Low and high flow analyses and wavelet application for characterization of the Blue Nile River system

The low and high flow characteristic of the Blue Nile River (BNR) basin is presented. The study discusses low and high flow, flow duration curve (FDC) and trend analysis of the BNR and its major tributaries. Different probability density functions were fitted to better describe the low and high flows of the BNR and major tributaries in the basin. Wavelet analysis was used in understanding the variance and frequency‐time localization and detection of dominant oscillations in rainfall and flow. FDCs were developed, and low flow (below 50% exceedance) and high flow (over 75% exceedance) of the curves were analysed and compared. The Gravity Recovery and Climate Experiment (GRACE) satellite‐based maps of monthly changes in gravity converted to water equivalents from 2003 to 2006 for February, May and September showed an increase in the moisture influx in the BNR basin for the month of September, and loss of moisture in February and May. It was also shown that 2004 and 2005 were drier with less moisture influx compared to 2003 and 2006. On the basis of the Kolmogorov‐Smirnov, Anderson‐Darling and Chi‐square tests, Gen. Pareto, Frechet 3P, Log‐normal, Log‐logistics, Fatigue Life and Phased Bi‐Weibull distributions best describe the low and high flows within the BNR basin. This will be beneficial in developing flow hydrographs for similar ungauged watersheds within the BNR basin. The below 50% and above 75% exceedance on the FDC for five major rivers in addition to the BNR showed different characteristics depending on size, land cover, topography and other factors. The low flow frequency analysis of the BNR at Bahir Dar showed 0·55 m³/s as the monthly low flow with recurrence interval of 10 years. The wavelet analysis of the rainfall (at Bahir Dar and basin‐wide) and flows at three selected stations shows inter‐ and intra‐annual variability of rainfall and flows at various scales. Copyright © 2009 John Wiley & Sons, Ltd.     

Origin of transverse folds in an extensional growth-fault setting: Evidence from an extensive seismic volume in the western Gulf of Mexico

Growth faults in gravity-driven extensional provinces are dominated by coast-parallel trends, but coast-perpendicular (transverse) trends are far less documented. The Clemente–Tomas fault in the inner Texas shelf has corrugations that are transverse to the fault and that plunge downdip. A large (8500 km²), high-quality, 3D seismic survey allows a uniquely encompassing perspective into hanging-wall deformation above this corrugated fault surface. Synextensional strata in the hanging wall are folded into alternating transverse ridges and synclines, typically spaced 10 km apart. Forward modelling in dip profiles of an extensional fault having three ramps produces ramp basin-rollover pairs that compare with the seismically revealed ridges and synclines. As they translated down the undulose fault plane, ramp basins and rollovers were juxtaposed along strike, forming the hanging-wall ridges and synclines observed offshore Texas. Fault-surface corrugations correlate broadly with footwall structure. We infer that corrugations on the Clemente–Tomas fault formed by evacuation of an allochthonous salt canopy emplaced in the late Eocene to early Oligocene. Early salt evacuation (Oligocene) created an undulose topography that influenced incipient Clemente-Tomas fault segments as they merged to form an inherently undulose fault. Late salt evacuation (early Miocene) further deformed this fault surface.     

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.     

Discrete wavelet‐based trend identification in hydrologic time series

Trend identification is a substantial issue in hydrologic series analysis, but it is also a difficult task in practice due to the confusing concept of trend and disadvantages of methods. In this article, an improved definition of trend was given as follows: ‘a trend is the deterministic component in the analysed data and corresponds to the biggest temporal scale on the condition of giving the concerned temporal scale’. It emphasizes the intrinsic and deterministic properties of trend, can clearly distinguish trend from periodicities and points out the prerequisite of the concerned temporal scale only by giving which the trend has its specific meaning. Correspondingly, the discrete wavelet‐based method for trend identification was improved. Differing from those methods used presently, the improved method is to identify trend by comparing the energy difference between hydrologic data and noise, and it can simultaneously separate periodicities and noise. Furthermore, the improved method can quantitatively estimate the statistical significance of the identified trend by using proper confidence interval. Analyses of both synthetic and observed series indicated the identical power of the improved method as the Mann–Kendall test in assessing the statistical significance of the trend in hydrologic data, and by using the former, the identified trend can adaptively reflect the nonlinear and nonstationary variability of hydrologic data. Besides, the results also showed the influences of three key factors (wavelet choice, decomposition level choice and noise content) on discrete wavelet‐based trend identification; hence, they should be carefully considered in practice. Copyright © 2012 John Wiley & Sons, Ltd.     

