ORIGINAL_ARTICLE
Investigation of concentration polarization in a cross-flow nanofiltration membrane: Experiment and CFD modelling
Numerous researches have been investigated on the mass transfer phenomena and hydrodynamics for the fluid in the vicinity of the membrane surface by the mathematical modelling and simulation. Due to complexities involved in solving transport phenomena within membranes, the application of CFD simulation study for determining the concentration polarization (CP) profile in the membrane channel is limited. In this study, a 2D CFD modelling and simulation of CP phenomena in nanofiltration of an aqueous solution of MgSO47H2O in a vertical spacer-filled flat sheet membrane module was presented. A response surface methodology (RSM) statistical analysis has been designed in order to fully capture effects of variations of the feed liquid flow and the transmembrane pressure (TMP) on the permeate flux and concentration. It was also shown that increasing TMP or the liquid flow rate led to enhancing the permeate flux while increasing the feed concentration decreased it. The simulated results were validated and compared with the available experimental data, showing a satisfactory agreement. Eventually, the mass transfer coefficient derived from CFD simulations and calculated from Sherwood empirical relationships were compared which showed 10% and 33% difference in lower and higher liquid flow rates, respectively.
https://jhs.scu.ac.ir/article_15256_e525cb28d2524e366a6d447556c527d6.pdf
2020-03-01
1
19
10.22055/jhs.2020.31418.1124
Concentration polarization
Nanofiltration
Sherwood number
Mass transfer coefficient
CFD modelling
Hossein
Asefi
h_asefi@sbu.ac.ir
1
Department of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehranpars, Tehran, Iran
LEAD_AUTHOR
Abolghasem
Alighardashi
a_ghardashi@sbu.ac.ir
2
Department of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehranpars, Tehran, Iran
AUTHOR
Mojtaba
Fazeli
m_fazeli@sbu.ac.ir
3
Department of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehranpars, Tehran, Iran
AUTHOR
Amir
Fouladitajar
fouladi@srbiau.ac.ir
4
College of Petroleum and Chemical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
AUTHOR
Abadikhah H., Zokaee Ashtiani F., Fouladitajar A., (2015). Nanofiltration of oily wastewater containing salt; experimental studies and optimization using response surface methodology, Desalin. Water Treat. 56:2783–2796.
1
Mulder M., Basic principles of membrane technology, Kluwer Academic, 1991.
2
Zeng K., Zhou J., Cui Z., Zhou Y., Shi C., Wang X., Zhou L., Ding X., Wang Z., Drioli E., (2018). Insight into fouling behavior of poly(vinylidene fluoride) (PVDF) hollow fiber membranes caused by dextran with different pore size distributions, Chinese J. Chem. Eng. 26: 268–277.
3
Geraldes V., Afonso M.D., (2007). Prediction of the concentration polarization in the nanofiltration/reverse osmosis of dilute multi-ionic solutions, J. Memb. Sci. 300: 20–27.
4
Prabhavathy C., De S., (2010). Estimation of transport parameters during ultrafiltration of pickling effluent from a tannery, Sep. Sci. Technol. 45:11–20.
5
Mercier-bonin M., Daubert I., Le D., Maranges C., Fonade C., Lafforgue C., (2001). How unsteady filtration conditions can improve the process efficiency during cell cultures in membrane bioreactors, Sep. Purif. Technol. 22–23: 601–615.
6
Liikanen R., Yli-Kuivila J., Laukkanen R., (2002). Efficiency of various chemical cleanings for nanofiltration membrane fouled by conventionally-treated surface water, J. Memb. Sci. 195:265–276.
7
Asefi H., Alighardashi A., Fazeli M., Fouladitajar A., (2019). CFD modeling and simulation of concentration polarization reduction by gas sparging cross-flow nanofiltration, J. Environ. Chem. Eng.7 : 103275, 1-7.
8
Asadi Tashvigh A., Zokaee Ashtiani F., Fouladitajar A., (2016). Genetic programming for modeling and optimization of gas sparging assisted microfiltration of oil-in-water emulsion, Desalin. Water Treat. 57 : 19160–19170.
9
Cui Z.F., Wright K.I.T., (1996). Flux enhancements with gas sparging in downwards crossflow ultrafiltration: Performance and mechanism, J. Memb. Sci. 117 : 109–116.
10
Jaffrin M.Y., (2008). Dynamic shear-enhanced membrane filtration: A review of rotating disks, rotating membranes and vibrating systems, J. Memb. Sci. 324 : 7–25.
11
Sarkara B., Deb S., (2010). Electric field enhanced gel controlled cross-flow ultrafiltration under turbulent flow conditions, Sep. Purif. Technol. 74 :73–82.
12
Ducom G., Puech F.P., Cabassud C., (2002). Air sparging with flat sheet nanofiltration: A link between wall shear stresses and flux enhancement, Desalination. 145 : 97–102.
13
Ahmed S., Seraji M.T., Jahedi J., Hashib M.A., (2012) . Application of CFD for simulation of a baffled tubular membrane, Chem. Eng. Res. Des. 90:600–608.
14
Schock A.M. G., (1987) Mass transfer and pressure loss in spiral wound modules, Desalination. 64 : 339 – 352.
15
B. hallstrom Vassilis gekas, (1987). Mass transffer in the membrane concentration polarization layer under turbulent cross flow, J. Memb. Sci. 30 : 153.
16
Li M., Bui T., Chao S., (2016) Three-dimensional CFD analysis of hydrodynamics and concentration polarization in an industrial RO feed channel, Desalination. 397 :194–204.
17
Fouladitajar A., Zokaee Ashtiani F., Rezaei H., Haghmoradi A., Kargari A., (2014). Gas sparging to enhance permeate flux and reduce fouling resistances in cross flow microfiltration, J. Ind. Eng. Chem. 20 : 624–632.
18
Shakaib M., Hasani S.M.F., Mahmood M., (2009) . CFD modeling for flow and mass transfer in spacer-obstructed membrane feed channels, J. Memb. Sci. 326 :270–284.
19
Monfared M.A., Kasiri N., Salahi A., Mohammadi T., (2012). CFD simulation of baffles arrangement for gelatin-water ultrafiltration in rectangular channel, Desalination. 284 : 288–296.
20
Ahmed S., Taif Seraji M., Jahedi J., Hashib M.A., (2011). CFD simulation of turbulence promoters in a tubular membrane channel, Desalination. 276 : 191–198.
21
Wardeh S., Morvan H., (2008). CFD simulations of flow and concentration polarization in spacer-filled channels for application to water desalination, Chem. Eng. Res. Des. 86 : 1107–1116.
22
Sutzkover I., Hasson D., Semiat R., (2009). Simple technique for measuring the concentration polarization level in a reverse osmosis system, 131 : 117–127.
23
Geraldes V., Afonso M.D., (2006). Generalized mass-transfer correction factor for nanofiltration and reverse osmosis, AIChE J. 52 : 3353–3362.
