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  • 标题:A high-resolution streamflow and hydrological metrics dataset for ecological modeling using a regression model
  • 本地全文:下载
  • 作者:Katie Irving ; Mathias Kuemmerlen ; Jens Kiesel
  • 期刊名称:Scientific Data
  • 电子版ISSN:2052-4463
  • 出版年度:2018
  • 卷号:5
  • DOI:10.1038/sdata.2018.224
  • 语种:English
  • 出版社:Nature Publishing Group
  • 摘要:Hydrological variables are among the most influential when analyzing or modeling stream ecosystems. However, available hydrological data are often limited in their spatiotemporal scale and resolution for use in ecological applications such as predictive modeling of species distributions. To overcome this limitation, a regression model was applied to a 1鈥塳m gridded stream network of Germany to obtain estimated daily stream flow data (m3 s鈭?) spanning 64 years (1950鈥?013). The data are used as input to calculate hydrological indices characterizing stream flow regimes. Both temporal and spatial validations were performed. In addition, GLMs using both the calculated and observed hydrological indices were compared, suggesting that the predicted flow data are adequate for use in predictive ecological models. Accordingly, we provide estimated stream flow as well as a set of 53 hydrological metrics at 1鈥塳m grid for the stream network of Germany. In addition, we provide an R script where the presented methodology is implemented, that uses globally available data and can be directly applied to any other geographical region.
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