四川省冕宁县华岩子沟火后泥石流成灾机理

以四川省冕宁县腊窝乡华岩子沟2019年7月发生的火后泥石流为典型案例,通过对火烧迹地现场地质勘察、降雨模拟试验研究了与火后泥石流形成相关的地形地貌、火行为分布、松散物源规模、产流产沙特征以及相应的灰烬层、斥水性、渗透性等相关试验。结果表明:（1）火烧迹地的斥水性强度越强,径流产流量越大;（2）严重火烧区的产沙量明显高于中度和轻度火烧区;（3）火烧迹地的斥水性与火烈度大致呈正相关,而渗透性恰好与之相反。研究揭示了火后泥石流的演变过程,为火后泥石流的防治和预警提供了理论依据。     

基于灰色关联法的地震灾区泥石流危险性评价以北川县泥石流为例

汶川地震后,地震灾区泥石流具有暴发临界雨量小,规模大,危险性高的特点。在考虑降雨和地震作用下,采用灰色关联法分析北川县72条泥石流沟的泥石流规模、流域面积、主沟长度、流域相对高差、流域切割密度、不稳定沟床比、年均降雨量和地震烈度8个影响因子的权重,在此基础上建立震区泥石流危险性评价模型并进一步对其进行危险性评价。结果表明:影响因子中,年均降雨量和地震烈度所占权重最大; 运用本文模型得到的评价结果与刘希林模型基本一致,但危险度值相对提高,其中有7条泥石流沟危险度提高一个等级。     

土壤侵蚀产沙量的人工神经网络模拟

在分析土壤侵蚀产沙过程和神经网络模型特点具有某些相信的基础上,采用三层前馈网络模型（ＢＰ算法）,模型的第一层有５个结点,分别代表降雨强度、降雨历时、降雨量、前期降雨量（用前１０天降雨总量表示）、径流深;第三层只有一个结点,表示土壤侵蚀产沙量;隐层的结点数采用“试错法”确定为３个。利用四川某地水土保持试验观测资料,对土壤侵蚀产沙量进行模拟及预测,通过分析比较,显示了具有较子的模拟预测效果。     

台风暴雨型泥石流单沟危险度研究

台风暴雨型泥石流分布广泛并常造成重大灾害,有必要对其危险度进行研究。选取温州山区的14处典型台风暴雨型泥石流为研究对象,构建了基于组合因子的单沟泥石流危险度评价模型。选取泥石流规模（M）、泥石流频率（F）、冲沟纵比降（J）、形成区完整系数（C）、台风降雨综合值（E）和地质综合因子（G）来表征泥石流危险程度,并基于信息熵理论获得各组合因子权重值。针对研究区均为低频泥石流的特点,引入麦尔登比率（R值）以间接确定基于不同R值范围的泥石流爆发频率（F）。将评价模型应用于所选的典型沟谷,得到各沟谷泥石流危险度,评价结果符合研究区泥石流沟谷为中、小型低频泥石流的实际情况,并且评价为危险度高的沟谷具备区域上最易形成泥石流的地质地貌条件。     

成昆铁路泥石流防治工程效益综合分析

成昆铁路泥石流防治工作已历时２０余年，投资达２亿余元，共防治了２００余条泥石流沟。为考察已建工程的效益，总结经验教训，加强防治，笔者于１９９２－１９９３年选择了４０条泥石流沟的防治工程进行回访调查。这些工点包括不同类型与不同严重程度的泥石流沟及各类防治工程，具有较好的代表性。调查评估结果为：安全效益良好（Ａ级）的工点２８个，占７０％；一般（Ｂ级）的工点９个，占２２．５％；待改进（Ｃ级）的工点３个，     

降雨驱动泥石流危险性评价

“5·12”汶川地震后大量滑坡崩塌体出现,伴随极端降雨极易向泥石流转换,其规模及危害程度远高于预期.2010年8月13日都江堰龙池场镇突发暴雨,导致八一沟泥石流暴发,冲毁拦挡坝,掩埋道路、房屋及农田.为了探索降雨驱动泥石流的危险性,选取八一沟泥石流作为研究对象,通过分析不同降雨频率下的泥石流暴发强度及周期,采用FLO-2D数值模拟方法开展危险性评价.经验证模拟精度可达78%,结合降雨频率（5年、20年、50年、100年、200年）、流速和堆积深度构建八一沟泥石流危险性评价模型并绘制分布图.结果表明,八一沟泥石流危险范围内高危险区占62%,中危险性区占28%,低危险区占10%,该结论为危险范围内重点设施的监测预警提供科学依据.      