24
Fernandez-Sempere J., Ruiz-Bevia F., Garcia-Algado P., Salcedo-Diaz R., (2010). Experimental study of concentration polarization in a crossflow reverse osmosis system using Digital Holographic Interferometry, Desalination. 257: 36–45.
25
Subramani A., Kim S., V Hoek E.M., (2006). Pressure, flow, and concentration profiles in open and spacer-filled membrane channels, J. Memb. Sci. 277 : 7–17.
26
Geraldes V., Semiao V., De Pinho M.N., (2004). Concentration polarisation and flow structure within nanofiltration spiral-wound modules with ladder-type spacers, Comput. Struct. 82 : 1561–1568.
27
Salcedo-D?az R., Garc?a-Algado P., Garc?a-Rodr?guez M., Fern?ndez-Sempere J., Ruiz-Bevi? F., (2014). Visualization and modeling of the polarization layer in crossflow reverse osmosis in a slit-type channel, J. Memb. Sci. 456 : 21–30.
28
Ahmad A.L., Lau K.K., (2006). Impact of different spacer filaments geometries on 2D unsteady hydrodynamics and concentration polarization in spiral wound membrane channel, J. Memb. Sci. 286 : 77–92.
29
Kim S., V Hoek E.M., (2005). Modeling concentration polarization in reverse osmosis processes, Desalination. 186 : 111–128.
30
Baker R.W.,( 2004). Membrane Technology and Applications, John Wiley & Sons, Ltd, Chichester, UK.
31
Khayet M., Seman M.N.A., N. Hilal, (2010). Response surface modeling and optimization of composite nanofiltration modified membranes, J. Memb. Sci. 349.
32
Khayet C.C. M., Essalhi M., (2011). Artificial neural network modeling and response surface methodology of desalination by reverse osmosis, J. Memb. Sci. 368 : 13.
33
Li R.G. Q.Y., Bellara S.R. , Cui Z.F. , Pepper D.S., (1998). Enhancement of ultrafiltration by gas sparging with flat sheet membrane modules, Sep. Purif. Technol. 14: 5.
34
Ishii M., Thermo-fluid dynamic theory of two-phase flow, Eyrolles, Paris, 1975
35
Issa R.I., (1986). Solution of the Implicit Discretized Fluid Flow Equations by Operator Splitting, J. Comput. Phys. 62 : 40–65.
36
Cavaco Mor?o A.I., Brites Alves A.M., Geraldes V., (2008). Concentration polarization in a reverse osmosis/nanofiltration plate-and-frame membrane module, J. Memb. Sci. 325: 580–591.
37
Karode S.K., Kumar A., (2001). Flow visualization through spacer filled channels by computational fluid dynamics I. Pressure drop and shear rate calculations for flat sheet geometry, J. Memb. Sci. 193 : 69–84.
38
Zare M., Fouladitajar A.,(2013). CFD modeling and simulation of concentration polarization in microfiltration of oil–water emulsions; Application of an Eulerian multiphase model, Desalination. 324:11.
39
ORIGINAL_ARTICLE
Evaluation of the Effects of Geology and Agricultural Development State on the Quality of Surface Water Resources Affected by Constructing Reservoir Dams (Case Study: Marun - Jarahi Basin)
The statistical tests such as T-test and Kruskall-Wallis test were used to study and analyze the difference between the quantitative parameters before and after constructing dams and the effect of different factors on water quality. The results of statistical tests showed that the values of the investigated water quality parameters (except EC value) before constructing (pre) dams were significantly different from the values after constructing dams in reservoir downstream stations. Sulfate (SO2-4) concentration in downstream stations of Marun Dam (Behbahan and Cham Nezam) reduced by 30 and 23 percent, respectively, and Cl- concentration increased 21 and 12 percent, respectively. Similarly, the difference between the values before and after constructing Jarreh dam at Mashin station was a 106% reduction in the concentration of sulfate ion (SO2-4) and a 78% increase in the concentration of chlorine ion (Cl-). The reason for this increase is probably due to the effect of river flows on the Formation and the relationship between the reservoir and the Formations where water has a long residence time and then reduced due to the exposure to the Geological Formations as well as the existence of agricultural activities downstream and before quality monitoring stations could be another reason for this claim. In addition, in the reservoir system, the concentration of soluble salt may be diluted by runoff from winter snowmelt and spring rains. Therefore, it can be concluded that water quality characteristics of Marun and Roudzard rivers in the studied basin has been affected by the constructed reservoir dams.
https://jhs.scu.ac.ir/article_15357_de93afe56d3fc3ebc5aa698f90f54ffd.pdf
2020-03-01
20
32
10.22055/jhs.2020.32575.1128
Geology
Agricultural Development
Reservoir Dams
water quality
Shahram
Moradi
sh_moradi76@yahoo.com
1
Department of Hydrology and Water Resources, Faculty of Water Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
LEAD_AUTHOR
Ali mohammad
Akhoond-Ali
aliakh@scu.ac.ir
2
Department of Hydrology and Water Resources, Faculty of Water Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
AUTHOR
freydon
radmanesh
radmanesh@scu.ac.ir
3
Department of Hydrology and Water Resources, Faculty of Water Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
AUTHOR
Heidar
Zarei
zareiheidar@gmail.com
4
Department of Hydrology and Water Resources, Faculty of Water Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
AUTHOR
Davis A. (1987). Chaparral conversion and streamflow: nitrate increase is balanced mainly by a decrease in bicarbonate. Water Resources Research 23: 215–224.
1
Egborge ABM. (1979). The effect of impoundment on the water chemistry of lake Asejire. Niger. Freshwater Biology 9: 403–412.
2
Hannan HH, Young WJ. (1974). The influence of a deep-storage reservoir on the physicochemical limnology of a central Texas river. Hydrobiologia 44: 177–204.
3
Jenkins A, Sloan WT, Cosby BJ. (1995). Stream chemistry in the middle hills and high mountains of the Himalayas, Nepal. Journal of Hydrology 166: 61–79.
4
Reynolds B, Hornung M, Hughes S. (1989). Chemistry of streams draining grassland and forest catchments at Plynlimon mid-Wales. Hydrological Sciences 34: 129–139.
5
Antonopoulos, V. Z., Papamichail, D. M., & Mitsiou, K. A. (2001). Statistical and trend analysis of water quality and quantity data for the Strymon River in Greece.
6
Edwards, A.M.C. (1973). The variation of dissolved constituents with discharge in some Norfolk rivers. Journal of Hydrology 18: 219–242.
7
Pinol, J., Avila, A., Roda, F., (1992). The seasonal variation of streamflow chemistry in three forested Mediterranean catchments. Journal of Hydrology 140 (1–4), 119–141.
8
Ahmad, S., Khan, I.H., Parida, B.P. (2001). Performance of stochastic approaches for forecasting river water quality. Water Research 18 : 4261–4266.
9
Huang and Foo 2002
10
Zarei, H., & Bilondi, M. P. (2013). Factor analysis of chemical composition in the Karoon River basin, southwest of Iran. Applied Water Science, 3(4), 753-761.
