Investigating the effect of complexity on groundwater flow modeling uncertainty

Considering complexity in groundwater modeling can aid in selecting an optimal model, and can avoid over parameterization, model uncertainty, and misleading conclusions. This study was designed to determine the uncertainty arising from model complexity, and to identify how complexity affects model uncertainty. The Ajabshir aquifer, located in East Azerbaijan, Iran, was used for comprehensive hydrogeological studies and modeling. Six unique conceptual models with four different degrees of complexity measured by the number of calibrated model parameters (6, 10, 10, 13, 13 and 15 parameters) were compared and characterized with alternative geological interpretations, recharge estimates and boundary conditions. The models were developed with Model Muse and calibrated using UCODE with the same set of observed data of hydraulic head. Different methods were used to calculate model probability and model weight to explore model complexity, including Bayesian model averaging, model selection criteria, and multicriteria decision-making (MCDM). With the model selection criteria of AIC, AICc and BIC, the simplest model received the highest model probability. The model selection criterion, KIC, and the MCDM method, in addition to considering the quality of model fit between observed and simulated data and the number of calibrated parameters, also consider uncertainty in parameter estimates with a Fisher information matrix. KIC and MCDM selected a model with moderate complexity (10 parameters) and the best parameter estimation (model 3) as the best models, over another model with the same degree of complexity (model 2). The results of these comparisons show that in choosing between models, priority should be given to quality of the data and parameter estimation rather than degree of complexity.     

REE Tetrad Effect as a Powerful Indicator of Formation Conditions of Karst Bauxites: A Case Study of the Shahindezh Deposit,NW Iran

Study of the concentration of major, trace, and rare earth elements (REE) in the Shahindezh karst bauxite deposit, northwestern Iran clarifies the relationship of the tetrad effect with geochemical parameters in the bauxite ores. The existence of irregular curves in the chondrite-normalized REE patterns as well as non-CHARAC behavior of geochemically isovalent pairs (Y/Ho) are related to the tetrad effect. The meaningful positive correlation between the sizes of the calculated T3 tetrad effect and some geochemical factors such as Y/Ho, ΣREE, La/Y, (La/Yb)N, and (LREE/HREE)N as well as some major oxides-based parameters like Al2O3 + LOI/SiO2 + Fe2O3, Al2O3/Fe2O3, Al2O3 + LOI, IOL, and SiO2 + Fe2O3 indicate that the studied bauxite horizon was likely deposited by different (acidic and/or alkalic) solutions at different stages. The lower part of the studied horizon with a thickness of ~4.7 m displays alkali characteristics whereas the upper parts of the horizon with a thickness of ~5.3 m are characterized by more acidic conditions. These results are fully supported by the co-occurrence of convex-concave tetrad effect curves in the chondrite-normalized REE patterns. Therefore, the tetrad effect phenomenon used in this study has proved to be a good and reliable geochemical proxy to assess the conditions of the depositional environment in the Shahindezh bauxite ores.     

Exploring Placer Gold Deposits through Integrated Geophysical and Geochemical Techniques at the Confluence of the Indus and Kabul Rivers, NW Pakistan

Analysis of the anomalous magnetic mineral intensities and geochemistry for placer gold deposits are presented for those of the Attock area at the confluence of the Indus and Kabul rivers in northwestern Pakistan. Two grids covering an area of 10 x18 m² and 8 x10 m² were analyzed using a G-858 Cesium Vapor Magnetometer. The anomalous zones obtained were plotted on contour maps, 2 D and 3 D magnetic intensity maps. Based on the magnetic anomalies, grid-1 of the study area wa...     

Water consumption of a six-year-old river red gum plantation in the Southern Zagros Mountains, Iran

Water is the most important limiting factor in the development of arid environments; therefore, optimization of water use is a necessity in desert reclamation projects. Evapotranspiration of a 6-year-old river red gum (Eucalyptus camaldulensis Dehnh.) plantation, located in a sedimentation basin of an artificial recharge of the Gareh Bygone Plain groundwater system in Iran’s southern Zagros Mountains, was studied during a 7-month period. A neutron moisture probe was used to monitor soil water changes in the root zone. Soil water depletion following rapid drainage for blocks receiving relatively high, medium and low volumes of floodwater were 809.6, 312.4, and 203.1 mm, respectively, for a 150-day period. Soil water potential during most of the study period was below -1.5 MPa. This study proves that river red gum in not a suitable species for desertification control where water conservation is of vital importance. Flood irrigation of trees on 8 Aug. 1991 provided the opportunity to throw doubt upon the claim of summer dormancy of river red gum.     

