青海玉树台钻孔应变降雨干扰特征分析

以青海玉树钻孔应变资料为基础,对观测数据变化特征进行分析,认为降雨所造成钻孔水位的变化不是影响钻孔应变数据变化的主要因素,降雨造成周围环境地表水的变化影响了数据的趋势变化。总结归纳降雨对观测数据变化的影响,有效识别地震前兆异常和降雨干扰因素。     

安徽肥东形变台短水准降雨干扰特征分析

运用安徽肥东形变台短水准和降雨观测数据,分析了降雨对该台短水准日均值与月均值的干扰特征。在此基础上,根据观测场地膨胀土土体特性,探讨了降雨干扰短水准观测的问题,由于地表水下渗,致使膨胀土膨胀,对标志杆产生围压,从而影响测线高差。     

常熟地震台DSQ水管倾斜仪干扰分析

对常熟地震台DSQ水管倾斜仪3年多的资料进行初步分析,雷电降雨、气温突变、气压变化会使观测数据发生畸变,雷电干扰时观测数据产生台阶、脉冲,甚至仪器故障、水管换水会使观测数据产生较大幅度漂移,伸缩仪标定时的检修也会对水管仪产生干扰,停电后恢复供电会使数据产生漂移现象.     

徐州地震台伸缩仪观测干扰分析

徐州地震台伸缩仪运行至今主要受降雨及气压干扰,分析研究了降雨及气压对伸缩仪的影响,并进行了定量分析。结果表明,EW分量降雨的响应较为灵敏;观测数据短时间大幅加速上行的主要原因是仪器传感器铁芯在差动变压传感器中未居中,偏置的传感器铁芯在差动变压传感器中的感应面积增加,使得感应电压在观测中大幅增加。      

阿克苏岩体断层形变观测影响因素分析

对阿克苏台岩体断层形变测量仪观测资料进行分析,雷电降雨、气温突变、气压变化会使观测数据发生畸变。雷电干扰在观测数据曲线上表现为台阶、脉冲,仪器故障和观测室架设其他仪器会造成观测数据产生较大幅度漂移,检修仪器和停电后恢复供电则导致数据曲线出现台阶和突跳变化。     

锦州义县地电场干扰因素的识别与分析

本文主要利用锦州义县地震站＂十五＂观测以来的地电场分钟值资料,对观测中常见的干扰因素进行了详细的识别与归类,并对干扰特征和判别原理进行了分析与探讨。研究结果表明：在地电场观测过程中存在地电阻率同场观测干扰、磁暴干扰、雷电干扰、降雨干扰、游散电流变化干扰、电极不稳定引起的数据漂移及观测系统变化干扰、仪器死机、重启仪器造成数据丢失干扰等。     

包头地震台形变干扰分析

整理包头地震台伸缩仪、水管倾斜仪和水平摆倾斜仪观测资料,依据干扰类型分析发现,风扰、雷电降雨、温度与气压、标定与地震,仪器故障与停电是影响3台仪器观测的主要因素,干扰特征主要表现为缺记断记、数据突跳、尖脉冲、高频干扰、台阶、毛刺,大幅度变化、曲线加粗和趋势性变化等。不同类型观测仪器对干扰的响应不同,识别并剔除干扰,有利于地震观测数据应用,对地震异常判定具有参考价值。     

降雨对地电阻率干扰的分析

在一些地电阻率观测台站受本身台址条件的限制，观测深度较浅，因此，观测值受到降雨等气象因素的影响。本文选取昌黎台ＥＷ向１９８３－１９９４年观测资料月均值，用褶积滤波和多元回归方法对ρｓ进行了降雨校正，将将之作为一个灰箱系统，将资料外推至１９９５４上，然后与原始观测值作残差分析，取得了一定效果。     

东平台分量应变与体应变可靠性分析及特殊事件影响对比

本文通过四分量自检、相对标定内精度分析对东平地震台YRY-4钻孔分量应变观测资料进行可靠性分析,并结合滑动相关性计算、潮汐因子内精度对2套仪器数据进行了对比,证明YRY-4钻孔分量应变2面应变相关性高,相关系数接近1,相对标定内精度高,与体应变数据整体趋势一致。2套仪器观测环境一致,运行稳定,数据正常可靠,但也存在降雨、场地干扰特殊时段曲线表现不一致的情况。针对2种特殊情况结合相关性计算、S变换进行了对比分析。结果表明,降雨时段,因钻孔条件差别较大,2套仪器数据相关性较差,接近0;场地干扰时段,2套仪器观测系统均受到影响,频率域同步出现干扰信号,影响数据曲线形态及潮汐因子精度,但2套仪器面应变相关性达0.91。综合分析,东平台钻孔应变仪与体应变2套仪器观测系统是可靠稳定的。      

洞体形变数字化观测干扰分析

对山东省洞体形变数字化观测资料的干扰进行了分析讨论.分析认为:①山东省泰安,马陵山和烟台3个台形变观测资料的主要干扰是气象因子变化,观测环境变化和仪器故障;②气压,大风,降雨是导致观测资料出现异常变化的主要因素;气压的短周期突变可导致固体潮曲线畸变,大风引起固体潮曲线出现毛刺;降雨导致观测曲线趋势变化;③马陵山地震台垂直摆倾斜仪NS向能够清晰记录到附近地区塌方,爆破和小震引起的弹性形变波;④供电系统,传感器和数采器故障是导致观测数据丢失的最主要原因.     