11
Ashrafi, S. M., & Mahmoudi, M. (2019). Developing a semi-distributed decision support system for great Karun water resources system. Journal of Applied Research in Water and Wastewater, 6(1), 16-24.
12
Bakhsipoor, I. E., Ashrafi, S. M., & Adib, A. (2019). Water Quality Effects on the Optimal Water Resources Operation in Great Karun River Basin. Pertanika, Journal of Science and Technology, 27 (4), 1881- 1900.
13
Ashrafi, S. M. (2019). Investigating Pareto Front Extreme Policies Using Semi-distributed Simulation Model for Great Karun River Basin. Journal of Hydraulic Structures, 5(1), 75-88.
14
Luo, Z., Shao, Q., Zuo, Q., & Cui, Y. (2020). Impact of land use and urbanization on river water quality and ecology in a dam dominated basin. Journal of Hydrology, 124655.
15
Zarei, H., and Ajdari, A. (2006). Chemical Quality of Water Resources in Aboulfares Dam Basin and Effect of Gachsaran Formation on it, 10th Iranian Geological Society Conference, Tarbiat Modares University, Tehran.
16
Zarei, H., Akhund Ali, AS. M., and Damough, n. A. (2006). Effects of Gachsaran Formation on Water Quality of Karun River in Khuzestan Province and Comparison with Dez River, 7th International Seminar on Ahwaz River Engineering, Shahid Chamran University of Ahvaz, Iran.
17
Sokal and Rohlf, (1995).Biometry: the principles and practice of statistics in biological research. W. H. Freeman, Newyork, USA.
18
ORIGINAL_ARTICLE
Air flow effect on the behavior of lock-exchange gravity current
The main goal of this study is investigating the effect of air flow above the free surface on the behavior of gravity current. Lock-release gravity current has been simulated in a channel, by using VOF method, for modeling free surface at the interface of gas and liquid phases. Eulerian approach is used to consider the presence of particles in the flow. The results of simulation with free surface assumption are in a well agreement with the previous experimental results. It is observed that the flows containing particles with larger diameter experience higher deposition rate, due to their higher terminal velocities which are 0.000129m/s, 0.000359m/s and 0.000808m/s for the particles with 12μm, 20μm and 30μm diameters respectively. Increasing the size of particles diameter leads to decrease in the driving force, the front position of flow containing particles with 30μm diameter is 11% less than that of flow containing particles with 12μm diameter, thereby the flow velocity decays quickly. The results show that the presence of particles leads to a reduction in the value of entrainment rate. It is concluded that the velocity of air-phase affects the shape of flow and instabilities. By considering three different values of 0.1m/s, 0.12m/s and 0.18m/s for the air-phase velocity, it is observed that the amount of run-out length, in the case where the air velocity is 0.18m/s, is nearly 3% more than that in other cases at the end of channel, moreover it leads to an increase in the value of entrainment rate.
https://jhs.scu.ac.ir/article_15396_bcdf37c3e3a03c7e162403f867c4f634.pdf
2020-03-01
33
54
10.22055/jhs.2020.31685.1125
Gravity current
lock exchange
multiphase flows
free surface
large eddy simulation
Ali
Koohandaz
alikoohandaz@gmail.com
1
Faculty of Mechanical engineering, University of Zanjan, Zanjan, Iran.
AUTHOR
Ehsan
Khavasi
khavasi@znu.ac.ir
2
Faculty of Mechanical engineering, University of Zanjan, Zanjan, Iran.
LEAD_AUTHOR
Hamid
Yousefi
h.yousefi_me@yahoo.com
3
Amirkabir University Of Technology.
AUTHOR
Hossein
Sadeghi sarsari
h.sadeghi1990@gmail.com
4
Faculty of Mechanical engineering, University of Zanjan, Zanjan, Iran.
AUTHOR
Khavasi, E., & Firoozabadi, B. (2019). Linear spatial stability analysis of particle-laden stratified shear layers. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 41(6), 246.
1
Khavasi, E., & Firoozabadi, B. (2018). Experimental study on the interfacial instability of particle-laden stratified shear flows. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 40(4), 193.
2
Nasr-Azadani, M. M., Meiburg, E., & Kneller, B. (2018). Mixing dynamics of turbidity currents interacting with complex seafloor topography. Environmental Fluid Mechanics, 18(1), 201-223.
3
Kyrousi, F., Leonardi, A., Roman, F., Armenio, V., Zanello, F., Zordan, J., Juez, C., & Falcomer, L. (2018). Large Eddy Simulations of sediment entrainment induced by a lock-exchange gravity current. Advances in Water Resources, 114, 102-118.
4
Ottolenghi, L., Adduce, C., Roman, F., & Armenio, V. (2019). Analysis of the flow in gravity currents propagating up a slope. International Journal of Sediment Research, 34(3), 240-250.
5
Zhao, L., Yu, C., & He, Z. (2019). Numerical modeling of lock-exchange gravity/turbidity currents by a high-order upwinding combined compact difference scheme. International Journal of Sediment Research, 34(3), 240-250.
6
Nasr-Azadani, M., Hall, B., & Meiburg, E. (2013). Polydisperse turbidity currents propagating over complex topography: comparison of experimental and depth-resolved simulation results. Computers & Geosciences, 53, 141-153
7
Bonnecaze, R. T., Huppert, H. E., & Lister, J. R. (1993). Particle-driven gravity currents. Journal of Fluid Mechanics, 250, 339-369.
8
De Rooij, F., & Dalziel, S. (2001). Time‐and space‐resolved measurements of deposition under turbidity currents. Particulate gravity currents, 207-215.
9
Gladstone, C., Phillips, J., & Sparks, R. (1998). Experiments on bidisperse, constant-volume gravity currents: propagation and sediment deposition. Sedimentology, 45(5), 833-843.
10
Kane, I. A., McCaffrey, W. D., Peakall, J., & Kneller, B. C. (2010). Submarine channel levee shape and sediment waves from physical experiments. Sedimentary Geology, 223(1-2), 75-85.
11
Luthi, S. (1981). Experiments on non-channelized turbidity currents and their deposits. Marine Geology, 40(3-4), M59-M68.
12
Peakall, J., Amos, K. J., Keevil, G. M., Bradbury, P. W., & Gupta, S. (2007). Flow processes and sedimentation in submarine channel bends. Marine and Petroleum Geology, 24(6-9), 470-486.
13
Cantero, M. I., Balachandar, S., Cantelli, A., Pirmez, C., & Parker, G. (2009). Turbidity current with a roof: Direct numerical simulation of self‐stratified turbulent channel flow driven by suspended sediment. Journal of Geophysical Research: Oceans, 114(C3).
14
Huang, H., Imran, J., & Pirmez, C. (2008). Numerical study of turbidity currents with sudden-release and sustained-inflow mechanisms. Journal of Hydraulic Engineering, 134(9), 1199-1209.
