Dataset for High-Locality Landslides in Zhuji, China: Hydrological Monitoring Data, Terrain Features, and Soil Thickness

This dataset includes five distinct data files collected from a series of field studies, remote sensing data, and hydrological monitoring conducted in an 80 km² study area in Zhuji, eastern China (29°43′05′′N, 120°01′25′′E). These data are essential for understanding the factors influencing the high-locality initiation of shallow landslides and the environmental conditions that contribute to such instability. The dataset includes information on soil thickness, terrain profiles, landslide inventories, and observation data for soil moisture and pore pressure. Measured Soil Thickness Data: This data contains ninety high-quality soil thickness measurements obtained from various locations within the study area. These data were collected in the study area using invasive methods, ground-penetrating radar (GPR), and UAV photogrammetry. Slope Terrain and Soil Thickness Profile Data: This data includes detailed terrain and soil thickness profiles from the study area. It combines topographical data, such as slope angle and curvature, with soil thickness across different points. These profiles are valuable for examining how terrain characteristics and soil depth vary along the slope and contribute to the initiation of shallow landslides. Researchers can use this data to model the relationship between slope topography, soil thickness, and landslide susceptibility. Shallow Landslide Inventories: These inventories record the coordinates and relative slope positions of initiation zones for eight rainfall-induced, group-occurring shallow landslides worldwide, extracted using remote sensing data. The eight clusters of landslides occurred in the following locations: Santa Barbara, USA (2023); Hiroshima City, Japan (2014) [Ref. 1]; Cinque Terre, Italy (2011); Teresópolis, Brazil (2011) [Ref. 2]; Mon State, Myanmar (2019) [Ref. 3]; Fuyang District, China (2023) [Ref. 4]; Mibei Village, China (2019) [Ref. 5]; and Zhuji City, China (2021) [Ref. 6]. Slope Type and Landslide Initiation Position Data: This data includes slope types and relative slope positions where landslides initiate. K-Means Clustering algorithm is used to classify slope profiles based on terrain features extracted from the relative elevation data using a 1D Convolutional Neural Network (CNN). The landslide initiation position data is derived from remote sensing and geomorphological analysis. Soil Moisture and Pore Pressure Observation Data: This data contains observation data on soil moisture and pore pressure collected at multiple monitoring sites in the study area. The monitoring site W-1 is located at 120°00'44.24''E, 29°42'04.77''N; W-2 at 120°00'44.34''E, 29°42'05.10''N; W-3 at 120°00'44.82''E, 29°42'05.68''N; and W-4 at 120°00'45.71''E, 29°42'06.80''N. Each monitoring site is equipped with an SCYG319 water level probe (Star Meter Ltd.; range: 0-10 kPa; accuracy: ±0.1% F.S.) for measuring pore pressure at the bedrock surface during rainfall, and a set of CSF13 soil moisture sensors (Star Meter Ltd.; resolution: 0.001 m³/m³; accuracy: ±0.02 m³/m³) for monitoring infiltration dynamics. Reference: Watakabe, T., & Matsushi, Y. (2019). Lithological controls on hydrological processes that trigger shallow landslides: Observations from granite and hornfels hillslopes in Hiroshima, Japan. Catena, 180, 55–68. https://doi.org/10.1016/j.catena.2019.04.010 Hungr, O., Leroueil, S., & Picarelli, L. (2014). The Varnes classification of landslide types, an update. Landslides, 11(2), 167–194. https://doi.org/10.1007/s10346-013-0436-y Panday, S., & Dong, J.