基于水槽试验的冰碛土泥石流启动机理分析

冰碛土启动形成泥石流在中巴公路沿线十分常见。前人对冰碛土特征变化及影响冰川泥石流形成的研究较少。为进一步探索泥石流暴发的原因,此次基于水槽试验,结合研究区的冰碛土物理力学特性进而探究冰碛土体形成泥石流的过程与机制。结果表明:（1）融水流量分别为8 L/min、12 L/min、16 L/min和56 L/min的情况下,冰碛土内部结构发生变化,其被冲刷时的破坏形式和启动过程具有差异性;（2）泥沙含量随融水流量和时间的不同而发生波动变化,泥石流冲刷启动和土体坍塌淤堵反复循环;（3）综合冰碛土特征变化及实验现象,将冰碛土泥石流的启动机理分为渗透饱和、侵蚀坍塌、冲刷启动三个部分;（4）冰碛土泥石流的稳定系数与水流流量呈幂函数关系。     

基于遗传规划的泥石流多因子融合预测方法

泥石流是一种多发的地质灾害，常对人民生命财产安全带来极大的威胁，其暴发不仅与降雨有关，还与众多地质环境因子相关。本文以流域面积、松散物质比率、沟床平均坡度为地质因子，以最大小时雨强（T）和总降雨量（R）的乘积作为降雨指数，在获取的泥石流地质因子和降雨指数因子综合样本库的基础上，采用遗传规划法建立了泥石流临界降雨指数智能预测模型，克服了以往以雨量为单一指标的预警模型的弊端，模型验证结果显示，泥石流预测精度高、适应性强。     

白龙江中上游泥石流形成条件与成灾模式探讨

白龙江中上游泥石流发育极为严重,危害、威胁巨大,研究该区泥石流的成灾模式和致灾模式对泥石流灾害防治、国土空间规划、生态文明建设具有重要的科学意义和实践指导价值。文中在分析白龙江流域中上游241条泥石流形成条件的基础上,对泥石流的成灾模式、致灾模式及泥石流防治进行了分析。研究结果表明:（1）泥石流受地形地貌影响显著,高差大于1000 m占总数74.3%,主沟沟床纵比降>200‰占总数93.4%;（2）众多滑坡体物源沿着坪定-化马断裂带呈条带状分布,泥石流物源主要来源于千枚岩、变质砂岩、残坡积层、黄土等软弱地层的崩塌、滑坡体,物源分布与下伏基岩坡向关系不大;（3）该区泥石流按地貌特征分主要有沟谷型泥石流（占总数69.7%）、坡面型泥石流（占总数30.3%）,基于固体物质补给方式划分泥石流成灾模式主要有重力侵蚀补给型（占总数71%）、坡面侵蚀冲蚀补给型（占总数10%）、沟床侵蚀补给型（占总数19%）;（4）泥石流主要的致灾模式为溃决-冲毁、冲积-淤埋、爬高-堆积、侵蚀-坍塌、淤埋-掩埋、堰塞-次生灾害;（5）重力侵蚀补给型泥石流重点防治沟内重大灾害体,坡面侵蚀冲蚀补给型泥石流防治以拦挡、排导工程为主,沟道侵蚀补给型泥石流防治以清淤、排导为主。     

甘肃省白龙江流域降雨型潜在泥石流危险性预报模型

【研究目的】泥石流灾害是白龙江流域分布广泛并常引起群死群伤的重大地质灾害,准确评价泥石流活动规模及其危险度,是泥石流危险性预警预报的前提,合理构建危险性预报模型是泥石流防灾减灾的关键。【研究方法】本文以研究区历史泥石流案例和对应降雨资料为基础数据,采用统计分析方法,通过分析形成泥石流关键地质环境条件及其相互关系,构建了白龙江流域潜在泥石流危险度定量评价模型,提出了两类泥石流危险级别临界判别模式。【研究结果】结果表明：（1）以泥石流活动规模、沟床平均比降、流域切割密度、不稳定沟床比例为判断因子的泥石流危险度动态定量计算模型,能快速准确预测未来不同工程情景和降雨频率工况下泥石流危险度;（2）影响降雨型泥石流发生的地形条件由流域面积、10°~40°斜坡坡度面积比、沟床平均纵比降等组成,降雨条件主要由泥石流爆发前的24 h累积降雨量、触发泥石流1 h降雨量或10 min降雨量等组成;（3）依据30条典型泥石流沟危险度计算结果,获得泥石流危险性临界判别值,提出了降雨型潜在泥石流危险性1 h预报模型（Ⅰ类）和10 min预报模型（Ⅱ类）,其中Ⅰ类模型高危险度以上泥石流预测精度大于87.5%,Ⅱ类模型中等危险度以上泥石流预测精度大于80%,而两类预报模型验证准确率为83.3%。【结论】研究成果为泥石流精准预警预报提供了技术支撑,对建立中小尺度泥石流实时化预警系统具有一定参考意义。创新点：通过确定与泥石流相对应关键地质环境因子,构建了泥石流危险度动态定量评价模型,依据泥石流危险性1 h和10 min临界判别模式可准确实现潜在泥石流预警预报。     

