The effects of hydrometer reading times on the spatial variability of soil textures in southeast Iran

Soil texture is an important physical soil property that may contribute to variations in many soil functions as well as nutrient storage and availability, water retention, and soil erosion. Although several methods for determining the texture classes of soil particles have been proposed, differences among hydrometer reading times have presented challenges in determining the precise soil texture classes. Therefore, this study was conducted to evaluate the effects of hydrometer reading time on the spatial variability of soil textures in the Rafsanjan area, southeast Iran. To accomplish this, 77 soil samples were collected on a 500-m square sampling grid from depths of 0–40, 40–80, and 80–120 cm, and their particle sizes were determined through analysis for 40 s, 2 h, 6.5 h, and 8 h using the Bouyoucos hydrometer method. The results showed a strong spatial correlation in the soil particles among sampling soil layers and across the study area. Moreover, the differences among hydrometer reading times did not have a significant impact on determination of coarse soil texture classes, although they did influence determination of the finer classes. Although the 8 h reading time provided the most accurate response with respect to mechanical analysis of a soil, after 6.5 h the hydrometer could also largely (more than 80.0 %, on average) achieve this goal. Additionally, the 2 h hydrometer reading time could also be useful for the initial assessment or general overview of the soil texture in a certain region; however, it is not recommended for precision agriculture or site-specific management.     

Modelling of infiltration using artificial intelligence techniques in semi-arid Iran

ABSTRACT

Infiltration plays a fundamental role in streamflow, groundwater recharge, subsurface flow, and surface and subsurface water quality and quantity. In this study, adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and random forest (RF) models were used to determine cumulative infiltration and infiltration rate in arid areas in Iran. The input data were sand, clay, silt, density of soil and soil moisture, while the output data were cumulative infiltration and infiltration rate, the latter measured using a double-ring infiltrometer at 16 locations. The results show that SVM with radial basis kernel function better estimated cumulative infiltration (RMSE = 0.2791 cm) compared to the other models. Also, SVM with M4 radial basis kernel function better estimated the infiltration rate (RMSE = 0.0633 cm/h) than the ANFIS and RF models. Thus, SVM was found to be the most suitable model for modelling infiltration in the study area.     

Biomonitoring of Ni and V Contamination Using Oysters (Saccostrea cucullata) at Lengeh Port,Persian Gulf,Iran

Vanadium and nickel are two important indicators of oil pollution. Lengthy exposure to these elements causes serious harmful effects in human health, different harsh allergies being examples. The accumulation of two heavy metals (Ni and V) in sediment and the soft and hard tissues of Saccostrea cucullata were analyzed at three sampling sites along the coast of Lengeh Port, Persian Gulf. Results indicated at all the sampling sites; the Ni levels in soft tissues (STs) were higher than in the shells (SHs) and sediments, whereas the V levels were higher in the sediments. In addition, meaningful relationship (r = 0.65; p < 0.05) was observed across Ni levels in ST of S. cucullata and sediment, while for V concentrations a strong relationship (r = 0.83; p < 0.01) was found in SH of S. cucullata and sediment. This indicates that ST and SH of oyster can be considered as a biomonitoring agent for Ni and V levels, in coastal waters, respectively. The exposure of the consumer is compared directly to minimal risk level and provisional maximum tolerable daily intake. Result indicated that levels of Ni and V were within the safety limits for human consumption.     

Impact of local site conditions on portfolio earthquake loss estimation for different building types

This article presents a sensitivity analysis investigating the impact of using high-resolution site conditions databases in portfolio earthquake loss estimation. This article also estimates the effects of variability in the site condition databases on probabilistic earthquake loss ratios and their geographical pattern with respect to structural characteristics of different building types. To perform the earthquake loss estimation here, the OpenQuake software developed by Global Earthquake Model is implemented in Clemson University’s supercomputer. The probabilistic event-based risk analysis is employed considering several notional portfolios of different building types in the San Francisco area as the inventory exposure. This analysis produces the stochastic event sets worth for 10,000 years including almost 8000 synthetically simulated earthquakes. Then, the ground motion prediction equations are used to calculate the ground motion per event and incorporate the effect of five site conditions, on amplifying or de-amplifying the ground motions on notional building exposure locations. Notional buildings are used to account for various building characteristics in conformance with the building taxonomy represented in HAZUS software. The HAZUS damage functions are applied to model the vulnerability of various structural types of buildings. Finally, the 50-year average mean loss and probabilistic loss for multiple values for probability of exceedance (2, 10, 20, and 40%) in 50 years are calculated, and the impact of different site condition databases on portfolio loss ratios is investigated for different structural types and heights of buildings. The results show the aggregated and geographical variation of loss and loss ratio throughout the region for various site conditions. Comparing the aggregated loss and loss ratio, while considering different databases, represents normalized differences that are limited to 6% for all building taxonomy with various heights and for all PoEs. However, site-specific loss ratio errors are significantly greater and in some cases are more than 20%.     

Numerical evaluation of liquefaction-induced damages in composite breakwaters and its application for performance-based improvement design

Breakwaters provide a calm sea basin for ships and protect harbor facilities by reflecting wave energy toward the open sea area. Their performance under environmental loadings is the main concern for coastal engineers. Liquefaction susceptibility of loose sediments of seabed threatens performance of these structures. The article investigates soil liquefaction effects on the seismic performance of Iran liquefied natural gas (LNG) composite breakwater. Performance-based design method, considering both grade of the breakwater and acceptable level of damages, was selected as design philosophy. Liquefaction-induced damages to the breakwater were determined by numerical analysis. Since the obtained level of deformations did not meet allowable damages, soil improvement against liquefaction was considered. Different improvement patterns were proposed based on distribution of pore pressure ratio (r_u) beneath the breakwater to control its seismic performance. This investigation revealed that the most important area for soil improvement is located near the toes of breakwater to control the slope instability and performance of the breakwater.     

