An analysis of Indian tide-gauge records

The paper presents an analysis of four Indian tide-gauge records. The stations were: Bombay, Madras, Cochin and Vishakhapatnam (Vizag). They were selected because of their reliability. There was no evidence of a monotonic rising trend at all four stations. The test by Mann and Kendall (loc. cit.) showed a rising trend at Bombay from 1940 to 1986 and at Madras from 1910 to 1933. The other records did not reveal a significant trend. The records reveal evidence of long-period cycles (50–60 year period), with shorter cycles (4.5 to 5.7-year period) riding on them. Spectral peaks corresponding to shorter cycles passed a false alarm probability test at 95% level of significance. The peaks were identified by computing periodograms and by maximizing the entropy of the time series. ARIMA models suggest a third order autoregressive model for Bombay and Madras (1953–1986). The remaining records only had a moving average component. Monthly tide-gauge data of Bombay reveal a 13.4-month cycle which was statistically significant. This was close to the 14.7-month Chandler wobble. But, an interaction between a 13.4-month and an annual cycle could not fully explain the observed short period cycles. Finally, the paper summarizes evidence to indicate that a pattern exists between fluctuations of monsoon rain and relative sea level at Bombay.     

北江干流年最大洪峰流量变化特征及其影响研究

利用北江干流4个水文站资料建立了年最大洪峰流量序列,运用线性回归分析序列的趋势,利用Mann–Kendall法识别突变点,应用经验模态分解(EMD)分析序列的周期,采用R/S法分析序列的持续性特征,并初步探讨了序列的影响因素。结果表明:4个测站的序列均呈不显著增加趋势,不存在显著突变点,有4~6a、8~10a等多个波动周期;4个序列都具有长期记忆性,未来仍将呈不显著增加趋势。相关性分析表明,南亚夏季季风是序列的显著影响因素,太阳活动则可能影响其周期性。     

A hybrid drought index combining meteorological,hydrological, and agricultural information based on the entropy weight theory

Drought assessment would be insufficient and unreliable when using the existing indicators based on a single variable (e.g., precipitation) or a combination of two variables (e.g., precipitation and runoff). Therefore, the entropy theory was utilized to develop a hybrid drought index (HDI) that combines meteorological, hydrological, and agricultural information based on precipitation, runoff, and soil moisture data, respectively, and it was applied to characterize the drought condition in Northwest China. Furthermore, the linkages between the atmospheric circulation anomaly/sunspot activities and the HDI series in Northwest China were explored through cross wavelet analysis. The results indicated that (1) HDI has a good performance to capture drought in Northwest China due to its consideration of multiple variables; (2) the annual HDI series in Northwest China was dominated by an insignificantly upward trend, except for Xinjiang, and this trend will be the opposite in the near future; and (3) generally, all of the sunspot activities, El Niño-Southern Oscillation (ENSO) events, AMO (Atlantic Multidecadal Oscillation), and PDO (Pacific Decadal Oscillation) had strong associations with the HDI series in Northwest China, in which sunspot activities had the strongest effects on drought conditions, whereas the AMO had the relatively lowest impacts. This study sheds new light on developing the hybrid drought index, and the findings are valuable for local drought mitigation.     

基于LSSVM-ARMA模型的基坑变形时间序列预测

如何准确预测和控制基坑变形是基坑工程的一个难点,提出了一种基于小波变换、粒子群优化的最小二乘支持向量机（PSO-LSSVM）和自回归移动平均模型（ARMA）的基坑变形时间序列预测方法。首先,利用小波变换将基坑变形时间序列分解和重构为2个子序列--趋势时间序列和随机时间序列,在该基础上,采用PSO-LSSVM模型与ARMA模型分别预测趋势时间序列与随机时间序列未来值,将2个子序列的预测值求和作为最终预测结果。最后,将该方法应用于昆明某基坑工程的深层水平位移预测,不断地利用前期工况的最新实测数据建模,对后期工况未来变形量进行滚动预测,获得了令人满意的结果。     

