loading page

Topological relationship-based flow direction modeling: stream burning and depression filling
  • +6
  • Chang Liao,
  • Tian Zhou,
  • Donghui Xu,
  • Zeli Tan,
  • Gautam Bisht,
  • Matthew G Cooper,
  • Darren Engwirda,
  • Hongyi Li,
  • L. Ruby Leung
Chang Liao
Pacific Northwest National Laboratory

Corresponding Author:[email protected]

Author Profile
Tian Zhou
Pacific Northwest National Laboratory
Author Profile
Donghui Xu
Pacific Northwest National Laboratory
Author Profile
Zeli Tan
Pacific Northwest National Laboratory (DOE)
Author Profile
Gautam Bisht
Lawrence Berkeley National Laboratory
Author Profile
Matthew G Cooper
Pacific Northwest National Laboratory
Author Profile
Darren Engwirda
Columbia University / NASA-GISS
Author Profile
Hongyi Li
University of Houston
Author Profile
L. Ruby Leung
PNNL
Author Profile

Abstract

Flow direction modeling consists of (1) an accurate representation of the river network and (2) digital elevation model (DEM) processing to preserve characteristics with hydrological significance. In part 1 of our study, we presented a mesh-independent approach to representing river networks on different types of meshes. This follow-up part 2 study presents a novel DEM processing approach for flow direction modeling. This approach consists of (1) a topological relationship-based hybrid breaching-filling method to conduct stream burning for the river network and (2) a modified depression removal method for rivers and hillslopes. Our methods minimize modifications to surface elevations and provide a robust two-step procedure to remove local depressions in DEM. They are mesh-independent and can be applied to both structured and unstructured meshes. We applied our new methods to the Susquehanna River Basin with different model configurations. The results show that topological relationship-based stream burning and depression-filling methods can reproduce the correct river networks, providing high-quality flow direction and other characteristics for hydrologic and Earth system models.