Enhancement of Carbon Dioxide Capture by Amine‐Functionalized Multi‐Walled Carbon Nanotube

In this study, crude multi‐walled carbon nanotubes (MWCNT) was functionalized by a two‐step process; first using strong mixed acids (H₂SO₄/HNO₃) and then treatment with 1,3‐phenylenediamine (mPDA). The equilibrium adsorption of CO₂ on pristine MWCNT and amine functionalized MWCNT (MWCNT‐NH₂) were investigated. Experiments were preformed via application of volumetric method in a dual sorption vessel at temperature range of 298–318 K and pressures up to 40 bars. The results obtained indicated that the equilibrium uptake of CO₂ increased after functionalizing of MWCNT. The increase in CO₂ capture by MWCNT‐NH₂ was attributed to the existence of great affinity between CO₂ molecules and amine sites on this adsorbent at low pressures. The experimental data were analyzed by means of Freundlich and Langmuir adsorption isotherm models. The data obtained revealed a fast kinetics for the adsorption of CO₂ in which most of adsorption occurred at initial period of adsorption experiments. This renders MWCNT as a suitable adsorbent for practical applications. Values of isosteric heat of adsorption were evaluated based on Clausius–Clapeyron equation. The results demonstrated that both chemisorption and physisorption played important role in CO₂ adsorption on MWCNT‐NH₂, whereas the physisorption process was dominant for CO₂ adsorption on MWCNT.     

Selection of Optimal Thresholds for Estimation and Simulation Based on Indicator Values of Highly Skewed Distributions of Ore Data

This study strives to outline a geostatistics model for estimation and simulation of the Qolqoleh gold ore deposit located in Saqqez, NW of Iran. Considering that this gold deposit contains high-grade values, accurate evaluation of such values is of high importance, and therefore different methods based on indicator values, such as full indicator kriging (FIK) and sequential indicator simulation (SIS), have been employed to improve the accuracy of estimation and simulation of high-grade values. FIK and SIS cover the full range of grades based on several thresholds on the indicator data. The cumulative distribution function (CDF) is typically used for selection of threshold values. Given the highly skewed distribution of gold grade and its intense fluctuations, the number of thresholds is increased using CDF, which in turn results in a whole lot of calculations. To reduce the volume of calculations, the number–size (N–S) fractal model has been used to select thresholds. From such a model, all optimal thresholds are chosen with respect to geology and the unnecessary thresholds are excluded from selection. Thus, a study of the selection of optimal thresholds for estimation and simulation of a gold ore resource by means of FIK and SIS, respectively, based on thresholds selected using the N–S fractal model is presented. Finally, it is proved that results of these geostatistical methods based on thresholds selection from the N–S model appear to be better-positioned to explain ore grade variability compared to thresholds selected from the CDF and threshold selection from the N–S model is more effective for reducing the volume of required calculations.     

Sensitivity analysis and accuracy assessment of the land transformation model using cellular automata

This study evaluates the effects of cellular automata (CA) with different neighborhood sizes on the predictive performance of the Land Transformation Model (LTM). Landsat images were used to extract urban footprints and the driving forces behind urban growth seen for the metropolitan areas of Tehran and Isfahan in Iran. LTM, which uses a back-propagation neural network, was applied to investigate the relationships between urban growth and the associated drivers, and to create the transition probability map. To simulate urban growth, the following two approaches were implemented: (a) the LTM using a top-down approach for cell allocation grounding on the highest values in the transition probability map and (b) a CA with varying spatial neighborhood sizes. The results show that using the LTM-CA approach increases the accuracy of the simulated land use maps when compared with the use of the LTM top-down approach. In particular, the LTM-CA with a 7 × 7 neighborhood size performed well and improved the accuracy. The level of agreement between simulated and actual urban growth increased from 58% to 61% for Tehran and from 39% to 43% for Isfahan. In conclusion, even though the LTM-CA outperforms the LTM with a top-down approach, more studies have to be carried out within other geographical settings to better evaluate the effect of CA on the allocation phase of the urban growth simulation.     

Performance evaluation of land change simulation models using landscape metrics

This study proposes a landscape metrics-based method for model performance evaluation of land change simulation models. To quantify model performance at both landscape and class levels, a set of composition- and configuration-based metrics including number of patches, class area, landscape shape index, mean patch area and mean Euclidean nearest neighbour distance were employed. These landscape metrics provided detailed information on simulation success of a cellular automata-Markov chain (CA-Markov) model standpoint of spatial arrangement of the simulated map versus the corresponding reference layer. As a measure of model simulation success, mean relative error (MRE) of the metrics was calculated. At both landscape and class levels, the MRE values were accounted for 22.73 and 10.2%, respectively, which are further categorised into qualitative measurements of model simulation performance for simple and quick comparison of the results. Findings of the present study depict a hierarchical and multi spatial level assessment of model performance.     

Simulation of the lately injected dykes in an Iranian porphyry copper deposit using the plurigaussian model

Ore deposits are usually composed of rock units or facies with different grade distributions and complex spatial structures. Being able to simulate the spatial layout of these facies are essential to have a comprehensive mining plan and an accurate resources and reserves evaluation. Modelers are faced with a set of challenges when creating the facies model such as: reproducing the facies proportions and spatial continuity as well as the topological contacts between facies, capturing post depositional overprinting, and honoring the data obtained from drill holes. Plurigaussian simulation (PGS) is a geostatistical approach that allows covering these challenges. This study addresses the application of PGS to Sungun porphyry copper deposit (Iran), in order to simulate the layout of three facies: mineralized porphyry and skarn and non-mineralized dykes. The aim of this study is to construct numerical models in which the dyke structures reflect the evolution observed in the geology.