Estuary/ocean exchange and tidal mixing in a Gulf of Maine Estuary: A Lagrangian modeling study

A Lagrangian particle method embedded within a 2-D finite element code, is used to study the transport and ocean–estuary exchange processes in the well-mixed Great Bay Estuarine System in New Hampshire, USA. The 2-D finite element model, driven by residual, semi-diurnal and diurnal tidal constituents, includes the effects of wetting and drying of estuarine mud flats through the use of a porous medium transport module. The particle method includes tidal advection, plus a random walk model in the horizontal that simulates sub-grid scale turbulent transport processes. Our approach involves instantaneous, massive [O(500,000)] particle releases that enable the quantification of ocean–estuary and inter-bay exchanges in a Markovian framework. The effects of the release time, spring–neap cycle, riverine discharge and diffusion strength on the intra-estuary and estuary–ocean exchange are also investigated.The results show a rather dynamic interaction between the ocean and the estuary with a fraction of the exiting particles being caught up in the Gulf of Maine Coastal Current and swept away. Three somewhat different estimates of estuarine residence time are calculated to provide complementary views of estuary flushing. Maps of residence time versus release location uncover a strong spatial dependency of residence time within the estuary that has very important ramifications for local water quality. Simulations with and without the turbulent random walk show that the combined effect of advective shear and turbulent diffusion is very effective at spreading particles throughout the estuary relatively quickly, even at low (1 m²/s) diffusivity. The results presented here show that a first-order Markov Chain approach has applicability and a high potential for improving our understanding of the mixing processes in estuaries.     

Modeling bed-load transport of coarse sediments in the Great Bay Estuary, New Hampshire

Current, sea level and bed-load transport are investigated in the Lower Piscataqua River section of the Great Bay Estuary, New Hampshire, USA—a well-mixed and geometrically complex system with low freshwater input, having main channel tidal currents ranging between 0.5 and 2 m s⁻¹. Current and sea level forced by the M₂M₄M₆ tides at the estuarine mouth are simulated by a vertically averaged, non-linear, time-stepping finite element model. The hydrodynamic model uses a fixed boundary computational domain and accounts for flooding–drying of tidal flats by making use of a groundwater component. Inertia terms are neglected in comparison with pressure gradient and bottom friction terms, which is consistent with the observed principal dynamic balance for this section of the system. The accuracy of hydrodynamic predictions in the study area is demonstrated by comparison with four tidal elevation stations and two cross-section averaged current measurements. Simulated current is then used to model bed-load transport in the vicinity of a rapidly growing shoal located in the main channel of the lower system. Consisting of coarse sand and gravel, the shoal must be dredged every five to eight years. Two approaches are taken—an Eulerian parametric method in which nodal bed-load flux vectors are averaged over the tidal cycle and a Lagrangian particle tracking approach in which a finite number of sediment particles are released and tracked. Both methods yield pathways and accumulations in agreement with the observed shoal formation and the long-term rate of sediment accumulation in the shoal area.     

EXPERIMENTAL AND FIELD STUDY ON MINING-PIT MIGRATION

1 INTRODUCTION Because of the large quantity of sand and gravel in their beds, rivers have always been considered as a major source of sand and gravel for civil works. Acceptable quality, ease of extraction and economy are some of the reasons could be mentioned. Unfortunately, specific laws and regulations regarding the safe in-stream mining have not been provided for users and officials. What should be taken into account are the effects of over-mining of sand and gravel, which can cause …     

Five young star clusters in the outer region of the Small Magellanic Cloud

Colour–magnitude diagrams in the Washington system are presented for the first time for five star clusters projected on to the outer region of the Small Magellanic Cloud (SMC). The clusters are found to have ages in the range 0.1–1.0 Gyr, as derived from the fit of isochrones with   Z = 0.004  . This sample increases substantially the number of young clusters in the outer SMC – particularly in the south-east quadrant – with well-derived parameters. We combine our results with those for other clusters in the literature to derive as large and homogeneous a data base as possible (totalling 49 clusters) in order to study global effects. We find no conclusive evidence for a dispersion in the cluster ages and metallicities as a function of their distance from the galaxy centre, in the SMC outer region. L 114 and 115, although very distant, are very young clusters, lying in the bridge of the SMC and therefore most likely formed during the interaction which formed this feature. We also find very good agreement between the cluster age–metallicity relation (AMR) and the prediction from a bursting model from Pagel & Tautvaišienė with a burst that occurred 3 Gyr ago. Comparing the present cluster AMR with that derived by Harris & Zaritsky for field stars in the main body of the SMC, we find that field stars and clusters underwent similar chemical enrichment histories during approximately the last couple of Gyr, but their chemical evolution was clearly different between 4 and 10 Gyr ago.     

