Discrete Global Grid System (DGG) is an emerging spatial data structure widely used to organize cross scale geographic spatial datasets. Although the discrete global grid system has been applied in multiple scientific disciplines such as atmospheric science and ecology, the integration of it into physics based hydrological models and Earth System Models (ESM) has been hindered due to the lack of a water flow path dataset based on the discrete global grid system. In response to this gap, this study first developed a new flow direction dataset using icosahedral Snyder equal area (ISEA) DGG and a novel grid independent flow direction model. We present water flow path datasets from two large watersheds, the tropical Amazon River Basin and the Arctic Yukon River Basin. These datasets demonstrate the potential of DGGs based water flow path datasets in improving hydrological model performance and provide observational water flow path inputs that can be immediately applied to the Amazon and Yukon river basins.
Vector river network data: from HydroSHEDS database. The HydroSHEDS v1 dataset is mainly based on elevation data obtained by NASA in 2000.
Amazon Basin Vector Boundary Data: Yukon Basin boundary vectors were obtained using Hydro65 droBASINS.
Grid terrain dataset: The high-resolution DEM dataset of 30 arc seconds (∼ 1 kilometer) in the Amazon Basin was obtained through NASA's ORNL DAAC. Similar to HydroSHEDS, this DEM is also a subset of SRTM DEM. The flow accumulation and length datasets with the same spatial resolution are used for data validation. The gap filling DEM and flow accumulation dataset with a resolution of 15 arc seconds (∼ 500 meters) in the Yukon Basin are also from HydroSHEDS.
1) The REACH model preprocesses vector river networks (i.e. HydroSHEDS) to generate simplified river networks.
2) The DGGRID model utilizes basin boundaries to generate DGGs grids.
3) The model generates a water flow path dataset using the output results of steps 1 and 2.
Due to limitations in computing power and input dataset quality, we only generated four spatial resolution water flow path datasets in the Amazon and Yukon river basins. To evaluate the performance and applicability of the model at finer spatial resolutions, more simulations are needed. In addition, once computational efficiency is improved, it will provide a global scale dataset.
This work is licensed under a
Creative
Commons Attribution 4.0 International License.
CSTR:11738.11.NCDC.ZENODO.DB6530.2024
10.5281/zenodo.10846223