ORIGINAL_ARTICLE
Drought Risk Analysis in Agricultural Water Delivery System, A Case Study of Roodasht Irrigation Districts
The occurrence of continuous water shortage as a natural hazard has dramatically impacted the country's performance of surface water delivery systems. In this research, drought risk analysis in surface water delivery systems in Roodasht irrigation network has been investigated. For this purpose, the streamflow Drought Index (SDI) has been used to assess the region's probability of drought. The consequence of drought was calculated by combining water distribution performance evaluation indicators, efficiency, adequacy, and justice with a simple additive weighting method (SAW) in five weight scenarios based on different management perspectives. Finally, by combining the probability and consequence of drought, the risk of this phenomenon in the agricultural water distribution system was calculated and analyzed. According to the SDI index, the results showed drought occurred in the monthly period with magnitude of -2.42 to 2.47, and in the annual period with magnitude of -2.54 to 1.65. The range of monthly drought risk values of the system in five weighted scenarios is as follows: evaluation perspective with emphasis on the opinion of the network administrator (0.668 to 0.804), With the emphasis on the beneficiary's opinion (0.636 to 0.803), the simultaneous emphasis of manager and beneficiary (0.647 to 0.802), emphasis on regional water opinion and provincial managers (0.684 to 0.804), and with emphasis on environmental considerations within the irrigation network (0.692 to 0.804). The obtained results of employing the risk analysis framework in both annual and monthly periods make the authorities present appropriate alternatives considering the magnitude and timing of the system's failure risk.
https://ijswr.ut.ac.ir/article_84028_1c2e006867e8778c9e27b123d181511b.pdf
2021-09-23
1709
1720
10.22059/ijswr.2021.320375.668912
Drought
probability
consequence
risk
Irrigation District
Javad
Pourmahmoud
j.pourmahmoud@ut.ac.ir
1
Department of Water Engineering, Aburaihan Campus, University of Tehran, Pakdasht, Iran
AUTHOR
Mehdy
Hashemy
mehdi.hashemy@ut.ac.ir
2
Department of Water Engineering, Aburaihan Campus, University of Tehran, Pakdasht, Iran
LEAD_AUTHOR
Abbas
Roozbahani
roozbahany@ut.ac.ir
3
Department of Water Engineering, Aburaihan Campus, University of Tehran, Pakdasht, Iran
AUTHOR
Arumi, J. L., Jones. D. (2001). Methodology for Risk Analysis of Irrigation Structures, Hydraulic Engineering in Mexico (in Spanish), vol. XVI, num. 3, pages 67-74.
1
Babaei, M., Roozbahani, A., Hashemy Shahdany, S. M. (2018). Risk Assessment of Agricultural Water Conveyance and Delivery Systems by Fuzzy Fault Tree Analysis Method. Water Resources Management, 4079–4101.
2
Bozorgi, A., Roozbahani, A. Hashemy Shahdany, S.M. (2020). Development of Drought Risk Analysis Model in Agricultural Water Supply Systems of Northern Roodasht Irrigation Network Using Bayesian Network. Journal of Water Research in Agriculture. 34.2 (2). 202-187. (In Persian).
3
Dejen, Z. A. (2015). Hydraulic and operational performance of irrigation schemes in view of water saving and sustainability: sugar estates and community managed schemes In Ethiopia. Doctorate Thesis, Delft University, Ntherlands.
4
Gachlou, M., Roozbahani, A., Banihabib, M. E. (2019). Comprehensive risk assessment of river basins using Fault Tree Analysis, Journal of Hydrology, 577, 123974.
5
Gunantara, N. (2018). A review of multi-objective optimization: Methods and its applications, Cogent Engineering, 5:1, 1502242.
6
Kaghazchi, A., Hashemy Shahdany, S.M., Roozbahani, A., (2020). Simulation and evaluation of agricultural water distribution and delivery systems with a Hybrid Bayesian network model. Agricultural Water Management. 245. 106578. 10.1016.
7
Molden, D., Gates, T. (1990). Performance Measures for Evaluation of Irrigation‐Water Delivery Systems. Journal of Irrigation and Drainage Engineering 116, 804-823.
8
Nalbantis,I., Tsakiris, G., (2009). Assessment of Hydrological Drought Revisited. Water Resources Management, 23(5):881-897.
9
Orojloo, M., Shahdany, S.M.H., Roozbahani, A. (2018). Developing an integrated risk management framework for agricultural water conveyance and distribution systems within fuzzy decision-making approaches. Science of the Total Environment. Pages 1363-1376.
10
Ozkaya, A., Zerberg, Y. (2019). A 40-Year Analysis of the Hydrological Drought Index for the Tigris Basin, Turkey. Water 11(4):657
11
Rahman, S., Devera, J., and Reynolds, J. (2014). Risk assessment model for pipe rehabilitation and replacement in a water distribution system, in pipelines. American Society of Civil Engineers. Pp: 1997-2006.
12
Roozbahani, A., Zahraie, B., Tabesh, M., (2012). Integrated risk assessment of urban water supply systems from source to tap. Stochastic Environmental Research and Risk Assessment, 923-944.
13
Rowe, W. D. (1977). An Anatomy Fo Risk: Wiley.
14
Salman, B., and Salem, O. (2012). Risk assessment of wastewater collection lines using failure models and criticality ratings. Pipe. Syst. Engin. Prac. J. 3: 3. 68-76.
15
Shukla, S., Wood, A. W., (2008). Use of a standardized runoff index for characterizing hydrologic drought. Geophysical research letters, 35(2).
16
Tigkas, D., Vangelis, H., Tsakiris, G., (2015). DrinC: a software for drought analysis based on drought indices. Earth Science Informatics 8(3):697–709.
17
Towler, E., Roberts, M., Rajagopalan, B., Sojda, R. (2013). Incorporating probabilistic seasonal climate forecasts into river management using a risk‐based framework, Water Resources Research, Volume 45, Issue 8.
18
Tsakiris, G., Vangelis, H. (2005). Establishing a Drought Index Incorporating Evapotranspiration. European Water. 9/1.
19
ORIGINAL_ARTICLE
Hedging approach in Multi-Objective Simulation-Optimization of operation of Ilam Dam Reservoir using MOPSO algorithm
In this research, the simulation and optimization models are integrated to apply the reservoir hedging policy. The simulation of the studied basin is executed using the WEAP model to conduct the system optimization and the multi-objective MOPSO model is utilized so that the first purpose is to maximize the percentage of supplying demands, while the second one is to minimize the violation of allowable capacities of the reservoir during the operation period. In this regard, the operation modeling from the reservoir was carried out based on the current condition for a 360-month period. Finally, by defining the optimized scenario and applying the reservoir hedging policy, the optimization of the operation from the reservoir is conducted and the results were compared with the outcomes of the reference scenario. In this study, by considering 24 decision variables including 12 hedging level variables and 12 hedging coefficient variables, the optimal answers were achieved after 1000 iterations. The results showed that the violation of the allowable capacities has not occurred in any periods, while in the reference scenario the reservoir level has reached the dead level in sequent months with more water shortage which might lead to the lack of water supply in such months and serious damages to the system. Due to the application of hedging policy in the optimized scenario, the percentage of supply in the critical months has increased between 20-35% compared to the reference scenario, which indicates a significant reduction in the failure rate in such months compared to the reference scenario.
https://ijswr.ut.ac.ir/article_84029_b4d341d4b621065cb364649c114095e4.pdf
2021-09-23
1721
1733
10.22059/ijswr.2021.319347.668901
Hedging Policy
MOPSO
Optimization
simulation
WEAP
Sedighe
Mansouri
mansoury.se19@yahoo.com
1
Department of Water Resources Engineering, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran
AUTHOR
Hossein
Fathian
fathian.h58@gmail.com
2
Department of Water Resources Engineering, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran
LEAD_AUTHOR
Alireza
Nikbakht shahbazi
nikbakhta@gmail.com
3
Department of Water Resources Engineering, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran
AUTHOR
Mehdi
Asadi lour
asadi379@yahoo.com
4
Department of Irrigation and Drainage, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran
AUTHOR
Ali
Asareh
ali_assareh_2003@yahoo.com
5
Department of Irrigation and Drainage, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran
AUTHOR
Azari, A., Hamzeh, S., and Naderi, S. (2018). Multi-objective optimization of the reservoir system operation by using the hedging policy. Water Resource Management, 32(6), 2061–2078.
