Investigation of hydraulics transport time scales within the Arvand River estuary,Iran

Transport time scales are key parameters for understanding the hydrodynamic and biochemical processes within estuaries. In this study, the flushing and residence times within the Arvand River estuary have been estimated using a two‐dimensional hydrodynamic model called CE‐QUAL‐W2. The model has been calibrated and verified by two different sets of field data and using the k‐ε vertical eddy diffusivity scheme. Flushing time has been estimated using different methods such as the tidal prism and fraction of freshwater methods. Moreover, residence times have been investigated using pulse residence time, estuarine residence time and remnant function approaches. The results have shown that different methods yield different time scales, and freshwater inflow has the greatest impact upon estimation of residence time, whereas tidal circulation hardly contributes to residence time at all. It has also been shown that the neap‐spring circulation and start phase of simulations have negligible effects on the Arvand's time scales. The investigation of bathymetry showed that two sills of the estuary tend to significantly increase residence time. Understanding the applicability of these time scales and their estimation approaches helps us to evaluate the water quality management of estuaries. Copyright © 2013 John Wiley & Sons, Ltd.     

Capabilities of satellite precipitation datasets to estimate heavy precipitation rates at different temporal accumulations

The importance of satellite datasets as alternative sources of precipitation information has been argued in numerous studies. Future developments in satellite precipitation algorithms as well as utilization of satellite data in operational applications rely on a more in‐depth understanding of satellite errors and biases across different spatial and temporal scales. This paper investigates the capability of satellite precipitation data sets with respect to detecting heavy precipitation rates over different temporal accumulations. In this study, the performance of Tropical Rainfall Measuring Mission real time (TRMM‐RT), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks and CPC MORPHing (CMORPH) is compared against radar‐based gauge‐adjusted Stage IV data. The results show that none of the high temporal resolution (3‐h) datasets are ideal for detecting heavy precipitation rates. In fact, the detection skill of all products drops as the precipitation thresholds (i.e. 75 and 90 percentiles) increase. At higher temporal accumulations (6, 12 and 24 h), the detection skill improves for all precipitation products, with CMORPH showing a better detection skill compared to all other products. On the other hand, all precipitation products exhibit high false alarm ratios above the heavy precipitation thresholds, although TRMM‐RT lead to a relatively smaller level of false alarms. These results indicate that further efforts are necessary to improve the precipitation algorithms so that they can capture heavy precipitation rates more reliably. Copyright © 2013 John Wiley & Sons, Ltd.     

Robust attenuation relations for peak time-domain parameters of strong ground motions

This study presents new attenuation models for the estimation of peak ground acceleration (PGA), peak ground velocity (PGV), and peak ground displacement (PGD) using a hybrid method coupling genetic programming and simulated annealing, called GP/SA. The PGA, PGV, and PGD were formulated in terms of earthquake magnitude, earthquake source to site distance, average shear-wave velocity, and faulting mechanisms. A worldwide database of strong ground motions released by Pacific Earthquake Engineering Research Center (PEER) was employed to establish the models. A traditional genetic programming analysis was performed to benchmark the proposed models. For more validity verification, the GP/SA models were employed to predict the ground-motion parameters of the Iranian plateau earthquakes. Sensitivity and parametric analyses were carried out and discussed. The results show that the GP/SA attenuation models can offer precise and efficient solutions for the prediction of estimates of the peak time-domain characteristics of strong ground motions. The performance of the proposed models is better than or comparable with the attenuation relationships found in the literature.     

