Abstract. Soil moisture storage capacity (SMSC) links the atmosphere and terrestrial ecosystems, which is required as spatial parameters for geoscientific models. However, there are currently no available common datasets of the SMSC on a global scale, especially for hydrological models since conventional evapotranspiration-derived estimates cannot represent the extra storage capacity for the lateral flow and runoff generation. Here, we produce a dataset of the SMSC parameter for global hydrological models. Joint parameter calibration of three commonly used monthly water balance models provides the labels for a deep residual network. The global SMSC is constructed based on the deep residual network at 0.5° resolution by integrating 15 types of meteorological forcings, underlying surface properties, and runoff data. SMSC products are validated with the spatial distribution against root zone depth datasets and validated in the simulation efficiency on global grids and typical catchments from different climatic regions. We provide the global SMSC parameter dataset as a benchmark for geoscientific modelling by users.

中文翻译：

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用于地球科学建模的 0.5° 分辨率的全球土壤水分储存能力

摘要。土壤水分储存能力 (SMSC) 将大气和陆地生态系统联系起来，这是地球科学模型所需的空间参数。然而，目前在全球范围内没有可用的 SMSC 通用数据集，特别是对于水文模型，因为传统的蒸散推导估计不能代表侧向流和径流产生的额外存储容量。在这里，我们为全球水文模型生成了 SMSC 参数的数据集。三种常用的月度水平衡模型的联合参数校准为深度残差网络提供了标签。全球SMSC是基于0.5°分辨率的深层残差网络，综合15种气象强迫、下垫面性质和径流数据构建而成。SMSC 产品通过根区深度数据集的空间分布验证，并在全球网格和不同气候区域的典型流域的模拟效率验证。我们提供全球 SMSC 参数数据集作为用户进行地球科学建模的基准。