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云南兰坪练登沟泥石流发育演化特征及成灾动力学过程模拟

刘刚, 胡卸文, 周永豪, 何坤, 刘文连, 眭素刚

刘刚,胡卸文,周永豪,等. 云南兰坪练登沟泥石流发育演化特征及成灾动力学过程模拟[J]. 中国地质灾害与防治学报,2025,36(0): 1-13. DOI: 10.16031/j.cnki.issn.1003-8035.202412023
引用本文: 刘刚,胡卸文,周永豪,等. 云南兰坪练登沟泥石流发育演化特征及成灾动力学过程模拟[J]. 中国地质灾害与防治学报,2025,36(0): 1-13. DOI: 10.16031/j.cnki.issn.1003-8035.202412023
LIU Gang,HU Xiewen,ZHOU Yonghao,et al. Development and evolution characteristics of debris flow and simulation of its catastrophic Dynamic process in Liandeng Gully, Lanping county, Yunnan Province[J]. The Chinese Journal of Geological Hazard and Control,2025,36(0): 1-13. DOI: 10.16031/j.cnki.issn.1003-8035.202412023
Citation: LIU Gang,HU Xiewen,ZHOU Yonghao,et al. Development and evolution characteristics of debris flow and simulation of its catastrophic Dynamic process in Liandeng Gully, Lanping county, Yunnan Province[J]. The Chinese Journal of Geological Hazard and Control,2025,36(0): 1-13. DOI: 10.16031/j.cnki.issn.1003-8035.202412023

云南兰坪练登沟泥石流发育演化特征及成灾动力学过程模拟

基金项目: 国家自然科学基金青年科学基金项目(42407212)。
详细信息
    作者简介:

    刘 刚(1999—),男,四川达州人,硕士研究生,主要从事地质灾害与防治工程研究,E-mail:dizhi123456@my.swjtu.edu.cn

    通讯作者:

    胡卸文(1963—),男,浙江金华人,博士,教授,博士生导师,主要从事工程地质、环境地质研究,E-mail:huxiewen@163.com

  • 中图分类号: P642.23

Development and evolution characteristics of debris flow and simulation of its catastrophic Dynamic process in Liandeng Gully, Lanping county, Yunnan Province

  • 摘要:

    练登沟历史上多次暴发泥石流灾害,频繁造成人员伤亡、阻断交通、堵塞河道等严重后果。通过野外实地调查和遥感解译,查明了练登沟流域沟道变化规律、物源变化特点及泥石流发育演化特征,运用数值模拟,分析了不同降雨频率下练登沟泥石流成灾动力学特征。结果表明,练登沟泥石流启动点位于主沟左侧的2号、4号、5号支沟源头,流域内沟道逐年扩宽,物源持续发育;在10年、20年、50年、100年一遇降雨频率下,泥石流沿途成灾过程相似,在沟口处最大流速分别为1.57 m/s、2.01 m/s、2.48 m/s、2.98 m/s,冲出规模分别为2.64万m3、5.17万m3、11.56万m3、18.76万m3;在10年一遇降雨频率下,其暴发泥石流就对沟口道路形成淤埋并堵塞河道,形成堰塞湖。针对练登沟泥石流成灾特点,建议沟域内采用固源固床、拦挡、排导等综合防治措施。结果可为高频泥石流发育演化特征研究及当地防灾减灾工程提供一定参考。

    Abstract:

