On seasonal and semi-annual approach for flood frequency analysis

As a supplementary method to the conventional flood frequency analysis based on annual peak flows, we propose an approach in this paper to infer the flood frequency distribution on quarterly and semi-annual time scale, which are then converted to annual time scale to obtain the floods corresponding to return periods in unit of year. Two criteria for test of data independence, namely, minimum 7 and 15-day interval between two consecutive peak flows, are tested. The proposed approach was applied to Des Moines River at Fort Dodge, Iowa, USA using its 62 years of observation daily flows. The results show that the estimated floods for given return periods from quarter-annual data series are in general higher than the corresponding estimated floods from semi-annual data series, which is further larger than estimated floods from annual peak flows. The floods estimated from semi-annual data series agree well with the results of previous US Geological Survey study.     

Trend detection in river flow series: 1. Annual maximum flow / Détection de tendance dans des séries de débit fluvial: 1. Débit maximum annuel

Abstract

Results of a study on change detection in hydrological time series of annual maximum river flow are presented. Out of more than a thousand long time series made available by the Global Runoff Data Centre (GRDC) in Koblenz, Germany, a worldwide data set consisting of 195 long series of daily mean flow records was selected, based on such criteria as length of series, currency, lack of gaps and missing values, adequate geographical distribution, and priority to smaller catchments. The analysis of annual maximum flows does not support the hypothesis of ubiquitous growth of high flows. Although 27 cases of strong, statistically significant increase were identified by the Mann-Kendall test, there are 31 decreases as well, and most (137) time series do not show any significant changes (at the 10% level). Caution is advised in interpreting these results as flooding is a complex phenomenon, caused by a number of factors that can be associated with local, regional, and hemispheric climatic processes. Moreover, river flow has strong natural variability and exhibits long-term persistence which can confound the results of trend and significance tests.     

陆地地震勘探随机噪声统计特性

在地震勘探随机噪声压制领域,噪声通常被假设为平稳、高斯随机过程的信息.然而,在某些情况下,这样的假设并不准确.本文应用现代统计检验方法对地震勘探随机噪声的平稳性、高斯性和线性进行了研究.结果表明地震勘探随机噪声并不是传统意义上认为的平稳随机过程,其平稳性受到噪声时长和采集环境复杂程度的影响.发现噪声时间越长,采集环境越复杂,随机噪声的平稳性越差,但是对于短时长随机噪声而言,其可以近似认为是平稳的.同时,采集环境的复杂程度也影响着随机噪声的高斯性和线性特性,环境条件越复杂,随机噪声高斯性越好,线性特性越差,但总的来说随机噪声可以归为线性非高斯随机过程.     

Temporal change of spatial heterogeneity and its effect on regional trend of annual precipitation heterogeneity indices

A statistical test on climate and hydrological series from different spatial resolution could obtain different regional trend due to spatial heterogeneity and its temporal variability. In this study, annual series of the precipitation heterogeneity indices of concentration index (CI) and the number of wet days (NW) along with annual total amount of precipitation were calculated based on at‐site daily precipitation series during 1962–2011 in the headwater basin of the Huaihe River, China. The regional trends of the indices were first detected based on at‐site series by using the aligned and intrablock methods, and field significance tests that consider spatial heterogeneity over sites. The detected trends were then compared with the trends of the regional index series derived from daily areal average precipitation (DAAP), which averages at‐site differences and thus neglects spatial heterogeneity. It was found that the at‐site‐based regional test shows increasing trends of CI and NW in the basin, which follows the test on individual sites that most of sites were characterized by increasing CI and NW. However, the DAAP‐derived regional series of CI and NW were tested to show a decreasing trend. The disparity of the regional trend test on at‐site‐based regional series and the DAAP‐derived regional series arises from a temporal change of the spatial heterogeneity, which was quantified by the generalized additive models for location, scale, and shape. This study highlights that compared with averaging indices, averaging at‐site daily precipitation could lead to an error in the regional trend inference on annual precipitation heterogeneity indices. More attention should be paid to temporal variability in spatial heterogeneity when data at large scales are used for regional trend detection on hydro‐meteorological events associated with intra‐annual heterogeneity.     

