Trichodesmium erythraeum (Ehrenberg) bloom along the southwest coast of India (Arabian Sea) and its impact on trace metal concentrations in seawater

The incidence of a large scale Trichodesmium erythraeum bloom along the southwest coast of India (Arabian Sea) observed in May 2005 is reported. Around 4802 filaments of T. erythraeum ml⁻¹ seawater was observed and a colony consisted of 3.6 × 10⁵ cells. The bloom was predominant off Suratkal (12° 59′N and 74° 31′E) with a depth of about 47 m, covering an area of 7 km in length and 2 km width. The concentrations of Zinc, Cadmium, Lead, Copper, Nickel and Cobalt were determined in samples collected from the bloom and non-bloom sites using stripping voltammetry. The observed hydrographical and meteorological parameters were found to be favorable for the bloom. The concentrations of Zinc, Cadmium and Nickel were found to be higher at bloom stations, while the concentrations of Lead, Copper and Cobalt were found to be very low at bloom stations. Elevated concentrations of Cadmium and Cobalt were observed at Valappad mainly due to the decomposition of detrital material produced in the bloom. Statistically significant differences (P > 0.01) in metal concentrations between the bloom and non-bloom stations were not observed except for Copper. Metals such as Lead, Copper and Cobalt were removed from the seawater at all places where bloom was observed. Cadmium was found to be slowly released during the decaying process of the bloom.     

Carbon cycling in a shallow turbid estuary of southeast Texas: The use of plant pigment biomarkers and water quality parameters

Particulate organic carbon (POC), dissolved organic carbon (DOC), and plant pigments (chlorophylls and carotenoids) were measured approximately bimonthly from March 1992 to October 1993 in the Sabine-Neches estuary (Sabine Lake region), located on the Texas-Louisiana border. High freshwater inflow into this shallow turbid estuary results in the shortest hydraulic residence time (ca. 7 d) of all Texas estuaries (Baskaran et al. in press). Annual averages of chlorophyll-a (3.0 μg l^?1) and particulate organic carbon (1.1 mg l^?1) in the water column were extremely low in comparison to other shallow estuaries. The highest chlorophyll-a concentrations were observed in October 1993, in the mid and lower regions of the estuary, during the lowest river discharge. Zeaxanthin and fucoxanthin concentrations suggested that much of the chlorophyll-a during this low flow period was represented by cyanobacteria and diatoms that entered from the Gulf of Mexico. The range of DOC concentrations was generally high (4.4–20.9 mg l^?1) and were significantly correlated with POC, but not with chlorophyll-a concentrations. When total suspended particulate (TSP) concentrations were below 20 to 30 mg l^?1, there were significant increases in %POC and %PON of the TSP. The unusually high POC: chlorophyll-a ratios (highest value of 1423) suggested that much of the POC contained low concentrations of chlorophyll-a that had degraded during transport from wetlands in the Sabine and Neches rivers. Based on these data, this estuary can be characterized as a predominantly heterotrophic system, with low light penetrance, short particle-residence times, high DOC, and low inputs from autochthonous carbon sources.     

Multivariate nonparametric resampling scheme for generation of daily weather variables

A nonparametric resampling technique for generating daily weather variables at a site is presented. The method samples the original data with replacement while smoothing the empirical conditional distribution function. The technique can be thought of as a smoothed conditional Bootstrap and is equivalent to simulation from a kernel density estimate of the multivariate conditional probability density function. This improves on the classical Bootstrap technique by generating values that have not occurred exactly in the original sample and by alleviating the reproduction of fine spurious details in the data. Precipitation is generated from the nonparametric wet/dry spell model as described in Lall et al. [1995]. A vector of other variables (solar radiation, maximum temperature, minimum temperature, average dew point temperature, and average wind speed) is then simulated by conditioning on the vector of these variables on the preceding day and the precipitation amount on the day of interest. An application of the resampling scheme with 30 years of daily weather data at Salt Lake City, Utah, USA, is provided.     