1
Bayesteh, M., and Azari, A. (2021). Stochastic Optimization of Reservoir Operation by Applying Hedging Rules. Journal of Water Resources Planning and Management, 147(2), 04020091-9.
2
Daraeikhah, M., Meraji, S.H., and Afshar, M.H. (2009). Application of Particle Swarm Optimization to Optimal Design of Cascade Stilling Basins. Scientia Iranica, 16(1), 50-57.
3
Deb, k., Pratap, A., Agarwal, S., and Meyarivan, T. (2002). A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans Evolutionary Computing, Indian. 6(2), 182–197.
4
Draper, A.J., and Lund, J.R. (2004). Optimal hedging and carry over storage value. Waterce Resource Planning and Management, ASCE, 130(1), 83–87.
5
Felfelani, F., Jalali Movahed, A., and Zarghami, M. (2013). simulating hedging rules for effective reservoir operation by using system dynamics: a case study of Dez Reservoir, Iran. Lake and Reservoir Management. 29(2), 126-140.
6
Izquierdo, J., Montalvo, I., Pérez, R., and Fuertes, V. S. (2008). Design optimization of wastewater collection networks by PSO. Computers & Mathematics with Applications, 56(3), 777-784.
7
Li, X., Zhao, Y., Shi, C., Sha, J., Wang, Z.L., and Wang, Y. (2015). Application of Water Evaluation and Planning (WEAP) model for water resources management strategy estimation in coastal Binhai New Area, China. Ocean & Coastal Management. 106, 97-109.
8
Loucks, D.P., and van Beek, E. )2005(. Water Resources Systems Planning and Management, An Introduction to Methods, Models and Applications. UNESCO Publication, PP: 677.
9
Moghaddam, A., Afsharnia, M., and Peirovi Minaee, R. (2020). Preparing the optimal emergency response protocols by MOPSO for a real-world water distribution network. Environmental Science and Pollution Research, 27(2), 30625–30637.
10
Mousavi, S.J., Anzab, N.R., Asl-Rousta, B., and Kim, J.H. (2017). Multi-Objective Optimization-Simulation for Reliability-Based Inter-Basin Water Allocation. Water resources management, 31(9), 1-20.
11
Nagesh Kumar, D., and Janga Reddy, M. (2007). Multipurpose reservoir operation using particle swarm optimization. Journal of Water Resources Planning and Management, 133(3), 192-201.
12
Neelakantan, T.R. and Pundarikanthan, N. V. (1999). Hedging rule optimization for water supply reservoirs system. Water Resources Management, 13(6), 409–426.
13
Rafiee Anzab, N., Mousavi, S.J., Rousta, B.A., and Kim, J.H. (2016). Simulation optimization for optimal sizing of water transfer systems. In Harmony Search Algorithm (pp. 365-375): Springer.
14
Reddy, M.J., and Kumar, D.N. (2007). Optimal reservoir operation for irrigation of multiple crops using elitist-mutated particle swarm optimization. Hydrological Sciences Journal, 52(4), 686-701.
15
Rezaei, F., Safavi, H.R. and Zekri, M. (2017). A Hybrid Fuzzy-Based Multi-Objective PSO Algorithm for Conjunctive Water Use and Optimal Multi-Crop Pattern Planning. Water resources management, 31, 1139–1155.
16
Rezaei, F. and Safavi, H.R. (2020). f-MOPSO/Div: an improved extreme-point-based multi-objective PSO algorithm applied to a socio-economic-environmental conjunctive water use problem. Environmental Monitoring Assessment. 192(12): 767. DOI: 10.1007/s10661-020-08727-y.
17
Sen, G.D., Sharma, J., Goyal, G.R., and Singh, A.K. (2017). A multi-objective PSO (MOPSO) algorithm for optimal active power dispatch with pollution control. Mathematical Modelling of Engineering Problems, 4(3), 113-119.
18
Shih, J.S., and ReVelle, C. (1994). Water-supply operations during drought: Continuous hedging rule. Water Resource Planning and Management, ASCE, 120(5), 613–629.
19
Taghian, M., Rosbjerg, D., Haghighi, A., and Madsen, H. (2014). Optimization of Conventional Rule Curves Coupled with Hedging Rules for Reservoir Operation. Water Resources Planning and Management, 140(5), 693–698.
20
Tennant, D.L. (1976). Instream flow regimens for fish, wildlife, recreation and related environmental resources. Fisheries, 1(4), 6-10.
21
Xilin, Z., Yuejin, T., and Zhiwei, Y. (2019). Resource allocation optimization of equipment development task based on MOPSO algorithm. Journal of Systems Engineering and Electronics, 30(6), 1132– 1143.
22
Vasan, A. (2013). Optimal Reservoir Operation for Irrigation Planning Using the Swarm Intelligence Algorithm. Metaheuristics in water, Geotechnical and Transport Engineering, 147-165.
23
Zhang, J., Wu, Z., Cheng, C.T., and Zhang, S.Q. (2011). Improved particle swarm optimization algorithm for multi-reservoir system operation. Water Science and Engineering, 4(1), 61-74.
24
ORIGINAL_ARTICLE
Effectiveness of Groundwater Resources Balancing Strategies for Landslide Control (Case Study: Varamin Study Area)
The main purpose of this study is to develop a multi-criteria decision model based on stakeholders in the study area of Varamin plain with the approach of aquifer subsidence control. One of the important tools for developing a decision model for land subsidence control is to use numerical models and evaluate different scenarios in these models. Due to the relationship and sensitivity of groundwater abstraction with subsidence, use of MODFLOW model to quantitatively simulate the aquifer and then use of SUB software package to simulate the amount of subsidence can determine this relationship well. Quantitative analysis and simulation of the subsidence model showed that the condition of the aquifer is critical and the rate of aquifer drop in a period of 5 years is more than 6 meters and subsequently the subsidence in the central parts of the aquifer will reach 37 cm. Accordingly, the effectiveness of these strategies was studied by considering 8 scenario strategies that are a combination of reducing the withdrawal of groundwater resources and artificial feeding of the aquifer. The results of weighting the criteria showed that the environmental criterion, which is related to the land subsidence adjustment index, has the highest weight with value of 0.27 and was introduced as the most important criterion in decision making. After evaluating the results and priorities of the solutions by COPRAS method, it was found that the A8 scenario is introduced as the first priority of aquifer treatment. The results also showed that by applying this scenario, the amount of subsidence will be reduced and the maximum amount of subsidence will be 23.5 cm in the central part of the aquifer. Finally, the quantitative status of the aquifer also improved by 76% compared to the forecast period (2024).
https://ijswr.ut.ac.ir/article_84030_5327ebf2dc12b1b57d86bd7b75b028a3.pdf
2021-09-23
1735
1751
10.22059/ijswr.2021.314786.668824
Groundwater balancing
Multi-Criteria Decision Making
subsidence
Varamin plain
Mojtaba
Zangeneh
zangeneh1363@gmail.com
1
Ph.D. Student of Water Resources, Department of Water Engineering and Sciences, Science and Research Branch, Islamic Azad University, Tehran, Iran.
AUTHOR
Mahdi
Sarai Tabrizi
mahdisarai@yahoo.com
2
Assistant Professor, Department of Water Engineering and Sciences, Science and Research Branch, Islamic Azad University, Tehran, Iran.
LEAD_AUTHOR
Amir
Khosrojerdi
khosrojerdi@srbiau.ac.ir
3
Assistant Professor, Department of Water Engineering and Sciences, Science and Research Branch, Islamic Azad University, Tehran, Iran.
AUTHOR
Ali
Saremi
saremi.ptmco@gmail.com
4
Assistant Professor, Department of Water Engineering and Sciences, Science and Research Branch, Islamic Azad University, Tehran, Iran.
AUTHOR
Al Heib, M., Duval, C., Theoleyre, F., Watelet, J.-M., Gombert, P., 2015. Analysis of the historical collapse of an abandoned underground chalk mine in 1961 in Clamart (Paris, France). Bull. Eng. Geol. Environ. 74 (3), 1001–1018.
