diff --git a/conda-recipe/conda_build_config.yaml b/conda-recipe/conda_build_config.yaml index 2883f56..c7ccb6f 100644 --- a/conda-recipe/conda_build_config.yaml +++ b/conda-recipe/conda_build_config.yaml @@ -23,7 +23,7 @@ metadata: # Package name name: pyflowline # Package version - version: "0.3.2" + version: "0.3.3" # Package summary summary: A mesh-independent river network generator for hydrologic models. # Package homepage diff --git a/conda-recipe/meta.yaml b/conda-recipe/meta.yaml index 6db30b1..8ea998b 100644 --- a/conda-recipe/meta.yaml +++ b/conda-recipe/meta.yaml @@ -1,6 +1,6 @@ {% set name = "hexwatershed" %} {% set git_rev = "main" %} -{% set version = "0.3.2" %} +{% set version = "0.3.3" %} package: name: {{ name|lower }} diff --git a/paper.bib b/paper.bib index bedd01d..4635878 100644 --- a/paper.bib +++ b/paper.bib @@ -1,5 +1,5 @@ @article{Engwirda:2021, - title={'Unified' laguerre-Power Meshes For Coupled Earth System Modelling}, + title={'Unified' {laguerre-Power Meshes For Coupled Earth System Modelling}}, author={Engwirda, Darren and Liao, Chang}, journal={29th International Meshing Roundtable (IMR), Virtual Conference}, year = {2021}, @@ -24,24 +24,24 @@ @article{Feng:2022 } -@article{Liao:2023, -author = {Liao, Chang and Zhou, Tian and Xu, Donghui and Cooper, Matthew G and Engwirda, Darren and Li, Hong-Yi and Leung, L. Ruby}, -title = {Topological relationship-based flow direction modeling: Mesh-independent river networks representation}, +@article{Liao:2023a, +author = {Liao, Chang and Zhou, Tian and Xu, Donghui and Cooper, Matthew G. and Engwirda, Darren and Li, Hong-Yi and Leung, L. Ruby}, +title = {{Topological} {Relationship}-{Based} {Flow} {Direction} {Modeling}: {Mesh}-{Independent} {River} {Networks} {Representation}}, journal = {Journal of Advances in Modeling Earth Systems}, -year = {2023}, -volume = {n/a}, -number = {n/a}, +volume = {15}, +number = {2}, pages = {e2022MS003089}, keywords = {watershed, land-river-ocean interaction, flow direction, river network, unstructured mesh, graph theory}, -doi = {10.1029/2022MS003089}, +doi = {https://doi.org/10.1029/2022MS003089}, url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2022MS003089}, eprint = {https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2022MS003089}, note = {e2022MS003089 2022MS003089}, -abstract = {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 network details, resulting in significant uncertainty in the projected flow direction. In this study, we developed a topological relationship-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.} +abstract = {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 network details, resulting in significant uncertainty in the projected flow direction. In this study, we developed a topological relationship-based river network representation method for spatially distributed hydrologic models. This novel method uses (a) graph theory algorithms to simplify real-world vector-based river networks and assist in mesh generation; and (b) a topological relationship-based method to reconstruct conceptual river networks. The main advantages of our method are that (a) it combines the strengths of vector-based and DEM raster-based river network extraction methods; and (b) 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.}, +year = {2023} } @article{mizukami_2016_GMD, - title = {{{mizuRoute}} Version 1: A River Network Routing Tool for a Continental Domain Water Resources Applications}, + title = {{{mizuRoute}} Version 1: {A River Network Routing Tool for a Continental Domain Water Resources Applications}}, shorttitle = {{{mizuRoute}} Version 1}, author = {Mizukami, Naoki and Clark, Martyn P. and Sampson, Kevin and Nijssen, Bart and Mao, Yixin and McMillan, Hilary and Viger, Roland J. and Markstrom, Steve L. and Hay, Lauren E. and Woods, Ross and Arnold, Jeffrey R. and Brekke, Levi D.