基于 SAFI-Flow-R 方法的通用雪崩建模指数在测量条件较差地区的应用

IF 2.8 3区 地球科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Uroš Durlević, Aleksandar Valjarević, Ivan Novković, Filip Vujović, Nemanja Josifov, Jelka Krušić, Blaž Komac, Tatjana Djekić, Sudhir Kumar Singh, Goran Jović, Milan Radojković, Marko Ivanović
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引用次数: 0

摘要

全球大多数高山地区在冬季或全年都很容易发生雪崩。本研究开发了通用雪崩模型指数,适用于确定山区的雪崩危险。研究的第一步是实地收集数据,并通过地理信息系统和遥感技术进行处理。在 2023-2024 年期间,实地绘制了雪崩地图,随后,地理信息系统(GIS)中的雪崩点与研究地区的主要自然条件重叠。第二步是确定主要标准(积雪覆盖、地形坡度和土地利用),并对其数值进行评估,以获得雪崩形成指数(SAFI)。通过实地研究和雪崩形成清单获得的阈值用于制定雪崩形成指数。采用该指数的目的是确定容易形成雪崩的地点(雪崩源区)。计算中使用的数值包括归一化差异积雪指数(NDSI > 0.6)、地形坡度(20-60°)和土地利用(牧场、草地)。第三步是根据气象条件(冬季降水量和冬季气温)分析 SAFI 的位置。第四步是在 Flow-R 软件 2.0 中建立雪崩其他部分的传播(模拟)模型。结果表明,282.9 平方公里的研究区域(塞尔维亚萨尔山区)容易发生雪崩,可能引发雪崩的雪层厚度为 50 厘米。如果雪层厚度为 5 米,则有 299.9 平方公里的区域容易发生雪崩。使用 ROC-AUC 方法进行的验证证实了其极高的预测能力(0.94)。SAFI-Flow-R方法可为没有雪崩清单的地区提供雪崩模型,这对所有没有历史数据的山区来说都是一种进步。研究结果可用于土地利用规划、脆弱地区分区以及采取适当的环境保护措施。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Universal Snow Avalanche Modeling Index Based on SAFI–Flow-R Approach in Poorly-Gauged Regions
Most high-mountain regions worldwide are susceptible to snow avalanches during the winter or all year round. In this study, a Universal Snow Avalanche Modeling Index is developed, suitable for determining avalanche hazard in mountain regions. The first step in the research is the collection of data in the field and their processing in geographic information systems and remote sensing. In the period 2023–2024, avalanches were mapped in the field, and later, avalanches as points in geographic information systems (GIS) were overlapped with the dominant natural conditions in the study area. The second step involves determining the main criteria (snow cover, terrain slope, and land use) and evaluating the values to obtain the Snow Avalanche Formation Index (SAFI). Thresholds obtained through field research and the formation of avalanche inventory were used to develop the SAFI index. The index is applied with the aim of identifying locations susceptible to avalanche formation (source areas). The values used for the calculation include Normalized Difference Snow Index (NDSI > 0.6), terrain slope (20–60°) and land use (pastures, meadows). The third step presents the analysis of SAFI locations with meteorological conditions (winter precipitation and winter air temperature). The fourth step is the modeling of the propagation (simulation) of other parts of the snow avalanche in the Flow-R software 2.0. The results show that 282.9 km2 of the study area (Šar Mountains, Serbia) is susceptible to snow avalanches, with the thickness of the potentially triggered layer being 50 cm. With a 5 m thick snowpack, 299.9 km2 would be susceptible. The validation using the ROC-AUC method confirms a very high predictive power (0.94). The SAFI–Flow-R approach offers snow avalanche modeling for which no avalanche inventory is available, representing an advance for all mountain areas where historical data do not exist. The results of the study can be used for land use planning, zoning vulnerable areas, and adopting adequate environmental protection measures.
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来源期刊
ISPRS International Journal of Geo-Information
ISPRS International Journal of Geo-Information GEOGRAPHY, PHYSICALREMOTE SENSING&nb-REMOTE SENSING
CiteScore
6.90
自引率
11.80%
发文量
520
审稿时长
19.87 days
期刊介绍: ISPRS International Journal of Geo-Information (ISSN 2220-9964) provides an advanced forum for the science and technology of geographic information. ISPRS International Journal of Geo-Information publishes regular research papers, reviews and communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. The 2018 IJGI Outstanding Reviewer Award has been launched! This award acknowledge those who have generously dedicated their time to review manuscripts submitted to IJGI. See full details at http://www.mdpi.com/journal/ijgi/awards.
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