地温、气温、气压对精河台水平摆观测的影响

形变台站观测的目标值是地球内力所导致的形变变化。由于形变台站建立在地壳表层,不可避免地要受到各种因素的影响,这也就意味着在观测值序列中不同程度上包含了来自地球外部的信息。为了更好地研究精河台水平摆观测数据中所包含的地球内力所导致的形变,本文分析了地温、气温、气压对精河台水平摆观测值的影响。结果表明,(1)地温和气温是影响精河台水平摆观测值序列年变化的主要因素,其间具有准线性关系;(2)通过直线拟合去掉趋势性变化之后,观测值与气象因素的线性相关性明显增强,说明观测值序列的趋势变化不是气象因素造成的。     

库尔勒地震台地倾斜数据一致性及气象因素分析

利用别尔采夫滤波、一般多项式拟合等方法将库尔勒地震台地倾斜、气温、气压观测数据分解为日波、半日波、1～2月周期的月波和年周期变化的年波,然后再利用相关性分析研究库尔勒地倾斜不同频段的观测数据一致性及与气象干扰因素的关系。结果表明：(1)库尔勒地倾斜在日波、半日波频段一致性较好,均能较好地反映出固体潮的变化;(2)库尔勒地倾斜在月波频段的一致性较差,仅垂直摆EW分量与气压月波具有一定的相关性;(3)库尔勒地倾斜年波经相位平移之后,均与气温具有较高的线性相关性,结合物理机理分析库尔勒地倾斜年周期变化受温度影响的原因。     

新疆精河6.6级地震前温泉水平摆异常特征分析

2017年8月9日新疆精河发生6.6级地震,本文以温泉台水平摆观测数据为研究对象,首先研究温度、水位对其影响,结果表明,水平摆NS向倾斜量滞后气温约114天,它们之间的相关系数为-0.620195;EW向倾斜量滞后气温44天,相关系数为0.8449978;NS向倾斜量滞后水位约17天,相关系数为-0.6886279。然后,利用回归分析,选取适当的回归模型剔除温度、水位对温泉台水平摆观测数据的影响。最后,对回归残差序列进行分析。研究认为,精河6.6级地震前温泉台水平摆倾斜量的异常特征为:(1)NS分量自2015年10月29日之后的年变畸变,精河地震发生后,于2017年8月11日转向N倾,异常结束;(2)EW向倾斜量自2016年5月13日起出现持续E倾变化,截至地震发生时E倾幅度达0.88″。     

新疆温泉水平摆年变影响因素分析

以温泉水平摆观测数据及温泉气象资料为研究对象,利用相关、回归分析,讨论温泉水平摆两分向年变的影响因素,研究结果表明:①气温、水位是影响温泉水平摆NS向年变主要因素,它们之间具有准线性关系,NS向相位滞后气温约51天、滞后水位约17天;②气温是影响温泉水平摆EW向年变的主要因素,它们之间具有准线性关系,EW向滞后气温27天,但是水位与温泉水平摆EW向年变基本无关;③气压与温泉水平摆两分向年变基本无关。     

气温、降雨对巴里坤水平摆观测的影响分析

选取2011~2016年巴里坤水平摆观测资料及降雨、气温数据,采用形态法、年变曲线、相关和回归分析方法,研究了气温、降雨对巴里坤水平摆的干扰特征及机理。结果表明:(1)持续降雨可造成巴里坤水平摆月频段和日频段曲线形态的变化,表现为NS分量加速南倾,EW分量西倾,NS分量较EW分量更易受降雨干扰的影响;(2)通过一般多项式曲线拟合去趋势后,气温和水平摆两分量观测值的相关系数明显变大。与NS分量相关性的显著提高说明巴里坤水平摆NS分量的趋势性异常并非由气温引起;与EW分量相关性的提高说明EW分量的整体西倾与气温无关。(3)通过曲线回归拟合可知,气温是巴里坤水平摆年变频段信息的重要影响因素,EW分量与气温的回归拟合结果优于NS分量。     