1
Nieuwenhuis, H., Schokking, F., 1997. Land subsidence in drained peat areas of the Province of Friesland, The Netherlands. Q. J. Eng. Geol. Hydrogeol. 30 (1), 37–48.
2
Strzalkowski, P., Tomiczek, K., 2015. Analytical and numerical method assessing the risk of sinkholes formation in mining areas. Int. J. Min. Sci. Technol. 25 (1), 85–89.
3
Choi, J.-K., Kim, K.-D., Lee, S., Won, J.-S., 2010. Application of a fuzzy operator to susceptibility estimations of coal mine subsidence in Taebaek City, Korea. Environ. Earth Sci. 59 (5), 1009–1022.
4
Deverel, S.J., Rojstaczer, S., 1996. Subsidence of agricultural lands in the Sacramento-San Joaquin Delta, California: role of aqueous and gaseous carbon fluxes. Water Resource. Res. 32 (8), 2359–2367.
5
Ghenai, C., Albawab, M., & Bettayeb, M. (2020). Sustainability indicators for renewable energy systems using multi-criteria decision-making model and extended SWARA/ARAS hybrid method. Renewable Energy, 146, 580-597.
6
Kaklauskas, A., Zavadskas, E., & Ditkevicius, R. (2006). An intelligent tutoring system for construction and real estate management master degree studies. Cooperative Design, Visualization, and Engineering, 12, 174–181.
7
Keršuliene, V., Zavadskas, E. K., & Turskis, Z. (2010). Selection of rational dispute resolution method by applying new step‐wise weight assessment ratio analysis (SWARA). Journal of business economics and management, 11(2), 243-258.
8
Khalifi, P., Nadiri, A. A., Novinpour, E. A., & Gharekhani, M. (2019). Estimation of Subsidence Potential Index Using the PCSM Method and Fuzzy Model in Ardabil Plain Aquifer. Iran-Watershed Management Science & Engineering, 13(45), 44-53.
9
Ministry of Energy. (2017). Water resources balance update studies in the study area of the Namak Lake basin. Department of basic studies of water resources management company, balance report no. 4134, 69 pp.
10
Moghaddam, H.K., Moghaddam, H.K., Kivi, Z.R., Bahreinimotlagh, M. and Alizadeh, M.J., 2019. Developing comparative mathematic models, BN and ANN for forecasting of groundwater levels. Groundwater for Sustainable Development, 9, 100-113.
11
Naderi, K., Nadiri, A. A., Asghari Moghaddam, A., & Kord, M. (2018). A new approach to determine probable land subsidence areas (Case study: The Salmas plain aquifer). Iranian Journal of ecohydrology, 5(1), 85-97.
12
Nadiri, A. A., Taheri, Z., Barzegari, Gh., & Dideban, Kh. (2018). A framework to estimation of aquifer subsidence potential using genetic algorithm. Iran-Water Resources Research, 14(2), 174-185.
13
Noorbeh, P., Roozbahani, A. and Moghaddam, H.K. (2020). Annual and Monthly Dam Inflow Prediction Using Bayesian Networks. Water Resources Management, pp.1-19.
14
Sadeghfam, S., Khatibi, R., Dadashi, S., & Nadiri, A. A. (2020). Transforming subsidence vulnerability indexing based on ALPRIFT into risk indexing using a new fuzzy-catastrophe scheme. Environmental Impact Assessment Review, 82, 106-122.
15
Tafreshi, G.M., Nakhaei, M. and Lak, R., 2019. Land subsidence risk assessment using GIS fuzzy logic spatial modeling in Varamin aquifer, Iran. Geo Journal, 11, 1-21.
16
Zavadskas, E. K., & Kaklauskas, A. (1996). Determination of an efficient contractor by using the new method of multicriteria assessment. In International Symposium for The Organization and Management of Construction. Shaping Theory and Practice 2, 94-104.
17
Zavadskas, E. K., Kaklauskas, A., & Vilutiene, T. (2009). Multicriteria evaluation of apartment blocks maintenance contractors: Lithuanian case study. International Journal of Strategic Property Management, 13(4), 319-338.
18
Zavadskas, E. K., Stević, Ž., Tanackov, I., & Prentkovskis, O. (2018). A novel multicriteria approach–rough step-wise weight assessment ratio analysis method (R-SWARA) and its application in logistics. Studies in Informatics and Control, 27(1), 97-106.
19
ORIGINAL_ARTICLE
Estimation of Field Capacity and Permanent Wilting Point of Plant Using Double-Rings Data and Inverse Numerical Solution in Different Soil Textures
In this study, HYDRUS-2D/3D software was used to estimate the field capacity (FC) and permanent wilting point (PWP) using double-rings infiltration data via inverse solution. For this purpose, the double rings infiltration data obtained from 95 points of different regions in Isfahan were used as model input. The studied soils were classified into seven textural classes including Sandy Loam (SL), Clay (C), Loam (L), Silty Loam (SiL), Clay Loam (CL), Silty Clay Loam (SiCL), and Silty Clay (SiC). For most soil samples, the simulated values of FC and PWP were less than the measured values. The results showed that the lowest error value in estimating FC was related to SL texture (R2 = 0.884 and RMSE = 0.021) and the highest error value for FC estimation was related to Clay texture (R2 = 0.1 and RMSE = 0.122). Furthermore, the lowest and the highest error values for PWP estimation were observed in Loam (R2 = 0.858 and RMSE = 0.003) and Clay (R2 = 0.21 and RMSE = 0.025) soils, respectively. In general, the simulation error increased with increasing clay content in the soil. The estimated PWP values were relatively more consistent than the estimated FC values with their measured values, in all soil samples. Coefficients of determination (R2) were 0.77 and 0.80 for FC and PWP in all soils, respectively. In general, the inverse numerical solution method had acceptable accuracy for estimating FC and PWP, especially in light textured soils.
https://ijswr.ut.ac.ir/article_84031_65218617534a96268c5a4bad2f3393cd.pdf
2021-09-23
1753
1763
10.22059/ijswr.2021.318649.668888
Double Rings
HYDRUS-2D/3D
Plant available water
simulation
parisa
MASHAYEKHI
mashayekhi_enj@yahoo.com
1
Soil and Water Research Department, Isfahan Agricultural and Natural Resources Research and Education Center. Agricultural Research, Education and Extension organization (AREEO), Isfahan, Iran.
LEAD_AUTHOR
Bahrami, A., Aghamir, F., Bahrami, M. and Khodaverdiloo, H. (2020). Inverse modeling towards parameter estimation of the nonlinear soil hydraulic functions using developed multistep outflow procedure. Journal of Hydrology, 590, 125446.
1
Blake, G.R. and Hartge, K.H. (1986). Bulk density. In: Methods of soil analysis. Part 1, 2nd edn (ed. A. Klute),. Agronomy Monographs. 9. ASA, Madison, WI. pp. 363–375
2
Dobarco, R.M., Cousin, I., Le Bas, C. and Martin, M. (2019). Pedotransfer functions for predicting available water capacity in French soils, their applicability domain and associated uncertainty. Geoderma, 336, 81–95.
3
Gribb, M. M., Forkutsa, I., Hansen, A., Chandler, D. G. and McNamara, J. P. (2009). The Effect of Various Soil Hydraulic Property Esti mates on Soil Moisture Simulations. Vadose Zone Journal, 8, 321–331. doi:10.2136/vzj2008.0088
4
Huang, J., Wu, P. and Xining, Z. (2013). Effects of rainfall intensity, underlying surface and slope gradient on soil infiltration under simulated rainfall experiments. Catena, 104, 93-102.
5
Kirkham, J.M., Smith, C., Doyle, R.B. and Brown. P.B. (2019). Inverse modelling for predicting both water and nitrate movement in a structured-clay soil (Red Ferrosol). Peer Journal, 6, e6002 https://doi.org/10.7717/peerj.6002
6
Klute, A. (1986). Methods of Soil Analysis. Part 1- Physical and Mineralogical Methods. 2nd ed., Agronomy No. 9. ASA/SSSA Inc., Madison, Wisconsin, USA.
7
Lai, J. and Ren, L. (2016). Estimation of effective hydraulic parameters in heterogeneous soils at field scale, Geoderma, 264, 28–41
8
Maqsoud A, Bussière B, Mbonimpa M and Aubertin M. )2004(. Hysteresis effects on the water retention curve: A comparison between laboratory results and predictive models. Pp. 8-15. Proceedings of the 57th geotechincal conference, Canada.