The mineralogy and petrology of the Pahnavar Fe skarn, In the Eastern Azarbaijan, NW Iran

The Pahnavar calcic Fe-bearing skarn zone is located in the Eastern Azarbaijan (NW Iran). This skarn zone occurs along the contact between Upper Cretaceous impure carbonates and an Oligocene granodioritic batholith. The skarnification process can be categorized into two discrete stages: prograde and retrograde. The prograde stage began immediately after the initial emplacement of the granodioritic magma into the enclosing impure carbonate rocks. The effect of heat flow from the batholith caused the enclosing rocks to become isochemically marmorized in the pure limestone layers and bimetasomatized (skarnoids) in the impure clay-rich carbonates. Segregation and evolution of an aqueous phase from the magma that infiltrated to the marbles and skarnoids through fractures and micro-fractures took place during the emplacement of magma. The influx of Fe, Si and Mg from the granodiorite to the skarnoids and marbles led to the crystallization of anhydrous calc-silicates (garnet and pyroxene). The retrograde stage can be divided, in turn, into two distinct sub-stages. During earliest sub-stage, the previously formed skarn assemblages were affected by intense hydro-fracturing; in addition, Cu, Pb, Zn, along with H₂S and CO₂ were added. Consequently, hydrous calc-silicates (epidote and tremolite-actinolite), sulfides (pyrite, chalcopyrite, galena and sphalerite), oxides (magnetite and hematite) and carbonates (calcite) deposited the anhydrous calc-silicates. The late-retrograde sub-stage was due the incursion of colder oxidizing fluids into the skarn system, causing the alteration of the previously formed calc-silicate assemblages and the development of fine-grained aggregates of chlorite, illite, kaolinite, hematite and calcite. The lack of wollastonite in the mineral assemblage, along with the garnet-clinopyroxene paragenesis, suggests that the prograde stage formed under temperature and fO₂ conditions of 430?C550°C and 10^?26?C10^?23, respectively.     

A mixture spatial point pattern model for the quasar luminosity function

Consider the problem of estimating the Quasar Luminosity Function (QLF). In a 2007 Ph.D. dissertation, Hugeback considers the QLF as a nonhomogeneous poisson process and estimates the intensity function under SDSS DR3 data (The University of Chicago, AAT 3273021). The present study follows Hugeback’s approach but introduces a mixture component which improves Hugeback’s model in several respects. Namely, the database is partitioned into two groups according to redshift: z < 2.75 and z ? 2.75. Next, a mixture model for the QLF was derived using the concept of pseudolikelihood, the addition of a K function to allow for inter-point interaction, and evaluation of residuals diagnostic plots. This mixture model (i) improves the deviance of Hugeback’s model, and (ii) satisfies residual assumptions that are violated under Hugeback’s model. Moreover, this study confirms Hugeback’s finding of inhomogeneity in the QLF, and provides stronger evidence for the existence of an interaction between redshift and absolute magnitude.     

Laboratory experiments of tsunami run-up and withdrawal in patchy coastal forest on a steep beach

Laboratory experiments were carried out to study tsunami flow dynamics in the presence of patchy macro-roughness, representing coastal forest, on a 1:10 steep beach. The experimental setup included four cross-shore rows of roughness patches affixed to the dry beach in a staggered array, such that 12 % of the staggered array region had higher roughness. The flow field during run-up and withdrawal was quantified using point measurements of velocity and flow depth at 20 locations, while high-resolution video was used to track bore position during run-up. Data analysis revealed that while inundated area was marginally impacted when patchy roughness was present, flow depths and flow force were, respectively, increased by more than 40 and 30 % in some areas within the patch array; a decrease in flow force was also observed in some areas. Alongshore variation in flow depth, induced by the roughness patches, was most pronounced during withdrawal. These findings suggest that patchy macro-roughness, like that created by coastal forest, will simultaneously lead to increased protection in some areas and decreased protection in others.     

Application of sequential Gaussian simulation to quantify the surface wave magnitude uncertainty within the faulted regions

Geostatistical simulations have been recently widely used in the geological and mining investigations. Variogram, the fundamental tools of geostatistics, can identify the spatial distribution of the regionalized variable within the area. One of the important issues of geostatistical simulation in seismotectonics is producing uncertainty maps, which could be applicable to predict earthquake parameters through the site locations especially for civil structures like bridges. It can help engineers to design the structure of interest better. Earthquake parameters as for example seismic fault and surface wave magnitude (Ms) have significant impact on the feasibility study of the civil structures. In this research, a method is presented to produce uncertainty maps for seismic fault and surface wave magnitude, Ms. For this aim, information related to surface wave magnitude and fault trace in Zagros region (SW of Iran) has been collected. Then, the relationships between them through the site location have been investigated and analyzed by conditional geostatistical simulation. In order to quantify the uncertainty of each parameter, the uncertainty formula after generating the E-type maps has been provided and discussed. Finally, in “Talgah Bridge” site, these uncertainty maps were produced to interpret the impact of the surface wave magnitude and fault trace in this specific civil structure.     

