Surface sediment carbonate mineralogy and water column chemistry: Nicaragua Rise versus the Bahamas

Periplatform surface sediments were studied for carbonate mineralogy in conjunction with analyses of the water column for carbonate chemistry on the eastern Northern Nicaragua Rise (NNR) in the Caribbean Sea. The results show a strong correspondence between variations and disappearance, with increasing water depth, of metastable carbonate minerals (fine aragonite and magnesian calcite) and their respective saturation levels in the overlying waters. Similar correspondence between variations in sediment proportions of fine aragonite and magnesian calcite and their respective saturation levels has previously been established in the Bahamas. There are, however, significant differences between the two areas. The sharp decrease in aragonite content and the measured aragonite saturation level occur at 4000 m in the Bahamas, compared to 1800 m on the eastern NNR. In both areas, magnesian calcite minima correspond to the in situ PCO₂ maxima in the water column. The magnesian calcite minimum, however, is at 950 m in the Bahamas and 750 m on the eastern NNR. Magnesian calcite disappears in the Bahamas at 3800 m and at 2000 m on the eastern NNR. These results demonstrate the importance of the influence of overlying water chemistry on the preservation of metastable carbonate minerals in off-bank periplatform sediments, and they clearly demonstrate the difference in terms of carbonate preservation between the poorly ventilated waters of the Caribbean Sea and the well-oxygenated waters of the adjacent Atlantic Ocean. They also open the possibility of obtaining paleoceanographic information on the depth of the CO₂ maximum (O₂ minimum) and its separation from the aragonite saturation depth in at least some areas.     

Regionalisation of the parameters of the log‐Pearson 3 distribution: a case study for New South Wales,Australia

The index flood method is widely used in regional flood frequency analysis (RFFA) but explicitly relies on the identification of ‘acceptable homogeneous regions’. This paper presents an alternative RFFA method, which is particularly useful when ‘acceptably homogeneous regions’ cannot be identified. The new RFFA method is based on the region of influence (ROI) approach where a ‘local region’ can be formed to estimate statistics at the site of interest. The new method is applied here to regionalize the parameters of the log‐Pearson 3 (LP3) flood probability model using Bayesian generalized least squares (GLS) regression. The ROI approach is used to reduce model error arising from the heterogeneity unaccounted for by the predictor variables in the traditional fixed‐region GLS analysis. A case study was undertaken for 55 catchments located in eastern New South Wales, Australia. The selection of predictor variables was guided by minimizing model error. Using an approach similar to stepwise regression, the best model for the LP3 mean was found to use catchment area and 50‐year, 12‐h rainfall intensity as explanatory variables, whereas the models for the LP3 standard deviation and skewness only had a constant term for the derived ROIs. Diagnostics based on leave‐one‐out cross validation show that the regression model assumptions were not inconsistent with the data and, importantly, no genuine outlier sites were identified. Significantly, the ROI GLS approach produced more accurate and consistent results than a fixed‐region GLS model, highlighting the superior ability of the ROI approach to deal with heterogeneity. This method is particularly applicable to regions that show a high degree of regional heterogeneity. Copyright © 2014 John Wiley & Sons, Ltd.     

Selection of the best fit flood frequency distribution and parameter estimation procedure: a case study for Tasmania in Australia

Selection of a flood frequency distribution and associated parameter estimation procedure is an important step in flood frequency analysis. This is however a difficult task due to problems in selecting the best fit distribution from a large number of candidate distributions and parameter estimation procedures available in the literature. This paper presents a case study with flood data from Tasmania in Australia, which examines four model selection criteria: Akaike Information Criterion (AIC), Akaike Information Criterion—second order variant (AIC_c), Bayesian Information Criterion (BIC) and a modified Anderson–Darling Criterion (ADC). It has been found from the Monte Carlo simulation that ADC is more successful in recognizing the parent distribution correctly than the AIC and BIC when the parent is a three-parameter distribution. On the other hand, AIC and BIC are better in recognizing the parent distribution correctly when the parent is a two-parameter distribution. From the seven different probability distributions examined for Tasmania, it has been found that two-parameter distributions are preferable to three-parameter ones for Tasmania, with Log Normal appears to be the best selection. The paper also evaluates three most widely used parameter estimation procedures for the Log Normal distribution: method of moments (MOM), method of maximum likelihood (MLE) and Bayesian Markov Chain Monte Carlo method (BAY). It has been found that the BAY procedure provides better parameter estimates for the Log Normal distribution, which results in flood quantile estimates with smaller bias and standard error as compared to the MOM and MLE. The findings from this study would be useful in flood frequency analyses in other Australian states and other countries in particular, when selecting an appropriate probability distribution from a number of alternatives.     