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Mashayekhi, P., Ghorbani Dashtaki, S., Mosaddeghi, M.R., Shirani, H. and Mohammadi Nodoushan, A. R. (2016). Different scenarios for inverse estimation of soil hydraulic parameters from double ring infiltrometer data using HYDRUS 2D/3D. International Agrophysics, 30(2), 203-210.
10
Mashayekhi P., Ghorbani Dashtaki S., Mosaddeghi M.R., Shirani H. and Nouri M.R. (2017). Estimation of soil hydraulic parameters using double-ring infiltrometer data via inverse method. Iranian Journal of Water and Soil Research, 47(4), 829-838. (In Persian)
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Minasny, B. and McBratney, A.B. (2002). The Neuro-m method for fitting neural network parametric pedotransfer functions. Soil Science Society of America Journal, 66,352– 361.
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Mirzaee, S., Zolfaghari, A. A, Gorjib, M Miles Dyckc, M., and Ghorbani Dashtakia, S. (2013). Evaluation of infiltration models with different numbers of fitting parameters in different soil texture classes Archives of Agronomy and Soil Science, http://dx.doi.org/10.1080/03650340.2013.823477
13
Mousavi Dehmurdi, A., Ghorbani Dashtaki, Sh. And Mashayekhi, P. (2018). Evaluation of double-ring infiltrometers method for measuring the vertical infiltration in different soil textures using HYDRUS. Journal of Water and Soil Conservation, 25(3), 241-253. (In Farsi).
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Mualem, Y. (1976). A new model for predicting the hydraulic conductivity of unsaturated porous media. Water Resources Research, 12(3), 513–522.
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Novák, V. and Havrila, J. (2006). Method to estimate the critical soil water content of limited availability for plants. Biologia Journal, 61(19), 289-293.
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Qiao, J., Zhu, Y., Jia, X., Huang, L. and Shao, M. (2018). Pedotransfer functions for estimating the field capacity and permanent wilting point in the critical zone of the Loess Plateau, China. Journal of Soils and Sediments. https://doi.org/10.1007/s11368-018-2036-x.
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Raoof, M. and Pilpayeh, A. R. (2013). Estimating soil wetting profile under saturated infiltration process by numerical inversion solution in land slopes. Middle-East Journal of Scientific Research, 13(6), 732–736.
18
Rucker, D.F. (2010). Inverse upscaling of hydraulic parameters during constant flux infiltration using borehole radar. Advances in Water Resources. http: //dx.doi.org/10.1016/j. advwatres. 2010.11.001.
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Toluee, R., Neyshabouri, M.R. and Rasoulzadeh, A. (2014). Estimating Parametrs of Brooks-Corey Soil Water Retention Curve for Drying and Wetting Branches by Pedotransfer Functions. Water and soil science, 25(3), 195-210. (In Persian).
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Tomasella, J., Pachepsky, Y.A., Crestana, S. and Rawls, W.J. )2003(. Comparison of two techniques to develop pedotransfer functions for water retention. Soil Science Society of America Journal, 67, 1085-1092.
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Silva, B.M., Silva, É.A., Oliveira, G.C., Ferreira, M.M. and Serafim, M.E. (2014). Plant-available soil water capacity: estimation methods and implications. Revista Brasileira de Ciência do Solo, 38, 464–475.
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Šimůnek, J., Šejna, M. and van Genuchten, M. Th. (2012). HYDRUS: model use, calibration and validation. American Society of Agricultural and Biological Engineers, 55(4), 1261-1274.
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Van Genuchten M. Th. 1980. A closed–form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Science Society of America Journal, 44(5), 892–898.
27
Vereecken, H., Weynants, M., Javaux, M., Pachepsky, Y., Schaap, M.G. and van Genuchten, M.Th. (2010). Using pedotransfer functions to estimate the van Genuchten–Mualem soil hydraulic properti es: A review. Vadose Zone Journal, 9, 795–820. doi:10.2136/vzj2010.0045
28
ORIGINAL_ARTICLE
Assessment of Heavy Metals Contamination of Soil Particle Size Fractions in Different Land Uses of Baghan Watershed, Bushehr province, Iran
Watersheds are the sources of drinking water for cities and villages, and their pollution with heavy elements threatens the health of the inhabitants who use their water and agricultural products. Considering the importance of Baghan watershed in Bushehr province, the purpose of this study was to evaluate the contamination of soil particle size fractions (<63 and <2000 µm) by some heavy metals (Cd, Mn, Ni, Pb, Zn, Cu, and Fe) in three major land uses (range lands, croplands and orchards). Location of 120 surficial composite soil samples (0-20 cm) were determined using the Latin Hypercube technique on the topographical map. After pretreatment of soil samples, heavy metals were extracted by the Sposito method and measured using an atomic absorption spectrometry and geochemical pollution indicators including contamination factor (CF), geo-accumulation index (Igeo) and the pollution load index (PLI) were calculated. A significant increase in the concentration of Cu, Cd, and Fe has been observed by decreasing the particle size in different land uses. The contamination factor (CF) for particle sizes <2000 and <63 were ordered as Cd>Mn>Pb>Ni>Cu>Zn>Fe and Cd>Mn>Cu>Ni>Pb>Zn>Fe, respectively. The CF index indicates that the orchard soils for Cd were considerably polluted and for other metals moderatly polluted. Positive and significant amount of geo-accumulation index (Igeo) for Cd and Mn was observed for both soil particle classes in all land uses. Overall, the results of this study confirmed concentration of some heavy metals in smaller particles size. Comparing contaminants concentration of Cd and Mn in croplands and orchards soils with the range lands soils indicated anthropogenic effects on soil pollution. The results revealed risk of heavy metals in the watershed and necessity of reconsidering management policies.
https://ijswr.ut.ac.ir/article_84032_3a1cfdb48ea34fea37d4bbee11060c0a.pdf
2021-09-23
1765
1778
10.22059/ijswr.2021.319317.668900
Soil contamination
Bagan watershed
Heavy metals
Contamination Factor
Geo-accumulation index
Somayeh
dehghani
somayehdehghany@yahoo.com
1
Soil Science Department, Faculty of Agriculture, University of Shahrekord,shahrekord, Iran
LEAD_AUTHOR
mehdi
naderi
khnaderi@yahoo.com
2
Soil Science Department, Faculty of Agriculture, University of Shahrekord,shahrekor, Iran
AUTHOR
jahangard
mohammadi
jahan.mohammad@ymail.com
3
Soil Science Department, Faculty of Agriculture, University of Shahrekord,shahrekord, Iran
AUTHOR
Ahmad
Karimi
karimiahmad1342@yahoo.com
4
Soil Science Department, Faculty of Agriculture, University of Shahrekord,shahrekord, Iran
AUTHOR
Acosta, J. A., Cano, A. F., Arocena, J. M., Debela, F., & Martínez-Martínez, S. (2009). Distribution of metals in soil particle size fractions and its implication to risk assessment of playgrounds in Murcia City (Spain). Geoderma, 149(1-2), 101-109.