1961-2004年新疆气候突变分析

利用1961-2004年新疆53个气象台站逐日平均最高气温、平均最低气温和降水数据,采用Mann-Kendall法分别对北疆、天山山区和南疆气温、日较差和降水年序列进行了突变分析,同时利用滑动t检验和Yamamoto法对突变点的真伪做了验证.结果表明:1961-2004年北疆、天山山区和南疆年平均最低气温上升速率均明显高于年平均最高气温,年平均日较差均呈显著下降趋势,年降水量均有增加趋势.北疆年均最低气温、年平均日较差及年降水量分别在1988年、1979年和1984年发生了突变;天山山区年均最低气温、年平均日较差及年降水量分别在1985年、1983年和1992年发生了突变;南疆年平均最高气温、年均最低气温及年平均日较差分别在1993年、1991年和1981年发生了突变.20世纪80年代中期以来北疆、天山山区、南疆均进入增温多雨时期.这些结果对进一步研究和预测新疆气候有着重要意义,为研究全国甚至全球气候变化提供重要依据,同时也为决策者提供决策依据.     

Minimum, maximum, and mean annual temperatures, relative humidity, and precipitation trends in arid and semi-arid regions of Iran

One of the most important scientific concerns of the last few decades is climate change, which is the result of a great many factors like global warming. Although a number of studies have been dedicated to understand the phenomenon of climate change, more attention is required to understand the potential effects of global warming on the ecosystems as well as on human life. The present study was designed to survey the trends of minimum, maximum and mean temperatures, relative humidity, and the time series of annual precipitation and 10-year moving average low-pass filter in the 13 synoptic weather stations of Iran’s arid and semi-arid regions during the last 55 years by using τ Kendall test. The analyses indicate a significantly increasing trend for the minimum and mean temperatures while a decreasing trend for the mean relative humidity in the arid and semi-arid regions, especially during the last few years up to the year 2000. Any clear increasing or decreasing trend was not found for the maximum temperature, while the precipitation did not show any increasing/decreasing trend for most of the surveyed stations. Further studies, with long-term programming, are recommended to be carried out to evaluate the climate change and its effects on such regions.     

Using of gene expression programming and climatic data for forecasting flow discharge by considering trend,normality, and stationarity analysis

In this research, the main hydrological characteristics (such as trend, stationarity, and normalization of hydrological data) of the Kasilian watershed are considered from 1970 to 2009. For forecasting of discharge, gene expression programming (GEP) method is applied. Normality and stationarity of time series are necessary for application of GEP method. For this purpose, third edition of Mann-Kendall trend test and skewness test are used for detection of trend and normalization of data, respectively. Also, five methods are applied for detection of stationarity of data. Modified Mann-Kendall trend test and Theil and Sen’s median slope method illustrate that annual and monthly precipitation data have slight decreasing trend, annual and monthly discharge data have insignificant decreasing trend, and annual and monthly temperature data have an increasing trend. Skewness test illustrates that annual, monthly, and daily discharge and precipitation data are not normal. By using logarithm function, skewness is minimized and symmetry of data is improved. After normalization of time series by logarithm function, five methods are applied for testing of stationarity of time series. These methods show that different normalized time series are stationarity and stationarity of time series is improved by elimination of periodic properties of data. For forecasting of daily discharge by GEP method, 85% of data are used for training and 15% of data are used for testing. By using data of 3 days ago, the GEP has the best efficiency. Coefficient of correlation (CC), root mean square error (RMSE), mean absolute error (MAE), and mean absolute relative error (MARE) are 0.9, 0.495 lit/s, 0.288 lit/s, and 0.053, respectively.     