    Liandeng Gully has historically experienced frequent debris flow disasters that often causes casualties, traffic disruptions, and river blockages. Based on field investigations and remote sensing interpretation, this study investigates the morphological changes in the gully, the characteristics of sediment sources, and the evolution patterns of debris flow. Using numerical simulation, the dynamic behavior of debris flow under various rainfall return periods was analyzed. The results indicate that debris flow initiation zones are concentrated at tributaries No. 2, 4, and 5 on the left side of the main gully, where channels have widened over time and material sources continue to accumulate. Under the rainfall frequencies of once every 10, 20, 50, and 100 years, debris flow disasters exhibit similar progression, with peak flow velocities at the gully outlet reaching 1.57 m/s, 2.01 m/s, 2.48 m/s, and 2.98 m/s, and debris volumes of 26,400 m3, 51,700 m3, 115,600 m3, and 187,600 m3, respectively. Under the 10-year return period rainfall scenario, debris flows can bury roads and block rivers, forming dammed lakes. In view of the characteristics of debris flow in Liandeng Gully, comprehensive mitigation and control measures are recommended, including source stabilization, channel bed reinforcement, barriers, and diversion works. The findings provide scitific supoprt for the study of high-frequency debris flow development and evolution, and serves as a reference for local disaster prevention and mitigation projects.

  • 地质灾害易发性评价是以地质环境条件为基础,参考地质灾害现状的静态因素来预测一定区域内发生地质灾害的可能性 [1]。地质灾害易发性评价方法分为定性和定量两类。定性方法主要包括专家评分 [2]、层次分析 [3]等。随着数据获取的便利、计算能力的提升以及评估模型的日趋完善,定量评价方法应用更为广泛,定量方法主要有信息量 [4]、确定性系数 [5]、证据权 [6]、逻辑回归 [7]、支持向量机 [8]、决策树 [9]、随机森林 [10]、神经网络 [11]等。其中确定性系数方法计算严密,可以解决多源数据类型的合并问题和影响因子内部不同特征区间对地质灾害易发性的影响 [12],但单一的确定性系数评价法没有考虑每个评价因素对地质灾害易发性的影响差异。逻辑回归( Logistic Regression,LR) 可以使用简单的线性回归来描述自然现象之间的复杂非线性关系,并根据影响因素与历史灾害点之间的关系确定影响因素的权重。文章基于地理信息系统,将研究区划分为栅格,选取海拔、坡度、坡向、地形曲率、归一化植被指数(Normalized Difference Vegetation Index,NDVI)、工程地质岩组、断层、道路、水系这9个孕灾、诱灾因素作为评价指标因子,采用频率比法(Frequency Ratio,FR)、确定性系数法(Certainty Factor,CF)量化评价指标因子,基于确定性系数法进行逻辑回归运算,计算研究区网格地质灾害发生的概率,得到地质灾害易发性分区图。

    频率比是建立在假设地质条件、孕育地质灾害的概率相似的地区。频率比重点考虑因子类别与地质灾害发生可能性的空间相关性,定量表示环境因子各属性区间对地质灾害发生的相对影响程度 [13-15],计算方法如式(1)。

    FRi=li/liLLsi/siSS (1)

    式中:FRi——频率比值;

    li——某个评价因子i类属性区间发生地质灾害的 个数;

    L——研究区内的总数;

    si——某个评价因子i类区间的面积;

    S——研究区总面积。

    FRi大于 1 表明该环境因子属性区间利于地质灾害发育,值越大表示对地质灾害发育的贡献也越大;反之,FRi小于 1 表明该环境因子属性区间不利于地质灾害发育。

    确定性系数模型假设将来发生地质灾害的条件和过去发生地质灾害的条件相同。CF 计算公式为:

    CF={PPaPPSPPS(1PPa)(PPa<PPS)PPaPPSPPa(1PPS)(PPaPPS) (2)

    式中:CF——地质灾害发生的确定性系数;

    PPa——地质灾害在因子分类数据a中发生的条件 概率,研究中通常用因子分类a中的地质 灾害个数与因子分类a的面积比值表示;

    PPS——地质灾害在整个研究区中发生的先验概率, 以研究区地质灾害总个数与研究区总面 积比值表示。

    由式(2)可知,CF的变化区间为[−1,1]。正值表示地质灾害发生的确定性大,越接近1越易于发生地质灾害;负值表示地质灾害发生确定性小,越接近−1越不易于发生地质灾害;值为 0 时表示条件概率和先验概率相同,不确定是否会发生地质灾害 [5]