Diagnostic study and modeling of the annual positive water temperature onset

A data-driven model is designed using artificial neural networks (ANN) to predict the average onset for the annual water temperature cycle of North-American streams. The data base is composed of daily water temperature time series recorded at 48 hydrometric stations in Québec (Canada) and northern US, as well as the geographic and physiographic variables extracted from the 48 associated drainage basins. The impact of individual and combined drainage area characteristics on the stream annual temperature cycle starting date is investigated by testing different combinations of input variables. The best model allows to predict the average temperature onset for a site, given its geographical coordinates and vegetation and lake coverage characteristics, with a root mean square error (RMSE) of 5.6 days. The best ANN model was compared favourably with parametric approaches.     

应用单台垂向记录进行区域地震事件实时检测和直达犘波初动自动识别

提出一种基于直达P波信号和其它背景噪声在能量、非高斯性、非线性和偏振特性的不同而进行区域地震事件实时检测的新方法信噪综合差异特征量方法（简写为EFGLP方法）,同时对比分析了应用信号的不同统计特性来精细识别震相初至的３种有效方法,其中的TOC AIC方法是新提出的．应用山东数字地震波资料处理的结果表明:①与常规的STA/LTA地震事件触发算法相比,EFGLP方法能够有效降低地震事件的错误报警率和漏报率;②与人机交互震相识别结果相比,当信噪比比较低、震相初至比较模糊时,3种震相精细识别方法中的TOC-AIC方法识别精度最高;当信噪比比较高、震相初至比较清晰时,基于VAR-AIC 和TOC-AIC方法所测量得到的震相初至识别基本一致．      

1950s以来鄱阳湖流域降水变化趋势及其持续性特征

以鄱阳湖流域1950s至2005年10个台站的日降水量为基础,采用距平分析、Mann-Kendall非参数检验对鄱阳湖流域1950s以来的年、季降水特征和变化趋势进行分析,并以此为基础,结合Hurat指数,从3年、5年、10年三个时间尺度上分析该流域未来降水的变化趋势.结果表明,鄱阳湖流域年内降水分配不均,年际变化较为...     

Impact of time-scale of the calibration objective function on the performance of watershed models

Many of the continuous watershed models perform all their computations on a daily time step, yet they are often calibrated at an annual or monthly time-scale that may not guarantee good simulation performance on a daily time step. The major objective of this paper is to evaluate the impact of the calibration time-scale on model predictive ability. This study considered the Soil and Water Assessment Tool for the analyses, and it has been calibrated at two time-scales, viz. monthly and daily for the War Eagle Creek watershed in the USA. The results demonstrate that the model's performance at the smaller time-scale (such as daily) cannot be ensured by calibrating them at a larger time-scale (such as monthly). It is observed that, even though the calibrated model possesses satisfactory ‘goodness of fit’ statistics, the simulation residuals failed to confirm the assumption of their homoscedasticity and independence. The results imply that evaluation of models should be conducted considering their behavior in various aspects of simulation, such as predictive uncertainty, hydrograph characteristics, ability to preserve statistical properties of the historic flow series, etc. The study enlightens the scope for improving/developing effective autocalibration procedures at the daily time step for watershed models. Copyright © 2007 John Wiley & Sons, Ltd.     

Entropy theory for analysing water resources in northeastern region of Brazil

Using the Shannon entropy, the space–time variability of rainfall and streamflow was assessed for daily rainfall and streamflow data for a 10-year period from 189 stations in the northeastern region of Brazil. Mean values of marginal entropy were computed for all observation stations and entropy maps were then constructed for delineating annual and seasonal characteristics of rainfall and streamflow. The Mann-Kendall test was used to evaluate the long-term trend in marginal entropy as well as relative entropy for two sample stations. The marginal entropy values of rainfall and streamflow were higher for locations and periods with the highest amounts of rainfall. The entropy values were higher where rainfall was higher. This was because the probability distributions of rainfall and the resulting streamflow were more uniform and less skewed. The Shannon entropy produced spatial patterns which led to a better understanding of rainfall and streamflow characteristics throughout the northeastern region of Brazil. The total relative entropy indicated that rainfall and streamflow carried the same information content at annual and rainy season time scales.     