Geological and geomorphological influences on a recent debris flow event in the Ice-scoured Mountain Quaternary domain,western Scotland

Debris flows in settings that have experienced net glacial erosion within the UK's Ice-scoured Quaternary domain are the result of a complex interaction of a range of geological and geomorphological factors. On the 11^th of August 2016 a rainfall-triggered debris flow deposited 100 t of sediment onto local road and rail infrastructure blocking transport between town of Fort William and port of Mallaig in north-west Scotland. The debris flow occurred in an ice-scoured setting, where current 1:50,000-scale geological maps suggest that little or no sediment is expected on the valley slopes. In this study, we show how weathering and mass-wasting processes have interacted with bedrock structures to fill localised depressions with sediment on the upper parts of the slope. The intense rainfall event of August 2016 caused the destabilisation of this localised sediment, with eventual failure along bedrock joint surfaces resulting in two debris flows. This study demonstrates the combination of processes that can result in thick accumulations of sediment on slopes that are otherwise generally lacking in superficial sediment cover. These sediment accumulations have the potential to pose a significant landslide hazard in areas that might previously have been thought of as lower susceptibility. The research illustrates a need to improve understanding and representation of sediment thickness and distribution on hill slopes – particularly those that show an absence of superficial deposits at the scale of currently available geological maps.     

中巴经济走廊泥石流活动性分析 ——以中巴公路奥布段为例

中巴经济走廊内的中巴公路奥布段泥石流频发且类型复杂,严重影响着安全出行和贸易流通。在对中巴公路奥布段沿线泥石流沟谷纵剖面形态分析的基础上,揭示其形态指数特征和活动程度,并从区域地形、地质和气象等因素方面探讨了泥石流的活动性差异成因及危害性。研究发现：公路沿线泥石流类型主要包括冰川型和降雨型两种,冰川型泥石流为27条,降雨型为26条。冰川型泥石流活动性强烈,形态指数N ≥ 1的沟谷占冰川型沟谷总数的81%,多数沟谷形态呈下凹状;降雨型泥石流活动性相对较弱,形态指数N ≥ 1的沟谷占其总数的50%,沟谷形态多呈上凸状。研究区大落差地形、不同物源供给和充沛水源条件等对泥石流的发育和活动具有重要影响,也是不同类型泥石流活动性差异的控制因素。研究结果可为研究区泥石流预测和防治提供指导,也可为中巴经济走廊区内交通工程选线和泥石流防治提供参考。     

Rainfall-triggering response patterns of post-seismic debris flows in the Wenchuan earthquake area

