Quantitative and qualitative simulation of groundwater by mathematical models in Rafsanjan aquifer using MODFLOW and MT3DMS

Agriculture sector by using 80% of freshwater is the greatest water consumer in Iran. Excessive use of agricultural fertilizers in last decade, caused accumulation of enormous amounts of salts and subsequence declined the physical properties of soil. In desert and dry regions such as Rafsanjan plain, use of the groundwater resources is more than the surface water resources. Therefore, information about the quality of these resources remains a necessary task for optimum management, protection of water resources, and stopping the future damages. In this study, the groundwater quantity and quality of Rafsanjan plain was investigated by MODFLOW and MT3DMS. The presented quantitative model for this aquifer was compared by observed data and calibrated. This model was used to predict a 10-year period. Results show that water elevation decreases approximately 15 m for 10 years to come in this plain. Qualitative model results show that most quality parameters will increase. Electrical conductivity will increase more than other parameters. As values of this parameter will reach 16,000 µs/l for next 5 years. Therefore, we suggest that exploitation of water from these resources should be reduced and discharge from some of agricultural wells stop; also we suggested that recharge to groundwater resources should be increased and agricultural activities should be limited or improved using of modern irrigation systems in this plain.     

Numerical modeling of two-phase flow in heterogeneous permeable media with different capillarity pressures

Contrast in capillary pressure of heterogeneous permeable media can have a significant effect on the flow path in two-phase immiscible flow. Very little work has appeared on the subject of capillary heterogeneity despite the fact that in certain cases it may be as important as permeability heterogeneity. The discontinuity in saturation as a result of capillary continuity, and in some cases capillary discontinuity may arise from contrast in capillary pressure functions in heterogeneous permeable media leading to complications in numerical modeling. There are also other challenges for accurate numerical modeling due to distorted unstructured grids because of the grid orientation and numerical dispersion effects. Limited attempts have been made in the literature to assess the accuracy of fluid flow modeling in heterogeneous permeable media with capillarity heterogeneity. The basic mixed finite element (MFE) framework is a superior method for accurate flux calculation in heterogeneous media in comparison to the conventional finite difference and finite volume approaches. However, a deficiency in the MFE from the direct use of fractional flow formulation has been recognized lately in application to flow in permeable media with capillary heterogeneity. In this work, we propose a new consistent formulation in 3D in which the total velocity is expressed in terms of the wetting-phase potential gradient and the capillary potential gradient. In our formulation, the coefficient of the wetting potential gradient is in terms of the total mobility which is smoother than the wetting mobility. We combine the MFE and discontinuous Galerkin (DG) methods to solve the pressure equation and the saturation equation, respectively. Our numerical model is verified with 1D analytical solutions in homogeneous and heterogeneous media. We also present 2D examples to demonstrate the significance of capillary heterogeneity in flow, and a 3D example to demonstrate the negligible effect of distorted meshes on the numerical solution in our proposed algorithm.     

Predicting groundwater level of wells in the Diyala River Basin in eastern Iraq using artificial neural network

Al-Mansourieh zone is a part of Al-Khalis City within the province of Diyala and located in the Diyala River Basin in eastern Iraq with a total area about 830 km2.Groundwater is the main water source for agriculture in this zone.Random well drilling without geological and hydraulic information has led the most of these wells to dry up quickly.Therefore,it is necessary to estimate the levels of groundwater in wells through observed data.In this study,Alyuda NeroIntelligance 2.1 software was applied to predict the groundwater levels in 244 wells using sets of measured data.These data included the coordinates of wells(x,y),elevations,well depth,discharge and groundwater levels.Three ANN structures(5-3-3-1,5-10-10-1 and 5-11-11-1)were used to predict the groundwater levels and to acquire the best matching between the measured and ANN predicted values.The coefficient of correlation,coefficient determination(R2)and sum-square error(SSE)were used to evaluate the performance of the ANN models.According to the ANN results,the model with the three structures has a good predictability and proves more effective for determining groundwater level in wells.The best predictor was achieved in the structure 5-3-3-1,with R2 about 0.92,0.89,0.84 and 0.91 in training,validation,testing and all processes respectively.The minimum average error in the best predictor is achieved in validation and testing processes at about 0.130 and 0.171 respectively.On the other hand,the results indicated that the model has the potential to determine the appropriate places for drilling the wells to obtain the highest level of groundwater.     