Regionalization of daily flow characteristics in part of the Eastern Cape,South Africa

Abstract

A method is described that allows long-term 1-day annual and seasonal flow duration curves at any ungauged location in one of the drainage regions of South Africa to be established. The method is based on normalization of observed flow duration curves by a long-term mean daily flow and subsequent averaging of normalized ordinates of the curves. The estimate of mean daily discharge for an ungauged site is obtained using the information from the existing national data base of flow characteristics. The established set of flow duration curves at a site is further translated into actual daily streamflow time series using a simple nonlinear spatial interpolation technique.     

香港GPS基准站坐标序列特征分析

利用香港GPS连续运行参考站网络2001年1月至2007年8月的观测资料,全面深入地分析了12个基准站坐标序列特征.本文采用主成分空间滤波算法去除公共误差,来提高坐标序列的信噪比,并采用最大似然估计准则定量估计滤波后坐标序列的噪声特性,计算了地球表面质量负荷（包括大气、非潮汐海洋、积雪和土壤水）对香港GPS基准站坐标序列的影响.研究结果表明：香港GPS基准站坐标序列具有高度的空间相关性,其公共误差具有较强的季节性变化特征;地表质量负荷变化引起的香港地壳形变可以解释公共误差序列中约为3mm的垂向周年变化,经过质量负荷改正后的公共误差序列与高阶电离层误差高度相关;滤波后坐标序列的噪声特性可以用可变白噪声加闪烁噪声模型来描述,顾及闪烁噪声所计算的速度误差要比只考虑可变白噪声计算的速度误差大2~6倍;基准站间存在达1.5 mm/yr的相对水平运动,揭示香港地区存在活动断层;部分基准站坐标具有明显的振幅为1～2 mm本地季节性变化,所有测站的残差序列也表现出强烈的季节性变化.     

Melt season hydrological characteristics of the Parlung No. 4 Glacier,in Gangrigabu Mountains,south‐east Tibetan Plateau

With global warming, hazards relating to glacial melt, such as glacial lake outburst floods, are becoming progressively more serious. However, glacial melt processes and their hydrological consequences are very poorly understood. This study collected glacier discharge data from the terminus of the Parlung No. 4 Glacier throughout the melt season (May–October) during 2008, 2010, 2011 and 2012 to study its specific hydrological characteristics. Time series and multivariate regression analyses were employed to investigate the relationships between discharge and meteorological factors involved, as well as their correlation to discharge estimations. The 0‐ to 3‐day time series analysis showed that discharge rates were highly autocorrelated and that discharge was significantly positively correlated to air temperature, vapour pressure and daily incoming shortwave radiation as well as weakly positively correlated to precipitation. A multiple‐regression exponential model using the independent variables of the daily mean temperature and the vapour pressure exclusively was applied to simulate daily discharge in the basin with a high degree of accuracy. On average, July yielded the maximum monthly mean discharge, followed by August. Discharge in July and August accounted for 53% of the total discharge during the main melt season. The daily cycle of discharge changed as the melt season progressed, reflecting hydrological processes and characteristics of snow melt and glacier ice/snow melt, as well as their transitional periods. Subsequently, regular variations in the characteristics of the diurnal cycle of discharge, storage and delay were observed as the melt season progressed. In addition, the reasons behind the inter‐annual variation in the characteristics of discharge and glacier discharge from the Tibetan Plateau and its surrounding areas are compared and discussed. Copyright © 2015 John Wiley & Sons, Ltd.     

