Chen Yueli, Zhao Linna, Wang Ying, et al. Review on forecast methods of rainfall-induced geo-hazards. J Appl Meteor Sci, 2019, 30(2): 142-153. DOI:  10.11898/1001-7313.20190202.
Citation: Chen Yueli, Zhao Linna, Wang Ying, et al. Review on forecast methods of rainfall-induced geo-hazards. J Appl Meteor Sci, 2019, 30(2): 142-153. DOI:  10.11898/1001-7313.20190202.

Review on Forecast Methods of Rainfall-induced Geo-hazards

DOI: 10.11898/1001-7313.20190202
  • Received Date: 2018-11-12
  • Rev Recd Date: 2019-01-18
  • Publish Date: 2019-03-31
  • The classification of geological hazards is very complicated for there being different methods according to different standards. Factors triggering geological hazards are divided into two categories:Internal and external. Internal factors mainly include geological and geomorphological factors, and external factors include precipitation, earthquake, volcanic eruption and human activities. The majority of the geological hazards are triggered by precipitation, especially heavy rainfall.Geological hazards including the debris flow, landslide and collapse triggered by rainfall are discussed. Geological hazard forecast plays a major role in the disaster risk reduction paradigm as cost-effective method to mitigate disaster damage. The geo-hazard forecasting mainly refers to the temporal and spatial warning in specific areas. Based on reviewing literatures related to rainfall-induced geological hazard prediction, related concepts are formulated, and previous researches are sorted and summarized. Afterwards, characteristics and application of the rainfall-induced geo-hazard prediction models are summarized, including the implicit statistics model, the explicit statistics model and the dynamic models. At present, the first-generation implicit statistic models considering precipitation characteristics are further developed into a second-generation explicit statistic models which consider rainfall factor, geology and geomorphology factors. Statistic models are widely used in the operational forecasting for their conciseness and convenience. However, the accuracy of the spatial and temporal simulating is limited because models can't simulate the physical mechanism of geological hazards. Geo-hazard early warning systems based on dynamic model can provide a better forecasting product with higher spatial and temporal resolution. The dynamic model is gradually developed from a slope stability model based on the theory of vertical infiltration to a coupled hydrological-geotechnical model.The geo-hazard forecasting model is the key of the early warning system. Lots of rainfall-induced geo-hazard early warning systems based on the statistic model have been set up in China. Meteorological models are used to forecast the rainfall in order to issue a warning with a given lead time. A complete geological hazards forecasting chain include the rainfall predicting, the disaster model, model results displaying, and the early warning releasing. The research foci of geological hazard forecasting have gradually expanded from the prediction model to the input of multi-source precipitation data, the display and release of early warning. Based on previous literature reviews and analysis, the coupled hydrological-geotechnical framework combined with multi-source forecasting precipitation data as an important direction for future development can be considered a useful geo-hazard risk mitigation measure to employ over widespread areas.
  • Fig. 1  Related articles about geological hazard forcast methods based on China National Knowledge Infrastructure

    Fig. 2  Geological hazard early warning systems all over the world except China(from Reference [3])

    Fig. 3  Illustration of the early warning system of regional rainfall-induced geo-hazards

    Fig. 4  Regions established geological hazard prediction system in China based on open data(the shaded)

    Table  1  Summary of geological hazard early warning systems in China

    预报区域 地质灾害类型 模型因子 降雨特征阈值 预警能力
    河北[98] 地质灾害 河北省山区地质灾害易发区分布图,1 h及3 h最大雨量,出现强降雨(1 h雨量大于20 mm)时多普勒天气雷达回波强度、回波顶高和垂直液态含水量 预警空间分辨率有待提高;预警等级分为高易发区、中易发区、低易发区和不易发区
    北京[101] 泥石流 以期建成单体化精确预报
    江苏[94] 滑坡、崩塌 易发性(山体坡度、地层岩性、斜坡结构、松散覆盖层厚度、人类活动特征,滑坡和崩塌发生密度),预报雨量,有效雨量 前5 d有效雨量、24 h预报雨量 预警空间分辨率有待提高;预警等级分为有一定风险、风险较高、高和很高
    浙江[40, 92] 滑坡 当日雨量,前1 d雨量,有效雨量,降雨持续时间,地质因子 预警空间分辨率有待提高;预警等级分为不易发生地质灾害、有可能发生、易发生和极易发生
    滑坡、泥石流 雨强-历时阈值;1 h,3 h,6 h,12 h和24 h累积雨量 预警空间分辨率有待提高
    湖北[99] 滑坡 前期雨量、气象台预报雨量 雨量-历时曲线 预警空间分辨率有待提高
    四川[50] 滑坡 滑坡危害危险性(坡度、地形起伏度、植被覆盖度、年平均雨量、地质岩性、地震烈度)、当日降雨概率化值、前1 d降雨概率化值、前2 d降雨概率化值 预警空间分辨率有待提高;预警等级分为一级预警、二级预警、三级预警(危险度最低)和无
    贵州[43] 地质灾害 地质灾害易发性(地震、地层、断层、土地利用、坡度、距河流远近、距主要道路远近),有效雨量 临界有效雨量 预警空间分辨率有待提高;预警等级分为一级(危险度最低)、二级和三级
    云南[104] 滑坡、泥石流 地理环境因子、临界雨量、前10 d中雨日数、临近24 h雨强 预警空间分辨率有待提高;预警等级分为可能性很小、可能性较小、可能性较大、可能性大和可能性很大
    广东[105] 地质灾害 地质环境背景、前期雨量、3 h预报雨量、地质灾害群测群防点资料 预警空间分辨率有待提高
    福建[41] 滑坡 雨强-有效降雨日数 预警空间分辨率有待提高
    新疆[49] 崩塌、滑坡、泥石流和地面塌陷 地质环境指标(海拔高度、斜坡坡向、地层岩性、地震动峰值加速度、水系密度、年平均雨量)、预报当日雨量、7 d有效雨量 预警空间分辨率有待提高;预警等级分为蓝色(危险度最低)、黄色、橙色和红色
    注:预报区域标注文献表示资料来源。
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    • Received : 2018-11-12
    • Accepted : 2019-01-18
    • Published : 2019-03-31

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