    逻辑回归模型是研究二分类因变量常用的多元统计分析方法。自变量Xi 为控制灾害发生的影响因子。因变量Y属于二分类变量,通常 0 代表地质灾害不存在,1 代表地质灾害存在。用线性回归来描述自然现象之间复杂的非线性关系,揭示因变量和多个自变量之间的多元回归关系,将每个评价因子视为自变量,能很好解决滑坡易发性评价中出现的二分类变量问题 [16],逻辑回归函数如式(3):

    Z=β0+β1x1+β2x2++βnxnP(Y=1)=11+eZ} (3)

    式中:P——地质灾害发生的概率;

    Z——地质灾害发生概率的目标函数,表达为各因素自变量x1x2x3,,xn的线性组合;

    β1,β2,,βn——逻辑回归系数;

    β0——常数表示在不受任何有利或不利于地质灾害发生因素影响的条件下,地质灾害发生与不发生概率之比的对数值 [17]

    通过确定性系数模型计算得到各评价因子类别的值,将其结果作为逻辑回归模型中的自变量,建立回归方程,进行逻辑回归运算,得到各评价因子的逻辑回归系数,以此进行确定性系数–逻辑回归模型(CF-LR)进行地质灾害易发性评价。

    研究区沿河土家族自治县位于贵州省东北部,隶属铜仁市,南北长98.28 km,东西宽53 km,行政区域总面积2483.51km2,占贵州省总面积的1.4%,占铜仁市总面积的13.7%。沿河县境内有乌江及其支流洪渡河、暗溪河、白泥河、坝坨河等26条河流,河道长548.7 km,河网密度0.23 km/km2。地貌轮廓明显受地质构造控制,全县地貌“轴部成山,翼部成谷”。区内出露地层从老到新有震旦系、寒武系、奥陶系、志留系、二叠系、三叠系及第四系。受乌江切割和地层、岩性、构造的影响,在内外营力综合作用下,形成山峦叠障、沟谷纵横、复杂多样的地形地貌景观。区内历史地质灾害以滑坡、崩塌为主,共计130处,滑坡、崩塌分别占全县地质灾害的55.38%、33.85%。研究区地理位置及地质灾害分布如图1所示。

    图  1  研究区地理位置及地质灾害点分布
    Figure  1.  Geographical location and distribution of geological hazard in the study area

    结合研究区的地质背景、地质灾害形成条件及发育特征,初步选取海拔、坡度、坡向、地形曲率、归一化植被指数(NDVI)、工程地质岩组、距断层距离、距道路距离、距水系距离9个影响因素作为评价指标因子。数据源为沿河县地质灾害数据库、地理空间数据云平台获取研究区30 m×30 m数字高程模型(Digital Elevation Model,DEM)、1∶50 000的地质图、Google影像地图,利用ArcGIS平台通过DEM数据提取分析得到研究区坡度、坡向、地形曲率、河流网评价因子图层,通过Google影像地图矢量化得到道路数据,利用 landsat8 影像获得该区的归一化植被指数(NDVI)专题图。

    影响地质灾害发育的因素之间存在一定的关联,当评价因子之间存在多重共线问题时,会降低模型的预测精度,因而需对评价因素进行相关性分析。利用ArcGIS计算相关矩阵如表1所示,相关性系数绝对值最大为0.324,说明本文选取的9个评价指标因子之间相关性较弱,均可纳入研究区评价模型 [18]

    表  1  评价指标因子相关性系数矩阵
    Table  1.  Correlation coefficient matrix of evaluation index factors
    评价因子海拔坡度坡向地形曲率NDVI工程地质岩组断层缓冲区道路缓冲区河流缓冲区
    海拔1
    坡度−0.0091
    坡向0.0090.0591
    地形曲率0.1380.045−0.0041
    NDVI0.1540.094−0.0730.0321
    工程地质岩组−0.0040.004−0.016−0.010−0.0061
    断层缓冲区0.182−0.0020.0020.0040.0240.1041
    道路缓冲区0.1130.0810.0040.0090.0430.0070.0601
    河流缓冲区0.324−0.0420.0060.0240.0590.0750.0940.1461
    下载: 导出CSV 
    | 显示表格

