Data and code from: Synergistic soil, land use, and climate influences on wind erosion on the Colorado Plateau: Implications for management - v2

Figure: Predicted aeolian flux near Monticello, UT. Click to view full-size image.
[ 2023-03-06 - Supersedes version 1, https://doi.org/10.15482/USDA.ADC/1528278 ]
Includes code and data to recreate analysis from the manuscript:
Nauman, T. W., Munson, S. M., Dhital, S., Webb, N. P., & Duniway, M. C. (2023). Synergistic soil, land use, and climate influences on wind erosion on the Colorado Plateau: Implications for management. Science of The Total Environment (p. 164605). https://doi.org/10.1016/j.scitotenv.2023.164605.
This includes R statistical code, aeolian monitoring data and associated soil, land use, and climate explanatory data for each site, and a raster map showing areas modeled to have more sediment transport.
Monitoring Data
Aeolian sediment horizontal mass flux (q, a proxy for potential wind erosion activity) measurements are recorded for 81 sites that are collected three times per year (Feb-March, June-July, and Oct-Nov.). For each collection data is summarized in the BSNE_Samples_RegrMatrix.* files (.txt is tab delimited, and .rds is an r archive file). These tables also include the associated land use descriptions determined from field visits and local land policy. All spatial datasets are also summarized in this table for each site. Static maps of topography and soils are simply extracted for each site and attached to all collections taken at a given site. Spatial data that is available for different time periods is summarized by summarizing extracted values for a given variable for the period of time matching the q collection period (e.g. mean windspeed of the site). A number of statistical summaries are used for the time varying variables which are documented in the BSNE_Samples_RegrMatrix_ColumnDescriptions.xlsx file.
Analysis
Random Forest Data Reduction
A random forest data reduction strategy was used as the first step to narrowing down potential wind erosion drivers in analysis. The merge_rfe_figs.R file includes all steps to reduce the number of variables considered for final model building that is done using linear mixed models in the next section. Some of the figures included in the paper looking at relationships between q and explanatory variables are also implemented in this script. Also included in the dataset are the caret recursive feature elimination object created in the script (rf.RFE_flux.rds), and two successive iterations of further pruned random forests created in the script (rf_pruned_flux.rds and rf_pruned2_flux.rds).
Linear Mixed Models
Linear mixed models were trained and ranked by a small sample size Akaike's Information Criterium to rank models. The LMMs_lme_flux.R file documents the process of training, ranking and interpretation of models. The highest-ranking models were interpreted by reporting slope estimates and effects sizes calculations. Interactions between explanatory variables were visualized using effect plots for the high ranking models.
Mapping erosion potential
After assessing model controls in the previous two sections, a conclusion was made that much of the variation in q could be represented by just the spatial data sources collected for the study. A random forest model was built for just important spatial variables that could then be rendered out to every 30-meter pixel in the study region. The rf_mapping_andFigs.R file documents the process of building the spatial model, rendering prediction maps, tabulating variable importances for the model, and plotting partial variable dependence plots to interpret model relationships. Also included from this script are the caret recursive feature elimination object (srf.RFE_flux.rds) and final pruned random forest model object (srf.pruned_flux.rds) used to predict q. Raster layers for each explanatory variable are provided for the summer 2018 collection used for making the map and are available in the finallayers_sum18.zip file with each raster filename matching the column names documented in BSNE_Samples_RegrMatrix_ColumnDescriptions.xlsx.
Erosion prediction map data
100cm_flux_sum18.* : Geotiff file of predicted q values across the study region.
flux_map.qgz : QGIS project file with pre-formatted visualization of the predicted q values.
Resources in this dataset:

Resource Title: Tabular data, R code models, and erosion prediction map
File Name: CO_Plat_dust_landuse_datarelease_v2.zip
Resource Description: This zip file includes all original sediment collection data, code used for modeling sediment transport, and the sediment flux map created for the summer of 2018.

Resource Title: Mapping covariate layers
File Name: finallayers_sum18.zip
Resource Description: This .zip file includes raster layers representing explanatory variables used to predict aeolian mass flux for the Colorado Plateau in the manuscript:

Nauman, T.W., Munson, S.M., Dhital, S, Webb, N.P., Duniway, M.C. In Prep. Synergistic soil, land use, and climate influences on wind erosion on the Colorado Plateau: Implications for management. Accepted with minor revisions, STOTEN.
These layers include soil properties, vegetation cover metrics, topography, and climate summaries. For the layers with temporal components (i.e. climate and vegetation), the layers are summarized for each pixel for the summer 2018 collection period (7/17/2018 to 11/27/2018). The 2018 sediment collections were the highest of the study and thus predictions were aimed to represent hotspots during a high erosion period. An R script (layerprep.R) is also included that documents how all the included rasters were summarized and prepared for use in predictions.