PM10 Source Apportionment in Ahvaz,Iran, Using Positive Matrix Factorization

Source apportionment of particulate matter <10 µm in diameter (PM₁₀), having considerable impacts on human health and the environment, is of high priority in air quality management. The present study, therefore, aimed at identifying the potential sources of PM₁₀ in an arid area of Ahvaz located in southwest of Iran. For this purpose, we collected 24‐h PM₁₀ samples by a high volume air sampler. The samples were then analyzed for their elemental (Al, As, B, Ba, Be, Ca, Cd, Co, Cr, Cu, Fe, Hg, K, Mg, Mn, Na, Ni, Pb, Se, Si, Sn, Sr, Li, Ti, V, Zn, Mo, and Sb) and ionic (NH, Cl^?, NO, and SO) components using inductively coupled plasma optical emission spectrometry and ion chromatography instruments, respectively. Eight factors were identified by positive matrix factorization: crustal dust (41.5%), road dust (5.5%), motor vehicles (11.5%), marine aerosol (8.0%), secondary aerosol (9.5%), metallurgical plants (6.0%), petrochemical industries and fossil fuel combustion (13.0%), and vegetative burning (5.0%). Result of this study suggested that the natural sources contribute most to PM₁₀ particles in the area, followed closely by the anthropogenic sources.     

Risk assessment of agricultural water requirement based on a multi-model ensemble framework,southwest of Iran

Water shortage and climate change are the most important issues of sustainable agricultural and water resources development. Given the importance of water availability in crop production, the present study focused on risk assessment of climate change impact on agricultural water requirement in southwest of Iran, under two emission scenarios (A2 and B1) for the future period (2025–2054). A multi-model ensemble framework based on mean observed temperature-precipitation (MOTP) method and a combined probabilistic approach Long Ashton Research Station-Weather Generator (LARS-WG) and change factor (CF) have been used for downscaling to manage the uncertainty of outputs of 14 general circulation models (GCMs). The results showed an increasing temperature in all months and irregular changes of precipitation (either increasing or decreasing) in the future period. In addition, the results of the calculated annual net water requirement for all crops affected by climate change indicated an increase between 4 and 10 %. Furthermore, an increasing process is also expected regarding to the required water demand volume. The most and the least expected increase in the water demand volume is about 13 and 5 % for A2 and B1 scenarios, respectively. Considering the results and the limited water resources in the study area, it is crucial to provide water resources planning in order to reduce the negative effects of climate change. Therefore, the adaptation scenarios with the climate change related to crop pattern and water consumption should be taken into account.

     

Early Eocene planktonic and benthic foraminifera from the Khangiran formation (northeast of Iran)

The Kopet-Dagh sedimentary basin is located in northeast of Iran and southeast of Turkmenistan in the Middle East. The Khangiran formation represents the last marine deposition in the Kopet-Dagh sedimentary basin. The early Eocene planktonic and benthic foraminifera from the Khangiran formation which belongs to the lower 376 m thickness of this formation are identified and biostratigraphically evaluated. Due to rarity of Morozovella species, planktonic foraminiferal zonation was difficult to determine in this formation. The determination of upper part of the late Paleocene for the lower 124 m of this formation is according to the lowest occurrence of Acarinina sibaiyaensis species. From E₅ to near middle of E₇ biozone, increasing trend of Acarinina frequency and a peak in Morozovella species and decreasing trend of frequency of Subbotina and Pseudohastigerina species indicate the warm and oligotrophic condition of the seawater during sedimentation of the studied interval. Toward the Ypresian-Lutetian boundary, the increasing trend of Pseudohastigerina and Subbotina species and decreasing in frequency of Acarinina species suggest the low-oxygen level, eutrophic and intermediate condition of the seawater. In this formation, the high abundance of the epifaunal taxa such as Anomalinoides spp., Cibicidoides spp., Gyroidinoides spp., and Lenticulina spp. from the base (late Paleocene sediments) up to E₆ biozone reflects oligotrophic and oxic shallow water conditions. The occurrence of several peaks in abundance of Bulimina and Uvigerina species at the Ypresian/Lutetian boundary suggests eutrophic condition. These paleoecological conditions could be correlated with fluctuations in the numbers of the planktonic foraminifera.