新疆库尔勒断层氢气浓度的影响因素及其地震预测的潜在效能评价

以库尔勒断层H_2观测数据为研究对象,通过计算气压、气温与断层H_2浓度间的相关系数,分析了断层H_2浓度动态变化的影响因素,并利用Molchan图表法对断层H_2浓度映震效能进行检验,进而定量化地提取预测指标。结果显示:(1)气压和温度对库尔勒断层H_2浓度的变化有影响,表现为气压与断层H_2浓度呈正相关,相关系数为0.6735,温度与断层H_2浓度呈负相关,相关系数为-0.4262,气压对断层H_2浓度变化影响较大,温度影响较小;(2)Molchan图表法的库尔勒断层H_2浓度变化与地震间关系的检验结果反映出该测点断层H_2浓度映震效果较好,且地震预测优势对应时段为2个月内,最佳阈值为0.3392×10~(-6),该值可作为库尔勒断层H_2浓度在相对应的时间段内地震活动异常的判别指标,为震情判定提供参考依据。     

高精度氢实验台网观测数据浅析

断裂带H2浓度变化与断裂活动、地震发生的短临阶段有密切关系。在川滇菱形块体东边界安宁河断裂带和则木河断裂带选取典型构造位置,建设断层逸出氢气连续观测固定点5个,定点流动观测点12个,组成实验观测台网。在连续观测固定点开展高精度氢气浓度、气温、气压同机观测,同时观测同深度地温,进行高精度氢地震观测示范研究。本文主要以5个连续观测固定点2017年的观测数据为样本,结合流动观测数据,分析总结实验台网观测数据变化的日动态、月动态和年动态特征,浅析氢气浓度数据变化与地温、气温、气压的关系。     

气温、气压、地下水理论固体潮对新04井静水位的影响分析

以新04井静水位为研究对象,分析气温、气压和地下水理论固体潮对新04井静水位的影响特征,并探讨其机理.结果表明:① 气温、气压对新04井静水位长趋势变化和短周期变化影响均比较大,而地下水固体潮对新04井静水位变化影响很小;② 气温是静水位年变化最主要的影响因素,表现为中高度线性负相关,参与计算的2段观测数据中,2007...     

2017年精河6.6级地震异常变化数据跟踪分析

利用数据跟踪分析信息库,提取2017年8月9日精河6.6级地震前3个月,距震中500 km范围内的观测数据跟踪分析记录,对与地震相关的不明原因事件进行分析讨论。结果表明：精河地震发生前,温泉地震台水平摆、精河地震台水平摆、博乐32井水位出现不明原因的疑似前兆异常变化,地震结束后逐渐恢复正常。巴仑台地震台分量应变在此次地震前也出现不明原因变化,但地震结束后异常持续变化,需后续重点跟踪分析。     

库尔勒台DSQ水管倾斜仪和SQ-70石英倾斜仪观测资料对比分析

从DSQ水管倾斜仪、SQ-70石英水平摆倾斜仪的原理出发,结合实际观测,对库尔勒地震台两套倾斜仪器所记录的资料进行对比分析。通过地倾斜的趋势分析及合成矢量分析,表明两套仪器的观测结果是基本一致的,能够较好地反映监测区域大地倾斜的变化特征。两套倾斜仪潮汐分析结果与监测场的局部构造环境是一致的,但水管倾斜仪的观测精度明显高于水平摆倾斜仪。     

精河定点形变受气象因素影响探索

收集整理新疆精河地震台数字化形变资料和该地区的气象要素观测资料,利用相关和回归分析,研究精河定点形变与气象要素之间的关系和气象因素对精河数字化形变观测资料的影响特征,并给出精河数字化形变观测资料与气象要素之间的回归方程.     

地震前兆数据的稳健回归与建模

基于正态密度ψ函数的M 估计,建立了含有趋势和周期项组合的稳健回归数学模型,对含有离群值的仿真数据进行了最小二乘估计、稳健估计和修正离群值后的最小二乘估计.结果表明稳健估计可以克服最小二乘估计受离群值影响较大的弊病,模型参数更接近实际.对地倾斜和地下气体等前兆观测数据的实际算例表明,用稳健回归方法建立的数学模型避免了少数离群值的干扰影响,更加真实地反映了前兆观测数据的变化趋势,是前兆数据趋势变化分析的强有力的数学工具.     

