Global Dominant River Tracing (DRT) based Hydrography Datasets for Macroscale Hydrological ModelingEntry ID: DRT_Dataset_Description
Abstract: We developed a hierarchical Dominant River Tracing (DRT) algorithm for automated extraction and spatial upscaling of basin flow directions and river networks using fine scale hydrography inputs (e.g. flow direction, river networks and flow accumulation). The DRT algorithms are based on the D8 single direction flow method. In contrast with previous upscaling methods, the DRT algorithms utilize ... information on global and local drainage patterns from baseline fine scale hydrography inputs to determine upscaled flow directions and other critical variables including upscaled basin area, basin shape and river lengths. The DRT algorithm preserves the original baseline hierarchical drainage structure by tracing each entire flow path from headwater to river mouth at fine scale while prioritizing successively higher order basins and rivers for tracing. We applied the algorithm to produce a series of global hydrography data sets from 1/16° to 2°spatial scales in two geographic projections (WGS84 and Lambert azimuthal equal area). The DRT results were evaluated against other alternative upscaling methods and hydrography datasets for continental USA and global domains. These results show favorable DRT upscaling performance in preserving baseline fine scale river network information,including: (1) improved, automated extraction of flow directions and river networks at any spatial scale without the need for manual correction; (2) consistency of river network, basin shape, basin area, river length and basin internal drainage structure between upscaled and
baseline fine scale hydrography; (3) performance largely independent of spatial scale, geographic region and projection. The DRT upscaling process also generates other products useful for hydrological modeling, including flow distance, upstream drainage area,channel gradient and fractional area of basin boundary cells. These data include a set of DRT upscaled global hydrography maps derived from HYDRO1K and HydroSHEDS baseline fine scale hydrography inputs.
Purpose: The DRT algorithms were developed to upscale hydrogrpahy dataset suitable for a range of continental to global scale studies, including GCM and macroscale hydrological modeling. A detailed description of the DRT algorithms and resulting global hydrography products are available from Wu et al. (2011 & 2012). These products are considered suitable for a range of regional and global hydrological applications including, terrestrial water balance, runoff routing and river
discharge modeling within large river basins.
Data Set Citation
Dataset Originator/Creator: Huan Wu and John Kimball
Other Citation Details: See Publications/References below
Latitude Resolution: 2, 1, 1/2, 1/4, 1/8, 1/10, 1/12, 1/16 degree
Longitude Resolution: 2, 1, 1/2, 1/4, 1/8, 1/10, 1/12, 1/16 degree
Quality Because of the robust performance of the DRT upscaling algorithm, the quality of the baseline hydrography inputs essentially determines the accuracy of the DRT upscaled results. Improved baseline hydrography inputs enable greater accuracy in DRT upscaled river networks, which in turn would facilitate better accuracy in regional and macroscale hydrological model simulations that utilize these data. ... HydroSHEDS provides surface hydrographic information with generally improved resolution and quality over previous global datasets. The new DRT results translate these improvements into more accurate upscaled hydrography layers relative to an earlier DRT record defined from HYDRO1k. The improvements include the quality of upscaled flow direction, drainage area, and river length calculations. These improvement may be potentially beneficial to other parameters that are critical to hydrological models such as drainage density, channel geometry, Manning’s roughness coefficient etc.
Role: DIF AUTHOR
Email: huanwu at umd.edu
5825 University Court, Suite 4001
City: College Park
Province or State: MD
Postal Code: 20740-3823
Wu, H., J. S. Kimball, N. Mantua, and J. Stanford, 2011. Automated Upscaling of River Networks for Macroscale Hydrological Modeling. Water Resources Research, 47,
Wu H., J. S. Kimball, H. Li, M. Huang, L. R. Leung, R. F. Adler, 2012, A New Global River Network Database for Macroscale Hydrologic modeling, Water Resour. Res. (submitted).
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Creation and Review Dates
DIF Creation Date: 2012-06-29
Last DIF Revision Date: 2017-08-23