Developing a Taxonomic Soil Dataset from SSURGO for Hydrological and Water Quality Modeling

The file provided has two R scripts that can be used to aggregate SSURGO map units up to their subgroup taxonomic level. The script divides the soils listed in the SSURGO data into five classes: soils having fragipan, wet soils, wet soils having fragipan, soils having shallow bedrock (lithic), and soils that do not exhibit any of these four characteristics (general).The first script, 'fun_standerdise_layers.R’ extracts the soil mukey data from 'SoilDB' R package (Beaudette et al., 2021), connects it with the SSURGO soil data obtained from the SWAT website (https://swat.tamu.edu/data/), and standardizes the soil data with uniform soil layer depths considering depth-weighted average. The second script, 'create_taxonomy_soils.R' reads this data and first classifies it into taxonomic groups and then clusters it for each of the four hydrologic group within each taxonomic group by considering similarity in soil hydraulic conductivity, soil available water capacity, soil erosivity, and soil depth. A SWAT model developed using the taxonomic data was shown to show similar nutrient and hydrologic loads when compared with a SWAT model developed using SSURGO. The parameter distribution of both the models were also found to be similar. However, the taxonomic model reduced simulation time by half, as the taxonomic dataset mitigates boundary-based discontinuity issues that arise in soil surveys when compiling the SSURGO dataset.本次提供的文件包含两份R脚本，可用于将SSURGO土壤调查地理数据库（Soil Survey Geographic Database）的图斑聚合至其亚类分类学层级。该脚本将SSURGO数据中收录的土壤划分为五大类别：含脆磐的土壤、湿生土壤、含脆磐的湿生土壤、含浅部基岩（岩性）的土壤，以及不具备上述四类特征的通用土壤。第一份脚本'fun_standerdise_layers.R'从'SoilDB' R包（Beaudette等，2021）中提取土壤单元键值（mukey）数据，将其与从水土评价工具（SWAT，Soil and Water Assessment Tool）官网（https://swat.tamu.edu/data/）获取的SSURGO土壤数据进行关联，并通过深度加权平均法将土壤数据统一为标准化土层深度格式。第二份脚本'create_taxonomy_soils.R'读取上述处理后的数据，首先将其划分为分类学群组，随后针对每个分类学群组内的四类水文组，基于土壤导水率、土壤有效持水量、土壤侵蚀力以及土壤深度的相似性进行聚类。采用该分类学数据集构建的SWAT模型，与采用原始SSURGO数据构建的SWAT模型相比，其养分与水文负荷模拟结果高度相似。两类模型的参数分布也被证实具有相似性。不过，分类学模型将模拟时长缩减了一半，这是因为分类学数据集缓解了在汇编SSURGO数据集时，土壤调查中出现的基于边界的不连续性问题。