}, year = {2016}, @@ -84,7 +84,7 @@ @article{Wu:2012 number = {9}, pages = {}, keywords = {upscaling, DRT, river network, hydrography}, -doi = {https://doi.org/10.1029/2012WR012313}, +doi = {10.1029/2012WR012313}, url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2012WR012313}, eprint = {https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2012WR012313}, abstract = {Coarse-resolution (upscaled) river networks are critical inputs for runoff routing in macroscale hydrologic models. Recently, Wu et al. (2011) developed a hierarchical dominant river tracing (DRT) algorithm for automated extraction and spatial upscaling of river networks using fine-scale hydrography inputs. We applied the DRT algorithms using combined HydroSHEDS and HYDRO1k global fine-scale hydrography inputs and produced a new series of upscaled global river network data at multiple (1/16° to 2°) spatial resolutions. The new upscaled results are internally consistent and congruent with the baseline fine-scale inputs and should facilitate improved regional to global scale hydrologic simulations.}, @@ -113,13 +113,13 @@ @article{Sahr:2011 @article{Liao:2023b, author = {Liao, Chang and Zhou, Tian and Xu, Donghui and Tan, Zeli and Bisht, Gautam and Cooper, Matthew G. and Engwirda, Darren and Li, Hong-Yi and Leung, L. Ruby}, -title = {Topological Relationship-Based Flow Direction Modeling: Stream Burning and Depression Filling}, +title = {{Topological Relationship-Based Flow Direction Modeling}: {Stream Burning and Depression Filling}}, journal = {Journal of Advances in Modeling Earth Systems}, volume = {15}, number = {11}, pages = {e2022MS003487}, keywords = {flow direction, depression filling, mesh independent, unstructured mesh, hydrology, flow routing}, -doi = {https://doi.org/10.1029/2022MS003487}, +doi = {10.1029/2022MS003487}, url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2022MS003487}, eprint = {https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2022MS003487}, note = {e2022MS003487 2022MS003487}, diff --git a/paper.md b/paper.md index 9a1e7f7..96ba49e 100644 --- a/paper.md +++ b/paper.md @@ -45,7 +45,7 @@ A mesh-independent river network representation method that preserves topologica PyFlowline is a Python package that provides a framework for generating river networks for hydrologic models, meeting the identified need. Using an object-oriented programming approach, PyFlowline represents river network elements and mesh cell relationships. It relies on open-source Python libraries like GDAL and Cython for data input/output and spatial data operations. The computational geometry algorithms used in PyFlowline are designed and implemented using a unified spherical framework, making it suitable for regional and global-scale simulations. PyFlowline uses topological relationships to capture the river networks so they are preserved even at coarse spatial resolutions. -Moreover, PyFlowline is mesh-independent, supporting both structured and unstructured meshes. It can quickly adopt other mesh types, such as triangulated irregular networks (TIN) or discrete global grid systems (DGGs) [@Sahr:2011]. PyFlowline is a core component of the HexWatershed model, a mesh-independent flow direction model. Several scientific studies focusing on coupled Earth system models [@Feng:2022; @Liao:2023; @Liao:2023b] have utilized PyFlowline. A workshop tutorial has also been provided online and in person to support its implementation. +Moreover, PyFlowline is mesh-independent, supporting both structured and unstructured meshes. It can quickly adopt other mesh types, such as triangulated irregular networks (TIN) or discrete global grid systems (DGGs) [@Sahr:2011]. PyFlowline is a core component of the HexWatershed model, a mesh-independent flow direction model. Several scientific studies focusing on coupled Earth system models [@Feng:2022; @Liao:2023a; @Liao:2023b] have utilized PyFlowline. A workshop tutorial has also been provided online and in person to support its implementation. # Model features diff --git a/setup.py b/setup.py index 8325775..2a7d74a 100644 --- a/setup.py +++ b/setup.py @@ -12,7 +12,7 @@ AUTHOR = "Chang Liao" AUTHOR_EMAIL = "chang.liao@pnnl.gov" URL = "https://github.com/changliao1025/pyflowline" -VERSION = "0.3.2" +VERSION = "0.3.3" REQUIRES_PYTHON = ">=3.8.0" KEYWORDS = "Earth Science"