Applicability of Wakeby distribution in flood frequency analysis: a case study for eastern Australia

Parametric method of flood frequency analysis (FFA) involves fitting of a probability distribution to the observed flood data at the site of interest. When record length at a given site is relatively longer and flood data exhibits skewness, a distribution having more than three parameters is often used in FFA such as log‐Pearson type 3 distribution. This paper examines the suitability of a five‐parameter Wakeby distribution for the annual maximum flood data in eastern Australia. We adopt a Monte Carlo simulation technique to select an appropriate plotting position formula and to derive a probability plot correlation coefficient (PPCC) test statistic for Wakeby distribution. The Weibull plotting position formula has been found to be the most appropriate for the Wakeby distribution. Regression equations for the PPCC tests statistics associated with the Wakeby distribution for different levels of significance have been derived. Furthermore, a power study to estimate the rejection rate associated with the derived PPCC test statistics has been undertaken. Finally, an application using annual maximum flood series data from 91 catchments in eastern Australia has been presented. Results show that the developed regression equations can be used with a high degree of confidence to test whether the Wakeby distribution fits the annual maximum flood series data at a given station. The methodology developed in this paper can be adapted to other probability distributions and to other study areas. Copyright © 2014 John Wiley & Sons, Ltd.     

Interannual variations in length of day with respect to El Niño - Southern Oscillation’s impact (1962–2015)

The objective of research done in this study is to examine the variability of the length of day (LOD) and to investigate its correlation with ENSO (El Niño-Southern oscillation) episodes. For this purpose, the LOD time series (1962–2015), from the International Earth Rotation and Reference Systems Service (IERS), is investigated using the Singular Spectrum Analysis (SSA) technique. The results show that the LOD time series is very complex and is composed of several components: the long-term trend explains 95.97% of the original series, the annual harmonic 1.76% and the semi-annual 1.35%. Considering sea surface temperature anomalies (SSTA) index over the Niño3, Niño4 and Niño3.4 regions, Southern Oscillation Index (SOI) and Multivariate ENSO Index (MEI), the residuals signal, that represents only 0.92% of the initial LOD series, indicate a significant correlation with ENSO occurred during 1965–66, 1972–73, 1982–83 and 1997–98 El Niño events and 1970–71, 1973–74, 1988–89, 2007–08, 2010–11 La Niña ones. This is a pertinent result that suggests that LOD variability is at least partly related to ENSO phenomena.     

Mixed carbonate-siliciclastic sedimentation on a Miocene fault-block,Gulf of Suez,Egypt

The Miocene sediments at Abu Shaar (Red Sea, Egypt) include both carbonates and detrital silicates whose distribution is closely related to a pre-Miocene basement fault-block. As such, they demonstrate the relationships between local distensional tectonics and sedimentation. Because of the vertical bathymetric relief of the basement block, the carbonates include both a spectacular talus and massive platform facies. In spite of the presence of scattered corals, sedimentation is not truly reefal as deposits consist essentially of muddy bioclastic and silicate sands. The most typical ecological elements are numerous spectacular stromatolites. Rigidity of the sedimentary mass was mainly induced by early marine cementation. Carbonate-siliciclastic transitions on and around the Abu Shaar platform were determined mainly by basement morphology and related pre-Miocene tectonics.     

Mediterranean Sea level trends from long-period tide gauge time series

The main purpose of this study is to highlight, on the basis of statistical tests, the significant long-term changes of the Mediterranean Sea level, through the analysis of historical tide gauge records. In this framework, 14 tide gauge monthly series selected from the Permanent Service of the Mean Sea Level （PSMSL） database were used. The search for the presence or not of trends within these series, that have a temporal coverage from 59 to 142 years, was carried out using the Mann-Kendall test and the Sen''s slope estimator. The obtained results show that the Split Rt Marjana series are the only ones which does not exhibit a significant trend. The other 13 series show significant increasing trends. This result seems sufficient to suppose the presence, in the past century, of a new climatic phase on the scale of the Mediterranean basin, where the rising sea level is one of the consequences.     