Inference and uncertainty of snow depth spatial distribution at the kilometre scale in the Colorado Rocky Mountains: the effects of sample size,random sampling,predictor quality,and validation procedures

Historically, observing snow depth over large areas has been difficult. When snow depth observations are sparse, regression models can be used to infer the snow depth over a given area. Data sparsity has also left many important questions about such inference unexamined. Improved inference, or estimation, of snow depth and its spatial distribution from a given set of observations can benefit a wide range of applications from water resource management, to ecological studies, to validation of satellite estimates of snow pack. The development of Light Detection and Ranging (LiDAR) technology has provided non‐sparse snow depth measurements, which we use in this study, to address fundamental questions about snow depth inference using both sparse and non‐sparse observations. For example, when are more data needed and when are data redundant? Results apply to both traditional and manual snow depth measurements and to LiDAR observations. Through sampling experiments on high‐resolution LiDAR snow depth observations at six separate 1.17‐km² sites in the Colorado Rocky Mountains, we provide novel perspectives on a variety of issues affecting the regression estimation of snow depth from sparse observations. We measure the effects of observation count, random selection of observations, quality of predictor variables, and cross‐validation procedures using three skill metrics: percent error in total snow volume, root mean squared error (RMSE), and R². Extremes of predictor quality are used to understand the range of its effect; how do predictors downloaded from internet perform against more accurate predictors measured by LiDAR? Whereas cross validation remains the only option for validating inference from sparse observations, in our experiments, the full set of LiDAR‐measured snow depths can be considered the ‘true’ spatial distribution and used to understand cross‐validation bias at the spatial scale of inference. We model at the 30‐m resolution of readily available predictors, which is a popular spatial resolution in the literature. Three regression models are also compared, and we briefly examine how sampling design affects model skill. Results quantify the primary dependence of each skill metric on observation count that ranges over three orders of magnitude, doubling at each step from 25 up to 3200. Whereas uncertainty (resulting from random selection of observations) in percent error of true total snow volume is typically well constrained by 100–200 observations, there is considerable uncertainty in the inferred spatial distribution (R²) even at medium observation counts (200–800). We show that percent error in total snow volume is not sensitive to predictor quality, although RMSE and R² (measures of spatial distribution) often depend critically on it. Inaccuracies of downloaded predictors (most often the vegetation predictors) can easily require a quadrupling of observation count to match RMSE and R² scores obtained by LiDAR‐measured predictors. Under cross validation, the RMSE and R² skill measures are consistently biased towards poorer results than their true validations. This is primarily a result of greater variance at the spatial scales of point observations used for cross validation than at the 30‐m resolution of the model. The magnitude of this bias depends on individual site characteristics, observation count (for our experimental design), and sampling design. Sampling designs that maximize independent information maximize cross‐validation bias but also maximize true R². The bagging tree model is found to generally outperform the other regression models in the study on several criteria. Finally, we discuss and recommend use of LiDAR in conjunction with regression modelling to advance understanding of snow depth spatial distribution at spatial scales of thousands of square kilometres. Copyright © 2012 John Wiley & Sons, Ltd.     

A model of the late universe with viscous Zel’ldovich fluid and decaying vacuum

Many have speculated about the presence of a stiff fluid in very early stage of the universe. Such a stiff fluid was first introduced by Zel’dovich. Recently the late acceleration of the universe was studied by taking bulk viscous stiff fluid as the dominant cosmic component, but the age predicted by such a model is less than the observed value. We consider a flat universe with viscous stiff fluid and decaying vacuum energy as the cosmic components and found that the model predicts a reasonable background evolution of the universe with de Sitter epoch as end phase of expansion. More over, the model also predicts a reasonable value for the age of the present universe. We also performed a dynamical system analysis of the model and found that the end de Sitter phase predicted by the model is stable.     