15
Kassem, A., & Imran, J. (2004). Three-dimensional modeling of density current. II. Flow in sinuous confined and uncontined channels. Journal of Hydraulic Research, 42(6), 591-602.
16
Nasr-Azadani, M. M., & Meiburg, E. (2011). TURBINS: an immersed boundary, Navier–Stokes code for the simulation of gravity and turbidity currents interacting with complex topographies. Computers & Fluids, 45(1), 14-28.
17
Necker, F., Härtel, C., Kleiser, L., & Meiburg, E. (2002). High-resolution simulations of particle-driven gravity currents. International Journal of Multiphase Flow, 28(2), 279-300.
18
Necker, F., Härtel, C., Kleiser, L., & Meiburg, E. (2005). Mixing and dissipation in particle-driven gravity currents. Journal of Fluid Mechanics, 545, 339-372.
19
Liu, X., & Jiang, Y. (2014). Direct numerical simulations of boundary condition effects on the propagation of density current in wall-bounded and open channels. Environmental Fluid Mechanics, 14(2), 387-407.
20
Scotti, A. (2008). A numerical study of the frontal region of gravity currents propagating on a free-slip boundary. Theoretical and Computational Fluid Dynamics, 22(5), 383
21
Benjamin, T. B. (1968). Gravity currents and related phenomena. Journal of Fluid Mechanics, 31(2), 209-248.
22
Härtel, C., Kleiser, L., Michaud, M., & Stein, C. (1997). A direct numerical simulation approach to the study of intrusion fronts. Journal of engineering mathematics, 32(2-3), 103-120.
23
Séon, T., Znaien, J., Salin, D., Hulin, J., Hinch, E., & Perrin, B. (2007). Transient buoyancy-driven front dynamics in nearly horizontal tubes. Physics of Fluids, 19(12), 123603.
24
Härtel, C., Meiburg, E., & Necker, F. (2000). Analysis and direct numerical simulation of the flow at a gravity-current head. Part 1. Flow topology and front speed for slip and no-slip boundaries. Journal of Fluid Mechanics, 418, 189-212.
25
Bonometti, T., Balachandar, S., & Magnaudet, J. (2008). Wall effects in non-Boussinesq density currents. Journal of Fluid Mechanics, 616, 445-475.
26
Longo, S., Ungarish, M., Di Federico, V., Chiapponi, L., & Petrolo, D. (2018). Gravity currents produced by lock-release: theory and experiments concerning the effect of a free top in non-Boussinesq systems. Advances in Water Resources,121, 456-471.
27
Musumeci, R. E., Viviano, A., Foti, E. (2017). Influence of Regular Surface Waves on the Propagation of Gravity Currents: Experimental and Numerical Modeling. Journal of Hydraulic Engineering, 143(8), 04017022.
28
Viviano, A., Musumeci, R. E., & Foti, E. (2018). Interaction between waves and gravity currents: description of turbulence in a simple numerical model. Environmental Fluid Mechanics, 18(1), 117-148.
29
Dallimore, C. J., Imberger, J., & Ishikawa, T. (2001). Entrainment and turbulence in saline underflow in Lake Ogawara. Journal of Hydraulic Engineering, 127(11), 937-948.
30
Fernandez, R. L., & Imberger, J. (2006). Bed roughness induced entrainment in a high Richardson number underflow. Journal of Hydraulic Research, 44(6), 725-738.
31
Hebbert, B., Patterson, J., Loh, I., & Imberger, J. (1979). Collie river underflow into the Wellington reservoir. Journal of the Hydraulics Division, 105(5), 533-545.
32
La Rocca, M., Adduce, C., Sciortino, G., & Pinzon, A. B. (2008). Experimental and numerical simulation of three-dimensional gravity currents on smooth and rough bottom. Physics of Fluids, 20(10), 106603.
33
Lauber, G., & Hager, W. H. (1998). Experiments to dambreak wave: Horizontal channel. Journal of Hydraulic Research, 36(3), 291-307.
34
Adduce, C., Sciortino, G., & Proietti, S. (2011). Gravity currents produced by lock exchanges: experiments and simulations with a two-layer shallow-water model with entrainment. Journal of Hydraulic Engineering, 138(2), 111-121.
35
Ottolenghi, L., Adduce, C., Inghilesi, R., Armenio, V., & Roman, F. (2016). Entrainment and mixing in unsteady gravity currents. Journal of Hydraulic Research, 54(5), 541-557.
36
Pelmard, J., Norris, S., & Friedrich, H. (2018). LES grid resolution requirements for the modelling of gravity currents. Computers & Fluids, 174, 256-270.
37
Nasr-Azadani, M. M., & Meiburg, E. (2013). Influence of seafloor topography on the depositional behavior of bi-disperse turbidity currents: a three-dimensional, depth-resolved numerical investigation. Environmental Fluid Mechanics, 14(2), 319-342. i:10.1007/s10652-013-9292-5.
38
Ooi, S. K., Constantinescu, G., & Weber, L. (2009). Numerical simulations of lock-exchange compositional gravity current. Journal of Fluid Mechanics, 635, 361-388.
39
Härtel, C., Carlsson, F., & Thunblom, M. (2000). Analysis and direct numerical simulation of the flow at a gravity-current head. Part 2. The lobe-and-cleft instability. Journal of Fluid Mechanics, 418, 213-229.
40
Lopes, P. (2013). Free-surface flow interface and air-entrainment modelling using OpenFOAM.
41
Weller, H., & Derivation, M. (2005). Solution of the Conditionally Averaged Two-Phase Flow Equations. OpenCFD, Ltd.
42
Ketabdari, M. J. (2016). Free Surface Flow Simulation Using VOF Method. Numerical Simulation: From Brain Imaging to Turbulent Flows, 365.
43
Elghobashi, S. (1991). Particle-laden turbulent flows: direct simulation and closure models Computational fluid Dynamics for the Petrochemical Process Industry (pp. 91- 104): Springer.
44
Ottolenghi, L., Adduce, C., Inghilesi, R., Roman, F., & Armenio, V. (2016). Mixing in lock-release gravity currents propagating up a slope. Physics of Fluids, 28(5), 056604.
45
Peer, A., Gopaul, A., Dauhoo, M., & Bhuruth, M. (2008). A new fourth-order non- oscillatory central scheme for hyperbolic conservation laws. Applied Numerical Mathematics, 58(5), 674-688.
