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

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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.
Building 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.
Lineage: 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.