共查询到19条相似文献，搜索用时 109 毫秒
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通过对η坐标数值预报模式预报的降水量检验分析，发现η坐标数值预报模式对青藏高原天气系统活动造成的四川盆地降水预报明显偏弱，且雨区偏北、偏西。我们使用风场资料对高原天气系统作自动识别，进行了对高原天气系统影响降水的强化与雨区漂移的处理，研究得出了η坐标数值预报模式释用强降水预报方法。通过对1991-2001年四川盆地发生泥石流、滑坡灾害的气象成因(强降水)分析，研究得出了四川盆地不同的地质地貌条件下泥石流、滑坡预测雨量标准。在上述研究基础上，建立了四川盆地泥石流、滑坡产生的强降水预报方法。经2003-08-09业务试运行，效果较好，较成功的预报了四川盆地西部、西南部3次大暴雨过程触发的多处泥石流、滑坡灾害，在防灾减灾中发挥了好的作用。 相似文献
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站点密度对泥石流当日雨量和前期有效雨量计算的影响 总被引：1，自引：0，他引：1
研究气象站点密度在当日雨量和前期有效雨量因子内插中的影响程度,对提高对这2个因子的内插精度有重要的意义。本文针对我国东南地区87个泥石流灾害点和207个站点的降水资料,建立站点密度在5%~100%间变化的空间采样方案,采用反距离加权法(IDW)计算不同站点密度下泥石流灾害点的当日雨量和前期有效雨量。利用分位数方法对站点密度与当日雨量和前期有效雨量的关系进行分析,结果表明:诱发泥石流灾害的当日雨量和前期有效雨量的内插值,均随着气象站点密度的降低呈减少趋势;前期有效雨量的内插值相对于当日雨量的内插要稳定一些。在站点密度大于5%的情况下内插的前期有效雨量可以保持60%以上的雨量值,而当日雨量只大于30%。 相似文献
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《山地学报》2019,(6)
在泥石流灾害预报模型研究中,科学确定泥石流灾害影响因子及保证模型较高的预报准确率和快速的训练速度是关键问题,也是泥石流灾害预报预警和防灾减灾的重要基础。本研究针对目前泥石流预报模型输入数据维度较大和训练时间较长的问题,采用快速多个主成分并行提取算法(Fast multiple principle components extraction algorithm,FMPCE),选取出6个泥石流灾害影响因子,包括降雨量、山坡坡度、沟床比降、相对高差、土壤含水率和孔隙水压力。基于宽度学习(Broad learning,BL)算法,以泥石流影响因子为输入,泥石流发生概率为输出,构建了泥石流预报模型,并用矩阵随机近似奇异值分解(矩阵随机近似SVD)对模型进行了优化,将优化后宽度学习模型的预报结果与梯度下降法优化的BP神经网络预报模型(GD-BP)、基于支持向量机的预报模型(SVM)、宽度学习预报模型(BL)的结果进行对比,同时,通过输入数据集的扩展,从训练时间上对不同模型进行比较。结果表明,优化宽度学习泥石流灾害预报模型的预报准确率为93.52%,较GD-BP模型、SVM模型和BL模型的预报准确率分别高出1.60%、1.15%和0.03%;优化宽度学习泥石流灾害预报模型的训练时间为0.9039s,较GD-BP模型、SVM模型和BL模型的训练时间分别节省了25.3867 s、17.2620 s和0.8974 s。本研究说明宽度学习算法可以用于对泥石流灾害的发生概率进行预报,同时也可为泥石流预报的实际应用提供新的思路。 相似文献
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良好植被区泥石流防治初探 总被引：10，自引：0，他引：10
通过对近年来发生在良好植被区的几次重大泥石流、滑坡灾害的考察,发现植被在泥石流、滑坡形成中的作用有待于进一步认识。通过分析,当中小强度降雨激发下,植被能够削减泥石流、滑坡灾害的规模,甚至抑制泥石流、滑坡灾害的发生;当降雨超过一定阀值后,在水作用下,植被不但不能削减灾害规模,反而增大灾害的规模。在这类地区,简单地使用一般的防治措施,已经不能满足防灾、减灾的需要,客观要求针对这些地区泥石流灾害的形成原因、危害特征,研究防治对策。经初步研究提出3点防治措施:1)加强泥石流滑坡灾害的预测预报工作;2)在重点区域设置自动雨量记录报警装置;3)特别针对漂木拦挡,采取新型结构减轻泥石流的危害。 相似文献
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玉溪滑坡泥石流与降水关系及气象预警预报研究 总被引：3，自引：1，他引：2
应用玉溪市的乡镇加密雨量站,分析2004-2006年不同地质结构山体滑坡泥石流与降水的关系.提出乡镇雨量点在气象地质灾害中三站平均雨量应用方法,重点分析了触发滑坡泥石流的降水类型、降水强度和地质结构.得出不同地质结构和降水条件下的滑坡泥石流等级预报指标,结合降水预报和实况降水的基础上建立玉溪市山体滑坡气象预警预报等级.2007年度试运行中准确率达83.3%,在气象防灾减灾中取得了突出成绩. 相似文献
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在收集广东省佛山市南海区西樵山一带已有地质环境、地质灾害调查、勘查与评价,以及气象、水文等资料的基础上,通过野外调查和测试试验等方法,全面查明西樵山泥石流的发育分布规律、形成条件、诱发因素,从泥石流的水源特征、物源条件和地形环境,系统研究西樵山泥石流的基本特征和形成原因。研究结果表明:强热带风暴"派比安"诱发形成的西樵山北侧山体斜坡一带的大量崩塌、滑坡及斜坡浅层松动土体是直接构成泥石流的固体物质来源;西樵山北侧山体斜坡中、下部地段沟谷地形高陡,为泥石流的运移提供了理想的地形地貌环境;2006-08-04西樵山一带发生的近50年来24小时最大降雨量(298mm)所引发的山洪,为西樵山泥石流活动提供了良好的水源环境。在此基础上,依据西樵山泥石流灾害的形成环境和危害状态,采取固坡、治沟、排水、清淤、排导、拦挡和恢复植被等综合措施治理泥石流灾害,西樵山泥石流治理工程的实施成效显著。 相似文献
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前期土壤湿度和降雨对小流域山洪预警指标的影响评估 总被引：1，自引：0，他引：1
