A priori global 250m parameters for the SAC-SMA model

The Sacramento Soil Moisture Accounting (SAC-SMA) model is extensively employed across various sectors due to its robust capability to simulate complex hydrological processes, such as the National Weather Service of United States. However, its effectiveness hinges on the availability of detailed soil parameters, some of which require comprehensive soil property information that can be challenging to acquire. Generally, the model necessitates either direct calibration against observed hydrological data or the derivation of soil parameters from existing soil information. This requirement underscores a significant hurdle in applying the SAC-SMA model, particularly in regions where soil surveys are limited or the observed hydrological data are not available. \nBuilding on the established need for detailed soil parameters in the SAC-SMA model, this report outlines the creation of an a priori dataset that includes 11 critical soil parameters. These parameters are indispensable for the global application of the SAC-SMA model. Through the integration of comprehensive soil property information, this dataset aims to mitigate the challenges associated with obtaining specific soil data, thereby facilitating more precise model application.\nLineage: The dataset was developed leveraging the Global 250m Soil Hydraulic Properties, https://data.csiro.au/collection/csiro:62126, derived from the comprehensive SoilGrids250m 2.0 soil property dataset (https://soilgrids.org). This includes essential soil water characteristics such as Saturated Water Content (θs), Field Capacity (θfld), Permanent Wilting Point (θwlt) and Saturated Hydraulic Conductivity (Ks). These parameters are provided across various soil depth intervals, specifically at 0-5 cm, 5-15 cm, 15-30 cm, 30-60 cm, 60-100 cm, and 100-200 cm, ensuring detailed vertical soil profile representation. Additionally, the Hydrologic Soil Groups (HSGs) extracted from this dataset, in conjunction with the 300m resolution Copernicus Climate Change Service (C3S) Global Land Cover data for the year 2020 (https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=overview), were instrumental in estimating the Curve Number (CN), which is used for calculating upper soil zone depth.

萨克拉门托土壤水分核算（Sacramento Soil Moisture Accounting, SAC-SMA）模型因其具备稳健模拟复杂水文过程的强大能力，已在诸多领域得到广泛应用，例如美国国家气象局。不过，其应用效果取决于详细土壤参数的可获取性，其中部分参数需要全面的土壤属性信息，而这类信息往往难以获取。通常而言，该模型要么需要针对实测水文数据直接进行率定，要么需要从已有土壤信息中推导土壤参数。这一需求使得SAC-SMA模型的应用面临显著障碍，尤其是在土壤调查资料匮乏或缺乏实测水文数据的地区。
鉴于SAC-SMA模型对详细土壤参数的既定需求，本报告介绍了一个先验数据集的构建过程，该数据集包含11项关键土壤参数，这些参数是SAC-SMA模型全球应用的必备条件。通过整合全面的土壤属性信息，本数据集旨在缓解获取特定土壤数据时面临的挑战，从而助力更精准的模型应用。
数据集溯源：本数据集依托全球250米分辨率土壤水力属性数据集（https://data.csiro.au/collection/csiro:62126）开发而成，该数据集源自全面的SoilGrids250m 2.0土壤属性数据集（https://soilgrids.org）。其包含了饱和含水量（Saturated Water Content, θs）、田间持水量（Field Capacity, θfld）、永久萎蔫点（Permanent Wilting Point, θwlt）以及饱和导水率（Saturated Hydraulic Conductivity, Ks）等核心土壤水力学特征参数。这些参数覆盖多个土壤深度层级，具体为0-5 cm、5-15 cm、15-30 cm、30-60 cm、60-100 cm以及100-200 cm，可精准表征垂直方向上的土壤剖面特征。此外，从该数据集中提取的水文土壤组（Hydrologic Soil Groups, HSGs），结合2020年300米分辨率的哥白尼气候变化服务（Copernicus Climate Change Service, C3S）全球土地覆盖数据（https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=overview），被用于估算用于计算上层土壤带深度的径流曲线数（Curve Number, CN）。