Assessment of mesoscale convective systems using IR brightness temperature in the southwest of Iran

In this research, the spatial and temporal distribution of Mesoscale Convective Systems was assessed in the southwest of Iran using Global merged satellite IR brightness temperature (acquired from Meteosat, GOES, and GMS geostationary satellites) and synoptic station data. Event days were selected using a set of storm reports and precipitation criteria. The following criteria are used to determine the days with occurrence of convective systems: (1) at least one station reported 6-h precipitation exceeding 10 mm and (2) at least three stations reported phenomena related to convection (thunderstorm, lightning, and shower). MCSs were detected based on brightness temperature, maximum areal extent, and duration thresholds (228 K, 10,000 km², and 3 h, respectively). An MCS occurrence classification system is developed based on mean sea level, 850 and 500 hPa pressure patterns.

The results indicated that the highest frequency of MCSs occurred in December and April. Assessment of MCSs spatial frequency showed that MCS occurrence is strongly correlated with topography in April and May unlike the cold months. In other words, the role of Zagros Mountains in developing MCSs varies based on the season so that its impact increases with enhancement of mean monthly temperature. In addition, the occurrence of MCSs depends closely on the configuration of the Sudan Low in the southwest of Iran.

     

An alternative direct method towards mean dynamic topography computations

An alternative “direct method” to “mean dynamic topography” (MDT) computations using satellite altimetry-derived “mean sea surface” (MSS) and “global geopotential model” (GGM), without direct application of the geoid, is devised. The developed approach, which is based on derivation of an equipotential surface of the gravity field of the Earth that fits to global MSS in least squares sense, is formulated via a constrained optimization problem. The validity of our method is numerically tested by computing a global MDT model based on DNSC08 MSS model and EGM2008 GGM as input data.     

On correct application of one-step inversion of gravity data

One of the main problems on the numerical solution of integral equations is the resolution of input data. Among the integral equations used in geodesy we have the “onestep inversion” based on the first derivative of the Poisson integral, which transforms gravity values on the Earth’s surface to the gravity potential on the reference ellipsoid. In this study, it is shown that the required spatial resolution of the input gravity data on the Earth’s surface for correct one-step inversion depends on the height of the computational region, the fact that if overlooked can cause totally wrong results. Consequently the following two major questions are posed: (i) How could one know whether the spatial resolution of the input gravity data for correct one-step inversion is sufficient? (ii) What should be done if the spatial resolution is not sufficient? By studying the behaviour of the integral kernel, an algorithm is presented which enables an appropriate answer to the former question. In order to address the latter question, a method is proposed to modify the integral kernel which overcomes the adverse effect of insufficient spatial resolution of the input gravity data. Our answers, which possess the novelty of the study, are numerically verified by means of real and simulated gravity data. The numerical results approve the efficiency of the proposed method in solving the problem of insufficient spatial resolution of the input gravity data for correct one-step inversion.     

Paleoenvironment of geomorphic surfaces of an alluvial fan in the eastern Isfahan,Iran, in the light of micromorphology and clay mineralogy

Alluvial fans are one of the most important landforms in geomorphological and paloenvironmental studies. The objective of this study was the application of clay mineral assemblages and micromorphological properties of the studied paleosols in the geomorphic surfaces of an alluvial fan in the eastern Isfahan as proxies for paleoenvironmental and paleoclimatic changes. Micromorphology, X-ray diffraction, and scanning electron microscopy approaches were used to study the representative pedons. The results indicated that the illuviation process in the calcareous soils of the arid regions of the eastern Isfahan was probably in response to Quaternary moist conditions. There was no significant difference between clay coating properties of the studied relict and buried paleosols. Clay mineralogical study suggested that kaolinite and illite were inherited from the parent materials, while smectite and palygorskite were formed in the soil environment. Paleoargillic horizon was characterized by smectite and calcic (especially the calcrete) horizons were dominated by palygorskite. Palygorskite was accumulated by both neoformation and illuviation processes. High clay content, high intensity of smectite peak, and activity of the illuviation process in paleoargillic horizon demonstrated the seasonality of climate (rainfall) even in the moist periods of Quaternary in Central Iran. Clay mineralogical assemblages suggested a trend of increasing environmental aridity in the study area. This study, therefore, highlighted the role of clay mineralogical investigations in arid lands’ geomorphological and paleoenvironmental researches.     

Use of hydrological methods for assessment of environmental flow in a river reach

Environmental flow assessment and maintenance are relatively new practices, especially in developing countries. This paper describes the desktop assessment of environmental flows in a river with insufficient data on ecological features and values. In this study, the potential environmental flows in a typical river reach of the Shahr Chai River in Iran were investigated using a newly developed hydrological method (flow duration curve (FDC) shifting) and Global Environmental Flow Calculator software. This approach uses monthly flow data to develop an environmental FDC and to generate flow requirements corresponding to different features of the river ecosystem. Results were compared with those from four alternative hydrological methods: the desktop reserve model (DRM), Tennant, low-flow index, and flow duration curve analysis (FDCA). Comparisons of these methods indicated that to maintain the basic function of the river ecosystem, the river flows should be managed within an acceptable environmental level. The predictions from the Tennant method and the low-flow index (7-day low flow with a 10-year return period), and from the FDCA (for flows exceeding 90?% of occurrence) are not as reliable as those from the FDC shifting technique and DRM. Comparative results indicate that a minimum flow rate of 1.2?m³/s (equivalent to 23?% of the natural mean annual runoff, or flow with 80?% occurrence depicted from the FDC) is required for the Shahr Chai River to run toward the internationally recognized Urmia Lake in Iran.     

