possible limitations of DEM data for eco-hydrological modeling of data-scarce rivers.

リンク情報
DOI
https://doi.org/10.3178/hrl.15.71
Scopus
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85113638163&origin=inward 本文へのリンクあり
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https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=85113638163&origin=inward
ID情報
  • DOI : 10.3178/hrl.15.71
  • eISSN : 1882-3416
  • SCOPUS ID : 85113638163

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