Comparative evaluation of statistical tests for time series analysis: application to hydrological time series / Evaluation comparative de tests statistiques pour l'analyse de séries temporelles: application à des séries temporelles hydrologiques

Abstract

Statistical analyses of hydrological time series play a vital role in water resources studies. Twenty-nine statistical tests for detecting time series characteristics were evaluated by applying them to analyse 46 years of annual rainfall, 47 years of 1-day maximum rainfall and consecutive 2-, 3-, 4-, 5- and 6-day maximum rainfalls at Kharagpur, West Bengal, India. The performance of all the tests was evaluated. No severe outliers were found, and both the annual and maximum rainfall series were found to be normally distributed. Based on the known physical parameters affecting the homogeneity, the cumulative deviations and the Bayesian tests were found to be superior to the classical von Neumann test. Similarly, the Tukey test proved excellent among all the multiple comparison tests. These tests indicated that all the seven rainfall series are homogeneous. Two parametric t tests and the non-parametric Mann-Whitney test indicated stationarity in all the rainfall series. Of 12 trend detection tests, nine tests indicated no trends in the rainfall series. The Kendall's Rank Correlation test and the Mann-Kendall test were found equally powerful. Moreover, the Fourier series analysis revealed no apparent periodicities in all the seven rainfall series. The annual rainfall series was found persistent with a time lag of nine years. All the rainfall series were subjected to stochastic analysis by fitting 35 autoregressive moving-average (ARMA) models of different orders. The best-fit models for the original annual rainfall and 1-, 2- and 3-day maximum rainfall series were found to be ARMA(0,4), ARMA(0,2), ARMA(0,2) and ARMA(3,0), respectively. The best-fit model for the logarithmically transformed 4-day maximum rainfall was found to be ARMA(0,2). However, for the inversely transformed 4-, 5- and 6-day maximum rainfall series, ARMA(0,1) was obtained as the best-fit model. It is concluded that proper selection of time series tests and use of several tests is indispensable for making useful and reliable decisions.     

Long-term trend analysis using discrete wavelet components of annual precipitations measurements in Marmara region (Turkey)

The purpose of this study is to determine the possible trends in annual total precipitation series by using the non-parametric methods such as the wavelet analysis and Mann-Kendall test. The wavelet trend (W-T) analysis is for the first time presented in this study. Using discrete wavelet components of measurement series, we aimed to find which periodicities are mainly responsible for trend of the measurement series. We found that some periodic events clearly affect the trend of precipitation series. 16-yearly periodic component is the effective component on Bal?kesir annual precipitation data and is responsible for producing a real trend founded on the data. Also, global wavelet spectra and continuous wavelet transform were used for analysis to precipitation time series in order to clarify time-scale characteristics of the measured series. The effects of regional differences on W-T analysis are checked by using records of measurement stations located in different climatic areas. The data set spans from 1929 to 1993 and includes precipitation records from meteorological stations of Turkey. The trend analysis on DW components of the precipitation time series (W-T model) clearly explains the trend structure of data.     

城市化对北京气象站极端气温指数趋势变化的影响

利用5个乡村气象站和北京气象站(简称北京站)1960～2008年日最高、最低气温资料,比较分析了北京地区城市和乡村极端气温指数年、季节的时间变化以及城市化对北京站各极端气温指数趋势变化的影响.结果表明:1960～2008年北京站霜冻日数、冷夜日数、冷昼日数和平均日较差均显著减少,暖夜日数、暖昼日数、平均最高气温和平均最...     

Trend and long-range correlation characteristics analysis of runoff in upper Fenhe River basin

In order to explore long-term evolution rule and future trend of runoff time series, and exactly detect its tendency and long-range correlation characteristics, runoff data covering 1952–2012 from 3 stations across the upper Fenhe River basin were analyzed. The moving average method, Empirical Mode Decomposition (EMD) method and Mann-Kendall (M-K) trend test method were simultaneously applied to analyze the trend characteristics firstly. Then Rescaled Range analysis (R/S) and Detrended Fluctuation Analysis (DFA) methods were employed to research the long-range correlation characteristics and length of non-periodic cycle of hydrological time series, they can systematically detect and overcome non-stationarity at all time scales. Finally, predict the future trend by combining the trend characteristics with the long-range correlation characteristics and length of non-periodic cycle. The results illustrate the annual runoff series is non-linear, non-normal time series, and have 10 years non-periodic cycle length and noticeable descending trend. This descending trend will continue in a period time of future.     