    工程地质岩组为离散型因子,根据野外地质调查以及已有分类标准进行分类,连续型指标因子分类根据地质灾害比例进行等距离划分,各指标因子分级如图2所示,利用式(1)进行频率比计算确定性系数计算,利用式(2)进行确定性系数计算,结果见表2

    图  2  评价指标因子分级图
    Figure  2.  Grading of evaluation index factors
    表  2  评价指标因子分级、频率比、确定性系数
    Table  2.  Evaluation index factor classification, frequency ratio and certainty coefficient
    评价指
    标因子
    分级地质灾
    害频数
    分级面积
    /km2
    频率比CF评价指
    标因子
    分级地质灾
    害频数
    分级面积
    /km2
    频率比CF
    工程地
    质岩组
    坚硬岩组19908.6500.399−0.374地形
    曲率
    <−0.254842.3201.2250.194
    较坚硬岩组12433.8410.528−0.485−0.2~0.241804.5090.974−0.028
    较软岩组24354.6241.2930.239≥0.235836.6810.799−0.210
    软岩组24156.9082.9220.694道路缓
    冲区/m
    0~20010121.6081.5710.384
    软硬相间岩组51629.4871.5480.373200~4007110.0301.2150.187
    海拔/m209~40019125.5282.8920.690400~6008102.0961.4970.350
    400~60035630.0161.0610.061600~800896.1481.5900.391
    600~80046781.4361.1250.117800~1000491.3580.836−0.206
    800~100023557.5910.788−0.221≥1000931962.2700.905−0.099
    1000~12005329.8690.290−0.721河流缓
    冲区/m
    0~20018292.0501.1770.159
    1200~1408259.0680.647−0.366200~40020270.7621.4110.307
    坡度/(°)0~88360.7770.424−0.589400~60016276.5471.1050.112
    8~1644774.5341.0850.083600~80015263.6641.0870.084
    16~2447726.4151.2360.202800~100013249.4470.996−0.005
    24~3224407.5031.1250.117≥1000481131.0390.811−0.198
    32~405150.8860.633−0.380断层缓
    冲区/m
    0~30015263.9221.0860.083
    ≥40263.3950.603−0.410300~60013246.9681.0060.006
    坡向平面09.0520.000−1.000600~90013230.3451.0780.077
    17249.9941.2990.243900~120010202.0120.946−0.057
    东北19325.9201.1140.1081200~15008176.9010.864−0.143
    32390.8191.5640.381≥1500711363.3630.995−0.005
    东南14338.8930.789−0.220NDVI−0.02~0.19219.3310.784−0.225
    9253.1270.679−0.3330.1~0.225459.4781.0390.040
    西南21287.8071.3940.2980.2~0.3611008.8611.1550.142
    西7326.1640.410−0.6030.3~0.434757.6560.857−0.149
    西北11301.7340.696−0.3150.4~0.54138.1830.500−0.513
    下载: 导出CSV 
    | 显示表格

    海拔高度与降雨量、植被类型、植被覆盖等有着密切的关系,影响着人类工程活动程度,因此海拔间接影响着地质灾害的发育 [19],海拔高度209~1408 m,将其分为6个类别。

    坡度定量描述地面的倾斜程度,它的大小对斜坡表面径流量、斜坡表体土层剩余下滑力等都影响巨大,一定程度上影响着地质灾害发育的规模与强度 [20],研究区坡度最高达75°,以8°等间距分为5类,大于40°为1类,共计6个类别。

    不同坡向与岩体结构面的组合关系差异导致地质灾害发育的程度不同 [21],将研究区坡向分为9个类别。

    地形曲率是局部地形曲面在各个截面方向上形状、凹凸变化的反映,其值为正时表明边坡是凸面坡,为 0 时表明为平面坡,为负时表明边坡为凹面坡 [22],由于研究区平面坡(曲率等于0)面积极小,所以用曲率为−0.2~0.2代表近似平面坡,将其分为凹坡(<−0.2),近似平面坡(−0.2~0.2),凸坡(≥0.2)3类。