山洪灾害临界雨量预警指标受多种因素影响,尤其是前期土壤湿度状况和降雨变化。基于分布式水文模型和情景分析法确定安徽省岳西流域的山洪灾害临界雨量集合,评估前期土壤湿度状况、雨型和预警时段对临界雨量变化的影响。研究表明：① 中国山洪水文模拟系统在研究区内具有较好的适用性,率定期和验证期的平均径流深和洪峰流量相对误差均在15%以内,平均峰现时间误差在1h以内,平均Nash-Sutcliffe系数为0.79和0.77;② 60种情景模式下,池墩组村的临界雨量集合为141~528 mm;③ 前期土湿状况和雨型均显著影响临界雨量变化,随着土壤饱和率从0.20分别增加到0.50和0.80,临界雨量分别减少13.7%~16.2%和26.8%~31.8%;短历时预警时,临界雨量由大到小的相应雨型分别为减弱雨型、中间雨型和增强雨型。研究可为山洪灾害预报和早期预警提供理论支持,也为中国山洪灾害防治提供参考和借鉴。 相似文献
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《山地学报》2015,(5)
2010-07-24,河南省栾川县境内普降暴雨,引发多处泥石流灾害(简称"7·24"泥石流),造成重大人员伤亡和财产损失。对泥石流的分布、类型、静动力学特征、灾害和损失进行总结,在此基础上,从降雨、地形和物源条件对泥石流成因进行了分析。结果表明:"7·24"泥石流分布范围主要沿着雨量高值区、地貌过渡区、人类活动区展布,泥石流类型多样,危害严重。丰富的前期降雨量,极高的日降雨量和短历时高雨强是泥石流的激发因素,充足的前期降雨量可以极大削弱物源地岩土体的强度。大部分泥石流沟的山坡坡度和沟床比降属于泥石流易发范围,说明栾川县的地形条件有利于泥石流的形成。区内泥石流物质来源丰富,通过"表观物源量"指标对物源进行分区发现,物源主要集中在区域性断裂和人类活动区附近。"7·24"泥石流是有利的地形、充足的物源和高强度的降雨共同作用的结果。 相似文献
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According to the principle of the eruption of debris flows, the new torrent classification techniques are brought forward. The torrent there can be divided into 4 types such as the debris flow torrent with high destructive strength, the debris flow torrent, high sand-carrying capacity flush flood torrent and common flush flood by the techniques. In this paper, the classification indices system and the quantitative rating methods are presented. Based on torrent classification, debris flow torrent hazard zone mapping techniques by which the debris flow disaster early-warning object can be ascertained accurately are identified. The key techniques of building the debris flow disaster neural network (NN)real time forecasting model are given detailed explanations in this paper, including the determination of neural node at the input layer, the output layer and the implicit layer, the construction of knowledge source and the initial weight value and so on. With this technique, the debris flow disaster real-time forecasting neural network model is built according to the rainfall features of the historical debris flow disasters, which includes multiple rain factors such as rainfall of the disaster day, the rainfall of 15 days before the disaster day, the maximal rate of rainfall in one hour and ten minutes. It can forecast the probability, critical rainfall of eruption of the debris flows, through the real-time rainfall monitoring or weather forecasting. Based on the torrent classification and hazard zone mapping, combined with rainfall monitoring in the rainy season and real-time forecasting models, the debris flow disaster early-warning system is built. In this system, the GIS technique, the advanced international software and hardware are applied, which makes the system′s performance steady with good expansibility. The system is a visual information system that serves management and decision-making, which can facilitate timely inspect of the variation of the torrent type and hazardous zone, the torrent management, the early-warning of disasters and the disaster reduction and prevention. 相似文献