Spatial and multivariate analysis of geochemical data from metavolcanic rocks in the Ben Nevis area, Ontario

A study of the lithogeochemistry of metavolcanics in the Ben Nevis area of Ontario, Canada has shown that factor analysis methods can distinguish lithogeochemical trends related to different geological processes, most notably, the principal compositional variation related to the volcanic stratigraphy and zones of carbonate alteration associated with the presence of sulphides and gold. Auto- and cross-correlation functions have been estimated for the two-dimensional distribution of various elements in the area. These functions allow computation of spatial factors in which patterns of multivariate relationships are dependent upon the spatial auto- and cross-correlation of the components. Because of the anisotropy of primary compositions of the volcanics, some spatial factor patterns are difficult to interpret. Isotropically distributed variables such as CO ₂ are delineated clearly in spatial factor maps. For anisotropically distributed variables (SiO ₂ ), as the neighborhood becomes smaller, the spacial factor maps becomes better. Interpretation of spatial factors requires computation of the corresponding amplitude vectors from the eigenvalue solution. This vector reflects relative amplitudes by which the variables follow the spatial factors. Instability of some eigenvalue solutions requires that caution be used in interpreting the resulting factor patterns. A measure of the predictive power of the spatial factors can be determined from autocorrelation coefficients and squared multiple correlation coefficients that indicate which variables are significant in any given factor. The spatial factor approach utilizes spatial relationships of variables in conjunction with systematic variation of variables representing geological processes. This approach can yield potential exploration targets based on the spatial continuity of alteration haloes that reflect mineralization.List of symbols c _i Scalar factor that minimizes the discrepancy between andU _i - D Radius of circular neighborhood used for estimating auto- and cross-correlation coefficients - d Distance for which transition matrixU is estimated - d _ij Distance between observed valuesi andj - E Expected value - E _i Row vector of residuals in the standardized model - F(d _ij) Quadratic function of distanced _ij F(d _ij)=a+bd _ij+cd _ij ² - L Diagonal matrix of the eigenvalues ofU - _i Eigenvalue of the matrixU;ith diagonal element ofL - N Number of observations - p Number of variables - Q Total predictive power ofU - R Correlation matrix of the variables - R _0j Variance-covariance signal matrix of the standardized variables at origin;j is the index related tod andD (e.g.,j=1 ford=500 m,D=1000 m) - R _1j Matrix of auto- and cross-correlation coefficients evaluated at a given distance within the neighborhood - R _m ² Multiple correlation coefficient squared for themth variable - S _i Column vectori of the signal values - s _k ² Residual variance for variablek - T _i Amplitude vector corresponding toV _i;ith row ofT=V ^–1 - T Total variation in the system - U Nonsymmetric transition matrix formed by post-multiplyingR ₀₁ ^–1 byR _ij - U _i Componenti of the matrixU, corresponding to theith eigenvectorV _i;U _i= _iV_iT_i - U* _i ComponentU _i multiplied byc _i - U _ij Sum of componentsU _i+U _j - V _i Eigenvector of the matrixU;ith column ofV withUV=VL - w Weighting factor; equal to the ratio of two eigenvalues - X _i Random variable at pointi - x _i Value of random variable at pointi - y _i Residual ofx _i - Z _i Row vectori for the standardized variables - z _i Standardized value of variable     

When do we need a trend model in kriging?

Under usual estimation practice with local search windows for data and for interpolation situations, universal kriging and ordinary kriging yield the same estimates, using a data set with apparent trend, for both the unknown attribute and its trend component. Modeling the trend matters only in extrapolation situations. Because conditions of the case study presented arise most frequently in practice, the simpler ordinary kriging is the preferred option.     

钻孔走向偏斜的初步探讨

在急倾斜构造部位大力使用硬合金回转钻进时,常常会产生弯曲度很大的走向偏斜情况。这是因为不同岩层的阻力差异使钻头受到一种平行于岩层走向的偏斜力,迫使钻头向侧方研磨井壁;在不同井径变换部位所产生的偏斜效果是不同的。这样造成钻孔沿地层走向发生有规律的定向偏斜。     

浅析云峰井变径前后气压效应与井径比的关系

本文通过改变云峰井孔径试验,探讨了提高地下水观测井映震能力的一种途径。     

塔里木盆地绿洲形成与演变

塔里木盆地的绿洲按形成历史可划分为古绿洲、旧绿洲和新绿洲,它们分别代表绿洲发展演变的三个阶段。本文对绿洲未来演变趋势作了预测,探讨了盆地古绿洲衰亡原因、认为除了风沙、盐碱及河流改道等自然原因外,人类活动引起的地表水资源时空分配发生变化是主要原因。     

Modelling the effects of motion on primary production in the mixed layer of lakes

A model of the time dependent relationship between productivity and light intensity following changes in light intensity is briefly described. The model incorporates two response timescales simulating initial response and photoinhibition, although additional timescales could easily be incorporated. The model is calibrated against one set of time dependent data, and applied to two simple models of motion in the upper mixed layer of a lake. The two models are: organised motion simulating Langmuir cells, and disorganised motion simulating the turbulent velocity field associated with surface wind stirring. The depth and therefore light histories for a number of photosynthesising particles are calculated by these models, and used by the productivity model to calculate mean productivities. The results show that the influence of the time dependent nature of the productivity relationship depends on the ratio of the mixed layer depth to the euphotic depth, and to a less extent, on the rate at which the particles circulate in the mixed layer.