Snow Avalanche Release Area Identification and Hazard Assessment Based on GIS and RAMMS Modeling: A Case Study of the Galongla Section of the Zhamo Highway, Xizang
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摘要: 随着人类活动和重大基础设施建设向高寒山区扩展，雪崩灾害风险日益凸显，生成雪崩危险性地图是一项至关重要的基础性工作.以西藏扎墨公路嘎隆拉段为研究区，结合GIS和RAMMS数值模拟技术，构建了基于DEM数据识别雪崩潜在释放区并进行大范围危险性评估的框架.设定了基于常规地形参数阈值的“一般情境”与评估潜在最大危险性的“极端情境”进行模拟对比.结果表明，在两种不同情境下分别识别出539个和526个潜在释放区. 一般情境下，雪崩影响面积为43.89 km2，占研究区总面积的54.58%；极端情境下，影响面积扩大至53.24 km2，占66.20%. 扎墨公路嘎隆拉段在两种情境下分别有16.7%和25.8%的路段处于高危险等级之中，最大雪崩冲击压力可达580 kPa以上.西藏扎墨公路嘎隆拉段的雪崩危险性可以划分为高、中、低、无4个危险等级，高危险区为防治工程的优先布局的“靶区”.将GIS-DEM参数识别与RAMMS大规模数值模拟相结合，提出了一种可移植的高效雪崩危险性评估框架，具有在类似数据匮乏的高寒山区推广应用的潜力.Abstract: As human activities and major infrastructure construction extend into high-altitude mountainous regions, the risk of snow avalanche disasters is increasingly severe. Generating avalanche hazard maps is a crucial foundational task. This study, focusing on the Galongla Section of the Zhamo Highway in Xizang, developed a framework for identifying potential avalanche release areas and conducting large-scale hazard assessments by integrating GIS and RAMMS numerical simulation technologies based on DEM data. This study defined a "general scenario" based on standard terrain parameter thresholds and an "extreme scenario" in order to assess the potential maximum hazard. The results indicate that under general and extreme scenarios, 539 and 526 potential release areas were identified, respectively. In the general scenario, the avalanche-affected area was 43.89 km2, accounting for 54.58% of the total study area; under the extreme scenario, the affected area expanded to 53.24 km2, representing 66.20%. Additionally, 16.7% and 25.8% of the highway section in the Galongla area were classified as high hazard level under the two scenarios, with maximum avalanche impact pressures exceeding 580 kPa. The avalanche hazard in the Galongla Section of the Zhamo Highway, Xizang, can be categorized into four hazard level levels: high, medium, low, and none. The high hazard zones are prioritized as target areas for mitigation engineering. This research establishes a portable and efficient avalanche hazard assessment framework by combining GIS-DEM analysis and RAMMS simulation, offering a practical solution for data-deficient high-mountain environments.
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Key words:
- snow avalanche /
- RAMMS /
- numerical simulation /
- hazard assessment /
- Zhamo highway /
- Qinghai-Xizang plateau
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图 3 识别PRA的输入数据
a.山体阴影；b.坡度；c. 地表粗糙度；d. 平面曲率；e. 坡向；f. 地表覆盖类型
Fig. 3. Input layers for PRA identification
图 5 RAMMS模型模拟雪崩的物理过程及其参数示意
Fig. 5. Schematic illustration of the physical processes and associated parameters in the RAMMS avalanche simulation model
图 6 一般情境下的PRA识别结果
a. PRA分布；b. 进一步细分的PRA；c. PRA的雪层断裂深度
Fig. 6. PRA identification results under general conditions
图 7 极端情境下的PRA识别结果
a. PRA分布；b. 进一步细分的PRA；c. PRA的雪层断裂深度
Fig. 7. PRA identification results under extreme conditions
图 9 基于光学卫星数据测绘的雪崩影响范围与模拟结果的对比
a. Sentinel-2卫星影像；b. 一般情景下雪崩遥感测绘结果与数值模拟结果的叠加；c. 局部放大图
Fig. 9. Comparison between avalanche-affected areas mapped from optical satellite data and simulation outputs
图 10 扎墨公路嘎隆拉段雪崩冲击压力及危险性分区
a. 一般情境下公路受到的雪崩冲击压力；b. 极端情境下公路受到的雪崩冲击压力；c. 雪崩危险性分区；底图来源于2022年2月7日的Sentinel-2卫星影像
Fig. 10. Avalanche impact pressure and risk zoning along the Galongla section of the Zhamo Highway
表 1 研究区不同情景下模拟结果的统计
Table 1. Statistics of simulation results under different scenarios in the study area
情境 PRA数量 PRA总面积(km2) PRA占研究区总面积的比例(100%=80.42 km2) 雪崩影响面积(km2) 雪崩影响面积占研究区总面积的比例(100%=80.42 km2) 一般情境 539 20.27 25.21% 43.89 54.58% 极端情境 526 30.70 38.17% 53.24 66.20% 
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