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According to the principle of the eruption of debris flows, the new torrent classification techniques are brought forward. The torrent there can be divided into 4 types such as the debris flow torrent with high destructive strength, the debris flow torrent, high sand-carrying capacity flush flood torrent and common flush flood by the techniques. In this paper, the classification indices system and the quantitative rating methods are presented. Based on torrent classification, debris flow torrent hazard zone mapping techniques by which the debris flow disaster early-warning object can be ascertained accurately are identified. The key techniques of building the debris flow disaster neural network (NN) real time forecasting model are given detailed explanations in this paper, including the determination of neural node at the input layer, the output layer and the implicit layer, the construction of knowledge source and the initial weight value and so on. With this technique, the debris flow disaster real-time forecasting neural network model is built according to the rainfall features of the historical debris flow disasters, which includes multiple rain factors such as rainfall of the disaster day, the rainfall of 15 days before the disaster day, the maximal rate of rainfall in one hour and ten minutes. It can forecast the probability, critical rainfall of eruption of the debris flows, through the real-time rainfall monitoring or weather forecasting. Based on the torrent classification and hazard zone mapping, combined with rainfall monitoring in the rainy season and real-time forecasting models, the debris flow disaster early-warning system is built. In this system, the GIS technique, the advanced international software and hardware are applied, which makes the system's performance steady with good expansibility. The system is a visual information system that serves management and decision-making, which can facilitate timely inspect of the variation of the torrent type and hazardous zone, the torrent management, the early-warning of disasters and the disaster reduction and prevention. 相似文献
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According to the principle of the eruption of debris flows, the new torrent classification techniques are brought forward.
The torrent there can be divided into 4 types such as the debris flow torrent with high destructive strength, the debris flow
torrent, high sand-carrying capacity flush flood torrent and common flush flood by the techniques. In this paper, the classification
indices system and the quantitative rating methods are presented. Based on torrent classification, debris flow torrent hazard
zone mapping techniques by which the debris flow disaster early-warning object can be ascertained accurately are identified.
