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.     

Present environmental status of Al-Kharrar Lagoon,central of the eastern Red Sea coast,Saudi Arabia

Al-Kharrar Lagoon is a fossil back-reef basin with hypersaline waters, situated 10 km northwest of Rabigh city, central of the eastern Red Sea coast, Saudi Arabia. About 130 stations were selected for measurements of the lagoon’s water temperature, salinity, pH, dissolved oxygen, and water depths during March 2014. The common macro-algae, flora, and fauna were also sampled and identified. The present study aims to investigate the prevailing environmental parameters and their impact on the macro-fauna/flora of the lagoon. The average water depth of the lagoon was around 5 m and reached maximum values of 8 and 16 at the lagoon centre and inlet, respectively. The results showed that the lagoon’s surface water temperature and salinity have mean values of 25 °C and 40‰, but with extreme values of 30 °C and 45‰ that occurred only at the enclosed intertidal areas, respectively. Their dissolved oxygen (DO) and pH were 6.5 mg/l and 8.3, respectively and the latter showing the highest values up to 8.5 in the intertidal areas dominated by the green cyanobacteria. These physicochemical conditions make the lagoon as a favorite place for the mangrove Avicennia marina, macro-algae, seagrasses (Halophila stipulacea and Cymodocea rotundata), and algal mats (Cyanobacteria) which dominate the intertidal and supratidal areas of the lagoon, tolerating extremely high-salinity and high-temperature conditions. On the other hand, corals were observed alive at the southern part of the lagoon, immediately south of the Al-Ultah Islet. Vertical profiles of temperature, salinity, and density in the lagoon’s water indicated that the water column consists of two layers throughout the year.     

The rainfall-runoff model GR4J optimization of parameter by genetic algorithms and Gauss-Newton method: application for the watershed Ourika (High Atlas,Morocco)

Hydrological modeling aims to place the disposal of water actors, very simple tools for hydrological simulation to start with, the basic observations made on rainfall until the reproduction of the response the flow of a basin. The aims of this work consist, first, of testing the model GR4J (Génie Rural à 4 paramètres Journalier) performances of the rain-flow in semi-arid environment watershed and secondly, of highlighting the contribution of new optimization techniques (genetic algorithm, Gauss-Newton) in determining the optimal parameters of the model. The results obtained show that those methods of optimization are robust and reliable in comparison with the “solver” optimization used in Excel that usually applies for GR4J model. In terms of the model performances, we obtained a very satisfying results. Also, the simulations clearly show the influence of the melting snow on the Ourika’s basin flows; indeed, taking into account this component in the model allowed a consecutive improvement of the simulations.     

3D geomechanical modeling and estimating the compaction and subsidence of Fahlian reservoir formation (X-field in SW of Iran)

A geomechanical model can reveal the mechanical behavior of rocks and be used to manage the reservoir programs in a better mode. Fluid pressure will be reduced during hydrocarbon production from a reservoir. This reduction of pressure will increase the effective stress due to overburden sediments and will cause porous media compaction and surface subsidence. In some oil fields, the compacting reservoir can support oil and gas production. However, the phenomena can also cause the loss of wells and reduced production and also cause irreparable damage to the surface structures and affect the surrounding environment. For a detailed study of the geomechanical behavior of a hydrocarbon field, a 3D numerical model to describe the reservoir geomechanical characteristics is essential. During this study, using available data and information, a coupled fluid flow-geomechanic model of Fahlian reservoir formation in X-field in SW of Iran was constructed to estimate the amount of land subsidence. According to the prepared model, in this field, the maximum amount of the vertical stress is 110 MPa and the maximum amount of the horizontal stress is 94 MPa. At last, this model is used for the prediction of reservoir compaction and subsidence of the surface. The maximum value of estimated ground subsidence in the study equals to 29 mm. It is considered that according to the obtained values of horizontal and vertical movement in the wall of different wells, those movements are not problematic for casing and well production and also the surrounding environment.     

Review of water and soil contamination in and around Salihli geothermal field (Manisa,Turkey)

The town of Salihli is situated in Gediz Graben in the western Anatolia. This region is important in terms of industry, mining, geothermal energy, water sources, and agricultural production. Geothermal flow and anthropogenic activities in Salihli threaten the surrounding environment due to the contamination of cold groundwater, surface water, and soil. The goal of the present study is to determine the environmental effects of the geothermal and anthropogenic activities in Salihli on soil, stream sediments, and water. Stream sediments and farm soil have been contaminated by substances derived from geothermal and industrial effluents. To this end, the quality review of the water was completed and the heavy metal levels in stream sediment samples were measured to determine the extent of contamination. The elements As, B, Br, Fe, and Ni are the major contaminants present in surface water and groundwater in the study area. The concentrations of these elements excess tolerance limits of international water standards. Gibbsite, K-mica, kaolinite, sepiolite, halite, sulfur, willemite, and Pb(OH)₂ might be precipitated as scales at low temperatures on the soil; this could be interpreted as a resultant from soil contamination. The concentrations of 17 elements (As, Ba, B, Cd, Co, Cr, Cu, Fe, Hg, Li, Mo, Mn, Ni, Pb, Sb, Sr, and Zn) were measured in samples from stream sediments and surface soils. In the study area, especially geothermal and anthropogenic activities give rise to environmental pollution.     