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51
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52
ORIGINAL_ARTICLE
Estimation of Recharge and Flow Exchange between River and Aquifer Based on Coupled Surface Water-Groundwater Model
Integrated operation of surface water and groundwater resources is one of the most important challenges facing water resources researchers. Integrated use is, in fact, the exploitation of surface and groundwater resources in order to increase the amount of available water and the sustainable use of available water resources. Therefore, one of the main goals of the present study is to simulate the interaction of surface water and groundwater by creating a dynamic couple between the WEAP surface water model and the MODFLOW groundwater model in the Miandarband plain. In this regard, the Soil Moisture Hydrological method was used to simulate the unsaturated zone of the soil. The results of simulation of surface and groundwater interaction were presented and the conditions for the use of water resources in the area was investigated for the continuous current policy. One of the most important achievements of this research is the simulation of saturated and unsaturated zones of the soil using complete hydroclimatology balance components as a coupled model of surface and groundwater. In the period of 6 years, the highest amount of aquifer recharge in the Miandarband plain, is about 10 to 19 million cubic meters in November to March. In some of these months, in addition to rainfall, the aquifer recharge is due to the infiltration of irrigation water. The highest rate of groundwater drowdown (7.5 meters) is related to the northern part of the plain and the average drowdown in the whole plain at the end of the 6-year operation period (October 2007 to September 2013) will be about 4 meters.
https://ijswr.ut.ac.ir/article_84033_8b9cf19849cc57bf9ae13819b83a7a76.pdf
2021-09-23
1779
1793
10.22059/ijswr.2021.318357.668883
River and aquifer interaction
Soil unsaturation zone
Recharge rate
WEAP-MODFLOW
Vida
Kamkar
vida.kamkar@yahoo.com
1
Department of Water Engineering, Razi University, Kermanshah, Iran
AUTHOR
Arash
Azari
a.azari@razi.ac.ir
2
Associate Professor, Department of Water Engineering, Razi University, Kermanshah, Iran
LEAD_AUTHOR
Seyed Ehsan
Fatemi
se.fatemi@razi.ac.ir
3
Assistant Professor, Department of Water Engineering, Razi University, Kermanshah, Iran
AUTHOR
Bayesteh, M and Azari, A. (2021). Stochastic Optimization of Reservoir Operation by Applying Hedging Rules. J. Water Resour. Plann. Manage., 147(2), 04020099.
1
Bear, J. (2010). Modeling Groundwater Flow and Contaminant Transport. Springer Verlag. Vol. 23. 834 P.
2
Brenot, A., Petelet-Giraud, E. and Gourcy, L. (2015). Insight from surface water-groundwater interactions in an alluvial aquifer: contributions of δ2H and δ18O of water, δ34SSO4 and δ18OSO4 of sulfates, 87Sr/86Sr ratio. Procedia Earth and Planetary Science, 13, 84 – 87.
3
Eastoe, C. J., Hutchison, W. R., Hibbs, B. J., Hawley, J. and Hogan, J. F. (2010). Interaction of a river with an alluvial basin aquifer: Stable isotopes, salinity and water budgets. Journal of Hydrology, 395, 67–78.
4
Engeler, I ., Hendricks Franssen H. J., Müller, R. and Stauffe, F. (2011). The importance of coupled modelling of variably saturated groundwater flow-heat transport for assessing river–aquifer interactions. Journal of Hydrology, 397, 295-305.
5
Fleckenstein, J. H., Krause, S., Hannah, D. M. and Boano, F. (2010). Groundwater-surface water interactions-New methods and models to improunderstanding of processes and dynamics. Journal of Advances in Water Resources, 33, 1291-1295.
6
Gorelick, S. M. (1983). A review of distributed parameter groundwater management modelling methods. Water Resources Research, 19 (2), 305-319.
7
Graham, P. W., Andersen, M. S., McCabe, M. F., Ajami, H., Baker, A. and Acworth, I. (2015). To what extent do long-duration high-volume dam releases influence river–aquifer interactions? A case study in New South Wales, Australia. Hydrogeology Journal, 23, 319–334.
8
Guzman, S. M., Paz, J. O., Tagert, M. L. M. and Mercer, A. E. (2019). Evaluation of Seasonally Classified Inputs for the Prediction of Daily Groundwater Levels: NARX Networks Vs Support Vector Machines. Environmental Modeling & Assessment, 24(2), 223-234.
9
Hu, L., Xu, Z. and Huang, W. (2016). Development of a river-groundwater interaction model and its application to a catchment in Northwestern China. Journal of Hydrology, 543, 483–500.
10
Ivkovic , K. M. (2009). A top–down approach to characterise aquifer–river interaction processes. Journal of Hydrology, 365, 145–155.
11
Jonoubi, R., Rezaei, H. and Bahmanesh, J. (2013). Underground water management through combining surface and sub-surface water using Modflow model in urmia plain. Journal of water and irrigation management, 3 (1), 49-68. (In Farsi)
12
Luo,Y. and Sophocleous, M. (2011). Tow-way coupling of unsaturated-saturated flow by integrating the SWAT and MODFLOW models with application in an irrigation district in arid region of West China. Journal of Arid Land, 3(3), http://doi.org/ 10.3724/SP.J.1227.2011.00164.
13
Nadiri, A. A., Naderi, K., Khatibi, R., and Gharekhani, M. (2019). Modelling groundwater level variations by learning from multiple models using fuzzy logic. Hydrological sciences journal, 64(2), 210-226.
14
Nazri, A. A. M., Syafalni., Abustan I., Rahman, M T A., Zawawi M H. and Dor N. (2012). Authentication Relation between Surface-Groundwater in Kerian Irrigation Canal System, Perak using Integrated Geophysical, Water Balance and Isotope Method. Procedia Engineering, 50, 284 – 296.
15
Pahar, G. and Dhar, A. (2014). A Dry Zone-Wet Zone Based Modeling of Surface Water and Groundwater Interaction for Generalized Ground Profile. Journal of Hydrology, 519(27), 2215-2223.
16
Ramírez-Hernández, J., Hinojosa-Huerta, O., Peregrina-Llanes, M., Calvo-Fonseca, A. and Carrera-Villa, E. (2013). Groundwater responses to controlled water releases in the limitrophe region of the Colorado River: Implications for management and restoration. Journal of Ecological Engineering, 59, 93-103.
17
Rugel, K., Golladay, S. W., Jackson, S. R. and Rasmussen, T. C. (2016). Delineating groundwater/surface water interaction in a karstwatershed: Lower Flint River Basin, southwestern Georgia, USA. Journal of Hydrology: Regional Studies, 5, 1–19.
18
Sanz, D., Castaño, S., Cassiraga, E., Sahuquillo, A., José Gómez-Alday, J., Peña, S. and Calera, A. (2011). Modeling aquifer–river interactions under the influence of groundwater abstraction in the Mancha Oriental System (SE Spain). Hydrogeology Journal, 19, 475–487.
19
Sieber, J. and Purkey, D. (2015) User guide for WEAP. Stockholm Environment Institute, U.S. Center.
20
Sophocleous. M. )2002(. Interaction between Ground Water and Surface Water: The State of the Science, Hydrogeology Journal, 10, 52-67.
21
Shamsaei., A., and Forghani, A. (2011). Integrated exploitation of surface water and groundwater resources in arid areas. Iranian Water Resources Research, 7(2), 26- 36. (In Farsi)
22
Weitz, J. and Demlie, M. (2013). Conceptual modelling of groundwater–surface water interactions in the Lake Sibayi Catchment, Eastern South Africa. Journal of African Earth Sciences, 99(2), 613-624.
23
Zampieri, M., Serpetzoglou, E., Anagnostou, E. N., Nikolopoulos. E. I. and Papadopoulos, A. (2012). Improving the representation of river–groundwater interactions in land surface modeling at the regional scale: Observational evidence and parameterization applied in the Community Land Model. Journal of Hydrology, 420(421), 72–86.
24
Zeinali, M., Azari, A. and Heidari, M. (2020a). Simulating Unsaturated Zone of Soil for Estimating the Recharge Rate and Flow Exchange Between a River and an Aquifer. Water Resources Management, 34, 425–443.
25
Zeinali, M., Azari, A. and Heidari, M. (2020b). Multiobjective Optimization for Water Resource Management in Low-Flow Areas Based on a Coupled Surface Water–Groundwater Model. Journal of Water Resource Planning and Management (ASCE), 146(5), 04020020.
26
Zibaei, M. H., Zibaei, M. and Ardokhani, K. (2013). Evaluation of scenarios of integrated use of surface and groundwater resources in Firoozabad plain of Fars. Journal of Agricultural Economics Research, 5(1), 157-181.