Short-run forecasting of United States coal production

Summary This paper focuses on short-range modelling and forecasting of aggregate US monthly coal production. The 1976–83 time-series data suggest a multiplicative autoregressive integrated moving average (ARIMA) model to replicate national level monthly coal production. The identified model required 12-month seasonal differencing and has an autoregressive component of lag 1 and a moving average component of lag 12. Model predictions for 1984 were very reasonable when compared with actual production: cyclical patterns were correctly replicated and the deterministic increasing trend was properly identified. The estimated model was enhanced by updating it with data for 1984. Intervention analysis was used to determine the impact of labour negotiations in coal production. Information relative to the identified ARIMA model was then used to model the intervening event of labour negotiations. Intervention modelling produced forecasts for 1984 superior to those identified by the ARIMA model. The mean predicted 1984 US monthly coal production of 1976–84 ARIMA and intervention models were 96.05 and 99.65% of the observed value of 74 178 thousand short tons per month, respectively. Simplicity of the ARIMA and intervention models, the realiability of their predictions, and the ease of updating make them very attractive when compared with large scale econometric models for use in short-term coal production forecasting.     

Analyses of precipitation,temperature and evapotranspiration in a French Mediterranean region in the context of climate change

This study is focused on the western part of the French Mediterranean area, namely the Pyrénées-Orientales and Aude administrative departments. The water resources (surface and groundwater) in the region are sensitive to climate change. The study addresses the question of whether any trend in the annual and monthly series of temperature, rainfall and potential evapotranspiration (PET) already appears at the scale of this region. Two data sources have been used: (a) direct local measurements using the meteorological network; and (b) spatially interpolated data from the French weather service model SAFRAN for the period 1970–2006. The non-parametric Mann–Kendall test was applied to identify significant trends at the local scale and, because of the natural spatial variability of the Mediterranean climate, regional interpretation was also performed. The trends observed in the 13 catchments of interest are consistent with those observed at a larger scale. An increase in annual mean temperature and annual PET was observed throughout the study area, whereas annual precipitation has not exhibited any trend. The monthly scale has revealed strong seasonal variability in trend. The trend for an increase in monthly PET has been observed mainly in the spring, and has not been seen in the coastal areas. A trend for an increase in monthly temperature has been observed in June and in the spring throughout the entire area. Monthly rainfall has been found to decrease in June and increase in November throughout the area. The significant trends observed in rainfall and temperature seem to be consistent between the different data sources.     

The latitude distributions of sunspots and its North-South asymmetry

The temporal variations observed in the monthly mean latitudes of sunspot groups are studied over 1874–2010 using the data of the Greenwich Catalog and its NOAA-USEF extension. The 11-year cycle is quite clear in the temporal variations of the monthly mean latitudes of sunspot groups (i.e., of the centers of spotting) in both the northern and southern hemispheres. The North-South (N-S) asymmetry in the latitudes of sunspot groups defined as the difference between the absolute values of sunspot latitudes observed in the N and S hemispheres is compared with the N-S asymmetry in the total area of sunspot groups determined on the scales of 11 years and longer. The N-S asymmetry is interpreted as an imbalance in the hemispheres’ powers (asymmetry in the total area of sunspot groups) and as spatial imbalance (asymmetry in the latitudes of the centers of spotting). This imbalance is most clearly seen at the solar minima, i.e., in the gradual transition from one cycle to the other, when the absolute values of the asymmetries observed both in the total sunspot area and in the sunspot latitudes reach their maxima. The results obtained here can be helpful for analyses of the solar dynamo.     

Latitude characteristics of the sunspot formation zone and the 11-year solar activity cycle

Using data of the extended Greenwich sunspot catalog for 1874–2006, annual average values of some quantities characterizing the latitude distribution of sunspot activity have been calculated. The quantity describing the width of the sunspot formation zone is closely correlated with the corresponding Wolf numbers. A latitude characteristic has been found that demonstrates in a particular time interval in the fourth year after the maximum of the current 11-year cycle a high correlation with the Wolf number at the maximum of the next cycle. This time interval is characterized by extreme differences between the speeds of the motion of the mean latitude and the upper boundary of the sunspot formation zone. A model displaying good stability and enabling forecasting of the amplitudes of the next 11-year cycles is constructed based on the found correlation. According to these forecasts, the activity of the next (24th) cycle will be 20–30% higher than in the previous one.     