Optimal water and waste load allocation in reservoir–river systems: a case study

In this paper, a new methodology is developed for optimization of water and waste load allocation in reservoir–river systems considering the existing uncertainties in reservoir inflow, waste loads and water demands. A stochastic dynamic programming (SDP) model is used to optimize reservoir operation considering the inflow uncertainty, and another model called PSO-SA is developed and linked with the SDP model for optimizing water and waste load allocation in downstream river. In the PSO-SA model, a particle swarm optimization technique with a dynamic penalty function for handling the constraints is used to optimize water and waste load allocation policies. Also, a simulated annealing technique is utilized for determining the upper and lower bounds of constraints and objective function considering the existing uncertainties. As the proposed water and waste load allocation model has a considerable run-time, some powerful soft computing techniques, namely, Regression tree Induction (named M5P), fuzzy K-nearest neighbor, Bayesian network, support vector regression and an adaptive neuro-fuzzy inference system, are trained and validated using the results of the proposed methodology to develop real-time water and waste load allocation rules. To examine the efficiency and applicability of the methodology, it is applied to the Dez reservoir–river system in the south-western part of Iran.     

Monthly suspended sediment load prediction using artificial intelligence: testing of a new random subspace method

ABSTRACT

The predictive capability of a new artificial intelligence method, random subspace (RS), for the prediction of suspended sediment load in rivers was compared with commonly used methods: random forest (RF) and two support vector machine (SVM) models using a radial basis function kernel (SVM-RBF) and a normalized polynomial kernel (SVM-NPK). Using river discharge, rainfall and river stage data from the Haraz River, Iran, the results revealed: (a) the RS model provided a superior predictive accuracy (NSE = 0.83) to SVM-RBF (NSE = 0.80), SVM-NPK (NSE = 0.78) and RF (NSE = 0.68), corresponding to very good, good, satisfactory and unsatisfactory accuracies in load prediction; (b) the RBF kernel outperformed the NPK kernel; (c) the predictive capability was most sensitive to gamma and epsilon in SVM models, maximum depth of a tree and the number of features in RF models, classifier type, number of trees and subspace size in RS models; and (d) suspended sediment loads were most closely correlated with river discharge (PCC = 0.76). Overall, the results show that RS models have great potential in data poor watersheds, such as that studied here, to produce strong predictions of suspended load based on monthly records of river discharge, rainfall depth and river stage alone.     

Exploring Real‐Time Geoprocessing in Cloud Computing: Navigation Services Case Study

Today, many real‐time geospatial applications (e.g. navigation and location‐based services) involve data‐ and/or compute‐intensive geoprocessing tasks where performance is of great importance. Cloud computing, a promising platform with a large pool of storage and computing resources, could be a practical solution for hosting vast amounts of data and for real‐time processing. In this article, we explored the feasibility of using Google App Engine (GAE), the cloud computing technology by Google, for a module in navigation services, called Integrated GNSS (iGNSS) QoS prediction. The objective of this module is to predict quality of iGNSS positioning solutions for prospective routes in advance. iGNSS QoS prediction involves the real‐time computation of large Triangulated Irregular Networks (TINs) generated from LiDAR data. We experimented with the Google App Engine (GAE) and stored a large TIN for two geoprocessing operations (proximity and bounding box) required for iGNSS QoS prediction. The experimental results revealed that while cloud computing can potentially be used for development and deployment of data‐ and/or compute‐intensive geospatial applications, current cloud platforms require improvements and special tools for handling real‐time geoprocessing, such as iGNSS QoS prediction, efficiently. The article also provides a set of general guidelines for future development of real‐time geoprocessing in clouds.     

Applying an optimized proxy-based workflow for fast history matching

History matching is still one of the main challenging parts of reservoir study especially in giant brown oil fields with lots of wells. In these cases, history matching with conventional manual technique needs many runs and takes months to get a match. In this work, an innovative approach was suggested for fast history matching in a real brown field. The workflow was employed based on an optimized proxy model for history matching of a field consisting of 14 active wells with multiple responses (which are production rate and pressure data) in the south part of Iran. The main important features of the proposed algorithm were defining a proxy model which is response surface method in which 21 model parameters were incorporated based on cubic centered face method. The proxy model was then optimized by one of the most famous algorithms which is genetic algorithm. Proxy model was successfully performed using 256 samples leading into p- value of 0.531 and R ² of 0.91 dataset. As a result, the proposed workflow and algorithm showed good and acceptable results for history matching of studied real model.     

Geodynamic setting and emplacement of mylonitic granitoids within the North Golpayegan shear zone,Iran

The metamorphic complex of the North Golpayegan is part of the Sanandaj-Sirjan Zone. There are at least three distinct stages of deformation in this complex. Throughout the first stage, Paleozoic and Mesozoic sedimentary rocks have experienced regional metamorphism during Late Jurassic tectonic events related to the subduction of the Neo-Tethys oceanic lithosphere under the Iranian microcontinent. During the second deformation stage in the Late Cretaceous-Paleocene, the rocks have been mylonitized. The third stage of deformation in the region has led to folding and faulting superimposed on previous structures, and to exhumation of the metamorphic complex. This stage has determined the current morphology and N70E strike of the complex. The mylonitic zones of the second stage of deformation have been formed along the dextral transpressional faults. During the third stage of deformation and exhumation of the metamorphic complex, the mylonitic zones have been uplifted to the surface. The granitoids in the metamorphic complex have been injected along the extensional shear fractures related to the dextral transpressional displacements. The granitoids have been transformed into mylonites within the synthetic or antithetic shear zones. These granitoids are recognized as syncollision type (CCG) and have been formed at the end of orogenic events synchronous to the collision between the Arabian and the Iranian plates at the Late Cretaceous-Paleocene.