The key techniques of building the debris flow disaster neural network (NN) real time forecasting model are given detailed
explanations in this paper, including the determination of neural node at the input layer, the output layer and the implicit
layer, the construction of knowledge source and the initial weight value and so on. With this technique, the debris flow disaster
real-time forecasting neural network model is built according to the rainfall features of the historical debris flow disasters,
which includes multiple rain factors such as rainfall of the disaster day, the rainfall of 15 days before the disaster day,
the maximal rate of rainfall in one hour and ten minutes. It can forecast the probability, critical rainfall of eruption of
the debris flows, through the real-time rainfall monitoring or weather forecasting. Based on the torrent classification and
hazard zone mapping, combined with rainfall monitoring in the rainy season and real-time forecasting models, the debris flow
disaster early-warning system is built. In this system, the GIS technique, the advanced international software and hardware
are applied, which makes the system’s performance steady with good expansibility. The system is a visual information system
that serves management and decision-making, which can facilitate timely inspect of the variation of the torrent type and hazardous
zone, the torrent management, the early-warning of disasters and the disaster reduction and prevention. 相似文献
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泥石流预报中前期有效降水量的确定 总被引：14，自引：2，他引：14
前期有效降水量是泥石流预报的重要参数之一,对不同类型泥石流的形成有不同的影响形式,对土力类泥石流的形成主要是影响泥石流形成区土体的土壤含水量.在每次前期降水增加的土壤含水量和其有效降水量遵循相同衰减规律,以及每次前期降水的有效降水量和其增加的土壤含水量衰减过程都是相互独立的假设条件下,通过分析土壤含水量随时间的变化关系,可以得到前期有效降水量与前期降水量随时间的变化关系,从而可以确定前期有效降水量.通过对云南蒋家沟降水和土壤含水量的实际观测,对这个关系进行了分析研究,并利用最小二乘法得出了前期有效降水量的计算公式. 相似文献
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浙江地处我国东南沿海,近几年频繁遭受泥石流等山地灾害的危害。由于所处地理地质和气候环境,发育的泥石流有其独特的地域特征。从泥石流的发育特征、形成过程等探讨了浙江泥石流形成的机理,分析了其成灾特点。结果表明,浙江省泥石流为滑坡、崩塌转化型泥石流,泥石流的形成机理及过程主要为:强降雨下滑坡、崩塌发生,持续强降雨使得滑坡、崩塌松散物流化从而形成泥石流。滑坡、崩塌转化型泥石流在流域特征上表现为形成区坡度大,易发滑坡、崩塌,水力条件充分等。泥石流灾害具有低频性、群发性、并发性、突发性、夜发性、危害大等特点。 相似文献
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进行泥石流区域危险性评价时,首先要选取合适的单元作为评价单元。在前人的研究成果中已有多种评价单元,如栅格单元、斜坡单元、单一条件单元等。作者认为国内评价工作中常采用的栅格单元、行政区划单元都没有体现泥石流发生特定的地貌条件,故提出以流域为评价单元的思路。根据水文学D8方法,介绍了在计算机协助下基于DEM数据自动完成流域单元划分的方法,并以辽宁省鞍山市岫岩满族自治县作为实例,将全区划分为1024个流域单元。将流域划分结果与历史灾害点位置相对比,每个流域内基本上只包含一个灾害点,该情况说明流域划分精度满足研究工作需要。在此基础上利用人工神经网络进行泥石流区域危险性评价。结果显示,以流域作为评价单元能充分体现泥石流发生的地貌特征和物理机制,利于评价指标取值,而且危险性结果与实际情况吻合较好,可为政府部门制定减灾防灾决策提供参考。 相似文献
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在干旱少雨的山区开展小流域的暴雨山洪预报预警关键技术研究,对防灾减灾意义重大。2018年7月31日新疆哈密北部山区出现特大暴雨,发生罕见的山洪灾害,致使射月沟流域水库漫坝溃口,下游受灾严重。射月沟流域气象观测站点少且缺乏水文监测资料,为客观定量分析射月沟流域大暴雨面雨量、形成的洪水汇水量以及致灾水库过程。通过采用空间插值法和多源融合逐时降水资料（CMPAS）计算了射月沟水库上游面雨量并进行检验分析。根据不同面雨量驱动Floodarea模型得出射月沟水库上游累计汇水量,结果表明：多源融合降水产品估算所得最大洪峰流量和累计汇水量与水利部门事后调查数据较吻合,最大洪峰量为1 756 m3·s-1,精确性达到调查值的95%,射月沟水库上游暴雨山洪总量为2.64×107 m3,远超该水库的防洪库容和溢洪道承载能力。 相似文献