Shift changes and monotonic trends in autocorrelated temperature series over Iran

Temperature data from 29 synoptic stations in Iran for a period of 40?years (1966–2005) were analyzed to test for the existence of monotonic trends and shift changes in the annual, seasonal, and monthly mean air temperature series using the Mann–Kendall and Mann–Whitney tests. The influences of significant lag-1 serial correlation were eliminated from data by the trend-free pre-whitening method prior to the trend analysis. The magnitude of the temperature trends was derived from the Theil–Sen’s slope estimator. It was found that annual mean air temperature increased at 25 out of the 29 stations, of which 17 stations showed significant monotonic trends. The magnitude of the annual mean air temperature trends averagely was (+)0.224°C per decade. Most of the stations with the significant positive monotonic trends had a significant upward shift change. The analysis indicated that the change point year of the significant upward shift changes was 1972 for the whole stations except the coastal ones. Moreover, the strongest monotonic increasing trends and upward shift changes were observed in summer especially in August and September. The spatial analysis of the mean air temperature trends revealed the highest numbers of significant monotonic trends in the big cities of Iran. These findings provide more insights for better understanding of regional temperature behavior in the study area.     

Regularization of geophysical ill-posed problems by iteratively re-weighted and refined least squares

The iteratively re-weighted least squares (IRLS) is a commonly used algorithm which has received significant attention in geophysics and other fields of scientific computing for regularization of discrete ill-posed problems. The IRLS replaces a difficult optimization problem by a sequence of weighted linear systems. The optimum solution of the original problem is usually determined by computing the solution for various regularization parameters λ, each needing several re-weighted iterations (usually 10–15). In this paper, in order to decrease the required computation time (iterations) while maintaining good properties of the algorithm such as edge-preserving, the IRLS is augmented with a refinement strategy and the value of λ is progressively updated in a geometrical form during the iterations. The new algorithm, called iteratively re-weighted and refined least squares (IRRLS), can be interpreted as a Landweber iteration with a non-stationary shaping matrix which is updated based on the solution obtained from previous iteration. Two main properties of IRRLS are (1) the regularization parameter is the stopping iteration and (2) it is equipped with a tuning parameter which makes it flexible for recovering models with different smoothness. We show numerically that both the residual and regularization norms are monotone functions of iteration and hence well behaved for automatic determination of stopping parameter. The Stain’s unbiased risk estimate (SURE), generalized cross validation (GCV), L-curve analysis, and discrepancy principle (DCP) techniques are employed for automatic determination of optimum iteration. Experimental results from seismic deconvolution and seismic tomography are included showing that the proposed methodology outperforms the conventional IRLS with significantly lower computational burden.     

Simulation of rainfall-runoff process using an artificial neural network (ANN) and field plots data

Theoretical and Applied Climatology - Rainfall-runoff modeling is necessary for many hydrological studies, such as estimating peak discharges and designing hydraulic structures. The intensity and...     

Aeolian sediment fingerprinting using a Bayesian mixing model

Identifying sand provenance in depositional aeolian environments (e.g. dunefields) can elucidate sediment pathways and fluxes, and inform potential land management strategies where windblown sand and dust is a hazard to health and infrastructure. However, the complexity of these pathways typically makes this a challenging proposition, and uncertainties on the composition of mixed‐source sediments are often not reported. This study demonstrates that a quantitative fingerprinting method within the Bayesian Markov Chain Monte Carlo (MCMC) framework offers great potential for exploring the provenance and uncertainties associated with aeolian sands. Eight samples were taken from dunes of the small (~58 km²) Ashkzar erg, central Iran, and 49 from three distinct potential sediment sources in the surrounding area. These were analyzed for 61 tracers including 53 geochemical elements (trace, major and rare earth elements (REE)) and eight REE ratios. Kruskal–Wallis H‐tests and stepwise discriminant function analysis (DFA) allowed the identification of an optimum composite fingerprint based on six tracers (Rb, Sr, ⁸⁷Sr, (La/Yb)_n, Ga and δCe), and a Bayesian mixing model was applied to derive the source apportionment estimates within an uncertainty framework. There is substantial variation in the uncertainties in the fingerprinting results, with some samples yielding clear discrimination of components, and some with less clear fingerprints. Quaternary terraces and fans contribute the largest component to the dunes, but they are also the most extensive surrounding unit; clay flats and marls, however, contribute out of proportion to their small outcrop extent. The successful application of these methods to aeolian sediment deposits demonstrates their potential for providing quantitative estimates of aeolian sediment provenances in other mixed‐source arid settings, and may prove especially beneficial where sediment is derived from multiple sources, or where other methods of provenance (e.g. detrital zircon U–Pb dating) are not possible due to mineralogical constraints. Copyright © 2017 John Wiley & Sons, Ltd.     