27
ORIGINAL_ARTICLE
Simulation of Scour Depth Around Twin and Three Piers Using Group Method of Data Handling
Estimation and computation of scouring around structures such as piers has a significant importance. In this study, scour depth in the vicinity of twin and three piers was simulated using Group Method of Data Handling (GMDH). First, effective parameters on scour depth were identified and then four different GMDH models were defined. To verify the simulation results, some experimental measurements were applied and 70% of these data were utilized to train the GMDH models, whereas 30% of the data were employed to test the models. Subsequently, the best GMDH model and the most influencing input parameters were introduced by conducting a sensitivity analysis. The sensitivity analysis showed that the GMDH models estimated the scour depth with acceptable accuracy. For instance, the correlation coefficient (R), scatter index (SI), and variance accounted for (VAF) for the best GMDH model were respectively calculated to be 0.949, 0.212, and 90.129. In addition, the Froude number was detected as the most important input variable to estimate the scour depth through GMDH model. Moreover, the mean discrepancy ratio (DRave) for the superior GMDH model was computed to be 1.228. For different GMDH models, four equations were presented and lastly a computer code was provided to simulate scour depth by means of the GMDH model.
https://ijswr.ut.ac.ir/article_84034_14db8216695164b9c5193d2bd3cb74db.pdf
2021-09-23
1795
1805
10.22059/ijswr.2021.320363.668911
Twin and three piers
Scouring
Group method of data handling
Sensitivity analysis
simulation
ehsan
moradi
ehsan.moradi@gmail.com
1
Ph.D. Candidate, Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran
AUTHOR
saeid
shabanlou
saeid.shabanlou@gmail.com
2
Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran
LEAD_AUTHOR
behrouz
yaghoubi
behrouz.yaghoubi.h@gmail.com
3
Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran
AUTHOR
Anastasakis, L. and Mort, N. (2001). The development of self-organization techniques in modelling: a review of the group method of data handling (GMDH). Research Report-University of Sheffield.
1
Atashkari, K, Nariman-Zadeh, N, Gölcü, M, Khalkhali, A. and Jamali, A. (2007). Modelling and multi-objective optimization of a variable valve-timing spark-ignition engine using polynomial neural networks and evolutionary algorithms. Energy Conversion and Management, 48(3), 1029-1041.
2
Ataie-Ashtiani, B., Baratian-Ghorghi, Z., and Beheshti, A.A. (2010). Experimental investigation of clear-water local scour of compound piers. Journal of Hydraulic Engineering, 136(6), 343-351.
3
Azamathulla, H.M. (2012). Gene-expression programming to predict scour at a bridge abutment. Journal of Hydroinformatics, 14(2), 324-331.
4
Azimi, H., Bonakdari, H., Ebtehaj, I., Gharabaghi, B., and Khoshbin, F. (2018). Evolutionary design of generalized group method of data handling-type neural network for estimating the hydraulic jump roller length. Acta Mechanica, 229(3), 1197-1214.
5
Azimi, H., Bonakdari, H., Ebtehaj, I., Talesh, S. H. A., Michelson, D. G., and Jamali, A. (2017). Evolutionary Pareto optimization of an ANFIS network for modeling scour at pile groups in clear water condition. Fuzzy Sets and Systems, 319, 50-69.
6
Azimi, H., Bonakdari, H., Ebtehaj, I., Shabanlou, S., Talesh, S. H. A., and Jamali, A. (2019). A pareto design of evolutionary hybrid optimization of ANFIS model in prediction abutment scour depth. Sādhanā, 44(7), 169.
7
Bateni, S. M., and Jeng, D. S. (2007). Estimation of pile group scour using adaptive neuro-fuzzy approach. Ocean Engineering, 34(8), 1344-1354.
8
Firat, M., and Gungor, M. (2009). Generalized regression neural networks and feed forward neural networks for prediction of scour depth around bridge piers. Advances in Engineering Software, 40(8), 731-737.
9
Liriano, S. L., and Day, R. A. (2001). Prediction of scour depth at culvert outlets using neural networks. Journal of Hydroinformatics, 3(4), 231-238.
10
Noori, R., Hoshyaripour, Gh., Ashrafi, Kh., and Nadjar Araabi B. (2010). Uncertainty analysis of developed ANN and ANFIS models in prediction of carbon monoxide daily concentration. Atmospheric Environment, 44(4), 476-482.
11
Shamshirband, S., Mosavi, A., and Rabczuk, T. (2020). Particle swarm optimization model to predict scour depth around a bridge pier. Frontiers of Structural and Civil Engineering, 14(4), 855-866.
12
Sharafi, H., Ebtehaj, I., Bonakdari, H., and Zaji, A. H. (2016). Design of a support vector machine with different kernel functions to predict scour depth around bridge piers. Natural Hazards, 84(3), 2145-2162.
13
Trent, R., Gagarin, N., and Rhodes, J. (1993). Estimating pier scour with artificial neural networks. In Hydraulic Engineering (pp. 1043-1048). ASCE.
14
Wang, H., Tang, H.W., Xiao, J.F., Wang, Y., and Jiang, S. (2016a). Clear-water local scouring around three piers in a tandem arrangement. Science China Technological Sciences, 59(6), 888–896.
15
Wang, H., Tang, H., Liu, Q., and Wang, Y. (2016b). Local scouring around twin bridge piers in open-channel flows. Journal of Hydraulic Engineering, 142(9), 060160081-8.
16
ORIGINAL_ARTICLE
Investigation of the Relationship between Natural Hydrophobicity and Physicochemical Properties of Soil in Different Land Uses in the Coastal Areas of West Guilan
Soil water repellency is a dynamic property that delays the infiltration of water into the soil and increases the potential for runoff and erosion. Accurate knowledge about the existence and severity of soil water repellency (SWR) in the coastal areas of Guilan, which are under different land uses, is very important. The present study evaluates the effects of land uses on soil water repellency in three areas: 1) Forested area with Pinus Teda, 2) forested area planted with Pinus Teda in some parts and covered with natural wild pomegranate in other parts 3) Agricultural land covered with Diaspyros Kaki. Soil water repellency was measured using three tests of the water drop penetration time (WDPT), the molarity of ethanol droplet (MED) and the soil wetted area (SWA). Significant differences in soil water repellency were found among the different land uses. Forest soils under pinus Teada showed the highest SWR and the soils under wild pomegranate and persimmon cultivated area showed the lowest SWR. Also, the relationship between SWR and soil properties (soil organic matter, pH, total nitrogen, phosphorus, Cation Exchangable Capacity, Electrical Conductivity, sodium, potassium, calcium, magnesium, soil texture, bulk and particle density) was investigated in 200 samples. Principal component analysis (PCA) showed that organic matter, total nitrogen with a positive effect and soil acidity with a negative effect are the most important parameters controlling repellency in these soils. To investigate which component of the soil particles have a more important role in creating water repellency, the intensity of water repellency was examined in six particle sizes of the soil (1-2, 0.5-1, 0.25-0.5, 0.125-0.25, 0.05-0.125 and less than 0.05 mm). The results showed that although coarse-textured soils are more prone to repellency, the smallest particle size in these soils plays a very important role in the intensity of soil water repellency.
https://ijswr.ut.ac.ir/article_84035_997a15ae7199c9b15830e8ddfb855c72.pdf
2021-09-23
1807
1823
10.22059/ijswr.2021.323653.668972
Soil water repellency
Water Drop Penetration Time
Molarity of Ethanol
soil wetted area
Seyedeh Mehrnoosh
Mirbabaei
mehrnooshmirbabaei@yahoo.com
1
pH.D. Student, Department of Soil Science, University of Guilan, 41635-1314, Rasht, Iran
AUTHOR
Mahmoud
Shabanpour
shabanpour@guilan.ac.ir
2
Department of Soil Science, University of Guilan, 41635-1314, Rasht, Iran
LEAD_AUTHOR
Mohammadreza
Khaledian
khaledian@guilan.ac.ir
3
Department of Water Engineering, University of Guilan, 41635-1314, Rasht, Iran
AUTHOR
Aliasghar
Zolfaghari
azolfaghari@semnan.ac.ir
4
Department of Desert Science, University of Semnan, Semnan, Iran
AUTHOR
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62
ORIGINAL_ARTICLE
Investigation of Land Surface Temperature Trends Relative to Land Use Changes in Dust Sources of South East Ahwaz Using Landsat 8 Satellite Data
Dust storms are known as one of the most important environmental hazards that affects various parts of the world. Following the intensification of dust storms in Khuzestan province, the internal sources of dust storms in Khuzestan province have been introduced in form of seven areas that southeast of Ahwaz was identified as the No.4 internal dust sources with the first priority of control and rehabilitation practices and the necessary executive measures for land reclamation in this region, including: management practices, biological operation and water distribution were on the agenda. The aim of this study was to investigate the land surface temperature (LST) changes and its relationship with land use changes as effective factors in creating a dust sources in south east Ahwaz. For this purpose, the Landsat 8 satellite data during the (2016-2020) were used and the land use maps of the study area were extracted using support vector machine (SVM) method and Split-Window method was used to extract the land surface temperature (LST) of the study area. The results showed that the area of barren land has been increased from 98.97% in 2016 to 99.81% in 2017 and has been reduced to 76.68% in 2020. The lowest areas of moderate vegetation, good vegetation and water bodies were corresponded to year 2017 which were equal to 0.05%, 0.01% and 0.03%, respectively. The highest areas of moderate vegetation and good vegetation were corresponded to year 2020 which were equal to 13.29% and 3.26%, respectively. The highest area of water body was corresponded to year 2019 which was equal to 7.73%. The results of mean LST estimation during 2016-2017 period showed 3.85℃ increase (from 32.62℃ to 36.47℃) and during 2017-2020 period showed 10.31℃ decrease, which reached to 26.16 ℃ in 2020. This trend has been affected by the land use changes, improved rainfall and the positive effects of modified measures taken to restore the vegetation of the study area.