    归一化植被指数(NDVI)是遥感影像中近红外波段(NIR)的反射值和红光波段(R)的反射值的差与两者之和的比值,NDVI值的范围为 [−1 , 1],负值表示对可见光高反射,地面为江、河、湖泊等水体或有雪覆盖,0表示NIR和R近似相等,为岩石或裸地等,正值表示有植被覆盖,数值越大表示植被覆盖率越高 [23],研究区NDVI在−0.02~0.54之间,将其分为5个类别。

    岩土体是地质灾害发生的物质来源基础,岩石类型、坚硬程度决定岩土体的力学强度、抗风化能力和抗侵蚀能力 [19],研究区工程地质岩组分为5类,分别为坚硬岩组、较坚硬岩组、较软岩组、软岩组和软硬相间岩组。

    地质构造影响着岩体结构及其组合特征,对山区地质灾害发育起着重要的控制作用 [24],利用ArcGIS领域分析功能将研究区断层以300 m等距离提取缓冲区,得到6个类别。

    道路修建开挖坡体改变原有地质环境,破坏岩土体结构 [25],以200 m等距离提取道路缓冲区,得到6个类别。

    河流的侵蚀、侧蚀作用影响地质灾害的发育、且河流是控制坡面侵蚀的重要原因 [26],将研究区河流200 m等距离提取缓冲区,得到6个类别。

    通过对因子类别进行分类后,利用式(1)对各评价因子类别进行频率比计算,当频率比大于1时,说明该因子类别对地质灾害发育具有促进作用,如表3所示。

    表  3  频率比大于1的属性区间
    Table  3.  Attribute intervals with frequency ratio greater than 1
    评价因子海拔/m坡度/(°)坡向地形曲率NDVI工程地质岩组断层缓冲区/m道路缓冲区/m河流缓冲区/m
    频率比大于
    1类别
    209~4008~16< −0.20.1~0.2较软质岩0~3000~2000~200
    400~60016~24东北0.2~0.3软质岩300~600200~400200~400
    600~80024~32软硬相间质岩600~900400~600400~600
    西南600~800600~800
    下载: 导出CSV 
    | 显示表格

    利用ArcGIS以500 m距离制作灾点缓冲区,在500 m以外提取随机点130个非地质灾害点,与灾害训练样本组成训练集共计260个点。将9个评价指标因子的属性提取至训练集样本,导出后替换成评价因子的CF值导入SPSS软件中进行逻辑回归运算,各评价因子分类级别的CF值作为自变量,是否发生滑坡灾害作为因变量(0 表示未发生地质灾害,1值表示已发生地质灾害),LR-CF模型的逻辑回归运算结果如表4所示,其计算得到的所有评价指标因子的逻辑回归系数均为正数,表明所有评价指标因子对模型均起正向作用。在逻辑回归计算过程中,显著性sig ≤ 0. 05 则该回归系数有效,评价指标因子具有统计意义 [22]

    表  4  逻辑回归系数和显著性
    Table  4.  Logistic regression coefficient and significance
    评价因子海拔坡度坡向地形曲率NDVI工程地质岩组断层缓冲区道路缓冲区河流缓冲区常量
    β3.8442.4953.4184.0851.1984.3773.2180.7342.7282.604
    sig0.0000.0030.0000.0190.0230.0000.0270.0360.1300.000
    下载: 导出CSV 
    | 显示表格

    基于GIS平台,将评价指标因子图层自定义添加属性字段,对应输入计算的确定性系数,利用栅格叠加得到确定性系数模型评价图,利用自然断点法将沿河县地质灾害易发性区划为低易发区、中易发区、高易发区、极高易发区,其面积(频率比)分别为361.265 km2(0.159)、784.269 km2(0.414)、895.197 km2(1.003)、442.779 km2(2.718),如图3(a)和表5所示。利用栅格计算器按照公式(3)计算得到CF-LR模型地质灾害发生概率图,利用自然断点法将其分为低易发区、中易发区、高易发区、极高易发区,其面积(频率比)分别为671.252 km2(0.142)、467.758 km2(0.327)、927.527 km2(0.741)、507.145 km2(3.051),如图3(b)和表5所示。CF模型和CF-LR模型地质灾害易发性等级的频率比值均从极低易发区到极高易发区明显增大,表明有效评价了研究区地质灾害易发性。CF模型和CF-LR模型计算的极高易发区频率比值分别占总频率比值为63.3%和71.6%。说明CF-LR模型比单一CF模型评价精度更高。

