Hazard characteristics and causes of the “7.22” 2021 debris flow in Shenshuicao gully, Qilian Mountains, NW China

Experimental Study on the Clogging Performance of Waste Slag

“…Therefore, based on the criterion of a coarse soil clogging pattern, combined with the variation in hydraulic gradient (Figure 3) and calculating the mass percentage of fine particles along the column (shown in Table 2) of the coarse slag, the clogging assessment criterion for coarse slags based on the ratio of coarse particles and fine particles is given: 1 ⃝ surface clogging pattern (SC), 4 < D e /d e ≤ 16, mass percentage of fine particle sediment on surface less than 50% and greater than 20%, and mass percentage of fine particles clogging in the front section of the column from 0 to 10 cm more than 50%, and the hydraulic gradient rising up significantly; 2 ⃝ internal partial pore blockage pattern (PB), 16 < D e /d e ≤ 32, mass percentage of fine particles clogging in the front section of the column from 0 to 10 cm less than 50% and mass percentage of fine particles clogging along the whole column more than 80%, and the hydraulic gradient rising up slightly slower than surface clogging; 3 ⃝ sediment or losing pattern, D e /d e > 32, and according to the mass percentage of sediment in the column and losing outside, sediment or loss pattern can be divided into two sub-patterns: 3 ⃝ (a) fine particle sediment (FS), 32 < D e /d e < 64, mass percentage of fine particles clogging along the whole column greater than 50% and loss greater than 30%, and the hydraulic gradient rising up slowly or no obvious change; Note: D denotes coarse slag particle size, mm; D e denotes the equivalent particle size of coarse slag particles, mm; d denotes fine particle size, mm; d e denotes the equivalent particle size of fine particles, mm; S denotes mass percentage of fine particles deposition on surface, %; C denotes mass percentage of fine particles clogging in the front section of column from 0 to 10 cm, %; W denotes mass percentage of fine particles clogging along the whole column, %; L denotes mass percentage of fine particles lost from the column, %; CP-S denotes clogging patterns for coarse slag; SC denotes surface clogging; PB denotes internal partial pore blockage; FS denotes fine particle sediment; UC denotes unclogging. In summary of the above experimental results, the critical limit of the mass percentage was 20% or 50% for coarse slag instead of 75%, different from the coarse soil clogging of the dam foundation.…”

“…With the continuous development of mineral resources, the accumulation rate of waste slag is much higher than that of natural sources [1]. At present, the waste slag in mining areas is mainly deposited on both sides of gullies or around river channels [2], forming the potential for a typical geologically and environmentally dangerous body with unstable deposits, that can easily induce slope instability, slag debris flow, river channel outbursts and blockages and other disasters [3]. The source of slag accumulation is different from that of generally accumulated soil and natural debris flow.…”

Assessment of geological hazard susceptibility based on remote sensing images

“…In order to better study geological hazards such as landslides, recent advances in satellite remote sensing and GIS technologies have played a key role [4]. Global Navigation Satellite System (GNSS) [5], Sentinel 2-satellite [6][7][8], Indian Remote Sensing Satellite (IRS)-1C [9], Google Earth Satellite Record (GESR) [10] and other remote sensing satellites are the essential data sources for obtaining time series data. In order to facilitate GIS analysis and vegetation assessment, highresolution remote sensing images are utilized to show the changes before and after regional catastrophes, and the report of Yan et al exemplifies the effectiveness of Sentinel-2 satellite images and provides a direct guide for the next step in the study of the triggering factors of mudslides [8].…”

“…Global Navigation Satellite System (GNSS) [5], Sentinel 2-satellite [6][7][8], Indian Remote Sensing Satellite (IRS)-1C [9], Google Earth Satellite Record (GESR) [10] and other remote sensing satellites are the essential data sources for obtaining time series data. In order to facilitate GIS analysis and vegetation assessment, highresolution remote sensing images are utilized to show the changes before and after regional catastrophes, and the report of Yan et al exemplifies the effectiveness of Sentinel-2 satellite images and provides a direct guide for the next step in the study of the triggering factors of mudslides [8]. Wang et al, in their study of spatial-temporal morphology before and after landslides' collapse, used the time of the occurrence of landslides as a node for the Sentinel-1 dataset before and after landslides were processed and analyzed [11].…”

“…As a method to improve the spatial resolution of images, image fusion is commonly used by researchers to fuse the information of high-resolution and geospatial-resolution image data for processing. For example, when processing images with different resolutions, the fusion of Landsat ETM+ panchromatic and multispectral data can preserve high spatial resolution panchromatic images with multispectral images to facilitate researchers' later interpretation of feature information [3,8,16,22].…”