离散全球网格系统（DGG）是一种新兴的空间数据结构，广泛用于组织跨尺度的地理空间数据集。虽然离散全球网格系统已在大气科学和生态学等多个科学学科中得到应用，但由于缺乏基于离散全球网格系统的水流路径数据集，将其集成到基于物理的水文模型和地球系统模型（ESM）中的工作一直受到阻碍。针对这一空白，本研究率先使用二十面体斯奈德等面积（ISEA）DGG 和一种新型网格无关流向模型开发了新的流向数据集。我们展示了两个大型流域（热带亚马逊河流域和北极育空河流域）的水流路径数据集。这些数据集证明了基于 DGGs 的水流路径数据集在提高水文模型性能方面的潜力，并提供了基于观测的水流路径输入，可立即应用于亚马逊河流域和育空河流域。", "ds_source": "
矢量河网数据：来自 HydroSHEDS 数据库。HydroSHEDS v1 数据集主要基于美国国家航空航天局（NASA）2000 年获得的高程数据。\n
亚马逊盆地矢量边界数据：育空盆地边界矢量通过 Hy65 droBASINS 获得。\n
栅格地形数据集：亚马逊盆地 30 弧秒（∼ 1 公里）的高空间分辨率 DEM 数据集是通过 NASA 的 ORNL DAAC。与 HydroSHEDS 类似，该 DEM 也是 SRTM DEM 的子集。相同空间分辨率的流量累积和长度数据集用于数据验证。育空盆地 15 弧秒（∼ 500 米）分辨率的空隙填充 DEM 和流量累积数据集也来自 HydroSHEDS。", "ds_process_way": "
1) REACH 模型对矢量河网（即 HydroSHEDS）进行预处理，生成简化河网。\n
2) DGGRID 模型利用盆地边界生成 DGGs 网格。\n
3) 模型利用步骤 1 和步骤 2 的输出结果生成水流路径数据集。", "ds_quality": "
由于计算能力和输入数据集质量的限制，我们只在亚马逊河流域和育空河流域生成了四种空间分辨率的水流路径数据集。要评估模型在更精细空间分辨率的模型性能和适用性，还需要进行更多的模拟。此外，一旦计算效率得到提高，将提供全球尺度的数据集。", "ds_acq_start_time": null, "ds_acq_end_time": null, "ds_acq_place": "亚马逊河流域,北极育空河流域", "ds_acq_lon_east": null, "ds_acq_lat_south": null, "ds_acq_lon_west": null, "ds_acq_lat_north": null, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 225553230, "ds_files_count": 2, "ds_format": "geojson", "ds_space_res": "", "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "73e82476-3d7d-4640-b435-13ad0be592c2.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "0a4269e1-65f4-45f1-aeba-88ea3068eebf", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.55" ], "quality_level": 3, "publish_time": "2024-06-21 10:52:43", "last_updated": "2026-01-14 10:12:09", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ZENODO.DB6530.2024", "i18n": { "en": { "title": "Amazon and Yukon Basin Water Flow Path Dataset Based on Discrete Global Grid System", "ds_format": "geojson", "ds_source": "
Vector river network data: from HydroSHEDS database. The HydroSHEDS v1 dataset is mainly based on elevation data obtained by NASA in 2000.\n
Amazon Basin Vector Boundary Data: Yukon Basin boundary vectors were obtained using Hydro65 droBASINS.\n
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.", "ds_quality": "
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.", "ds_ref_way": "", "ds_abstract": "
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.
", "ds_time_res": "", "ds_acq_place": "Amazon River Basin, Yukon River Basin in the Arctic", "ds_space_res": "", "ds_projection": "", "ds_process_way": "1) The REACH model preprocesses vector river networks (i.e. HydroSHEDS) to generate simplified river networks.\n
2) The DGGRID model utilizes basin boundaries to generate DGGs grids.\n
3) The model generates a water flow path dataset using the output results of steps 1 and 2.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "https://creativecommons.org/licenses/by/4.0/", "doi_reg_from": "reg_outside", "cstr_reg_from": "reg_outside", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "亚马逊河", "DGG", "DGGRID", "分水岭" ], "ds_subject_tags": [ "水文学" ], "ds_class_tags": [], "ds_locus_tags": [ "亚马逊河流域", "北极育空河流域" ], "ds_time_tags": [], "ds_contributors": [ { "true_name": "Chang Liao", "email": "changliao.climate@gmail.com", "work_for": "Pacific Northwest National Laboratory", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "Chang Liao", "email": "changliao.climate@gmail.com", "work_for": "Pacific Northwest National Laboratory", "country": "中国" } ], "ds_managers": [ { "true_name": "Chang Liao", "email": "changliao.climate@gmail.com", "work_for": "Pacific Northwest National Laboratory", "country": "中国" } ], "category": "水文" }