Regional flood frequency analysis using Bayesian generalized least squares: a comparison between quantile and parameter regression techniques

Regression‐based regional flood frequency analysis (RFFA) methods are widely adopted in hydrology. This paper compares two regression‐based RFFA methods using a Bayesian generalized least squares (GLS) modelling framework; the two are quantile regression technique (QRT) and parameter regression technique (PRT). In this study, the QRT focuses on the development of prediction equations for a flood quantile in the range of 2 to 100 years average recurrence intervals (ARI), while the PRT develops prediction equations for the first three moments of the log Pearson Type 3 (LP3) distribution, which are the mean, standard deviation and skew of the logarithms of the annual maximum flows; these regional parameters are then used to fit the LP3 distribution to estimate the desired flood quantiles at a given site. It has been shown that using a method similar to stepwise regression and by employing a number of statistics such as the model error variance, average variance of prediction, Bayesian information criterion and Akaike information criterion, the best set of explanatory variables in the GLS regression can be identified. In this study, a range of statistics and diagnostic plots have been adopted to evaluate the regression models. The method has been applied to 53 catchments in Tasmania, Australia. It has been found that catchment area and design rainfall intensity are the most important explanatory variables in predicting flood quantiles using the QRT. For the PRT, a total of four explanatory variables were adopted for predicting the mean, standard deviation and skew. The developed regression models satisfy the underlying model assumptions quite well; of importance, no outlier sites are detected in the plots of the regression diagnostics of the adopted regression equations. Based on ‘one‐at‐a‐time cross validation’ and a number of evaluation statistics, it has been found that for Tasmania the QRT provides more accurate flood quantile estimates for the higher ARIs while the PRT provides relatively better estimates for the smaller ARIs. The RFFA techniques presented here can easily be adapted to other Australian states and countries to derive more accurate regional flood predictions. Copyright © 2011 John Wiley & Sons, Ltd.     

Design rainfall estimation in Australia: a case study using L moments and Generalized Least Squares Regression

Design rainfall is an important input to rainfall runoff models and is used for many other water resources planning and design applications. The estimation of design rainfall is generally done by applying a regional frequency analysis technique that uses data from a large number of rainfall stations in the region. This paper presents a regional rainfall frequency analysis technique that uses an L moments based index method coupled with Generalized Least Squares Regression (GLSR). The particular advantages of the GLSR method are that it accounts for the differences in record lengths across various sites in the region and inter-station correlation in deriving regional prediction equations. The proposed method has been applied to a data set consisting of 203 rainfall stations across Australia. It has been found that the proposed method can be applied successfully in deriving reasonably accurate design rainfall estimates from 1 to 72 h durations. It has also been found that the proposed method provides quite consistent estimates where a third order polynomial is adequate in smoothing the intensity–frequency–duration (IFD) curves. The method can readily be extended to a larger data set of Australia and other countries to derive generalized IFD data.     

Application of a SPH depth-integrated model to landslide run-out analysis

Hazard and risk assessment of landslides with potentially long run-out is becoming more and more important. Numerical tools exploiting different constitutive models, initial data and numerical solution techniques are important for making the expert’s assessment more objective, even though they cannot substitute for the expert’s understanding of the site-specific conditions and the involved processes. This paper presents a depth-integrated model accounting for pore water pressure dissipation and applications both to real events and problems for which analytical solutions exist. The main ingredients are: (i) The mathematical model, which includes pore pressure dissipation as an additional equation. This makes possible to model flowslide problems with a high mobility at the beginning, the landslide mass coming to rest once pore water pressures dissipate. (ii) The rheological models describing basal friction: Bingham, frictional, Voellmy and cohesive-frictional viscous models. (iii) We have implemented simple erosion laws, providing a comparison between the approaches of Egashira, Hungr and Blanc. (iv) We propose a Lagrangian SPH model to discretize the equations, including pore water pressure information associated to the moving SPH nodes.