Evaluation of the physico-mechanical parameters affecting the deterioration rate of Ahlat ignimbrites (Bitlis,Turkey)

The paper principally focuses on the durability assessment of various stratigraphic levels of Ahlat ignimbrites collected from the eastern region of Turkey. A total of four different ignimbrite types with dissimilar color, texture and particularly welding degree were tested in laboratory. The laboratory tests performed on the ignimbrite specimens indicate that the welding degree as well as the lithic material content mainly controls the strength and capillarity properties of the ignimbrites. In addition, the durability of highly porous ignimbrites strongly depends upon the degree of welding. The effect of several weathering agents on the ignimbrites was evaluated on the basis of decay constant parameter. Accordingly, salt and ice crystallization pressures are a couple of major destructive agents acting within the micropores of the ignimbrites. Conversely, the investigated specimens are relatively durable against cyclic wetting–drying. Statistical evaluations reveal that the pore diameter is the major controlling factor on the deterioration rate of the ignimbrites after specifically recurrent freeze–thaw cycles. Moreover, the dry unit weight of the ignimbrites is more significant than the uniaxial compressive strength considering the deterioration rates during wetting–drying and salt crystallization. A less significant relationship was obtained between pore diameter and salt crystallization decay constant.     

Uncertainty assessment of the multilayer perceptron (MLP) neural network model with implementation of the novel hybrid MLP-FFA method for prediction of biochemical oxygen demand and dissolved oxygen: a case study of Langat River

Accurate prediction of the chemical constituents in major river systems is a necessary task for water quality management, aquatic life well-being and the overall healthcare planning of river systems. In this study, the capability of a newly proposed hybrid forecasting model based on the firefly algorithm (FFA) as a metaheuristic optimizer, integrated with the multilayer perceptron (MLP-FFA), is investigated for the prediction of monthly water quality in Langat River basin, Malaysia. The predictive ability of the MLP-FFA model is assessed against the MLP-based model. To validate the proposed MLP-FFA model, monthly water quality data over a 10-year duration (2001–2010) for two different hydrological stations (1L04 and 1L05) provided by the Irrigation and Drainage Ministry of Malaysia are used to predict the biochemical oxygen demand (BOD) and dissolved oxygen (DO). The input variables are the chemical oxygen demand (COD), total phosphate (PO₄), total solids, potassium (K), sodium (Na), chloride (Cl), electrical conductivity (EC), pH and ammonia nitrogen (NH₄-N). The proposed hybrid model is then evaluated in accordance with statistical metrics such as the correlation coefficient (r), root-mean-square error, % root-mean-square error and Willmott’s index of agreement. Analysis of the results shows that MLP-FFA outperforms the equivalent MLP model. Also, in this research, the uncertainty of a MLP neural network model is analyzed in relation to the predictive ability of the MLP model. To assess the uncertainties within the MLP model, the percentage of observed data bracketed by 95 percent predicted uncertainties (95PPU) and the band width of 95 percent confidence intervals (d-factors) are selected. The effect of input variables on BOD and DO prediction is also investigated through sensitivity analysis. The obtained values bracketed by 95PPU show about 77.7%, 72.2% of data for BOD and 72.2%, 91.6% of data for DO related to the 1L04 and 1L05 stations, respectively. The d-factors have a value of 1.648, 2.269 for BOD and 1.892, 3.480 for DO related to the 1L04 and 1L05 stations, respectively. Based on the values in both stations for the 95PPU and d-factor, it is concluded that the neural network model has an acceptably low degree of uncertainty applied for BOD and DO simulations. The findings of this study can have important implications for error assessment in artificial intelligence-based predictive models applied for water resources management and the assessment of the overall health in major river systems.     

Development of functionalized carrageenan,chitosan and alginate as polymeric chelating ligands for water softening

Chitosan, carrageenan and alginate are among the most abundant biopolymers in nature. They were prepared in uniform beads shape with a diameter of 2 mm ± 10%, using the encapsulator for removal of calcium, magnesium and iron cations from hard water. Solutions of 100–500 mg/L were prepared from each cation, and the detection of cations was carried out using atomic absorption spectrometer. Carrageenan and chitosan were able to chelate the three cations without further modification. However, alginate beads succeeded to chelate iron and magnesium and failed to chelate any calcium ions; in contrast, it increased the initial calcium concentration! That could be due to the pre-cross-linking of alginate beads using calcium chloride solution, which may be leaked back to the solution. However, grafting the alginate beads with polyethyleneimine and bromoacetic acid rectified this problem and the new functional group, –COOH, has been proved using the FT-IR. Optimization of the results in terms of beads weight (0.25–3.0 g) and cations concentrations (100–500 mg/L) has shown that most biopolymeric beads can chelate 85–100% of the cations in concentrations up to 500 mg/L. According to our finding, we came up with the recommendation to use chitosan for chelation of calcium and iron as it showed 100% chelation efficiency of both cations, whereas carrageenan is highly recommended for chelation of iron and magnesium, as it showed 100 and 98% chelation efficiency, respectively. Further work can be done on the reusability of the beads and scale up for the industrial use.     

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.     

Denoising and improving the quality of seismic data using combination of DBM filter and FX deconvolution

Seismic data denoising, random noise attenuation (RNA) and spike-like noise suppression, is a main consideration for improving the quality of records. RNA could increase signal to noise ratio (S/N) to avoid misinterpretation of seismic data. In this research, a novel method is created by using the combination of frequency-offset deconvolution (FXD) and decision-based median (DBM) filter for RNA from seismic data. The method is applied in two main phases; FXD is focused to remove the Gaussian noise and DBM filter is focused to attenuate the impulsive noise and spikes. To implement and verify the method, three types of data are used: two synthetic models (a model with linear events and a model with hyperbolic events) and an observed seismic section. The ability of the proposed method (FXD-DBM) in comparison of applying each in seismic RNA application is proven. The noise level is reduced obviously, and hence, the S/N of all examined seismic records is increased considerably after denoising by the combination of FX deconvolution and DBM filter. About the real seismic section, suppressing random noise and spikes show up improving the seismic reflector continuity and hence enhancing the interpretability of data. Moreover, some masked events by random noise are clarified in different parts of data after denoising using the planned method.