46
ORIGINAL_ARTICLE
Experimental and numerical investigation the effect of pier position on local scouring around bridge pier at a 90° convergent bend
Natural rivers have several bends along the path that are not generally uniform and some are convergent. Installing the bridge piers in river convergent bends may result in complicated flow and erosion patterns around the bridge piers. Most of previous studies on the flow and the scour pattern around piers were carried out in straight channels and fixed-width bends. Studying the local scouring around pier located at a converging bend, experimentally and numerically, has brought novelty to this paper. In this research, a physical hydraulic model with a 90° convergent bend and central radius of 170 cm was built. A cylindrical pier with a diameter of 60 mm was installed in positions of 0, 30, 45, 60, and 75 degrees and local scour were studied under clear-water conditions. The SSIIM-2 numerical model was also used to simulate the scour pattern and the results were compared with experimental results. The results showed that, increasing the convergence and changing the pier position in a bend leads to an increment in the continuity between the flow lines and secondary currents, respectively, so that the maximum depth and volume of the scour hole occurred in the second half of the bend at an angle of 75 degrees. The comparison between experimental and numerical data shows that SSIIM-2 model can efficiently simulate the scour pattern in a 90° convergent bend. Furthermore, in all cases by increasing the Froude number, maximum depth and volume of the scour hole were increased.
https://jhs.scu.ac.ir/article_15439_d27fff9ed01ada0e49c985e1e32061a6.pdf
2020-03-01
55
76
10.22055/jhs.2020.32753.1134
Bridge pier
pier position
Local Scouring
90° convergent bend
the SSIIM-2 numerical model
Mousa
Rasaei
rasaei.iau@gmail.com
1
Faculty member of Civil Engineering, Islamshahr Branch, Islamic azad university, Iran
AUTHOR
Sohrab
Nazari
nazari.soh@gmail.com
2
Department of Civil Engineering, Eqhlid Branch, Islamic Azad University, Eqhlid, Iran
LEAD_AUTHOR
Saeid
Eslamian
saeid@cc.iut.ac.ir
3
Department of Water Engineering, Isfahan University of Technology, Isfahan, Iran
AUTHOR
Vijayasree BA, Eldho TI, Mazumder BS, Ahmad N, (2019). Influence of bridge pier shape on flow field and scour geometry. International Journal of River Basin Management, 17(1):109-129.
1
Rozovskii I.L., Flow of Water in Bend of Open Channels, Academy of Sciences of the Ukrainian SSR, Kiev, 1957.
2
Breusers H.N.C., Raudkivi A.J., Scouring. Hydraulic structures design manual, Balkema, Rotterdam, 1991.
3
Vaghefi M, Ghodsian M, Salimi S, (2016). The effect of circular bridge piers with different inclination angles toward downstream on scour. Indian Academy of Sciences (SADHANA), 41:75-86.
4
Shukry A, (1950). Flow around bends in an open flume. Transactions of the American Society of Civil Engineers, 15(1):751-779.
5
Booij R, (2003). Measurements and large eddy simulations of some curved flumes. Journal of Turbulence, 4(1):8-16.
6
Najafzadeh M, Barani GA, (2014). Experimental study of local scour around a vertical pier in cohesive soils. Scientia Iranica, Trans A, 21(2):241–250.
7
Blanckaert, K., Graf, W.H. (1999). Outer-bank cell of secondary circulation and boundary shear stress in open-channel bends. InProc. 1st RCEM symp, pp:533-543.
8
Wildhagen j., Applied Computational Fluid Dynamics with sediment Transport in a Sharply Curved Meadering Channel, Institute for Hydromechanics, Germany, University of Karlsruhe (TH), 2004.
9
Vaghefi M, Tabib Nazhad Motlagh MJ, Hashemi SSH, Moradi S, (2018). Experimental study of bed topography variations due to placement of a triad series of vertical piers at different positions in a 180° bend. Arabian Journal of Geosciences, 11(5).
10
Georgiadou AD, Smith KVH, (1986). Flow in curved converging channel. Journal of Hydraulic Engineering ASCE, 112(6):476-496.
11
Tabarestani MK, Zarrati AR, Mashahir MB, Mokallaf E, (2015). Extent of riprap layer with different stone sizes around rectangular bridge piers with or without an attached collar. Scientia Iranica. Transaction A, Civil Engineering, 22(3):709-716.
12
Ghodsian M, Mousavi SK, (2006). Experimental study on bed scour in a 90O channel bend. International Journal of Sediment Research, 21(4):321-328.
13
Ghobadian R, Mohammadi K, (2011). Simulation of subcritical flow pattern in 180° uniform and convergent open-channel bends using SSIIM 3-D model. Water Science and Engineering. 4(3):270-283.
14
Mansuri AR., 3-D Numerical Simulation of Bed Changes in 180 Degree Bends, M.S. Dissertation. Tarbyat Modares University, Tehran, Iran, 2006.
15
Gholami A, Akhtari AA, Minatour Y, Bonakdari H, Javadi AA, (2014). Experimental and numerical study on velocity fields and water surface profile in a strongly-curved 90° open channel bend. Engineering Applications of Computational Fluid Mechanics (EACFM), 8(3):447−461.
16
Abdallah Mohamed Y, Mohamed Abdel-Aal G, Hemdan Nasr-Allah T, Shawky A, (2016). Experimental and theoretical investigations of scour at bridge abutment. Journal of King Saud University – Engineering Sciences, 28(1):32–40.
17
Akib SH, Basser H, Karami H, Jahangirzadeh A, (2014). Retrofitting of Bridge Piers against the Scour Damages: Case Study of the Marand-Soofian Route Bridge. World Academy of Science, Engineering and Technology, International Journal of Civil, Architectural Science and Engineering, 8(1):56-60.
18
Ehteram M, Mahdavi Meymand A, (2015). Numerical modeling of scour depth at side piers of the bridge. Journal of Computational and Applied Mathematics. 280:68–79.
19
Hamidi A, Siadatmousavi SM, (2017). Numerical simulation of scour and flow field for different arrangements of two piers using SSIIM model. Ain Shams Engineering Journal, 9(4):2415-2426.
20
Emami, Y., Salamatian, S.A., Ghodsian, M. (2008). Scour at cylindrical bridge pier in a 180-degree channel bend. Fourth International Conference on Scour and Erosion, Tokyo, Japan, pp: 256-262.
21
Masjedi A, Bejestan MS, Kazemi H, (2010). Effect of Bridge Pier Position in a 180 Degree Flume Bend on Scour Hole Depth. Journal of Applied Sciences, 10(8):670-675.
22
Wang H, Tang H, Xiao J, Wang Y, Jiang S, (2016). Clear-water local scouring around three piers in a tandem arrangement. Science China Technological Sciences, 59(6):888-896.
23
Khajeh SBM, Vaghefi M, Mahmoudi A, (2017). The scour pattern around an inclined cylindrical pier in a sharp 180-degree bend: an experimental study. International Journal of River Basin Management, 15(2):207-218.
24
Raudkivi AJ, Ettema R, (1983). Clear-water scour at cylindrical piers. Journal of Hydraulic Engineering (ASCE), 109(3):339-350.
25
Melville BW, Sutherland AJ, (1988). Design method for local scour at bridge piers. Journal of the Hydraulics Division, 114(10):1210-1225.
26
Guemou B, Seddini A, Ghenim NA, (2016). Numerical investigations of the round-nosed bridge pier length effects on the bed shear stress. Progress in Computational Fluid Dynamics, 16(5):313-321.
27
Melville BW, Chiew YM, (1999). Time scale for local scour at bridge piers. Journal of Hydraulic Engineering ASCE, 125(1):59-65.