Univariate modelling of summer-monsoon rainfall time series: Comparison between ARIMA and ARNN

The present article reports studies to develop a univariate model to forecast the summer monsoon (June–August) rainfall over India. Based on the data pertaining to the period 1871–1999, the trend and stationarity within the time series have been investigated. After revealing the randomness and non-stationarity within the time series, the autoregressive integrated moving average (ARIMA) models have been attempted and the ARIMA(0,1,1) has been identified as a suitable representative model. Consequently, an autoregressive neural network (ARNN) model has been attempted and the neural network has been trained as a multilayer perceptron with the extensive variable selection procedure. Sigmoid non-linearity has been used while training the network. Finally, a three-three-one architecture of the ARNN model has been obtained and after thorough statistical analysis the supremacy of ARNN has been established over ARIMA(0,1,1). The usefulness of ARIMA(0,1,1) has also been described.     

基于DEM的天山山区气温和降水序列推算方法研究

为了计算天山山区区域气候要素平均值序列,提出了一个新的气候序列计算方案.对天山山区17个气象站和10个水文站的1961-2000年的年气温、降水资料进行了自然正交分解(EOF),并以DEM(Digital Elevation Model)的1 km×1 km网格数据为基础,结合多元回归等方法,分别建立了前3个特征向量与经度、纬度及海拔高度因子的插值模型,由此推算出天山山区(海拔≥1500 m)年平均气温、降水序列.误差分析表明,27站实测的年平均气温序列与计算的区域平均序列值的相关系数为0.996,系统性偏差小,平均相对误差为5.5%;年降水量序列的实测值与计算值平均相对误差略大,为14.8%,相关系数为0.972.区域平均序列的计算值与测站简单的算术平均序列在量值上存在明显的差异,计算出的气温平均偏低4.3℃,降水量平均偏高43.2 mm.此方法为计算站点稀少、地形复杂的区域平均的要素时间序列方面提供了一个解决手段.     

气候模式应用中的不确定性分析

为了分析和评价气候变化对水文水资源的影响,根据中国东部地区1956～2000年实测年降水量系列数据,采用多年均值、系列趋势变化指标和反映系列可持续特性的Hurst系数等3项指标,对CGCMA3、MPI-ECHAM5和平均GCM等3种气候模式模拟的同期年降水系列数据进行了检验。检验结果表明,3种气候模式模拟结果与实测值之间,以及不同模式模拟结果之间,不仅定量方面差异很大,而且在定性方面甚至出现相悖的结果,不确定性十分显著。同样,根据3种气候模式预测的未来2001～2050年年降水量和季平均气温推得的未来年径流量系列数据,其多年均值、系列趋势变化指标和干旱年年径流量多项指标也存在明显的不确定性。据此对气候模式及其应用提出了相应的建议。     

Trend analysis and change point detection of annual and seasonal precipitation and temperature series over southwest Iran

This paper presents results of trend analysis and change point detection of annual and seasonal precipitation, and mean temperature (TM), maximum temperature (TMAX) and minimum temperature (TMIN) time series of the period 1950–2007. Investigations were carried out for 50 precipitation stations and 39 temperature stations located in southwest Iran. Three statistical tests including Pettitt’s test, Sequential Mann–Kendall test (SQ-MK test) and Mann–Kendall rank test (MK-test) were used for the analysis. The results obtained for precipitation series indicated that most stations showed insignificant trends in annual and seasonal series. Out of the stations which showed significant trends, highest numbers were observed during winter season while no significant trends were detected in summer precipitation. Moreover, no decreasing significant trends were detected by statistical tests in annual and seasonal precipitation series. The analysis of temperature trends revealed a significant increase during summer and spring seasons. TMAX was more stable than TMIN and TM, and winter was stable compared to summer, spring and autumn seasons. The results of change point detection indicated that most of the positive significant mutation points in TM, TMAX and TMIN began in the 1990s.     