    图  3  地质灾害易发性区划
    Figure  3.  Division of geological hazard susceptibility
    表  5  地质灾害易发性评价频率比值
    Table  5.  Frequency ratio of geological hazard susceptibility evaluation
    评价模型易发性
    等级
    分级面积
    /km2
    面积
    占比
    灾害点
    频数
    灾害
    占比
    频率比
    CF低易发区361.2650.14530.0230.159
    中易发区784.2690.316170.1310.414
    高易发区895.1970.360470.3621.003
    极高易发区442.7790.178630.4852.718
    CF-LR低易发区671.2520.27050.0380.142
    中易发区467.7580.18880.0620.327
    高易发区927.5270.373360.2770.741
    极高易发区507.1450.204810.6233.051
    下载: 导出CSV 
    | 显示表格

    本文使用ROC曲线来表示拟合数据和实测数据之间的关系,评价成功率或有效性以AUC值来表示(图4)。曲线中纵轴为敏感度,即实际地质灾害数量百分比累加量,横轴为特异性,即易发性面积百分比累积量,ROC曲线下面积AUC值越大表明模型评估效果越好 [27-28]。CF模型和CF-LR模型AUC值分别为0.722和0.818,说明CF和CF-LR评价模型均能够较为客观准确地对沿河县地质灾害易发性进行评价,且CF法进行逻辑回归后的CF-LR模型评价精度更高。

    图  4  ROC曲线
    Figure  4.  ROC curve

    (1)文中从选取的9个地质灾害影响因素的各类别的频率比值可以看出,在海拔209~800 m,坡度8°~32°,坡向朝向北、东北、东、西南,地形曲率小于−0.2,NDVI为0.1~0.3,较软质岩、软质岩、软硬相间质岩,距断层900 m、道路和河流800 m以内对沿河县地质灾害发育具有促进作用。

    (2)CF模型评价低易发区、中易发区、高易发区、极高易发区,其面积(频率比)分别为361.265 km2(0.159)、784.269 km2(0.414)、895.197 km2(1.003)、442.779 km2(2.718);CF-LR模型评价低易发区、中易发区、高易发区、极高易发区,其面积(频率比)分别为671.252 km2(0.142)、467.758 km2(0.327)、927.527 km2(0.741)、507.145 km2(3.051)。CF模型和CF-LR模型地质灾害易发性等级的频率比值从极低易发区到极高易发区明显增大,均有效评价了研究区地质灾害易发性。CF模型和CF-LR模型计算的极高易发区频率比值分别占总频率比值为63.3%和71.6%。

    (3)CF模型和CF-LR模型AUC值分别为0.722和0.818,均能够较为客观准确地对沿河县地质灾害易发性进行评价。但单一CF法没有考虑评价因素对地质灾害易发性的影响差异,在此基础上,LR法用线性回归来表示评价因子之间复杂非线性关系,考虑了评价因子的权重,使得AUC值提高了0.096,CF-LR模型具有更高的评价精度。

    由于研究区的地质灾害研究样本偏少,不为理想研究实验区,将影响评价效果和精度,对地质灾害易发性评价的精度还需进一步探索。

  • 图  1   练登沟泥石流工程地质平面图

    Figure  1.   Engineering geological plan of debris flow in Liandeng Gully

    图  2   练登沟主沟道纵断面图1-1’

    Figure  2.   Longitudinal section of the main gully in Liandeng Gully 1-1’

    图  3   研究区各月降雨量(2000年—2023年)

    Figure  3.   Monthly rainfall in the study area (2000—2023)

