Modeled daily salinity derived from multiple machine learning methodologies for 91 salinity monitoring sites in the northern Gulf of Mexico, 1980–2021

This data release consists of statistical predictions of daily salinity time series generated from the makESTUSAL software repository described by Asquith and others (2023b). The statistical methods included multiple methods of machine learning, which produced the daily salinity prediction and attendant credible uncertainties included in the data release. The geographic scope includes the predictions for 91 locations within bays and estuaries of the Gulf of Mexico, United States. The 91 locations are organized across 15 salinity groups and represented in the organizational structure of this data release. The input data files of imputed salinity (observations, response variable) and covariates (predictor variables) for the makESTUSAL software were created by use of a companion software (covESTUSAL) (Asquith and others, 2023a). These input data are provided by Banks and others (2024).

本数据发布包包含由Asquith等人（2023b）所描述的makESTUSAL软件仓库生成的每日盐度时间序列统计预测结果。本次预测采用的统计方法涵盖多种机器学习方法，可生成每日盐度预测值及其附带的可信不确定范围，相关内容均收录于本数据发布包中。本数据集的地理覆盖范围涵盖美国墨西哥湾沿岸各海湾与河口内的91个监测点位，这91个点位被划分为15个盐度组别，并依照本数据发布包的组织结构进行编排。针对makESTUSAL软件所需的插补盐度（观测值，即响应变量 (response variable)）与协变量（即预测变量 (predictor variables)）的输入数据文件，由配套软件covESTUSAL生成（Asquith等人，2023a），上述输入数据由Banks等人（2024）发布提供。