江苏省若干代表站年降水量的多年变化和系列代表性分析

利用长系列雨量站资料，采用数理统计法分析了江苏省年降水量的多年变化，对1956—2000年、1956—1979年、1971—2000年、1980—2000年等短系列年降水量的系列代表性做出了分析评价，为合理选用水文系列提供了依据，对江苏省水资源调查评价和水资源综合规划具有重要的参考价值，可在生产中应用。     

黔中气溶胶传输的210 Pb和7 Be示踪:Ⅱ.月及年时间尺度的剖析

自2001年12月连续逐周在贵阳观风山附近对近地面空气210Pb-7Be浓度和沉降量的观测表明:区域性降水和气温制约近地面空气210Pb具高浓度"U"型年分布;降水量和平流层向下输送影响近地面空气7Be月均浓度的季节性变化;210Pb/7Be月均浓度比率受控于210Pb变化和富7Be气团下沉的影响.观风山与瓦里关山之间因纬度和海拔的关系,7Be比率急剧震荡,显示出海洋性贫7Be气团入侵对观风山地区的明显影响.2002-2008年间贵阳观风山近地面空气210Pb的年均浓度(2.8±0.6)mBq/m3,约为全球若干站点中最高平均浓度值的4倍;而7Be的年均浓度(4.8±0.6)mBq/m3,与全球高海拔站点长期观测的平均值相当,大约为北半球中纬度对流层顶部附近7Be浓度值(18.0 mBq/m3)的1/3.8,显示出低纬度、较高海拔地区的预期水平.2003年,瓦里关山和观风山近地面空气7Be的年均浓度比为3.8,与预期的大气输送和混合作用基本一致.7Be月均浓度的增大趋势反映出受太阳黑子数减少的变化关系,其影响底线大约为4 mBq/m3;7Be年均浓度波动的增大趋势反映出受太阳黑子数降低的可能影响.贵阳观风山降水中7Be和210Pb的体积加权浓度分别为0.72 Bq/L和0.27 Bq/L;年沉降通量分别为0.080 Bq/(cm2·a)和0.031 Bq/(cm2·a).数值模拟显示出全球空气210Pb高浓度区和高沉降通量环带分布,印证了洱海和红枫湖沉积物7Be和157Cs蓄积的模拟结果.     

The Waldmeier rule and early diagnostics of the maximum of the current solar cycle

The well known Waldmeier rule and its modifications applied to forecasting of the solar activity are discussed. The maximum annual mean rate of the increase in the magnetic flux observed on the growth branch of the cycle is proposed as a predicting (diagnosing) parameter for the amplitude of the cycle currently in progress. The maximum Wolf number of the current (24th) cycle is estimated to reach 104 (±12) or more, and to occur in 2013 or later. The yearly average Wolf numbers observed during the 24th cycle are presented.     

北京天津等地气温降水量历史资料诊断

采用北京、天津、沈阳、哈尔滨等6站近百年的实测资料,诊断分析了华北、东北地区的气温和降水量的变化趋势及相互关系。分析结果表明:近百年来北京等地年平均气温和冬季1月的月平均气温均有上升趋势,且东北地区比华北地区上升的幅度大,冬季1月的月平均气温比年平均气温上升幅度大。部分地区的夏季7月的月平均气温呈下降趋势。同期月平均气温和月降水量有负相关关系,尤其以5月、6月、7月最为明显。     

Estimation of the transfer function by autoregressive deconvolution techniques—An application to time series analysis

The transfer function of time-dependent models is classically inferred by the ordinary least squares (OLS) techniques. This OLS technique assumes independence of the residuals with time. However, in practical cases, this hypothesis is often not justified producing inefficient estimation of the transfer function. When the residuals constitute an autoregressive process, we propose to apply the Box-Jenkins' method to model the residuals, and to modify in a simple manner the primary convolution equation. Then, a multivariate regression technique is used to infer the transfer function of the new equation producing time-independent residuals. This three-step autoregressive deconvolution technique is particularly efficient for time series analysis. The reconstitution and the forecasting of real data are improved efficiently. Theoretically, the proposed method can be extended to the convolution equations for which the residuals follow a moving average or an autoregressive-moving average process, but the mathematical formulation is no longer direct and explicit. For this general case, we propose to approximate the moving average or the autoregressive-moving average process by an autoregressive process of sufficient order, and then the transfer function. Two case studies in hydrogeology will be used to illustrate the procedure.