山西临汾中心地震台数字化定点形变观测异常与地震对应关系分析

针对近几年临汾中心地震台的数字化定点形变资料,参照台站仪器工作日志,排除人为干扰、气象因素等台站已知干扰因素,综合分析总结临汾台数字化定点形变日均值、整点值、分钟值曲线在震前出现的各种异常图像,找出一定的判断规律,从而为地震预测及震后趋势判断提供科学依据。     

四川九寨沟7.0级、新疆精河6.6级地震山西定点形变异常分析

文章运用山西定点形变观测数据,分析2017年8月8日四川九寨沟7.0级地震、8月9日新疆精河6.6级地震的前兆异常,讨论了异常的信度。结果表明,九寨沟和新疆精河地震前,山西地区部分倾斜、应变仪器的观测数据出现了较为可靠的短临前兆异常变化,异常出现在震前3~180 d,主要表现为短时间内的破年变、转折、加速等。各定点形变台站在异常出现的时间上呈明显的准同步性特征。     

中国区域地理、地形因子对降水分布影响的估算和分析

不同与以往基于最小二乘的多元线性回归方法，本文首次尝试将新型的第二代回归分析方法——偏最小二乘回归分析方法应用到中国区域的降水建模中．利用区域内394个气象观测站建站到2000年45年(及以上)的降水资料，建立了一个简单的年、季降水量和地理、地形因子(包括纬度、经度、地形高程、坡度、坡向和遮蔽度)的关系模型，估算了区域降水量中地理、地形的影响部分，并分析了这种影响的特征．结果表明，用此方法建立的模型能够解释70%以上的因变量的变异，相关系数基本都在0.84以上，经交叉有效性检验，模型的回归效果较显著．分析表明，在多元线性回归不适用的情况下，本文基于偏最小二乘法的简单模型能够比较准确地定性、定量地再现实际降水分布．     

Dynamic factor analysis and artificial neural network for estimating pan evaporation at multiple stations in northern Taiwan

Abstract

Evaporation is an important reference for managers of water resources. This study proposes a hybrid model (BD) that combines back-propagation neural networks (BPNN) and dynamic factor analysis (DFA) to simultaneously precisely estimate pan evaporation at multiple meteorological stations in northern Taiwan through incorporating a large number of meteorological data sets into the estimation process. The DFA is first used to extract key meteorological factors that are highly related to pan evaporation and to establish the common trend of pan evaporation among meteorological stations. The BPNN is then trained to estimate pan evaporation with the inputs of the key meteorological factors and evaporation estimates given by the DFA. The BD model successfully inherits the advantages from the DFA and BPNN, and effectively enhances its generalization ability and estimation accuracy. The results demonstrate that the proposed BD model has good reliability and applicability in simultaneously estimating pan evaporation for multiple meteorological stations.

Citation Chang, F.J., Sun, W., and Chung, C.H., 2013. Dynamic factor analysis and artificial neural network for estimating pan evaporation at multiple stations in northern Taiwan. Hydrological Sciences Journal, 58 (4), 813–825.     

An integrated statistical approach to identify the nonlinear trend of runoff in the Hotan River and its relation with climatic factors

A number of studies have indicated a transition from warm-dry to warm-wet climate in Northwest China after the 1980s. This transition was characterized by an increase in temperature and precipitation, added river runoff volume, increased lake water surface elevation and area, and elevated groundwater table. However, some literatures showed that the Hotan River has presented a contrary situation, i.e. the runoff decreased, whereas temperature and precipitation increased. In order to discover the nonlinear runoff trend and its causes in the Hotan River, based on the related data from hydrological stations, ground and air sounding meteorological stations, this study applied a comprehensive method combing correlation analysis, wavelet analysis and regression analysis to investigate the runoff change in the Hotan River with its relevant climatic factors over the past decades. The main findings are: (a) the hydrological process of the Hotan River is a nonlinear system, with a periodicity of 24 year cycle, and it shows different nonlinear trends at different time scales; (b) the data from the ground meteorological stations in the Hotan area shows a false appearance that there is almost no correlation between runoff and temperature, and a little negative correlation between runoff and precipitation; (c) but the data from air sounding meteorological stations shows the truth that there is a close relation between the runoff in the Hotan River and the 0°C level height in summer on the north slope of Kunlun Mountains. The two variables present a same periodicity, i.e. 24-year cycle, having similar nonlinear trends and significant correlations at different time scales.     

