Inventory of shallow landslides triggered by extreme precipitation in July 2023 in Beijing, China.

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Hao Ma, Fawu Wang
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引用次数: 0

Abstract

The extreme meteorological events caused by climate change have increasingly caused serious clustered landslides. A systematic and timely inventory of triggered landslides is a prerequisite for quantifying and evaluating the impact of extreme meteorological events. In addition, a landslide inventory can provide basic data for any subsequent analysis of the event or be used innovatively in landslide risk analysis. The Rainfall-induced Landslides in Beijing (RLBJ) inventory presented here contains data on 15,383 rainfall-induced shallow landslides triggered by a single extreme precipitation event in July 2023 in an area of ~3,250 km2 in western mountainous areas of Beijing, China. High-resolution satellite images before and after this rainstorm event were used to visually analyze the landslides. All landslides were reported as vectorized points and polygon features and classified according to their motion forms. This inventory is now freely available for the benefit of international geohazard researchers.

2023 年 7 月中国北京极端降水引发的浅层滑坡盘点。
气候变化导致的极端气象事件越来越多地引发了严重的群发滑坡。对引发的滑坡进行系统和及时的清查是量化和评估极端气象事件影响的先决条件。此外,山体滑坡清单还可为事件的后续分析提供基础数据,或创新性地用于山体滑坡风险分析。本文介绍的北京降雨诱发滑坡(RLBJ)清单包含 2023 年 7 月在中国北京西部山区约 3250 平方公里范围内由单次极端降水事件引发的 15383 次降雨诱发浅层滑坡的数据。此次暴雨前后的高分辨率卫星图像用于对滑坡进行直观分析。所有滑坡都以矢量化点和多边形特征的形式进行了报告,并根据其运动形式进行了分类。该清单现已免费提供,供国际地质灾害研究人员使用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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