DOI: 10.5281/zenodo.16748046
title: Dataset for High-Locality Landslides in Zhuji, China: Hydrological Monitoring Data, Terrain Features, and Soil Thickness

description: 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:



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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

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