https://ijswr.ut.ac.ir/article_84036_d67ccb2aa107f214b2e293d9a010b16d.pdf
2021-09-23
1825
1840
10.22059/ijswr.2021.324040.668978
Khuzestan Province
Spatial‑Temporal Detection
Split Window
vegetation
remote sensing
mohammadreza
ansari
ansari386@yahoo.com
1
Department of Soil Sciences, Faculty of Agriculture, University of Khuzestan Agricultural Sciences and Natural Resources, Mollasani, Iran.
LEAD_AUTHOR
Azin
Norouzi
norouzi.azin@gmail.com
2
Department of Soil Sciences, Faculty of Agriculture, University of Khuzestan Agricultural Sciences and Natural Resources, Mollasani, Iran.
AUTHOR
Abdul Athick, A. S. M., Shankar, K., & Raja Naqvi, H. (2019). Data on time series analysis of land surface temperature variation in response to vegetation indices in Twelve Wereda of Ethiopia using mono window, split window algorithm and spectral radiance model. Journal of Data in Brief, 27, 1-12.
1
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2
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3
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4
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33
Qin, Z., and Karnieli, A. (2001). A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region, International Journal of Remote Sensing, 22(18), 3719–3746.
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Rahdari, V., Soffianian, A., Khajaldin., S. J., & Maleki Najafabadi, S. (2014). Identification of Satellite image ability for vegetation cover crown percentage mapping in arid and semi arid region (case study: Mouteh wild life sanctuary). Journal of environmental Science and Technology, 4(4), 43-54. (In Farsi)
35
Rajeshwari, A., & Mani N. D. (2014). Estimation of land surface temperature of Dindigul district using landsat 8 data. International Journal of Research in Engineering and Technology, 3(5), 122-126.
36
Ranjbar, A., Valia, A., Mokarramb, M., & Taripanahc. (2020). Analyzing of the spatio-temporal changes of vegetation and its response to environmental factors in north of Fars province, Iran. Iranian Remote Sensing & GIS, 11(4), 61-82. (In Farsi)
37
Rongali, G., Keshari, A. K., Gosain, A. K., & Khosa R. (2018). Split-Window Algorithm for Retrieval of Land Surface Temperature Using Landsat 8 Thermal Infrared Data. Journal of Geovisualization and Spatial Analysis, 2(14), 1-19.
38
Rusta, Z., Monavvari, S. M., Darvishi, M., & Falahati, F. (2012). Application of remote sensing and geographic information system in extraction of Shiraz land use maps. Town and Country Planning, 4(6), 149-164. (In Farsi)
39
Sekertekin, A., & Zadbagher, E. (2021). Simulation of future land surface temperature distribution and evaluating surface urban heat island based on impervious surface area. Ecological Indicators, 122, 1-11.
40
Thakur, P.K., & Gosavi V. E. (2018). Estimation of Temporal Land surface temperature using thermal remote sensing of landsat-8 (oli) and landsat-7 (etm+): a study in Sainj river basin, himachal pradesh, India. Society for Environment and Development, 13, 29-45.
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Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8, 127–150.
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43
Vali, A., Ranjbar, A., Mokarram, M., & Taripanah, F. (2019). An investigation of the relationship between land surface temperatures, geographical and environmental characteristics, and biophysical indices from Landsat images. Journal of RS & GIS for Natural Resources, 10(3), 35-58. (In Farsi)
44
Wang, M., He, G., Zhang, Z., Wang, G., Wang, Z., Yin, R., Cui, S., Wu, Z., & Cao, X. (2019). A radiance-based split-window algorithm for land surface temperature retrieval: Theory and application to MODIS data. International Journal of Applied Earth Observation and Geoinformation, 76, 204–217.
45
Wang, R., & Murayama, Y. (2020). Geo-simulation of land use/cover scenarios and impacts on land surface temperature in sapporo, japan. Sustainable Cities and Society, 63, 1-11.
46
Weng, Q., Karimi firozjaei, M., Kiavarz, M., Alavipanah S. K., & Hamzeh, S. (2019). Normalizing land surface temperature for environmental parameters in mountainous and urban areas of a cold semi-arid climate. Science of the Total Environment, 650, 515-529.
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48
ORIGINAL_ARTICLE
Influence of the Application of Zeolite and Nitrogen on Quality, Yield and Yield Components of Potato under Field Condition
For evaluation of the effect of different levels of zeolite and nitrogen on yield and some quality traits of potato under field condition, an experiment was carried out as a split plot based on a randomized complete blocks design in three replications in research fields of University of Kurdistan in 2018. The experimental treatments were clinoptilolite zeolite application at four levels (0, 5, 10 and 15 ton ha-1) as the main plot and nitrogen application at five levels (0, 50, 100 150 and 200 kg N ha-1) as the sub plot. The results indicated that with the application of different levels of zeolite, tuber yield, biological yield, average tuber weight, tuber dry matter, significantly increased, while, tuber NO3- concentration significantly decreased. Also, the results indicated that the nitrogen application led to significant increase in the tuber yield, biological yield, average tuber weight, tuber dry matter and NO3- concentration. The greatest tuber yield was recorded at 200 kg ha-1 N application treatment. The highest tuber yield and harvest index were recorded at 10 ton ha-1 zeolite application treatment. The tuber yield in this treatment was 13.9% higher than the control treatments (no zeolite application). In general, the results of this study demonstrated that the zeolite application at 10 ton ha-1 can be a suitable practice for improving potato yield and quality.
https://ijswr.ut.ac.ir/article_84037_e85347c6c621318c20c99c332a741fb3.pdf
2021-09-23
1841
1852
10.22059/ijswr.2021.323539.668969
potato
Nitrate
soil amendment
Sustainable agriculture
Urea fertilizer
Kohsar
Ahmadi
ahmadikosar1371@gmail.com
1
Graduated MSc Student, Department of Soil Sciences, Faculty of Agriculture, University of Kurdistan, Sanandaj, Iran.
AUTHOR
Mohammad Ali
Mahmoodi
m_mahmoodi81@yahoo.com
2
Department of soil science, Collage of Agriculture, University of Kurdistan, Sanandaj, Iran
LEAD_AUTHOR
Masoud
Davari
m.davari@uok.ac.ir
3
Department of Soil Sciences, Faculty of Agriculture, University of Kurdistan, Sanandaj, Iran
AUTHOR
Farzad
Hosseinpanahi
f.hosseinpanahi@agri.uok.ac.ir
4
Department of Agronomy and Plant Breeding, Faculty of Agriculture, University of Kurdistan, Sanandaj, Iran
AUTHOR
Akbar
Karimi
akbar.karimi84@yahoo.com
5
Department of Agronomy Research, Khuzestan Sugarcane Development and By-products Research and Training Institute, Ahvaz, Iran
AUTHOR
Amini, R., Dabbagh Mohammadi Nasab, A., and Mahdavi, S. (2017). Effect of organic fertilizers in combination with chemical fertilizer on tuber yield and some qualitative characteristics of potato (Solanum tuberosum L.). Journal of Agroecology, 9(3), 734-748.
1
Ashfaque, F., Inam, A., Iqbal, S. and Sahay, S. (2017). Response of silicon on metal accumulation, photosynthetic inhibition and oxidative stress in chromium-induced mustard (Brassica juncea L.). South African Journal of Botany, 111, 153-160.