Fast hyperbolic deconvolutive Radon transform using generalized Fourier slice theorem

The hyperbolic Radon transform has a long history of applications in seismic data processing because of its ability to focus/sparsify the data in the transform domain. Recently, deconvolutive Radon transform has also been proposed with an improved time resolution which provides improved processing results. The basis functions of the (deconvolutive) Radon transform, however, are time-variant, making the classical Fourier based algorithms ineffective to carry out the required computations. A direct implementation of the associated summations in the time–space domain is also computationally expensive, thus limiting the application of the transform on large data sets. In this paper, we present a new method for fast computation of the hyperbolic (deconvolutive) Radon transform. The method is based on the recently proposed generalized Fourier slice theorem which establishes an analytic expression between the Fourier transforms associated with the data and Radon plane. This allows very fast computations of the forward and inverse transforms simply using fast Fourier transform and interpolation procedures. These canonical transforms are used within an efficient iterative method for sparse solution of (deconvolutive) Radon transform. Numerical examples from synthetic and field seismic data confirm high performance of the proposed fast algorithm for filling in the large gaps in seismic data, separating primaries from multiple reflections, and performing high-quality stretch-free stacking.     

Design,manufacture and calibration of the SARI portable rainfall simulator for field and laboratory experiments

The Simulator of Artificial RaInfall (SARI) rainfall simulator (RS) is a newly designed, constructed and calibrated, portable, two-nozzle RS with low water consumption, accurate measurement, easy management and low cost. The raindrop size distribution and velocity and mean rainfall intensity were measured. The best rainfall spatial distribution was achieved with nozzles separated by 50, 60 and 70 cm, and with oscillation angles of 30, 45 and 60°, at a pressure of 60 kPa. The uniformity coefficient varied from 57 to 61% and rainfall intensity from 48 to 101 mm h^?1. The raindrop diameter varied from 0.2 to 9.9 mm. The raindrop velocity at the optimum pressure of 60 kPa, which was measured with high-speed photography, ranged from 1.1 to 7.1 m s^?1. Comparison with other RSs shows that the SARI simulator is a suitable apparatus to research soil erosion and runoff generation under laboratory and field conditions.     

Statistical Multivariate Analysis in the Assessment of River Water Quality in the Vicinity of KRS Dam, Karnataka, India

Surface water quality assessment is a basic and critical tool for informing sustainable management of water resources. The aim of this study is to evaluate the reservoir and river water quality of the Cauvery River in the vicinity of the Krishna Raja Sagar Dam, Karnataka, India. The results will assist water management in the study area for varied future demands including, irrigation, industries, and river conservation. Ecologically important parameters such as dissolved oxygen, chemical oxygen demand, biochemical oxygen demand, and physico-chemical parameters were analyzed. The results were compared with standard permissible limits. The differences in various parameters were statistically significant (P < 0.01) when comparing the upstream and downstream stretches of the river, particularly in summer. To identify a good technique for water quality assessment, all of the water quality parameters have been calculated by correlating various parameters and comparing them over two critical dry seasons. The dendrogram of the water-quality parameters clearly indicate that river water is safe for drinking, fishing, irrigation, and industrial purposes.     

