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).

中文翻译：

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亚马逊和育空盆地基于离散全球网格系统的流量路由数据集

摘要。离散全球网格系统 (DGG) 是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 DGG 已在包括大气科学和生态学在内的各种科学学科中得到应用，但由于缺乏基于 DGG 的流量路由数据集，它们与基于物理的水文模型和地球系统模型 (ESM) 的集成受到阻碍。为了弥补这一差距，本研究开创性地使用二十面体斯奈德等面积 (ISEA) DGG 和新颖的与网格无关的流向模型开发了新的流路由数据集。我们提供了两个大型流域（热带亚马逊河流域和北极育空河流域）的流量演算数据集。这些数据集展示了基于 DGG 的流量演算数据集在增强水文模型性能并提供基于观测的流量演算输入以立即应用于亚马逊和育空河流域方面的潜力。数据可在 https://doi.org/10.5281/zenodo.8377765 上获取（Liao，2023）。