The Runoff Declining Process and Water Quality in Songhuajiang River Catchment,China under Global Climatic Change

The runoff in Songhuajiang River catchment has experienced a decreasing trend during the second half of the 20th century. Serially complete daily rainfall data of 42 rainfall stations from 1959 to 2002 and daily runoff data of five meteorological stations from 1953 to 2005 were obtained. The Mann–Kendall trend test and the sequential version of Mann–Kendall test were employed in this study to test the monthly and annual trends for both rainfall and runoff, to determine the start point of abrupt runoff declining, and to identify the main driving factors of runoff decline. The results showed an insignificant increasing trend in rainfall but a significant decreasing trend in runoff in the catchment. For the five meteorological stations, abrupt runoff decline occurred during 1957–1963 and the middle 1990s. Through Mann–Kendall comparisons for the area‐rainfall and runoff for the two decreasing periods, human activity, rather than climatic change, is identified as the main driving factor of runoff decline. Analysis of land use/cover shows that farmland is most related with runoff decline among all the land use/cover change in Nenjiang catchment. From 1986 to 1995, the area of farmland increased rapidly from 6.99 to 7.61 million hm². Hydraulic engineering has a significant influence on the runoff decline in the second Songhuajiang catchment. Many large‐scale reservoirs and hydropower stations have been built in the upstream of the Second Songhuajiang and lead to the runoff decline. Nenjiang and the Second Songhuajiang are the two sources of mainstream of Songhuajiang. Decreased runoff in these two sub‐catchments then results in runoff decrease in mainstream of Songhuajiang catchment. It is, therefore, concluded that high percent agricultural land and hydraulic engineering are the most probable driving factors of runoff decline in Songhuajiang River catchment, China.     

Northern Dvina runoff simulation using land-surface model SWAP and global databases

The potentialities of a method for evaluating runoff from Northern Dvina basin, which is based on a model of heat and water exchange between land surface and the atmosphere (SWAP) in combination with input data based on global databases on land surface parameters and different variants of meteorological data (derived from reanalysis data; reanalysis data hybridized with ground based and satellite observations; observational data of meteorological stations situated in the river basin). In all three cases, an optimization was applied to some key model parameters, including the characteristics of the land surface and correction factors for precipitation and incoming radiation.     

Prediction of hourly actual evapotranspiration using neural networks,genetic programming,and statistical models

The complexity of the evapotranspiration process and its variability in time and space have imposed some limitations on previously developed evapotranspiration models. In this study, two data‐driven models: genetic programming (GP) and artificial neural networks (ANNs), and statistical regression models were developed and compared for estimating the hourly eddy covariance (EC)‐measured actual evapotranspiration (AET) using meteorological variables. The utility of the investigated data‐driven models was also compared with that of HYDRUS‐1D model, which makes use of conventional Penman–Monteith (PM) model for the prediction of AET. The latent heat (LE), which is measured using the EC method, is modelled as a function of five climatic variables: net radiation, ground temperature, air temperature, relative humidity, and wind speed in a reconstructed landscape located in Northern Alberta, Canada. Several ANN models were evaluated using two training algorithms of Levenberg–Marquardt and Bayesian regularization. The GP technique was used to generate mathematical equations correlating AET to the five climatic variables. Furthermore, the climatic variables, as well as their two‐factor interactions, were statistically analysed to obtain a regression equation and to indicate the climatic factors having significant effect on the evapotranspiration process. HYDRUS‐1D model as an available physically based model was examined for estimating AET using climatic variables, leaf area index (LAI), and soil moisture information. The results indicated that all three proposed data‐driven models were able to approximate the AET reasonably well; however, GP and regression models had better generalization ability than the ANN model. The results of HYDRUS‐1D model exhibited that a physically based model, such as HYDRUS‐1D, might be comparable or even inferior to the data‐driven models in terms of the overall prediction accuracy. Based on the developed GP and regression models, net radiation and ground temperature had larger contribution to the AET process than other variables. Copyright © 2010 John Wiley & Sons, Ltd.