Climate change outlook for water resources management in a semiarid river basin: the effect of the environmental water demand

Iran is a developing country with arid and semiarid regions. Poor management of water resources combined with the effects of climate change is leading to the drying of several rivers and wetlands. Several planned water development projects, primarily for agricultural expansion, will be implemented in the coming years which could worsen impacts on vulnerable aquatic ecosystems. Proper water resources management is essential to meet present and future residential, environmental, industrial, and agricultural demands in semiarid regions. This paper presents projections of how the availability of water resources will change in the Karkheh river basin of Iran for the period 2010–2059 employing sustainability criteria in the form of time-based reliability, volumetric reliability, resiliency, and vulnerability. This paper’s results show that consideration of environmental receptors as a stakeholder of water use places limitations on agricultural development within the Karkheh river basin.     

Prediction of Compressional, Shear, and Stoneley Wave Velocities from Conventional Well Log Data Using a Committee Machine with Intelligent Systems

Measurement of compressional, shear, and Stoneley wave velocities, carried out by dipole sonic imager (DSI) logs, provides invaluable data in geophysical interpretation, geomechanical studies and hydrocarbon reservoir characterization. The presented study proposes an improved methodology for making a quantitative formulation between conventional well logs and sonic wave velocities. First, sonic wave velocities were predicted from conventional well logs using artificial neural network, fuzzy logic, and neuro-fuzzy algorithms. Subsequently, a committee machine with intelligent systems was constructed by virtue of hybrid genetic algorithm-pattern search technique while outputs of artificial neural network, fuzzy logic and neuro-fuzzy models were used as inputs of the committee machine. It is capable of improving the accuracy of final prediction through integrating the outputs of aforementioned intelligent systems. The hybrid genetic algorithm-pattern search tool, embodied in the structure of committee machine, assigns a weight factor to each individual intelligent system, indicating its involvement in overall prediction of DSI parameters. This methodology was implemented in Asmari formation, which is the major carbonate reservoir rock of Iranian oil field. A group of 1,640 data points was used to construct the intelligent model, and a group of 800 data points was employed to assess the reliability of the proposed model. The results showed that the committee machine with intelligent systems performed more effectively compared with individual intelligent systems performing alone.     

A new approach for developing comprehensive agricultural drought index using satellite-derived biophysical parameters and factor analysis method

The accurate assessment of drought and its monitoring is highly depending on the selection of appropriate indices. Despite the availability of countless drought indices, due to variability in environmental properties, a single universally drought index has not been presented yet. In this study, a new approach for developing comprehensive agricultural drought index from satellite-derived biophysical parameters is presented. Therefore, the potential of satellite-derived biophysical parameters for improved understanding of the water status of pistachio (Pistachio vera L.) crop grown in a semiarid area is evaluated. Exploratory factor analysis with principal component extraction method is performed to select the most influential parameters from seven biophysical parameters including surface temperature (T _s), surface albedo (α), leaf area index (LAI), soil heat flux (G _o), soil-adjusted vegetation index (SAVI), normalized difference vegetation index (NDVI), and net radiation (R _n). T _s and G _o were found as the most effective parameters by this method. However, T _s, LAI, α, and SAVI that accounts for 99.6 % of the total variance of seven inputs were selected to model a new biophysical water stress index (BPWSI). The values of BPWSI were stretched independently and compared with the range of actual evapotranspiration estimated through well-known METRIC (mapping evapotranspiration at high resolution with internal calibration) energy balance model. The results showed that BPWSI can be efficiently used for the prediction of the pistachio water status (RMSE of 0.52, 0.31, and 0.48 mm/day on three image dates of April 28, July 17, and August 2, 2010). The study confirmed that crop water status is accounted by several satellite-based biophysical parameters rather than single parameter.     

Neuro-fuzzy reaping of shear wave velocity correlations derived by hybrid genetic algorithm-pattern search technique

Shear wave velocity is a critical physical property of rock, which provides significant data for geomechanical and geophysical studies. This study proposes a multi-step strategy to construct a model estimating shear wave velocity from conventional well log data. During the first stage, three correlation structures, including power law, exponential, and trigonometric were designed to formulate conventional well log data into shear wave velocity. Then, a Genetic Algorithm-Pattern Search tool was used to find the optimal coefficients of these correlations. Due to the different natures of these correlations, they might overestimate/underestimate in some regions relative to each other. Therefore, a neuro-fuzzy algorithm is employed to combine results of intelligently derived formulas. Neuro-fuzzy technique can compensate the effect of overestimation/underestimation to some extent, through the use of fuzzy rules. One set of data points was used for constructing the model and another set of unseen data points was employed to assess the reliability of the propounded model. Results have shown that the hybrid genetic algorithm-pattern search technique is a robust tool for finding the most appropriate form of correlations, which are meant to estimate shear wave velocity. Furthermore, neuro-fuzzy combination of derived correlations was capable of improving the accuracy of the final prediction significantly.     