合肥台地电阻率NS向趋势异常变化原因分析

合肥台NS测向地电阻率自2015年以来出现趋势下降异常,经核实2015—2016年降雨量增多和地下水位抬升与地电阻率趋势下降异常变化显著相关。通过褶积滤波法和相关分析法分别消除降雨和水位影响后,趋势下降变化不明显,从而认为此变化为干扰所致,非地震前兆异常,并探讨了这种干扰影响机制,为今后判断引起地电测值变化的原因提供了科学依据。     

CLUSTERING ANALYSIS OF DEBRIS-FLOW STREAMS

The Chi-Chi earthquake in 1999 caused disastrous landslides, which triggered numerous debris flows and killed hundreds of people, A critical rainfall intensity line for each debris-flow stream is studied to prevent such a disaster. However, setting rainfall lines from incomplete data is difficult, so this study considered eight critical factors to group streams, such that streams within a cluster have similar rainfall lines. A genetic algorithm is applied to group 377 debris-flow streams selected from the center of an area affected by the Chi-Chi earthquake, These streams are grouped into seven clusters with different characteristics, The results reveal that the proposed method effectively groups debris-flow streams.     

Study of the Difference between Seismogenic Deformation and Deformation by Permanent Tectonic Movement

Ground deformation may be caused by crustal movement and non-crustal movement.The non-crustal movements include those caused by temperature,rainfall,ground water,etc.Deformation caused by crustal movement includes seismogenic deformation(that appearing in the process of earthquake preparation and that directly caused by the earthquake)and general tectonic activities.The key point in the study of the relationship between ground deformation and earthquakes is how to eliminate information of deformation caused by non-crustal movement and permanent(or slow)tectonic deformation in observed data.In this paper an analysis is made on the short leveling data recorded at Dahuichang Deformation-Observing Station,Beijing,for 22 years(1970-1991)by using the LMS algorithm method after lateral adaptive filtering of the modern digital signal processing technique.Eliminating the approximate annual variation in deformation caused by non-crustal movement such as temperature,rainfall,ground water,etc.,a unified standard is     

Incorporating rainfall intensity into daily rainfall records for simulating runoff and infiltration into soil profiles

Hydrological modelling often implies the use of rainfall data. Its quality and resolution directly affect the accuracy of the simulation results. This study illustrates that a simple approach of incorporating rainfall intensity information in daily rainfall records significantly improves the simulation of surface runoff and rainfall infiltration into soil profiles. The procedure is developed using a frequency analysis on rainfall data of the Royal Meteorological Institute of Belgium, collected with a resolution of 10 min and for a consecutive period of 61 years. The frequency analysis of the data allowed the incorporation of rainfall intensity information into daily rainfall records. To test the effect of this approach the surface runoff and water flow into three different soil types was simulated using the HYDRUS‐1D model for a typical dry, normal and wet year. The simulation results whereby the observed 10‐min rainfall data was used as input were considered as the reference. Comparative analysis revealed that the simulations using the 10 min rainfall data deducted from the incorporation of rainfall intensity into daily rainfall records, deviate a maximum 1·2% from the reference and produce much better results than the Soil Conservation Service (SCS) runoff curve‐number method because rainfall intensity is considered in the procedure presented. The SCS curve‐number method typical overestimates surface runoff during periods of low rainfall intensity (winter) and underestimate runoff during periods of high rainfall intensities (summer). Copyright © 2002 John Wiley & Sons, Ltd.     

A two steps disaggregation method for highly seasonal monthly rainfall

 The need for high resolution rainfall data at temporal scales varying from daily to hourly or even minutes is a very important problem in hydrology. For many locations of the world, rainfall data quality is very poor and reliable measurements are only available at a coarse time resolution such as monthly. The purpose of this work is to apply a stochastic disaggregation method of monthly to daily precipitation in two steps: 1. Initialization of the daily rainfall series by using the truncated normal model as a reference distribution. 2.␣Restructuring of the series according to various time series statistics (autocorrelation function, scaling properties, seasonality) by using a Markov chain Monte Carlo based algorithm. The method was applied to a data set from a rainfall network of the central plains of Venezuela, in where rainfall is highly seasonal and data availability at a daily time scale or even higher temporal resolution is very limited. A detailed analysis was carried out to study the seasonal and spatial variability of many properties of the daily rainfall as scaling properties and autocorrelation function in order to incorporate the selected statistics and their annual cycle into an objective function to be minimized in the simulation procedure. Comparisons between the observed and simulated data suggest the adequacy of the technique in providing rainfall sequences with consistent statistical properties at a daily time scale given the monthly totals. The methodology, although highly computationally intensive, needs a moderate number of statistical properties of the daily rainfall. Regionalization of these statistical properties is an important next step for the application of this technique to regions in where daily data is not available.     