An ANN‐based model for spatiotemporal groundwater level forecasting

This paper evaluates the feasibility of using an artificial neural network (ANN) methodology for estimating the groundwater levels in some piezometers placed in an aquifer in north‐western Iran. This aquifer is multilayer and has a high groundwater level in urban areas. Spatiotemporal groundwater level simulation in a multilayer aquifer is regarded as difficult in hydrogeology due to the complexity of the different aquifer materials. In the present research the performance of different neural networks for groundwater level forecasting is examined in order to identify an optimal ANN architecture that can simulate the piezometers water levels. Six different types of network architectures and training algorithms are investigated and compared in terms of model prediction efficiency and accuracy. The results of different experiments show that accurate predictions can be achieved with a standard feedforward neural network trained usung the Levenberg–Marquardt algorithm. The structure and spatial regressions of the ANN parameters (weights and biases) are then used for spatiotemporal model presentation. The efficiency of the spatio‐temporal ANN (STANN) model is compared with two hybrid neural‐geostatistics (NG) and multivariate time series‐geostatistics (TSG) models. It is found in this study that the ANNs provide the most accurate predictions in comparison with the other models. Based on the nonlinear intrinsic ANN approach, the developed STANN model gives acceptable results for the Tabriz multilayer aquifer. Copyright © 2008 John Wiley & Sons, Ltd.     

Confirmation of the 25.5-day fundamental period of the Sun using the north-south asymmetry of the flare index

Short-term periodicities of solar activity were studied. To perform the study, a north-south asymmetry time series was constructed by using the northern and the southern hemisphere flare index values for solar cycle 22. The statistical significance of this time series was calculated. It indicates that in most of cases the asymmetry is highly significant during cycle 22. Power spectral analysis of this time series reveals a periodicity around 25.5 days, which was announced before as a fundamental period of solar activity (Bai and Sturrock, 1991). To investigate the time agreement between the two hemispheres, the phase distribution was studied and a phase shift of about 0.5 was found. An activity trend from the north to the south was found.     

WIYN Open Cluster Study – XVI. Optical/infrared photometry and comparisons with theoretical isochrones

We present combined optical/near-infrared photometry ( BVIK ) for six open clusters – M35, M37, NGC 1817, NGC 2477, NGC 2420 and M67. The open clusters span an age range from 150 Myr to 4 Gyr and have metal abundances from  [Fe/H]=−0.27  to +0.09 dex. We have utilized these data to test the robustness of theoretical main sequences constructed by several groups as denoted by the following designations – Padova, Baraffe, Y², Geneva and Siess. The comparisons of the models with the observations have been performed in the  [ M_V , ( B − V )₀], [ M_V , ( V − I )₀]  and  [ M_V , ( V − K )₀]  colour–magnitude diagrams as well as the distance-independent  [( V − K )₀, ( B − V )₀]  and  [( V − K )₀, ( V − I )₀]  two-colour diagrams. We conclude that none of the theoretical models reproduces the observational data in a consistent manner over the magnitude and colour range of the unevolved main sequence. In particular, there are significant zero-point and shape differences between the models and the observations. We speculate that the crux of the problem lies in the precise mismatch between theoretical and observational colour–temperature relations. These results underscore the importance of pursuing the study of stellar structure and stellar modelling with even greater intensity.     

Relation of the fluctuations of some of the solar activity indices

In this Letter we obtained the fluctuation curves from the differences of unsmoothed and smoothed values of the different solar activity indices. We compared the pattern of fluctuations of these curves. Also using monthly mean values of each index we studied the degree of correlation between the pairs of indices during the selected time intervals.     

Estimation of relative humidity based on artificial neural network approach in the Aegean Region of Turkey

The aim of this study is to estimate the monthly mean relative humidity (MRH) values in the Aegean Region of Turkey with the help of the topographical and meteorological parameters based on artificial neural network (ANN) approach. The monthly MRH values were calculated from the measurement in the meteorological observing stations established in Izmir, Mugla, Aydin, Denizli, Usak, Manisa, Kutahya and Afyonkarahisar provinces between 2000 and 2006. Latitude, longitude, altitude, precipitation and months of the year were used in the input layer of the ANN network, while the MRH was used in output layer of the network. The ANN model was developed using MATLAB software, and then actual values were compared with those obtained by ANN and multi-linear regression methods. It seemed that the obtained values were in the acceptable error limits. It is concluded that the determination of relative humidity values is possible at any target point of the region where the measurement cannot be performed.