The dissolved Beryllium isotope composition of the Arctic Ocean

We present the first comprehensive set of dissolved ¹⁰Be and ⁹Be concentrations in surface waters and vertical profiles of all major sub-basins of the Arctic Ocean, which are complemented by data from the major Arctic rivers Mackenzie, Lena, Yenisey and Ob. The results show that ¹⁰Be and ⁹Be concentrations in waters below 150 m depth are low and only vary within a factor of 2 throughout the Arctic Basin (350-750 atoms/g and 9-15 pmol/kg, respectively). In marked contrast, Be isotope compositions in the upper 150 m are highly variable and show systematic variations. Cosmogenic ¹⁰Be concentrations range from 150 to 1000 atoms/g and concentrations of terrigenous ⁹Be range from 7 to 65 pmol/kg, resulting in ¹⁰Be/⁹Be ratios (atom/atom) between 0.5 and 14 × 10⁻⁸. Inflowing Atlantic water masses in the Eurasian Basin are characterized by a ¹⁰Be/⁹Be signature of 7 × 10⁻⁸. The inflow of Pacific water masses across the Bering Strait is characterized by lower ratios of 2-3 × 10⁻⁸, which can be traced into the central Arctic Ocean, possibly as far as the Fram Strait. A comparison of the high dissolved surface ¹⁰Be and ⁹Be concentrations (corresponding to low ¹⁰Be/⁹Be signatures of ∼2 × 10⁻⁸) in the Eurasian Basin with hydrographic parameters and river data documents efficient and rapid transport of Be with Siberian river waters across the Siberian Arctic shelves into the central Arctic Basin, although significant loss and exchange of Be on the shelves occurs. In contrast, fresh surface waters from the Canada Basin also show high cosmogenic ¹⁰Be contents, but are not enriched in terrigenous ⁹Be (resulting in high ¹⁰Be/⁹Be signatures of up to 14 × 10⁻⁸). This is explained by a combination of efficient scavenging of Be in the Mackenzie River estuary and the shelves and additional supply of cosmogenic ¹⁰Be via atmospheric fallout and melting of old sea ice. The residence time of Be in the deep Arctic Ocean estimated from our data is 800 years and thus similar to the average Be residence time in the global ocean.     

Analysis of sedimentary facies and depositional environments of Paleogene sequence of Cambay basin,India

A study on the sedimentary facies characterization and depositional environment interpretations for the K#Field (K-Oil Field) in Cambay petroleum basin of western onshore, India was conducted based on the sub-surface data from drilled wells, including well logs, borehole images, cores and the regional knowledge of the basin. In this work, an effort is made to integrate the current data from seismics and well logging, to study and analyze its depositional environments and establish the petroleum systems. The study regions for the present work are K45 and K48 blocks. The target strata includes 2 oil-bearing formations of Paleogene, which is about 3600 ft; they are M#Fm (M-Formation) of the Eocene and N#Fm (N-Formation) of Oligocene, subdivided into 11 zones. The sediment fill is mostly of Tertiary. The research attempts to decipher the oil - depositional source correlation problems of the basin. Sedimentary models were established referring to the core analysis, core photographs and well logs. Reservoir and heterogeneity study included reservoir lithology features, physical properties and pore structure features.Well facies analysis of oil well WELL-0297 and WELL-0129 was done and the results were analyzed for further drilling of new wells for oil and gas exploration. The study found that the Eocene, Oligocene, Miocene and Paleogene are fluviatile facies sand and mud interbed sediment with the thickness 2000-4000 ft, which are main oil-bearing formations in these areas. Studies concluded that the fluvial reservoirs of the K#Field are characterized by large variations from laterally extensive bodies with good interconnectedness and high net-to-gross ratios, multi-storey ribbon bodies with poor interconnections and low net-to-gross ratios.     

Use of daily precipitation uncertainties in streamflow simulation and forecast

Among other sources of uncertainties in hydrologic modeling, input uncertainty due to a sparse station network was tested. The authors tested impact of uncertainty in daily precipitation on streamflow forecasts. In order to test the impact, a distributed hydrologic model (PRMS, Precipitation Runoff Modeling System) was used in two hydrologically different basins (Animas basin at Durango, Colorado and Alapaha basin at Statenville, Georgia) to generate ensemble streamflows. The uncertainty in model inputs was characterized using ensembles of daily precipitation, which were designed to preserve spatial and temporal correlations in the precipitation observations. Generated ensemble flows in the two test basins clearly showed fundamental differences in the impact of input uncertainty. The flow ensemble showed wider range in Alapaha basin than the Animas basin. The wider range of streamflow ensembles in Alapaha basin was caused by both greater spatial variance in precipitation and shorter time lags between rainfall and runoff in this rainfall dominated basin. This ensemble streamflow generation framework was also applied to demonstrate example forecasts that could improve traditional ESP (Ensemble Streamflow Prediction) method.