Spatiotemporal Dynamics and Driving Mechanisms of Water Erosion in the Shiyang River Basin, Northwest China (2001–2020)

This dataset was developed to investigate the spatiotemporal patterns and driving factors of water erosion in the Shiyang River Basin from 2001 to 2020. Water erosion intensity in this region is influenced not only by natural factors such as precipitation and topography, but also significantly by land use change and vegetation dynamics. Moreover, these driving factors may interact to amplify erosion risks. The dataset includes the following components:
(1)Simulated Water Erosion Data:
Annual raster layers of potential soil erosion, transport capacity, and net water erosion rate, generated using an integrated RUSLE and TLSD model. These data capture both the potential for soil detachment and the actual sediment transport across the basin landscape. In addition, multi-year spatial trend data and inter-annual change maps are provided to illustrate erosion dynamics over time.
(2)Erosion Classification and Transition Data:
Net water erosion intensity was categorized into seven classes. The spatial variation in erosion severity and the area transition matrix across different years were compiled to assess temporal changes and spatial dynamics in erosion patterns.
(3)Driving Factor Analysis (Random Forest):
Output from the Random Forest model quantifies the relative importance of various natural and anthropogenic drivers of erosion. Summary tables and maps illustrate erosion responses under different environmental gradients, including topography, LUCC, and NDVI.
(4)Supplementary Data:
Raster datasets for all RUSLE input factors (rainfall erosivity, soil erodibility, slope and slope length, cover management, and support practice), NDVI, topsoil texture, and LUCC data are included. The dataset also provides statistics on net soil erosion rate, sediment deposition, and NDVI values across different slope classes and under various land use change scenarios.
Data Collection and Processing:
Climate and topographic data were obtained from national and international geospatial databases. Slope and slope length were derived from DEM data, and rainfall data were used to calculate rainfall erosivity. NDVI and land use/land cover (LUCC) data were extracted from MODIS and Landsat imagery and reclassified based on regional characteristics.
Soil erosion was simulated using a calibrated RUSLE-TLSD model. Model inputs include rainfall erosivity, soil erodibility, topographic factors, vegetation cover, and land management practices. Sediment yield observations were used for model validation.
Random forest analysis was conducted in R using the “randomForest” and “rfPermute” packages. The model was trained with 500 trees to evaluate the importance of environmental variables in driving net water erosion.
All raster datasets were resampled to a uniform 90 m resolution for spatial analysis.

本数据集旨在探究2001—2020年石羊河流域的水蚀时空格局及其驱动因子。该区域水蚀强度不仅受降水、地形等自然因素影响，同时也显著受土地利用变化与植被动态调控；且各类驱动因子间可能存在交互作用，进一步加剧侵蚀风险。本数据集包含以下组成部分：
(1) 模拟水蚀数据：采用耦合修正通用土壤流失方程（Revised Universal Soil Loss Equation，RUSLE）与土壤输移限制模型（Transport Limited Sediment Delivery，TLSD）生成的潜在土壤侵蚀量、输沙能力及净水蚀速率年度栅格图层。此类数据可同时表征流域景观内的土壤剥离潜力与实际输沙过程。此外，数据集还提供多年空间趋势数据与年际变化图，以展示侵蚀随时间的动态特征。
(2) 侵蚀分类与转移数据：将净水蚀强度划分为7个等级，并编制了侵蚀强度空间分布数据与不同年份间的面积转移矩阵，用于评估侵蚀格局的时间变化与空间动态。
(3) 驱动因子分析（随机森林（Random Forest））：通过随机森林模型量化各类自然与人为侵蚀驱动因子的相对重要性。配套的汇总统计表与空间分布图可展示不同环境梯度下的侵蚀响应特征，涵盖地形、土地利用/土地覆盖变化（Land Use and Land Cover Change，LUCC）与归一化植被指数（Normalized Difference Vegetation Index，NDVI）等维度。
(4) 补充数据：包含所有RUSLE输入因子栅格数据集，即降雨侵蚀力、土壤可蚀性、坡度与坡长、植被覆盖管理措施与水土保持措施；同时提供归一化植被指数（NDVI）、表层土壤质地与土地利用/土地覆盖（LUCC）数据。此外，数据集还包含不同坡度等级与各类土地利用变化情景下的净土壤侵蚀速率、泥沙沉积量与NDVI值统计数据。

数据采集与处理：
气候与地形数据来源于国内外地理空间数据库。坡度与坡长由数字高程模型（Digital Elevation Model，DEM）数据提取得出，降雨侵蚀力通过降水数据计算得到。归一化植被指数（NDVI）与土地利用/土地覆盖（LUCC）数据分别从中分辨率成像光谱仪（MODIS）与陆地卫星（Landsat）影像中提取，并结合区域特征进行重分类。
土壤侵蚀采用经过校准的RUSLE-TLSD耦合模型进行模拟，模型输入项包括降雨侵蚀力、土壤可蚀性、地形因子、植被覆盖与土地管理措施。研究使用产沙观测数据对模型进行验证。
随机森林分析通过R语言的"randomForest"与"rfPermute"包完成，模型以500棵决策树进行训练，以评估环境变量对净水蚀的驱动重要性。
所有栅格数据集均重采样至统一的90米分辨率，以支撑空间分析工作。