图示：犹他州蒙特西洛附近的风成通量预测。点击查看全尺寸图像。
[2023年3月6日 - 修订版1，https://doi.org/10.15482/USDA.ADC/1528278]
包含用于重现论文中分析的代码和数据：
Nauman, T. W., Munson, S. M., Dhital, S., Webb, N. P., & Duniway, M. C. (2023). 科罗拉多高原上土壤、土地利用和气候对风蚀的协同影响：管理启示. 总环境科学 (第164605页). https://doi.org/10.1016/j.scitotenv.2023.164605.
此部分包括用于模拟沉积物运输的 R 统计代码、风成监测数据以及每个站点相关的土壤、土地利用和气候解释数据，以及展示模拟具有更多沉积物搬运区域的栅格地图。
监测数据
风成沉积物水平质量通量（q，潜在风蚀活动的一个代理）的测量记录于81个站点，每年收集三次（二月至三月、六月到七月和十月至十一月）。对于每次收集，数据总结在 BSNE_Samples_RegrMatrix.* 文件中（.txt 为制表符分隔，.rds 为 r 存档文件）。这些表格还包括从现场访问和当地土地政策中确定的关联土地利用描述。每个站点的所有空间数据集也总结在此表中。每个站点的地形和土壤的静态地图简单提取并附加到该站点的所有收集数据中。不同时间段可用的空间数据通过总结与 q 收集时间段匹配的给定变量的提取值进行总结（例如，站点的平均风速）。在 BSNE_Samples_RegrMatrix_ColumnDescriptions.xlsx 文件中记录了用于时间变化变量的多个统计摘要。
分析
随机森林数据降维
在分析中，首先使用随机森林数据降维策略来缩小潜在风蚀驱动因素。merge_rfe_figs.R 文件包括所有步骤，用于减少用于最终模型构建的变量数量，该步骤在下一节中使用线性混合模型完成。该脚本还包括了论文中查看 q 和解释变量之间关系的部分图。数据集中还包括在脚本中创建的 caret 递归特征消除对象（rf.RFE_flux.rds），以及脚本中创建的两个连续剪枝随机森林迭代（rf_pruned_flux.rds 和 rf_pruned2_flux.rds）。
线性混合模型
使用小样本大小的 AIC（赤池信息量准则）对模型进行训练和排名。LMMs_lme_flux.R 文件记录了模型的训练、排名和解释过程。通过报告斜率估计和效应量计算来解释排名最高的模型。使用效应图可视化解释变量之间的交互作用。
侵蚀潜力制图
在评估前两节中的模型控制之后，得出结论，q 的很大一部分变化可以通过仅收集研究中的空间数据源来表示。仅针对重要的空间变量构建了一个随机森林模型，然后将其渲染到研究区域的每个30米像素中。rf_mapping_andFigs.R 文件记录了构建空间模型、渲染预测地图、汇总模型变量重要性以及绘制部分变量依赖图以解释模型关系的流程。还包括此脚本中使用的 caret 递归特征消除对象（srf.RFE_flux.rds）和用于预测 q 的最终剪枝随机森林模型对象（srf.pruned_flux.rds）。每个解释变量的栅格层提供用于制作地图的2018年夏季收集的数据，可在 finallayers_sum18.zip 文件中找到，每个栅格文件名与 BSNE_Samples_RegrMatrix_ColumnDescriptions.xlsx 中记录的列名相匹配。
侵蚀预测地图数据
100cm_flux_sum18.*：研究区域预测 q 值的地理 TIFF 文件。
flux_map.qgz：QGIS 项目文件，具有预格式化的预测 q 值的可视化。
数据集中包含的资源：
资源标题：表格数据、R 代码模型和侵蚀预测地图
文件名：CO_Plat_dust_landuse_datarelease_v2.zip
资源描述：此 zip 文件包含所有原始沉积物收集数据、用于模拟沉积物运输的代码以及为2018年夏季创建的沉积物通量地图。
资源标题：映射协变量层
文件名：finallayers_sum18.zip
资源描述：此 .zip 文件包括代表用于预测科罗拉多高原风成质量通量的解释变量的栅格层。
Nauman, T.W., Munson, S.M., Dhital, S, Webb, N.P., Duniway, M.C. 在准备中。科罗拉多高原上土壤、土地利用和气候对风蚀的协同影响：管理启示。经过小幅修订后已接受，STOTEN。
这些层包括土壤属性、植被覆盖指标、地形和气候摘要。对于具有时间成分的层（即气候和植被），层针对每个像素在2018年夏季收集期（2018年7月17日至11月27日）进行总结。2018年的沉积物收集量是研究中的最高值，因此预测旨在表示高侵蚀期间的热点。还包括一个 R 脚本（layerprep.R），记录了所有包含的栅格如何总结和准备用于预测的流程。