-J. (2021). Topographical features of rainfall-triggered landslides in Mon State, Myanmar, August 2019: Spatial distribution heterogeneity and uncommon large relative heights. Landslides, 18(12), 3875–3889. https://doi.org/10.1007/s10346-021-01758-7 Lü, Q., Wu, J., Liu, Z., Liao, Z., & Deng, Z. (2024). The Fuyang shallow landslides triggered by an extreme rainstorm on 22 July 2023 in Zhejiang, China. Landslides. https://doi.org/10.1007/s10346-024-02314-9 Feng, W., Bai, H., Lan, B., Wu, Y., Wu, Z., Yan, L., & Ma, X. (2022). Spatial-temporal distribution and failure mechanism of group-occurring landslides in Mibei village, Longchuan County, Guangdong, China. Landslides, 19(8), 1957–1970. 0 1. https://doi.org/10.1007/s10346-022-01904-9 Wang, F., Yan, K., Nam, K., Zhu, G., Peng, X., & Zhao, Z. (2022). The Wuxie debris flows triggered by a record-breaking rainstorm on 10 June 2021 in Zhuji City, Zhejiang Province, China. Landslides, 19(8), 1913–1934. https://doi.org/10.1007/s10346-022-01903-w本数据集包含5个独立数据文件，数据采集自中国东部诸暨市一处80 km²的研究区域（北纬29°43′05′′，东经120°01′25′′）内开展的一系列野外调查、遥感监测与水文监测工作。该数据集可为解析浅表层滑坡局地高发启动的影响因子及诱发此类失稳的环境条件提供核心支撑。 实测土层厚度数据：本数据集包含90组高质量土层厚度实测数据，采集自研究区域内多个点位，采用侵入式探测法、探地雷达（ground-penetrating radar, GPR）与无人机摄影测量技术完成数据获取。 坡面地形与土层厚度剖面数据：本数据集包含研究区域内的精细化地形与土层厚度剖面数据，整合了坡度、曲率等地形参数与不同点位的土层厚度信息。该剖面数据可用于探究坡面地形特征与土层厚度沿坡面的变化规律及其对浅表层滑坡启动的驱动机制，支持研究者构建坡面地形、土层厚度与滑坡易发性之间的关联模型。 浅表层滑坡编目数据：本数据集收录了全球范围内8处降雨诱发型群发性浅表层滑坡的启动区坐标与相对坡位信息，数据通过遥感解译提取得到。该8组滑坡群分别发生于以下区域：美国圣巴巴拉（2023年）、日本广岛市（2014年）[参考文献1]、意大利五渔村（2011年）、巴西特雷索波利斯（2011年）[参考文献2]、缅甸孟邦（2019年）[参考文献3]、中国富阳区（2023年）[参考文献4]、中国米贝村（2019年）[参考文献5]以及中国诸暨市（2021年）[参考文献6]。 坡型与滑坡启动位置数据：本数据集包含坡型分类与滑坡启动位置相关信息。研究采用K-Means聚类算法，结合一维卷积神经网络（1D Convolutional Neural Network, CNN）从相对高程数据中提取地形特征，对坡面剖面进行分类；滑坡启动位置数据则通过遥感与地貌分析方法获取。 土壤含水率与孔隙水压力监测数据：本数据集包含研究区域内多个监测点位的土壤含水率与孔隙水压力监测数据。各监测点位坐标如下： W-1：东经120°00'44.24''，北纬29°42'04.77''； W-2：东经120°00'44.34''，北纬29°42'05.10''； W-3：东经120°00'44.82''，北纬29°42'05.68''； W-4：东经120°00'45.71''，北纬29°42'06.80''。 每个监测点位均配备SCYG319型水位探头（Star Meter Ltd.; 量程: 0-10 kPa; 精度: ±0.1% F.S.），用于监测降雨期间基岩面的孔隙水压力；同时搭载一套CSF13型土壤含水率传感器（Star Meter Ltd.; 分辨率: 0.001 m³/m³; 精度: ±0.02 m³/m³），用于监测入渗动态。 参考文献： Watakabe, T., & Matsushi, Y. (2019). Lithological controls on hydrological processes that trigger shallow landslides: Observations from granite and hornfels hillslopes in Hiroshima, Japan. Catena, 180, 55–68. https://doi.org/10.1016/j.catena.2019.04.010 Hungr, O., Leroueil, S., & Picarelli, L. (2014). The Varnes classification of landslide types, an update. Landslides, 11(2), 167–194. https://doi.org/10.1007/s10346-013-0436-y Panday, S., & Dong, J.-J. (2021). Topographical features of rainfall-triggered landslides in Mon State, Myanmar, August 2019: Spatial distribution heterogeneity and uncommon large relative heights. Landslides, 18(12), 3875–3889. https://doi.org/10.1007/s10346-021-01758-7 Lü, Q., Wu, J., Liu, Z., Liao, Z., & Deng, Z. (2024). The Fuyang shallow landslides triggered by an extreme rainstorm on 22 July 2023 in Zhejiang, China. Landslides. https://doi.org/10.1007/s10346-024-02314-9 Feng, W., Bai, H., Lan, B., Wu, Y., Wu, Z., Yan, L., & Ma, X. (2022). Spatial-temporal distribution and failure mechanism of group-occurring landslides in Mibei village, Longchuan County, Guangdong, China. Landslides, 19(8), 1957–1970. https://doi.org/10.1007/s10346-022-01904-9 Wang, F., Yan, K., Nam, K., Zhu, G., Peng, X., & Zhao, Z. (2022). The Wuxie debris flows triggered by a record-breaking rainstorm on 10 June 2021 in Zhuji City, Zhejiang Province, China. Landslides, 19(8), 1913–1934. https://doi.org/10.1007/s10346-022-01903-w