Seismic directional random noise suppression by radial‐trace time–frequency peak filtering using the Hurst exponent statistic

Radial‐trace time–frequency peak filtering filters a seismic record along the radial‐trace direction rather than the conventional channel direction. It takes the spatial correlation of the reflected events between adjacent channels into account. Thus, radial‐trace time–frequency peak filtering performs well in denoising and enhancing the continuity of reflected events. However, in the seismic record there is often random noise whose energy is concentrated in certain directions; the noise in these directions is correlative. We refer to this kind of random noise (that is distributed randomly in time but correlative in the space) as directional random noise. Under radial‐trace time–frequency peak filtering, the directional random noise will be treated as signal and enhanced when this noise has same direction as the signal. Therefore, we need to identify the directional random noise before the filtering. In this paper, we test the linearity of signal and directional random noise in time using the Hurst exponent. The time series of signals with high linearity lead to large Hurst exponent value; however, directional random noise is a random series in time without a fixed waveform and thus its linearity is low; therefore, we can differentiate the signal and directional random noise by the Hurst exponent values. The directional random noise can then be suppressed by using a long filtering window length during the radial‐trace time–frequency peak filtering. Synthetic and real data examples show that the proposed method can remove most directional random noise and can effectively recover the reflected events.     

Statistical moment-based structural damage detection method in time domain

A novel structural damage detection method with a new damage index,i.e.,the statistical moment-based damage detection(SMBDD) method in the frequency domain,has been recently proposed.The aim of this study is to extend the SMBDD method in the frequency domain to the time domain for building structures subjected to non-Gaussian and non-stationary excitations.The applicability and effectiveness of the SMBDD method in the time domainis verified both numerically and experimentally.Shear buildings with various damage scenarios are first numerically investigated in the time domain taking into account the effect of measurement noise.The applicability of the proposed method in the time domain to building structures subjected to non-Gaussian and non-stationary excitations is then experimentally investigated through a series of shaking table tests,in which two three-story shear building models with four damage scenarios aretested.The identified damage locations and severities are then compared with the preset values.The comparative results are found to be satisfactory,and the SMBDD method is shown to be feasible and effective for building structures subjected to non-Gaussian and non-stationary excitations.     

Effect of autocorrelation on temporal trends in rainfall in a valley region at the foothills of Indian Himalayas

This study examines the effect of autocorrelation on step and monotonic trends in seasonal and annual rainfall. Initially, for step change, modified-Pettitt test is applied in two ways. First, using the corrected and unbiased trend-free-pre-whitening (TFPWcu) approach. Second, using a new approach in which time series is modelled by intervention analysis for modified Pettitt test. Subsequently, for monotonic trends, Mann–Kendall (MK) and six approaches of modified Mann–Kendall (MMK) test are applied to NCDC data for period 1901–2012 and its sub-periods. Approaches of MMK include pre-whitening (PW), trend-free-pre-whitening (TFPW), TFPWcu, two Variance Correction Approaches (VCAs) based on empirical formula (VCA:CF1) and Monte-Carlo-Simulations (VCA:CF2) and long term persistence (MK-LTP). A single change point is identified in 1970 for annual and monsoon rainfall from original and modified-Pettitt’s test using TFPWcu, while time series modelling approach has not exhibited any change point. Process shift in rainfall series is also studied using CUSUM and multiple change points are identified using Segment-Neighbourhood method. Outcomes of MMK show that TFPWcu is able to efficiently limit the effect of autocorrelation and may be preferred over PW and TFPW. The VCA:CF2 is not dependent on whole autocorrelation structure and corrects variance of all data series using lag-1 autocorrelation and may be preferred over VCA:CF1. MK-LTP considers long term persistence and it has exhibited presence of weaker trends than exhibited by other approaches. VCA:CF2 and MK-LTP are used to study trends of rainfall in Dehradun.