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.

     

Statistical analysis of surface urban heat island intensity variations: A case study of Babol city,Iran

The urban heat island is considered as one of the most important climate change phenomena in urban areas, which can result in remarkable negative effects on flora, concentration of pollutants, air quality, energy and water consumption, human health, ecological and economic impacts, and even on global warming. The variation analysis of the surface urban heat island intensity (SUHII) is important for understanding the effect of urbanization and urban planning. The objective of this study was to present a new strategy based on the Shannon’s entropy and Pearson chi-square statistic to investigate the spatial and temporal variations of the SUHII. In this study, Landsat TM, ETM+, OLI and TIRS images, MODIS products, meteorological data, topographic and population maps of the Babol city, Iran, from 1985 to 2017, and air temperature data recorded by ground recorder devices in 2017 were used. First, Single-Channel algorithm was used to estimate land surface temperature (LST), and the maximum likelihood classifier was employed to classify Landsat images. Then, based on LST maps, surface urban heat island ratio index was employed to calculate the SUHII. Further, several statistical methods, such as the degree-of-freedom, degree-of-sprawl and degree-of-goodness, were used to analyse the SUHII variation along different geographic directions and in various time periods. Finally, correlation between various parameters such as air temperature, SUHII, population variation and degree-of-goodness index values were investigated. The results indicated that the SUHII value increased by 24% in Babol over different time periods. The correlation coefficient yielded 0.82 between the values of the difference between the mean air temperature of the urban and suburbs and the SUHII values for the geographic directions. Furthermore, the correlation coefficient between the population variation and the degree-of-goodness index values reached 0.8. The results suggested that the SUHII variation of Babol city had a high degree-of-freedom, high degree-of-sprawl and negative degree-of-goodness.     

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.     

Simulating urban growth in a megalopolitan area using a patch‐based cellular automata

Although traditional cellular automata (CA)‐based models can effectively simulate urban land‐use changes, they typically ignore the spatial evolution of urban patches, due to their use of cell‐based simulation strategies. This research proposes a new patch‐based CA model to incorporate a spatial constraint based on the growth patterns of urban patches into the conventional CA model for reducing the uncertainty of the distribution of simulated new urban patches. In this model, the growth pattern of urban patches is first estimated using a developed indicator that is based on the local variations in existing urban patches. The urban growth is then simulated by integrating the estimated growth pattern and land suitability using a pattern‐calibrated method. In this method, the pattern of new urban patches is gradually calibrated toward the dominant growth pattern through the steps of the CA model. The proposed model is applied to simulate urban growth in the Tehran megalopolitan area during 2000–2006–2012. The results from this model were compared with two common models: cell‐based CA and logistic‐patch CA. The proposed model yields a degree of patch‐level agreement that is 23.4 and 7.5% higher than those of these pre‐existing models, respectively. This reveals that the patch‐based CA model simulates actual development patterns much better than the two other models.     

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.     

Assessing desertification by using soil indices

Desertification generally refers to land degradation in arid, semiarid, and dry semi-humid climatic zones. It involves five principal processes: vegetation degradation, water erosion, wind erosion, salinization and waterlogging, and soil crusting and compaction. The aim of this study is assessing desertification using soil criteria. For this purpose, nine indices including sodium absorption ratio (SAR), soil gypsum percentage, soil texture, the content of HCO₃ ^?, the percentage of the organic matter, electrical conductivity (EC), pH, the content of the soil sodium, and chloride were used. The soil samples were taken in the north of Zayandeh-Rood River in Isfahan province of Iran, using soil data randomly sampled in a depth of 0–20 cm. After assessing the normality of the samples using Kolmogorov-Smirnov test, indices were imported into GIS environment and interpolated with IDW and normal and discrete kriging methods for delineating soil characteristics maps based on MEDALUS model. In this model, the data were firstly changed from 100 to 200. Thus 100 and 200 are estimated as the best and worst quality, respectively. Then the final map of soil criteria has been created by geometric mean of its indicators. The results showed that the maximum area is related to the medium class of desertification and is equal to 44,746 ha. The areas of severe and very severe classes of desertification are equal to 30,949 and 351 ha, respectively. The results also revealed that the indices of the organic matter and soil gypsum percentage are the most influential indices which affect desertification phenomenon.     

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.     

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.     

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.     

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.