Geochemical Prospectivity Mapping Through a Feature Extraction–Selection Classification Scheme

Natural Resources Research - Machine learning (ML) schemes can enhance success in geochemical prospectivity mapping. This study has examined the effectiveness of several feature extraction or...     

Historical tsunami in the Makran Subduction Zone off the southern coasts of Iran and Pakistan and results of numerical modeling

Tsunami hazard in the Makran Subduction Zone (MSZ), off the southern coasts of Iran and Pakistan, was studied by numerical modeling of historical tsunami in this region. Although the MSZ triggered the second deadliest tsunami in the Indian Ocean, among those known, the tsunami hazard in this region has yet to be analyzed in detail. This paper reports the results of a risk analysis using five scenario events based on the historic records, and identifies a seismic gap area in western Makran off the southern coast of Iran. This is a possible site for a future large earthquake and tsunami. In addition, we performed numerical modeling to explain some ambiguities in the historical reports. Based on the modeling results, we conclude that either the extreme run-up of 12–15 m assigned for the 1945 Makran tsunami in the historical record was produced by a submarine landslide triggered by the parent earthquake, or that these reports are exaggerated. The other possibility could be the generation of the huge run-up heights by large displacements on splay faults. The results of run-up modeling reveal that a large earthquake and tsunami in the MSZ is capable of producing considerable run-up heights in the far field. Therefore, it is possible that the MSZ was the source of the tsunami encountered by a Portuguese fleet in Dabhul in 1524.     

Effects of wastewater stabilization pond effluent on agricultural crops

Wastewater reclamation and reuse is one of the best alternatives for compensating water shortages. Water supply and environmental conservation can be met through wastewater reclamation. Principally, treated wastewater is a reliable water resource, especially for periodic droughts and in arid areas. This study designed and implemented to investigate the full scale application of effluent for irrigation use. A major objective of this study is to assess on health effects and feasibility of crop irrigation by using stabilization ponds effluent of southern Hovaizeh Wastewater Treatment Plant located in Khuzestan Province. Two experimental plots of about 0.5 ha. were constructed. One of the plots irrigated by stabilization pond effluent and the other irrigated by Nisan River water. Basic parameters in two plots such as type of cultivated crops, amount of fertilizer use and lack of soil contamination have been similar in both. The only difference was the type of water applied for irrigation of agricultural crops. It was shown that high salinity of soil reduced the growth rate of agricultural crops. So, removing salinity from area should be performed before cultivation. Results gained on agricultural crops growth in two studied plots showed the growth rate and quality of crops were increased by using of stabilization pond effluent in comparison with Nissan River water.     

Sector-zoned phlogopites in igneous rocks

Sector-zoned micas are described in three occurrences of igneous rocks. Basal sectors (001) and lateral sectors (010) are well defined. Analyses of probably isochronous growth points indicate that there are consistent chemical composition differences between the sectors, the (010) sector being richer in Si and poorer in Ti, Fe, Al and Ba relative to the (001) sector. Within each sector type Fe/ (Fe + Mg) increases from core to rim. These differences vary in amplitude from one occurrence to the other, but are systematic for micas with extremely different tetrahedral cations. Possible factors influencing this type of crystallization are briefly reviewed: growth rate, geometry of protosites and bulk composition of the liquid.     

Use of magnetic measures to assess soil redistribution following deforestation in hilly region

Limited information is available about the use of magnetic susceptibility property to determine soil redistribution in hilly areas of the semi-arid regions. This study was conducted to evaluate the use of magnetic properties to determine soil redistribution along a hill slope following deforestation. The study area is located in the Quaternary hilly region of Lordegan district in west Iran. Ten transects were established in two land uses that included natural Querqus forested and cultivated lands. Soil samples were collected at different positions along the slope and magnetic properties (χlf, χhf, SIRM, ARM, and χfd) and selected physico-chemical properties were determined. The results (based on the χfd, SIRM/ARM) showed that the magnetic susceptibly in the calcareous materials is pre-dominantly derived during the pedogenic processes and the superparamagnetic particles which were transported to lower positions of hill slope following deforestation. The results confirmed that this methodology could be applied for monitoring soil redistribution along the slope in calcareous hilly areas in the semi-arid regions.