28
Oliveto G, Hager WH, (2002). Temporal Evolution of Clear-Water Pier and Abutment Scour. Journal of Hydraulic Engineering ASCE, 128(9):811-820.
29
Olsen NRB, Jimenes OF, Abrahamsen L, Lovoll A, (1999). 3D CFD modeling of water and sediment flow in a hydropower reservoir. International Journal of Sediment Research, pp.16-24.
30
Olsen, N.R.B., A three-dimensional numerical model for simulation of sediment movements in water intakes with multi block option, Online User’s manual, 2011.
31
ORIGINAL_ARTICLE
A Conceptual Framework of a Surrogate-based Quality-Quantity Decision Support System (Q2DSS) for Water Resources Systems
The water crisis in different countries of the world has made the earth undergo tremendous changes compared to the past years. Therefore, it is very necessary to have intelligent systems that can help managers make correct and optimal decisions in various possible conditions. In recent years, the biggest challenge faced by water resource managers in the Karun Basin in Iran has been the decline in the quality of surface waters in the downstream areas of the basin. In this research, a surrogate-based model has been developed for predicting and controlling the quantity and quality of water in different parts of the basin. As a decision support system, this model can evaluate the quantity of water at different points in the basin and also predict its quality in various probable conditions. This model will also be used to extract optimal operating policies with the aim of satisfying quality constraints in different conditions. The model can help decision makers in the optimal management of the system and also greatly reduce the losses caused by quality issues in possible future situations.
https://jhs.scu.ac.ir/article_15464_382d07c0c7b112edea22619167a3e9d0.pdf
2020-03-01
77
89
10.22055/jhs.2020.32755.1135
Quality Management
Surrogate
Water resources
Intelligent computational modelling
Seyed Mohammad
Ashrafi
ashrafi@scu.ac.ir
1
Department of Civil Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
LEAD_AUTHOR
Tayebeh
Moradpoor
gisu.moradpour@gmail.com
2
Department of Civil Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
AUTHOR
Vaghefi, S. A., Mousavi, S. J., Abbaspour, K. C., Srinivasan, R., & Arnold, J. R. (2015). Integration of hydrologic and water allocation models in basin-scale water resources management considering crop pattern and climate change: Karkheh River Basin in Iran. Regional environmental change, 15(3), 475-484.
1
Bakhsipoor, I. E., Ashrafi, S. M., & Adib, A. (2019). Water Quality Effects on the Optimal Water Resources Operation in Great Karun River Basin. Pertanika J. Sci. & Technol. 27 (4), pp: 1881–1900.
2
Ashrafi, S. M. (2019). Investigating Pareto Front Extreme Policies Using Semi-distributed Simulation Model for Great Karun River Basin. Journal of Hydraulic Structures, 5(1), 75-88.
3
Labadie, J. W. (2004). Optimal operation of multireservoir systems: state-of-the-art review. Journal of water resources planning and management, 130(2), 93-111.
4
Ashrafi, S. M., & Dariane, A. B. (2017). Coupled Operating Rules for Optimal Operation of Multi-Reservoir Systems. Water Resources Management, 31(14), 4505-4520.
5
Lk Velikanov, A. (1987). Problems of large-scale water resources systems development. In International symposium on water for the future, pp. 621-625.
6
Rahaman, M. M., & Varis, O. (2005). Integrated water resources management: evolution, prospects and future challenges. Sustainability: science, practice and policy, 1(1), 15-21.
7
Tundisi, J. G. (2008). Water resources in the future: problems and solutions. estudos avançados, 22(63), 7-16.
8
Wang, K. W., Chang, L. C., & Chang, F. J. (2011). Multi-tier interactive genetic algorithms for the optimization of long-term reservoir operation. Advances in Water Resources, 34(10), 1343-1351.
9
Cosgrove, W. J., & Loucks, D. P. (2015). Water management: Current and future challenges and research directions. Water Resources Research, 51(6), 4823-4839.
10
Ashrafi, S. M., & Mahmoudi, M. (2019). Developing a semi-distributed decision support system for great Karun water resources system. Journal of Applied Research in Water and Wastewater, 6(1), 16-24.
11
Fa'al, F., Ghafouri, H. R., & Ashrafi, S. M. (2020). Predicting Saltwater Intrusion into Coastal Aquifers Using Support Vector Regression Surrogate Models. Journal of Water and Wastewater, 31(2).
12
Raad, D. N., Sinske, A. N., & Van Vuuren, J. H. (2010). Comparison of four reliability surrogate measures for water distribution systems design. Water Resources Research, 46(5).
13
Tanyimboh, T. T., Tietavainen, M. T., & Saleh, S. (2011). Reliability assessment of water distribution systems with statistical entropy and other surrogate measures. Water Science and Technology: Water Supply, 11(4), 437-443.
14
Herrera, M., Abraham, E., & Stoianov, I. (2015). Graph-theoretic surrogate measures for analysing the resilience of water distribution networks. Procedia Engineering, 119, 1241-1248.
15
Gheisi, A., & Naser, G. (2015). Multistate reliability of water-distribution systems: comparison of surrogate measures. Journal of Water Resources Planning and Management, 141(10), 04015018.
16
Ayati, A. H., Haghighi, A., & Lee, P. (2019). Statistical review of major standpoints in hydraulic transient-based leak detection. Journal of Hydraulic Structures, 5(1), 1-26.
17
Wan, W., Guo, X., Lei, X., Jiang, Y., & Wang, H. (2018). A Novel Optimization Method for Multi-Reservoir Operation Policy Derivation in Complex Inter-Basin Water Transfer System. Water Resources Management, 32(1), 31-51.
18
Ashrafi, S. M., Ashrafi, S. F., & Moazami, S. (2017). Developing Self-adaptive Melody Search Algorithm for Optimal Operation of Multi-reservoir Systems. Journal of Hydraulic Structures, 3(1), 35-48.
19
Adib, A., Mirsalari, S. B., & Ashrafi, S. M. (2018). Prediction of meteorological and hydrological phenomena by different climatic scenarios in the Karkheh watershed (south west of Iran). Scientia Iranica (In Press). DOI: 10.24200/SCI.2018.50953.1934.
20
Moazami, S., Abdollahipour, A., Zakeri Niri, M., & Ashrafi, S. M. (2016). Hydrological Assessment of Daily Satellite Precipitation Products over a Basin in Iran. Journal of Hydraulic Structures, 2(2), 35-45.
21
Peng, H., Wang, Y., Zhang, W., Li, Y., Wu, K. B., & Zhu, Q. (2009, June). A coupled water quality-quantity model for water resource allocation. In 2009 3rd International Conference on Bioinformatics and Biomedical Engineering (pp. 1-5). IEEE.
22
Zhang, W., Wang, Y., & Peng, H. (2009). An Integrated Water Quality-Quantity Method for Water Resource Management. In 2009 International Conference on Environmental Science and Information Application Technology (Vol. 2, pp. 178-181). IEEE.
