Liao. et al. 2022. Journal of Advances in Modeling Earth Systems

Topological relationships-based flow direction modeling: mesh-independent river networks representation

Chang Liao^1*, Donghui Xu¹, Tian Zhou¹, Matt Cooper¹, Darren Engwirda², Hong-Yi Li³, and L. Ruby Leung¹

¹ Atmospheric Sciences and Global Change, Pacific Northwest National Laboratory, Richland, WA, USA

² T-3 Fluid Dynamics and Solid Mechanics Group, Los Alamos National Laboratory, Los Alamos, NM, USA

³ University of Houston, Houston, TX, USA

* corresponding author: [email protected]

Abstract

River networks are important features in surface hydrology. However, accurately representing river networks in spatially distributed hydrologic and Earth system models is often sensitive to the model's spatial resolution. Specifically, river networks are often misrepresented because of the mismatch between the model's spatial resolution and river networks details, resulting in significant uncertainty in projected flow direction. In this study, we developed a topological relationships-based river network representation method for spatially distributed hydrologic models. This novel method uses (1) graph theory algorithms to simplify real-world vector-based river networks and assist in mesh generation; and (2) a topological relationship-based method to reconstruct conceptual river networks. The main advantages of our method are that (1) it combines the strengths of vector-based and DEM raster-based river network extraction methods; and (2) it is mesh-independent and can be applied to both structured and unstructured meshes. This method paves a path for advanced terrain analysis and hydrologic modeling across different scales.

Journal reference

Liao. et al. (2022). Topological relationships-based flow direction modeling: river networks representation

Code reference

References for each software release for all code involved.

Darren Engwirda: Generalised primal-dual grids for unstructured co-volume schemes, J. Comp. Phys., 375, pp. 155-176, https://doi.org/10.1016/j.jcp.2018.07.025, 2018.

Liao, Chang, & Cooper, Matt. (2022). Pyflowline: a mesh-independent river networks generator for hydrologic models (0.1.22). Zenodo. https://doi.org/10.5281/zenodo.6604337

Data reference

Input data

Reference for each data source for your input data. For example:

Data	Source	Download website	Usage
River flowline	USGS National Hydrography Dataset	USGS national map	Raw river flowline
Coastal line	USGS	USGS national map	Coastal line for the MPAS mesh generation

Output data

Reference for each data source for your output data. For example:

Data	Format	Content	Usage
Mesh	GeoJSON	The mesh file	Hydrologic model
Conceptual river flowline	GeoJSON	The modeled river flowline	Hydrologic model
Mesh info	JSON	Information of both mesh and conceptual flowline	HexWatershed model

Contributing modeling software

Model	Version	Repository Link	DOI
PyFlowline	latest	https://doi.org/10.5281/zenodo.6604337	10.5281/zenodo.6604337

Reproduce my experiment

You need to follow these two steps:

1. Run the Mesh generation workflow
2. Run the PyFlowline tool

Reproduce my figures

You are recommended to generate the plots using QGIS for all the GeoJSON files.