Morphometric classification of the biosphere reserve “Eastern Carpathians” (Central Europe) with self-organizing map

In this paper, we propose a semiautomatic method for landscape analysis of biosphere reserve Eastern Carpathians with both spectral and morphometric constituents. The Shuttle Radar Topography Mission (SRTM) has provided digital elevation models for approximately 80 % of the earth’s land surface. SRTM data are used to calculate first derivatives (slope) and second derivatives of elevation (such as minimum curvature, maximum curvatures, and cross-sectional curvature) by fitting a bivariate quadratic surface with a window size of 9 by 9. Together with multispectral remote sensing data like Landsat 7 ETM+ with 28.5 m raster elements, these data provide comprehensive information for the analysis of the landscape in the study area. Unsupervised neural network algorithm—self-organizing map—divided all input vectors into inclusive and exhaustive classes on the basis of similarity between attribute vectors. An optimal self-organizing map with 21 classes using 1,000 iterations and a final neighborhood radius of 0.05 provided a low average quantization error of 0.3394 and was used for further analysis. Morphometric analysis, spectral signature analysis, and feature space analysis are used to assign semantic meaning to the classes as landscape elements according to form, cover, and slope, e.g., deciduous forest on ridge (convex landform) with steep slopes. The results revealed the efficiency of self-organizing map to integrate SRTM and Landsat data for landscape classification. This makes it possible to develop an alternative method for fast assessment and comparison of landscapes over large areas. This procedure is reproducible for the same applications with consistent results.     

Earthquake prediction activities and Damavand earthquake precursor test site in Iran

Iran has long been known as one of the most seismically active areas of the world, and it frequently suffers destructive and catastrophic earthquakes that cause heavy loss of human life and widespread damage. The Alborz region in the northern part of Iran is an active EW trending mountain belt of 100 km wide and 600 km long. The Alborz range is bounded by the Talesh Mountains to the west and the Kopet Dagh Mountains to the east and consists of several sedimentary and volcanic layers of Cambrian to Eocene ages that were deformed during the late Cenozoic collision. Several active faults affect the central Alborz. The main active faults are the North Tehran and Mosha faults. The Mosha fault is one of the major active faults in the central Alborz as shown by its strong historical seismicity and its clear morphological signature. Situated in the vicinity of Tehran city, this 150-km-long N100° E trending fault represents an important potential seismic source. For earthquake monitoring and possible future prediction/precursory purposes, a test site has been established in the Alborz mountain region. The proximity to the capital of Iran with its high population density, low frequency but high magnitude earthquake occurrence, and active faults with their historical earthquake events have been considered as the main criteria for this selection. In addition, within the test site, there are hot springs and deep water wells that can be used for physico-chemical and radon gas analysis for earthquake precursory studies. The present activities include magnetic measurements; application of methodology for identification of seismogenic nodes for earthquakes of M ≥ 6.0 in the Alborz region developed by International Institute of Earthquake Prediction Theory and Mathematical Geophysics, IIEPT RAS, Russian Academy of Science, Moscow (IIEPT&MG RAS); a feasibility study using a dense seismic network for identification of future locations of seismic monitoring stations and application of short-term prediction of medium- and large-size earthquakes is based on Markov and extended self-similarity analysis of seismic data. The establishment of the test site is ongoing, and the methodology has been selected based on the IASPEI evaluation report on the most important precursors with installation of (i) a local dense seismic network consisting of 25 short-period seismometers, (ii) a GPS network consisting of eight instruments with 70 stations, (iii) magnetic network with four instruments, and (iv) radon gas and a physico-chemical study on the springs and deep water wells.