    图  4   流域内典型滑坡物源

    Figure  4.   Typical landslide material source in the watershed

    图  5   流域内典型沟道物源

    Figure  5.   Typical channel material sources in the Watershed

    图  6   雨季沟道典型影像图及照片[13]

    Figure  6.   Typical imagery and photos of the gully during rainy season

    图  7   主沟中游段2016—2023年形态变化图

    Figure  7.   Morphological changes in the middle section of the main gully (2016—2023)

    图  8   4号支沟及周围沟道2016—2023年形态变化图

    Figure  8.   Morphological changes in No. 4 branch and surrounding gullies (2016—2023)

    图  9   练登沟典型区域沟道宽度和滑坡滑动面积2017—2023年变化率

    Figure  9.   Change rate of gully width and landslide sliding area in typical sections of Liandeng Gully (2017—2023)

    图  10   1号滑坡2016—2023年形态变化图

    Figure  10.   Morphological changes of Landslide No. 1 (2016—2023)

    图  11   2号滑坡2016—2023年形态变化图

    Figure  11.   Morphological changes of Landslide No. 1 (2016—2023)

    图  12   练登沟“8•7”泥石流模拟运动过程图(左图为流速,右图为泥深)

    Figure  12.   Simulated movement process of the “8•7” debris flow in Liandeng Gully (velocity on the left, flow-height on the right)

    图  13   “8•7”泥石流实际堆积范围与模拟堆积范围

    Figure  13.   Comparison between actual and simulated accumulation ranges of "8•7" debris flow

    图  14   各降雨频率下监测点处泥石流流速、泥深变化图(a、b为1号监测点,c、d为2号监测点)

    Figure  14.   Changes in debris flow velocity and height at the monitoring points under different rainfall frequencies (a and b represent monitoring point No. 1, c and d represent monitoring point No. 2)

    图  15   各降雨频率下泥石流沟口堆积范围和深度

    Figure  15.   Accumulation extent and depth of debris flow at the gully mouth under different rainfall frequencies

    表  1   2000—2023年练登沟泥石流灾害史

    Table  1   Debris flow disaster History of Liandeng Gully (2000—2023)

    序号 暴发时间 灾害特征
    1 2000年5月 暴发中型泥石流灾害,造成数人死亡、失踪
    2 2002年7月 暴发小型泥石流灾害,损毁矿区矿坑
    3 2003年6—10月 暴发十余次小型泥石流,损毁沟内道路,淹没农田
    4 2005年7月 暴发小型泥石流灾害,造成轻微财产损失
    5 2010年9月 暴发小型泥石流灾害,轻微堵塞河流,
    影响交通道路安全
    6 2013年8月 暴发小型泥石流灾害,冲毁少量房屋和农田
    7 2018年7月19日 暴发中型泥石流灾害,阻断交通,
    堵塞河流,冲毁上百亩农田和8宅房屋
    8 2018年7月23日 暴发小型泥石流灾害,
    加剧了“7•19”泥石流灾害程度
    9 2019年8月7日 暴发中型泥石流灾害,阻断交通,
    堵塞河流,冲毁八十余亩农田和5宅房屋
    下载: 导出CSV

    表  2   练登沟流域主要地形特征

    Table  2   Main topographic features of the Liandeng Gully watershed

    区域 面积
    /(km2
    主沟长
    /(km)
    主沟纵
    坡降
    形态特征
    全流域 16.0 9.8 134‰ 整体呈树叶状,地势较陡峭,
    汇水条件好
    形成区 14.2 7.6 224‰ 面积大,地形较陡,呈漏斗状
    流通区 1.5 1.3 117‰ 沟道岸坡较陡,沟谷呈“U”型
    堆积区 0.3 0.9 61‰ 地势平坦,沟口开阔,呈喇叭状
    下载: 导出CSV
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出版历程
  • 收稿日期:  2024-12-12
  • 修回日期:  2025-01-12
  • 录用日期:  2025-05-13
  • 网络出版日期:  2025-05-24

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