Probabilistic reservoir operation using Bayesian stochastic model and support vector machine

Available water resources are often not sufficient or too polluted to satisfy the needs of all water users. Therefore, allocating water to meet water demands with better quality is a major challenge in reservoir operation. In this paper, a methodology to develop operating strategies for water release from a reservoir with acceptable quality and quantity is presented. The proposed model includes a genetic algorithm (GA)-based optimization model linked with a reservoir water quality simulation model. The objective function of the optimization model is based on the Nash bargaining theory to maximize the reliability of supplying the downstream demands with acceptable quality, maintaining a high reservoir storage level, and preventing quality degradation of the reservoir. In order to reduce the run time of the GA-based optimization model, the main optimization model is divided into a stochastic and a deterministic optimization model for reservoir operation considering water quality issues.The operating policies resulted from the reservoir operation model with the water quantity objective are used to determine the released water ranges (permissible lower and upper bounds of release policies) during the planning horizon. Then, certain values of release and the optimal releases from each reservoir outlet are determined utilizing the optimization model with water quality objectives. The support vector machine (SVM) model is used to generate the operating rules for the selective withdrawal from the reservoir for real-time operation. The results show that the SVM model can be effectively used in determining water release from the reservoir. Finally, the copula function was used to estimate the joint probability of supplying the water demand with desirable quality as an evaluation index of the system reliability. The proposed method was applied to the Satarkhan reservoir in the north-western part of Iran. The results of the proposed models are compared with the alternative models. The results show that the proposed models could be used as effective tools in reservoir operation.     

Deformation condition determination and strain analysis: Application of microstructural and microthermometry study of the Zamanabad Shear Zone (East of Iran)

Microstructural analysis and microthermometry are useful methods for determining the deformation evolution. To address this issue, rheological behavior of quartz, feldspar and calcite in veins and host rocks during deformation, are presented in the mylonite zone of the dextral reverse Zamanabad Shear Zone (ZSZ), in northern part of Sistan Suture Zone (SSZ), in east of Iran. Microstructure evidences revealed two evolution stages of high and low temperature deformation. Quartz microstructures in the ZSZ show abundant evidences for early high-temperature plastic deformation (e.g. Bulging recrystallization (BLG)) which are as microstructures with SW directed ductile shearing in the central parts of the ZSZ. This shear zone shows progressively decreasing strain away from the central of shear zone toward the wall. High-temperature microstructures are overprinted partly or completely during shearing by the later low-temperature deformation (e.g. Pressure solution, fractures, veinlets). Microstructural observations of veins (quartz and calcite) confirms the results of microstructures in the host rock, as quartz veins occurred from peak metamorphic conditions (<400°C) and then in lower P–T conditions have been formed calcite veins (~250°C). According to microthermometric studies, two primary fluid groups are observed in quartz veins: (1) fluids trapped during peak deformation conditions, with higher-salinity, They were initially trapped at ~300–400°C, (2) smaller fluids by trapping of low-salinity inclusions at ~240–180°C that related to subsequent phases of shear zone exhumation in lower deep. Microthermometry results and microstructural analysis indicate deformation under lower greenschist facies conditions for the ZSZ, and then exhumation of the early of high-temperature rocks within regime of ductile-brittle transition to brittle.     

Further Study on the Kinetics of Bioremediation of Soils Contaminated with the Herbicide 2,4‐Dichlorophenoxy Acetic Acid

The bioremediation of soil contaminated with the herbicide 2,4‐dichlorophenoxy acetic acid has been studied applying the bacterium Ralstonia eutropha. The effects of temperature, pH, and moisture content of soils on the rate of degradation of this herbicide have been investigated. The Taguchi experimental design method was applied to determine the relative impacts of the pertinent system variables. According to this approach, four series of experiments were performed under various operating conditions. In all four series the herbicide was degraded efficiently, however, the highest rate was observed at a temperature of 35°C, pH 6, and at 80% moisture content of the soil. A kinetic model for the biodegradation of the herbicide was developed for this particular conditions based on the Monod type growth equation and autocatalytic behavior of the decomposition reaction. A close correlation has been observed between the experimental data and those predicted from the model. The degree of agreement was between 85–95%.