Several giant debris flows occurred in southwestern China after the Wenchuan earthquake, causing serious casualties and economic losses. Debris flows were frequently triggered after the earthquake. A relatively accurate prediction of these post-seismic debris flows can help to reduce the consequent damages. Existing debris flow prediction is almost based on the study of the relationship between post-earthquake debris flows and rainfall. The relationship between the occurrence of post-seismic debris flows and characteristic rainfall patterns was studied in this paper. Fourteen rainfall events related to debris flows that occurred in four watersheds in the Wenchuan earthquake area were collected. By analyzing the rainfall data, characteristics of rainfall events that triggered debris flows after the earthquake were obtained. Both the critical maximum rainfall intensity and average rainfall intensity increased with the time. To describe the critical conditions for debris flow initiation, intensity–duration curves were constructed, which shows how the threshold for triggering debris flows increased each year. The time that the critical rainfall intensities of debris flow occurrences return to the value prior to the earthquake could not be estimated due to the absent rainfall data before the earthquake. Rainfall-triggering response patterns could be distinguished for rainfall-induced debris flows. The critical rainfall patterns related to debris flows could be divided on the basis of antecedent rainfall duration and intensity into three categories: (1) a rapid triggering response pattern, (2) an intermediate triggering response pattern, and (3) a slow triggering response pattern. The triggering response patterns are closely related to the initiation mechanisms of post-earthquake debris flows. The main difference in initiation mechanisms and difference in triggering patterns by rainfall is regulated by the infiltration process and determined by a number of parameters, such as hydro-mechanical soil characteristics, the thickness of the soil, and the slope gradient. In case of a rapid triggering response rainfall pattern, the hydraulic conductivity and initial moisture content are the main impact factors. Runoff erosion and rapid loading of solid material is the dominant process. In case of a rainfall pattern with a slow triggering response, the thickness and strength of the soil, high hydraulic conductivity, and rainfall intensity are the impact factors. Probably slope failure is the most dominant process initiating debris flows. In case of an intermediate triggering response pattern, both debris flow initiation mechanisms (runoff erosion and slope failure) can play a role.     

浅层滑坡诱发沟谷泥石流的地形和降雨条件

2011年贵州省望谟县打易镇的大范围浅层滑坡诱发的沟谷泥石流提供了研究这类泥石流地形和降雨条件的机会。在地质条件一致和小区域内的降雨条件基本一致的情况下,地形条件就是这些泥石流暴发与否的唯一决定因素。对比一些重要的地形因素与泥石流暴发的关系,得出了由流域面积、沟床纵比降和25°~45°山坡坡度面积比组成的泥石流综合地形因子T。在地形因子T的基础上,研究获得了由前期降雨量、1 h降雨强度、年平均降雨量等组成的降雨因子R。由地形因子T和降雨因子R获得的临界条件P可以判断该区域的泥石流暴发。由于研究工作部分基于泥石流的形成机理,研究成果还可用于其他区域的泥石流形成预测,为泥石流的预测预报提供了一个较好的方法。     

甘肃省舟曲8.7特大泥石流调查研究

本文通过对甘肃省舟曲县城后山三眼峪沟和罗家峪沟特大泥石流灾害的现场调查,从泥石流形成的地形、地质和降雨条件入手,分析了特大泥石流灾害的特征与成因:三眼峪沟和罗家峪沟泥石流形成区在2010年8月7日23～24时的1h降雨量达77.3mm,暴雨形成强大洪水依次冲毁两条沟内的天然堆石坝和人工拦挡坝,形成规模巨大的高容重黏性泥石流,泥石流冲出总量和泥沙总量分别为 144.2104m3和97.7104m3; 泥石流携带具有强大冲击力的巨石冲毁房屋5500余间; 在白龙江内形成长约550m,宽约70m,高约10m的堰塞坝并形成堰塞湖,堰塞湖回水长3km,使县城一半被淹; 泥石流造成1744人死亡和失踪。分析研究表明,三眼峪沟和罗家峪沟泥石流如果在近期遭遇强降雨还会暴发泥石流,但规模比87特大泥石流小;如果强降雨发生在数年后,暴发的泥石流规模比87特大泥石流略小;在20a或更长的时期内,没有发生新的地震影响下,在三眼峪沟和罗家峪沟经历一次大规模泥石流暴发后,泥石流的规模将回到汶川地震前的水平。     

Wildfire impacts on the processes that generate debris flows in burned watersheds