23
Bin, Z., & Zengchuan, D. (2004). Allocation model of regional water supply in different quality. Yangtze River, 35(2), 21-31.
24
Abolpour, M. J., M. Karamouz (2005). Water allocation improvement in river basin using adaptive neural fuzzy reinfourcement learning approach. Applied Soft Computing 7(2007): 265-285.
25
Xing, L., Y. Kan, et al. (2006). Allocation model of regional water resources in water supply in different quality. Journal of Heilongjiang Hydraulic Engineering College (2): 67-70.
26
Karamouz M, Moridi A and Fayyazi HM (2008) Dealing with conflict over water quality and quantity allocation: a case study. Journal of Scientia Iranica 15(1): 34–49.
27
Abbasnia, I. and Mosavi, J. (2010). A basin-scale quantity and quality allocation model of surface water resources. Proceedings of the 8th International Congress of Civil Engineering. University of Shiraz, Shiraz, Iran.
28
Zhang, Z., & Johnson, B. E. (2016). Aquatic nutrient simulation modules (NSMs) developed for hydrologic and hydraulic models (No. ERDC/EL TR-16-1). US Army Engineer Research and Development Center Vicksburg United States.
29
Azmi, M., & Heidarzadeh, N. (2013). Dynamic modelling of integrated water resources quality management. In Proceedings of the Institution of Civil Engineers-Water Management (Vol. 166, No. 7, pp. 357-366). Thomas Telford Ltd.
30
Fontane D, Labadie J and Loftis B (1981). Optimal control of reservoir discharge quality through selective withdrawal. Water Resources Research 17(6): 1594–1604.
31
Sasikumar K. and Mujumdar PP. (1998). Fuzzy optimization model for water quality management of a river system. Water Resources Planning and Management 124(2): 79–88.
32
Azevedo, L. G. T. D., Gates, T. K., Fontane, D. G., Labadie, J. W., & Porto, R. L. (2000). Integration of water quantity and quality in strategic river basin planning. Journal of water resources planning and management, 126(2), 85-97.
33
Erbe V and Schutze M (2005) An integrated modelling concepts for emission-based management of sewer system, wastewater treatment plant and river. Water Science and Technology 52(5): 95–103.
34
Kerachian R and Karamouz M (2005) Waste load allocation model for seasonal river water quality management: application of sequential dynamic genetic algorithm. Journal of Scientia Iranica 12(2): 117–130.
35
Schmitt, T. G., & Huber, W. C. (2006). The scope of integrated modelling: system boundaries, sub-systems, scales and disciplines. Water science and technology, 54(6-7), 405-413.
36
Winz, I., Brierley, G., & Trowsdale, S. (2009). The use of system dynamics simulation in water resources management. Water resources management, 23(7), 1301-1323.
37
Freni, G., Mannina, G., & Viviani, G. (2010). Urban water quality modelling: a parsimonious holistic approach for a complex real case study. Water Science and Technology, 61(2), 521-536.
38
Saadatpour M, Afshar A, Edinger JE (2017). Meta-model assisted 2D hydrodynamic and thermal simulation model (CE-QUAL-W2) in deriving optimal reservoir operational strategy in selective withdrawal scheme. Water Resource Manage. 31(9):2729–2744.
39
Rousta, B. A., & Araghinejad, S. (2015). Development of a multi criteria decision making tool for a water resources decision support system. Water Resources Management, 29(15), 5713-5727.
40
Ashrafi, S. M., & Kourabbaslou, N. E. (2015). An Efficient Adaptive Strategy for Melody Search Algorithm. International Journal of Applied Metaheuristic Computing (IJAMC), 6(3), 1-37.
41
Bozorg-Haddad, O., Garousi-Nejad, I., & Loáiciga, H. A. (2017). Extended multi-objective firefly algorithm for hydropower energy generation. Journal of Hydroinformatics, 19(5), 734-751.
42
Yang, Z., Yang, K., Wang, Y., Su, L., & Hu, H. (2019). The improved multi-criteria decision-making model for multi-objective operation in a complex reservoir system. Journal of Hydroinformatics, 21(5), 851-874.
43
Soghrati, F., & Moeini, R. (2019). Deriving optimal operation of reservoir proposing improved artificial bee colony algorithm: standard and constrained versions. Journal of Hydroinformatics.
44
Caloiero, T., Coscarelli, R., & Ferrari, E. (2019). Assessment of seasonal and annual rainfall trend in Calabria (southern Italy) with the ITA method. Journal of Hydroinformatics.
45
Lerma, N., Paredes-Arquiola, J., Molina, J. L., & Andreu, J. (2014). Evolutionary network flow models for obtaining operation rules in multi-reservoir water systems. Journal of Hydroinformatics, 16(1), 33-49.
46
Gordillo, G., Morales-Hernández, M., & García-Navarro, P. (2019). Finite volume model for the simulation of 1D unsteady river flow and water quality based on the WASP. Journal of Hydroinformatics.
47
Maier, H. R., Kapelan, Z., Kasprzyk, J., Kollat, J., Matott, L. S., Cunha, M. C., ... & Ostfeld, A. (2014). Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions. Environmental Modelling & Software, 62, 271-299.
48
Aydin, N. Y., Zeckzer, D., Hagen, H., & Schmitt, T. (2015). A decision support system for the technical sustainability assessment of water distribution systems. Environmental Modelling & Software, 67, 31-42.
49
Rani, D., & Moreira, M. M. (2010). Simulation–optimization modeling: a survey and potential application in reservoir systems operation. Water resources management, 24(6), 1107-1138.
50
Falalakis, G., & Gemitzi, A. (2020). A simple method for water balance estimation based on the empirical method and remotely sensed evapotranspiration estimates. Journal of Hydroinformatics.
51
Li, Y., Liang, Z., Hu, Y., Li, B., Xu, B., & Wang, D. (2019). A multi-model integration method for monthly streamflow prediction: modified stacking ensemble strategy. Journal of Hydroinformatics.
52
Lee, Y., Kim, S. K., & Ko, I. H. (2008). Multistage stochastic linear programming model for daily coordinated multi-reservoir operation. Journal of Hydroinformatics, 10(1), 23-41.
53
Ehteram, M., Mousavi, S. F., Karami, H., Farzin, S., Singh, V. P., Chau, K. W., & El-Shafie, A. (2018). Reservoir operation based on evolutionary algorithms and multi-criteria decision-making under climate change and uncertainty. Journal of Hydroinformatics, 20(2), 332-355.