Fractal analysis of rainfall observed in two different climatic regions

Abstract

The importance of high-resolution rainfall data to understand the intricacies of the dynamics of hydrological processes and describe them in a sophisticated and accurate way has been increasingly realized. The present study investigates the general suitability of fractal (or scaling) theory for understanding the rainfall behaviour and transforming rainfall data from one time scale to another. The study, employing a multi-fractal approach, follows the research undertaken earlier by the author (Sivakumar, 2000) employing a mono-fractal approach in which some preliminary indication as to the possibility of existence of (multi-) fractals was obtained. Rainfall data of three different resolutions, six-hourly, daily, and weekly, observed over a period of 25 years in two different climatic regions: a subtropical climatic region (Leaf River basin, Mississippi, USA); and an equatorial climatic region (Singapore) are analysed. The existence of multi-fractal behaviour in the rainfall data is investigated using (a) the power spectrum method; (b) the empirical probability distribution function (PDF) method; (c) the statistical moment scaling method; and (d) the probability distribution multiple scaling (PDMS) method. The results achieved from all these methods for the six different rainfall data sets considered indicate the existence of multi-fractal behaviour of rainfall observed in Leaf River basin and Singapore, providing further support to the results obtained using the mono-fractal approach (Sivakumar, 2000). The suitability of a multi-fractal framework to characterize the behaviour of rainfall observed in the above two significantly different climatic regions, subtropical and equatorial, seems to suggest the general suitability of the fractal theory for transforming rainfall from one time scale to another. Investigations with rainfall data from several other climatic regions are underway with a view to strengthening the above conclusions.     

Validity and extension of the SCS‐CN method for computing infiltration and rainfall‐excess rates

A criterion is developed for determining the validity of the Soil Conservation Service curve number (SCS‐CN) method. According to this criterion, the existing SCS‐CN method is found to be applicable when the potential maximum retention, S, is less than or equal to twice the total rainfall amount. The criterion is tested using published data of two watersheds. Separating the steady infiltration from capillary infiltration, the method is extended for predicting infiltration and rainfall‐excess rates. The extended SCS‐CN method is tested using 55 sets of laboratory infiltration data on soils varying from Plainfield sand to Yolo light clay, and the computed and observed infiltration and rainfall‐excess rates are found to be in good agreement. Copyright © 2004 John Wiley & Sons, Ltd.     

The effects of climate change on historical and future extreme rainfall in Antalya,Turkey

Abstract

There is increasing concern that flood risk will be exacerbated in Antalya, Turkey as a result of global-warming-induced, more frequent and intensive, heavy rainfalls. In this paper, first, trends in extreme rainfall indices in the Antalya region were analysed using daily rainfall data. All stations in the study area showed statistically significant increasing trends for at least one extreme rainfall index. Extreme rainfall datasets for current (1970–1989) and future periods (2080–2099) were then constructed for frequency analysis using the peaks-over-threshold method. Frequency analysis of extreme rainfall data was performed using generalized Pareto distribution for current and future periods in order to estimate rainfall intensities for various return periods. Rainfall intensities for the future period were found to increase by up to 23% more than the current period. This study contributed to better understanding of climate change effects on extreme rainfalls in Antalya, Turkey.     

Analysing the Relationship Between Typhoon‐Triggered Landslides and Critical Rainfall Conditions

Many landslides are triggered by rainfall. Previous studies of the relationship between landslides and rainfall have concentrated on deriving minimum rainfall thresholds that are likely to trigger landslides. Though useful, these minimum thresholds derived from a log–log plot do not offer any measure of confidence in a landslide monitoring or warning system. This study presents a new and innovative method for incorporating rainfall into landslide modelling and prediction. The method involves three steps: compiling radar reflectivity data in a QPESUMS (quantitative precipitation estimation and segregation using multiple sensors) system during a typhoon (tropical hurricane) event, estimating rainfall from radar data and using rainfall intensity and rainfall duration as explanatory variables to develop a landslide logit model. Given the logit model, this paper discusses ways in which the model can be used for computing probabilities of landslide occurrence for a real‐time monitoring system or a warning system, and for delineating and mapping landslides. Copyright © 2007 John Wiley & Sons, Ltd.