Every year, and in many countries worldwide, wildfires cause significant damage and economic losses due to both the direct effects of the fires and the subsequent accelerated runoff, erosion, and debris flow. Wildfires can have profound effects on the hydrologic response of watersheds by changing the infiltration characteristics and erodibility of the soil, which leads to decreased rainfall infiltration, significantly increased overland flow and runoff in channels, and movement of soil. Debris-flow activity is among the most destructive consequences of these changes, often causing extensive damage to human infrastructure. Data from the Mediterranean area and Western United States of America help identify the primary processes that result in debris flows in recently burned areas. Two primary processes for the initiation of fire-related debris flows have been so far identified: (1) runoff-dominated erosion by surface overland flow; and (2) infiltration-triggered failure and mobilization of a discrete landslide mass. The first process is frequently documented immediately post-fire and leads to the generation of debris flows through progressive bulking of storm runoff with sediment eroded from the hillslopes and channels. As sediment is incorporated into water, runoff can convert to debris flow. The conversion to debris flow may be observed at a position within a drainage network that appears to be controlled by threshold values of upslope contributing area and its gradient. At these locations, sufficient eroded material has been incorporated, relative to the volume of contributing surface runoff, to generate debris flows. Debris flows have also been generated from burned basins in response to increased runoff by water cascading over a steep, bedrock cliff, and incorporating material from readily erodible colluvium or channel bed. Post-fire debris flows have also been generated by infiltration-triggered landslide failures which then mobilize into debris flows. However, only 12% of documented cases exhibited this process. When they do occur, the landslide failures range in thickness from a few tens of centimeters to more than 6 m, and generally involve the soil and colluvium-mantled hillslopes. Surficial landslide failures in burned areas most frequently occur in response to prolonged periods of storm rainfall, or prolonged rainfall in combination with rapid snowmelt or rain-on-snow events.     

Application of back-propagation networks in debris flow prediction

Debris flows have caused serious loss of human lives and a lot of damage to properties in Taiwan over the past decades. Moreover, debris flows have brought massive mud causing water pollution in reservoirs and resulted in water shortage for daily life locally and affected agricultural irrigation and industrial usages seriously. A number of methods for prediction of debris flows have been studied. However, the successful prediction ratio of debris flows cannot always maintain a stable and reliable level. The objective of this study is to present a stable and reliable analytical model for occurrence predictions of debris flows. This study proposes an Artificial Neural Networks (ANN) model that was constructed by seven significant factors using back-propagation (BP) algorithm. These seven factors include (1) length of creek, (2) average slope, (3) effective watershed area, (4) shape coefficient, (5) median size of soil grain, (6) effective cumulative rainfall, and (7) effective rainfall intensity. A total of 178 potential cases of debris flows collected in eastern Taiwan were fed into the ANN model for training and testing. The average ratio of successful prediction reaching 93.82% demonstrates that the presented ANN model with seven significant factors can provide a stable and reliable result for the prediction of debris flows in hazard mitigation and guarding systems.     

The RUSLE erosion index as a proxy indicator for debris flow susceptibility

Debris flows represent dangerous occurrences in many parts of the world. Several disasters are documented due to this type of fast-moving landslides; therefore, natural-hazard assessment of debris flows is crucial for safety of life and property. To this aim, much current work is being directed toward developing geotechnical-hydraulic models for the evaluation of debris flow susceptibility. A common base for such current models is parameterization of background predisposing and triggering factors such as inherent characteristics of geo-materials, topography, landscape and vegetation cover, rainfall regime, human activities, etc. which influence the occurrence of these processes on slopes. The same factors are also taken into account in soil erosion prediction models. Consequently, it seems worth investigating the effectiveness of the soil erosion index as debris flows susceptibility indicator. To this aim, a logistic regression analysis was carried out between the erosion index assessed by means of the Revised Universal Soil Loss Equation (RUSLE) model and the inventory of debris flows that have occurred in an area in Sicily (Southern Italy). Model assumptions were verified and validated by means of a series of statistical tools. Different possible scenarios were also evaluated by considering hypothetical changes in soil erosion rate under different rain erosivity conditions. Notwithstanding the rough approximations in model data collection, the outcomes appear encouraging.     

An empirical approach for evaluation of the potential of debris flow occurrence in mountainous areas

Debris flows can occur relatively suddenly and quickly in mountainous areas, resulting in major structural damage and loss of life. The establishment of a model to evaluate the occurrence probability of debris flows in mountainous areas is therefore of great value. The influence factors of debris flows are very complex; they can basically be divided into background factors and triggering factors. Background factors include the mechanical characteristics of geo-materials, topography and landscape, and soil vegetation; and triggering factors include hydrological and rainfall conditions, and human activities. By assessing the dynamic characteristics of debris flows in mountainous areas, some important influence factors are selected here for analysis of their impacts on the occurrence probability of debris flow. A mathematical model for evaluation of the occurrence probability of debris flows is presented and combined with probability analysis. Matlab software is used for the numerical implementation of the forecasting model, and the influences of rainfall, lithology and terrain conditions on the occurrence probability of debris flows are analyzed. Finally, the presented model is applied to forecast the occurrence probability of debris flows in the mountainous area around Qingping Town; the simulation results show that many loose landslide deposits and heavy rainfall are the key factors likely to trigger debris flows in this region.