Preprints
https://doi.org/10.5194/essd-2023-398
https://doi.org/10.5194/essd-2023-398
02 Apr 2024
 | 02 Apr 2024
Status: this preprint is currently under review for the journal ESSD.

Discrete Global Grid System-based Flow Routing Datasets in the Amazon and Yukon Basins

Chang Liao, Darren Engwirda, Matthew Cooper, Mingke Li, and Yilin Fang

Abstract. Discrete Global Grid systems (DGGs) are emerging spatial data structures widely used to organize geospatial datasets across scales. While DGGs have found applications in various scientific disciplines, including atmospheric science and ecology, their integration into physically based hydrologic models and Earth System Models (ESMs) has been hindered by the lack of flow-routing datasets based on DGGs. In response to this gap, this study pioneers the development of new flow routing datasets using Icosahedral Snyder Equal Area (ISEA) DGGs and a novel mesh-independent flow direction model. We present flow routing datasets for two large basins, the tropical Amazon River Basin and the Arctic Yukon River Basin. These datasets demonstrate the potential of DGGs-based flow routing datasets to enhance the performance of hydrologic models and provide observationally-based flow routing inputs for immediate application to the Amazon and Yukon River Basins. The data are available at https://doi.org/10.5281/zenodo.8377765 (Liao, 2023).

Chang Liao, Darren Engwirda, Matthew Cooper, Mingke Li, and Yilin Fang

Status: open (until 09 May 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
Chang Liao, Darren Engwirda, Matthew Cooper, Mingke Li, and Yilin Fang

Data sets

Discrete Global Grid System-based Flow Routing Datasets in the Amazon and Yukon Basins Chang Liao https://doi.org/10.5281/zenodo.8377765

Chang Liao, Darren Engwirda, Matthew Cooper, Mingke Li, and Yilin Fang

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Short summary
Discrete Global Grid systems, or DGGs, are digital frameworks that help us organize information about our planet. Although scientists have used DGGs in areas like weather and nature, using them in the water cycle has been challenging because some core datasets are missing. We created a way to generate these datasets. We then developed the datasets in the Amazon Basin, which plays an important role in our planet's climate. These datasets may help us improve our water cycle models.
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