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Title
Japanese: 
English:New and better global terrain elevation data for global hydrology modelling 
Author
Japanese: 山崎大, 池嶋大樹, 田渡竜乃介, 鼎信次郎.  
English: Dai Yamazaki, Daiki Ikeshima, Ryunosuke Tawatari, Shinjiro Kanae.  
Language English 
Journal/Book name
Japanese: 
English: 
Volume, Number, Page        
Published date Dec. 16, 2016 
Publisher
Japanese: 
English: 
Conference name
Japanese: 
English:American Geophysical Union 2016 Fall meeting 
Conference site
Japanese:サンフランシスコ 
English:San Francisco 
Official URL https://agu.confex.com/agu/fm16/meetingapp.cgi/Paper/146224
 
Abstract Global-scale Digital Elevation Models (DEMs) are essential for many studies, such as land surface hydrology modelling, flood inundation modelling, and terrain analysis. The Shuttle Radar Topography Mission (SRTM) DEM is the most widely used global-scale elevation data. However, the SRTM DEM contains height errors which come from different error sources. Long- and medium-wavelength (>500m) errors are mainly due to residual motion error of the interferometric mast. While short-wavelength errors (i.e. radar speckle) are caused by pixel-scale variations of surface brightness and slope. In addition to these random error components, the SRTM DEM contains systematic tree height bias because the radar beam cannot penetrate into forest canopy. We have developed a global-scale method for removing height errors from the SRTM DEM, by combining the statistical and multi-satellite approach. First, we removed the medium-wavelength "striping error" using a 2D-Fourier-transform filter. Second, we have corrected the long-wavelength "absolute error" using ICESat Lasar altimerty "centroid" elevations. Third, we calculated the "tree height bias" by subtracting ICESat “lowest” elevation from SRTM elevation. We estimated tree height bias in pixels which do not have ICESat measurements, by using the Landsat tree density map and the global forest height map. Last, the short-wavelength "radar speckles" were removed by an adaptive smoothing filter. By applying this 4-step method, the 90 percentile absolute bias was reduced from ~10m to ~3m, and remaining bias is mostly considered to be due to sub-pixel topography. We executed global flood simulations with the error-removed SRTM and the original SRTM, and found that the simulated inundated area showed much better agreement to observations when the error-removed DEM was used. The flood simulation results suggests that the height error removal is essential for better understanding of the global surface water dynamics and the global hydrological cycle.

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