An alternative method for density variation modeling of the crust based on 3-D gravity inversion

In this paper we are proposing an alternative method for determination of density variations of the crust from constrained inversion of the terrestrial gravity data. The main features of the method can be summarized as follows: (i) Constructing a band-pass filter to remove the long and short wavelength signals from the terrestrial gravity data. (ii) Using an iterative method for stabilization and solution of the inverse problem. The mentioned regularization method is first validated by simulated gravity data and next the methodology is used for development of a new regional density variation model of the crust in three layers based on real gravity data in geographical area of Iran. Application of the band-pass filter to the latter data resulted the residual gravitation variations in the range of − 300 to 50 (mGal) which next based on the iterative method resulted following ranges for residual densities: −120 to 40 (kg/m³) in first layer, −40 to 40 (kg/m³) in second layer, and − 40 to 40 (kg/m³) in third layer.     

The Positive Effects of Nanoclay on the Hydraulic Conductivity of Compacted Kahrizak Clay Permeated With Landfill Leachate

This study evaluates the effect of nanoclay on permeability, swelling, compressive strength, and cation exchange capacity of a compacted Kahrizak landfill clay liner. The results show that 4% nanoclay significantly reduces permeability (3 × 10^?9 to 7.74 × 10^?11 cm/s in neutral, 3.66 × 10^?9 to 7.9 × 10^?10 cm/s in acidic, and 3.25 × 10^?9 to 5.24 × 10^?10 cm/s in alkaline condition), and increases compressive strength (by 36.28%) and the percentage of swelling (from 16.67 to 41.82, 23.33 to 45.45, and 15 to 38.18 at pH 7, 4.8, and 9, respectively) compare to raw clay samples. Moreover, the results of cation exchange capacity tests show that adding 4% nanoclay to the Kahrizak clay, permeated with landfill leachate, helps the sample maintain its mono‐valent ions between layers and remains dispersed. The results of SEM and XRD analyses show that by adding nanoclay, nanoclay clusters are formed in the sample; as a result, the interlayer spacing decreases which makes it remain dispersed. XRF analyses also demonstrate that by adding nanoclay to the mixture, the permeability and therefore, the amount of heavy metals which can penetrate into it decreases. The results justify the construction of clay barriers with nanoclay in order to prevent leachate penetration, and consequently reduce the operation costs.     

An Improvement on the Clustering of High-Resolution Satellite Images Using a Hybrid Algorithm

The challenge of obtaining training data for supervised classifications of satellite images has led researchers to unsupervised algorithms, i.e. cluster analysis. Numerous researches have been conducted to improve quality and decrease uncertainty of results of this analysis. This study proposes a hybrid cost function as well as a hybrid clustering algorithm-Artificial Bee Colony optimization approach for the clustering of high-resolution satellite images. In order to evaluate viability of the proposed methodology, it is compared to some other classic clustering algorithms such as modified K-Means, K-Medoids, Fuzzy C-Means, and Kernel-based Fuzzy C-Means methods over three different study areas selected from a WorldView-2 satellite image. The Shannon entropy technique, Kappa coefficient, compactness, and separation criteria are used as quality and uncertainty indicators for the evaluation. The results of the study show that, compared to other methods, the hybrid algorithm obtained from the proposed cost function, Kernel-based Fuzzy C-Means method, and ABC algorithm provide clustering capabilities of higher quality and lower uncertainty levels.     

The influence of environmental variables on distribution patterns of Irano-Turanian forests in Alborz Mountains,Iran

Topographic and edaphic variables are the main ecological factor determining species spatial variability on mountainous forests. A field study was performed in central Alborz to investigate how the edaphic and topographic parameters can affect the tree and shrub communities. Initially, 27 forest stands were identified and the homogeneous units were separated regarding physiognomy. In each single homogeneous unit, one random sample plot (1000 m2) and totally 43 plots were established. In each plot, the presence and abundance of all trees and shrubs were recorded and four soil samples were taken from depths of 0-5 and 5-20 cm. Concerning classification results, eight different forest communities were identified. The lowest and highest soil pH values were observed in Malus orientalis and pistacia-Amygdalus communities, respectively. The water saturation percent of pure- and mixed Juniperus excelsa and Rhus coriaria was the highest amongst communities. The clay content was the highest in pure J. excelsa. The 0-5 organic matter and Nitrogen content in mixed J. excelsa were significantly higher than pure J. excelsa and other communities. The CCA (Canonical Correspondence Analysis) results indicated that the altitude, precipitation, pH, EC, SP, clay and CaCO₃ are the most important factors determine the distribution of trees and shrub in central Alborz     

Scale,governance and the management of river basins: A case study from Central Iran

Aquatic socio-ecological systems show pervasive cross-scale interactions and problems of fit between ecosystems and institutions. Nested bio-hydrological processes within river basins are prone to third-party impacts, and equitable/sustainable management of water resources requires adequate governance patterns that both cover relevant scalar levels and handle cross-scale interactions. This paper provides the example of the Zayandeh Rud basin, in central Iran, and describes the historical evolution of water use at three different nested scales. It shows how the gradual overallocation of water resources (basin closure) and the manipulation of the hydrological cycle by the state and other actors have resulted in a constant spatial and social redistribution of water use and associated benefits and costs. State-centered modes of governance characterized by the priority to large-scale infrastructure, vested political and financial interests, lack of attention to local processes and hydrological interconnectedness, and the neglect of environmental degradation, must give way to forms of comanagement that better articulate the different levels of control and governance.