Soil thickness map at two hillslopes near the pumphouse in the east river watershed, Colorado

The soil thickness maps were created by using a hybrid model-data approach. Field sampling and remote sensing data of the spatial distribution of two hillslopes in the Pump House area in the East River Watershed in the CO., the U.S. The data package includes the geospatial data of the soil thickness maps at two hillslopes near the pumphouse, and the associated remote sensing data, including lidar DEM and a shape file of the boundary of the study area. The data can be viewed in GIS software such as QGIS or ArcGIS desktop. The geospatial data can also be viewed in Python or Matlab. The data were generated for the purpose of modeling surface hydrology and near-surface chemistry. This work shows how to combine sampling data and a process-based model to predict one of the highest uncertainty in the land surface process, the soil thickness.本土壤厚度图集采用模型与数据融合的混合方法构建而成。数据源自美国科罗拉多州（Colorado）东河流域泵房区域内两处山坡的野外采样与遥感观测数据，用于表征该区域山坡的空间分布特征。本数据包涵盖泵房附近两处山坡的土壤厚度图地理空间数据，以及配套遥感数据集，包括激光雷达数字高程模型（LiDAR DEM）与研究区边界形状文件（Shapefile）。该数据集可通过QGIS、ArcGIS Desktop等地理信息系统（GIS）软件进行查看，其地理空间数据亦可在Python或Matlab环境中完成可视化与解析。本数据集的构建初衷为地表水文与近地表化学过程建模提供支撑。本研究展示了如何结合采样数据与基于过程的机理模型，对地表过程中不确定性最高的要素之一——土壤厚度进行预测。