54
ORIGINAL_ARTICLE
The Impact of outlier detection to estimate groundwater fluctuations using GRACE satellite data; Case Study: Khuzestan Province, Iran
Groundwater aquifers are one of the most significant freshwater resources in the world. Hence, the monitoring of these resources is particularly important for available water resources planning. Piezometric wells have traditionally been used to monitor groundwater. This approach is costly and pointwise, which is not feasible for places with steep topography and mountainous areas. Nowadays, remote sensing techniques are widely used in various fields of engineering as appropriate alternatives to traditional methods. In water resources management, the Gravity Recovery and Climate Experiment (GRACE) satellites can monitor groundwater changes with acceptable accuracies. This paper applied the GRACE satellite data for a 40-month period to assess the variation of the groundwater level in Khuzestan province. The Global Land Data Assimilation System (GLDAS) model was used to counteract the soil moisture effect in final results. The observed data from piezometric wells were pre-processed to detect outliers using the Mahalanobis algorithm in Khuzestan province. At last, the outputs of GRACE were compared with these processed observed data. Despite the relatively small size of the area in question, the results indicated the efficiency of GRACE data (RMSE = 0.8, NRMSE = 0.2) for monitoring the groundwater level changes.
https://jhs.scu.ac.ir/article_15510_8117957763a1f8ef3d7f0e9125ef4a4d.pdf
2020-03-01
90
104
10.22055/jhs.2020.32747.1133
Groundwater Monitoring
GRACE data
GLDAS Model
Outlier Detection
Mahalanobis Algorithm
Mohammad Mehdi
Riyahi
mo_riyahi@yahoo.com
1
Department of Civil Engineering, Faculty of Civil Engineering and Architecture,Shahid Chamran University of Ahvaz, Ahvaz, Iran.
LEAD_AUTHOR
Mohammad
Jafarpour
m.jafarpour13@gmail.com
2
Department of Civil Engineering, Faculty of Civil Engineering and Architecture,Shahid Chamran University of Ahvaz, Ahvaz, Iran.
AUTHOR
Lotfollah
Emadali
bemad1390@alumni.ut.ac.ir
3
Department of Civil Engineering, Engineering Faculty, Khatam Al-Anbia University of Technology, Behbahan, Iran
AUTHOR
Mohammad Ali
Sharifi
sharifi@ut.ac.ir
4
Department of Geodesy, Faculty of Surveying and Spatial Information, College of Engineering, University of Tehran, Tehran, Iran
AUTHOR
Esmaeili, Mand Motagh, M (2016) Improved persistent scatterer analysis using amplitude dispersion index optimization of dual polarimetry dataISPRS Journal of Photogrammetry and Remote Sensing, 117, pp.108-114.
1
Rodell, M., Houser, P., Jambor, U., Gottschalck, J., Mitchell, K., Meng, C.-J., Arsenault, K., Cosgrove, B., Radakovich, J., & Bosilovich, M (2004) The global land data assimilation systemBulletin of the American Meteorological Society, 85, pp.381-394
2
Abart, C (2005) Assessment of solution strategies for GRACE gravity field processing.
3
Wahr, J., Swenson, S., Zlotnicki, V., & Velicogna, I (2004) Time‐variable gravity from GRACE: First resultsGeophysical Research Letters, 31.
4
Ramillien, G., Famiglietti, J.S., & Wahr, J (2008) Detection of continental hydrology and glaciology signals from GRACE: a reviewSurveys in geophysics, 29, pp.361-374
5
Awange, J., Sharifi, M., Keller, W., & Kuhn, M (2009) GRACE application to the receding Lake Victoria water level and Australian droughtObserving our changing earth: Springer, pp387-396
6
Longuevergne, L., Scanlon, B.R., & Wilson, C.R (2010) GRACE Hydrological estimates for small basins: Evaluating processing approaches on the High Plains Aquifer, USAWater Resources Research, 46.
7
Voss, K.A., Famiglietti, J.S., Lo, M., De Linage, C., Rodell, M., & Swenson, S.C (2013) Groundwater depletion in the Middle East from GRACE with implications for transboundary water management in the Tigris‐Euphrates‐Western Iran regionWater Resources Research, 49, pp.904-914
8
Ferreira, V.G., Gong, Z., & Andam-Akorful, S.A (2012) Monitoring mass changes in the Volta River basin using GRACE satellite gravity and TRMM precipitationBoletim de Ciências Geodésicas, 18, pp549-563
9
Ahmed, M., Sultan, M., Wahr, J., Yan, E., Milewski, A., Sauck, W., Becker, R., & Welton, B (2011)Integration of GRACE (Gravity Recovery and Climate Experiment) data with traditional data sets for a better understanding of the time-dependent water partitioning in African watershedsGeology, 39 , pp479-482
10
Ahmed, M., Sultan, M., Wahr, J., & Yan, E (2014) The use of GRACE data to monitor natural and anthropogenic induced variations in water availability across AfricaEarth-Science Reviews, 136, pp.289-300
11
Rodell, M., Famiglietti, J., Wiese, D., Reager, J., Beaudoing, H., Landerer, F., & Lo, M (2019) Emerging trends in global freshwater availability Nature, 565, E7-E7, vol 557, pg 651.
12
Tapley, B.D., Bettadpur, S., Watkins, M., & Reigber, C (2004) The gravity recovery and climate experiment: Mission overview and early resultsGeophysical Research Letters, 31.
13
Buis, A (2012) At 10, GRACE Continues Defying, and Defining, GravityNASA.
14
Rummel, R., Balmino, G., Johannessen, J., & Visser, P.a.W., P (2002) Dedicated gravity field missions-principles and aimsJGeodyn, 33, pp3-20
15
Wahr, J., Molenaar, M., & Bryan, F (1998) Time variability of the Earth's gravity field: Hydrological and oceanic effects and their possible detection using GRACEJournal of Geophysical Research: Solid Earth, 103, pp30205-30229
16
Joodaki, G (2014) Earth mass change tracking using GRACE satellite gravity data.
17
Houser, P., & Rodell, M (2002) GLDAS: an important contribution to CEOPGEWEX Newsletter, May, 2, 9.
18
Fang, H., Hrubiak, P., Kato, H., Rodell, M., Teng, W.L., & Vollmer, B.E (2008) Global land data assimilation system (GLDAS) products from NASA hydrology data and information services center (HDISC).
19
Fang, H., Beaudoing, H.K., Teng, W.L., & Vollmer, B.E (2009) Global Land data assimilation system (GLDAS) products, services and application from NASA hydrology data and information services center (HDISC).
20
Rui, H., & Beaudoing, H (2015) Global Land Data Assimilation System Version 2 (GLDAS-2) Products, Last revisedNational Aeronautices and space administration.
21
Grubbs, F.E (1969) Procedures for detecting outlying observations in samplesTechnometrics, 11, pp.1-21
22
Barnett, V.L., T (1994) Outliers in statistical dataSchool of Mathematical & Physical Sciences, National Technical University of Athens, Greece.
23
Das, P., & Haimes, Y.Y (1979) Multiobjective optimization in water quality and land managementWater Resources Research, 15, pp1313-1322
24
Hair, J.F., & R. E (1998) Andersen, R.LMultivariate data analysisRentice Hall, Upper Saddle River, New Jersey,.
25
Filzmoser, P., Garrett, R.G., & Reimann, C (2005) Multivariate outlier detection in exploration geochemistryComputers & geosciences, 31, pp.579-587
26