2
Beig, B., Niazi, M. B. K., Jahan, Z., Hussain, A., Zia, M. H. and Mehran, M. T. (2020). Coating materials for slow release of nitrogen from urea fertilizer: A review. Journal of Plant Nutrition, 43(10), 1510-1533.
3
Bunce, J.A. (2006). How do leaf hydraulics limit stomatal conductance at high water vapour pressure deficits? Plant Cell and Environment, 29, 1644-1650.
4
Carter M.R., and Gregorich E.G. 2008. Soil Sampling and Methods of Analysis (2nd Ed.). CRC Press. Boca Raton, Florida, 1204p.
5
Cataldo, D. A., Maroon, M., Schrader, L. E. and Youngs, V. L. (1975). Rapid colorimetric determination of nitrate in plant tissue by nitration of salicylic acid 1. Communications in Soil Science and Plant Analysis, 6(1): 71-80.
6
Chen, J., Wu, H., Qian, H. and Gao, Y. (2017). Assessing nitrate and fluoride contaminants in drinking water and their health risk of rural residents living in a semiarid region of northwest China. Exposure and Health, 9(3), 183-195.
7
Elrys, A. S., Abdo, A. I. and Desoky, E. S. M. (2018). Potato tubers contamination with nitrate under the influence of nitrogen fertilizers and spray with molybdenum and salicylic acid. Environmental Science and Pollution Research, 25(7), 7076-7089.
8
Elrys, A. S., El-Maati, M. F. A., Abdel-Hamed, E. M. W., Arnaout, S. M., El-Tarabily, K. A. and Desoky, E. S. M. (2021). Mitigate nitrate contamination in potato tubers and increase nitrogen recovery by combining dicyandiamide, moringa oil and zeolite with nitrogen fertilizer. Ecotoxicology and Environmental Safety, 209, 111839.
9
Elrys, A. S., Raza, S., Abdo, A. I., Liu, Z., Chen, Z. and Zhou, J. (2019). Budgeting nitrogen flows and the food nitrogen footprint of Egypt during the past half century: Challenges and opportunities. Environment international, 130, 104895.
10
Gao, X., Li, C., Zhang, M., Wang, R. and Chen, B. (2015). Controlled release urea improved the nitrogen use efficiency, yield and quality of potato (Solanum tuberosum L.) on silt loamy soil. Field crops research, 181, 60-68.
11
Jones, J. B. Jr. 1991. Kjeldahl Method for Nitrogen Determination. Micro-Macro Publishing, Athens, GA
12
Jumadi, O., Hala, Y., Iriany, R. N., Makkulawu, A. T., Baba, J. and Inubushi, K. (2020). Combined effects of nitrification inhibitor and zeolite on greenhouse gas fluxes and corn growth. Environmental Science and Pollution Research, 27(2), 2087-2095.
13
Karami, S., Hadi, H., Tajbaksh, M., and Modarres-Sanavy, S. A. M. (2020). Effect of zeolite on nitrogen use efficiency and physiological and biomass traits of Amaranth (Amaranthus hypochondriacus) under water-deficit stress conditions. Journal of Soil Science and Plant Nutrition, 20(3), 1427-1441.
14
Latifah, O., Ahmed, O. H. and Majid, N. M. A. (2017). Enhancing nitrogen availability from urea using clinoptilolite zeolite. Geoderma, 306, 152-159.
15
Madani, H., Farhadi, A., Pazoki, A. and Changizi, M. (2009). Effects of different levels of nitrogen and zeolite on traits qualitative and quantitative of potato in Arak region. New Finding in Agriculture, 3(4), 379-391. (In Farsi)
16
Maghsoodi, M. R., Najafi, N., Reyhanitabar, A., & Oustan, S. (2020). Hydroxyapatite nanorods, hydrochar, biochar, and zeolite for controlled-release urea fertilizers. Geoderma, 379, 114644.
17
Malakouti, M. J. (2011). Relationship between balanced fertilization and healthy agricultural products (A Review). Journal of Crop and Weed Ecophysiology. 4(16), 133-150. (In Farsi)
18
Mihok, F., Macko, J., Oriňak, A., Oriňaková, R., Kovaľ, K., Sisáková, K., Petruš, O. and Kostecká, Z. (2020). Controlled nitrogen release fertilizer based on zeolite clinoptilolite: Study of preparation process and release properties using molecular dynamics. Current Research in Green and Sustainable Chemistry, 3, 100030.
19
Ozbahce, A., Tari, A. F., Gonulal, E. and Simsekli, N. (2018). Zeolite for enhancing yield and quality of potatoes cultivated under water-deficit conditions. Potato Research, 61(3), 247-259.
20
Pati, S., Pal, B., Badole, S., Hazra, G. C. and Mandal, B. (2016). Effect of silicon fertilization on growth, yield, and nutrient uptake of rice. Communications in Soil Science and Plant Analysis, 47(3), 284-290.
21
Petropoulos, S. A., Fernandes, Â., Polyzos, N., Antoniadis, V., Barros, L. and CFR Ferreira, I. (2020). The impact of fertilization regime on the crop performance and chemical composition of potato (Solanum tuberosum L.) cultivated in central Greece. Agronomy, 10(4), 474.
22
Pirzad, A., Yoosefi, M., Darvishzadeh, R. and Raei, Y. (2013). Effect of different rates of zeolite and nitrogen fertilizer on yield and harvest index of flower, grain, essential oil and seed oil of calendula officinalis L. Journal of Agricultural Science and Sustainable Production, 23(2), 61-75.
23
Qi, W., Guimin, Xia., Taotao, C., Daocai, C., Ye, J. and Dehuan, S. (2016). Impacts of nitrogen and zeolite managements on yield and physicochemical properties of rice grain. International Journal of Agricultural and Biological Engineering, 9(5), 93-100.
24
Souza, E. F., Soratto, R. P., Fernandes, A. M. and Rosen, C. J. (2019). Nitrogen source and rate effects on irrigated potato in tropical sandy soils. Agronomy Journal, 111(1), 378-389.
25
WHO. (1978). Nitrates, Nitrites and N-Nitrozo Compounds. Geneva, Environmental Health Criteria 5.
26
Yarmohammadi. V. Sajedi. N.V. and Mirzakhani. M. (2014). The effect of irrigation cycle and application of manure and zeolite on agronomic characteristics and potato yield of Agria cultivar. New Agricultural Findings. 9(2), 158-149. (In Farsi)
27
Yeganeh, M. and Bazargan, K. (2016). Human health risks arising from nitrate in potatoes consumed in Iran and calculation nitrate critical value using risk assessment study, Human and Ecological Risk Assessment: An International Journal, 22(3), 817-824.
28
Zheng, J., Chen, T., Xia, G., Chen, W., Liu, G., and Chi, D. (2018). Effects of zeolite application on grain yield, water use and nitrogen uptake of rice under alternate wetting and drying irrigation. International Journal of Agricultural and Biological Engineering. 11(1), 157-164.
29
ORIGINAL_ARTICLE
Quasi Two-Dimensional Modeling of Flow Hydraulics and Bed Load Transport in Zaremrood River
Determining the amount of sediment carried by rivers is important in several ways. This parameter is effective in design of dimensions and geometric characteristics of flow regulation and diversion structures, reservoir dams as well as pumping stations. In this study, the calculation of flow discharge and bed load of Zaremrood river located in Mazandaran province has been investigated using Shiono and Knight quasi-two-dimensional model. This model is based on the Navier-Stokes continuity and momentum equations and has been simplified by depth averaged concept. For this purpose, using the finite element method, this model was solved numerically and the lateral velocity distribution was calibrated at the Garmrood hydrometric station. Comparison of obtained results by Shiono and Knight model in different flow discharges against measured data indicates the high accuracy of the model for lateral velocity distribution. Then, by using the computed lateral velocity distribution, the distribution of bed load across the river was simulated. The results showed that among the 17 empirical bed load equations selected in this study, the Duboy formula (1879) has the best accuracy in both one and quasi-two-dimensional modeling cases. In 1D modeling case, this formula with standard deviation of the discrepancy ratio of 0.34 percent had better agreement with the measured bed load in comparison to the Frijlink (1952) and Meyer-Peter and Mueller (1948) equations with the standard deviation of 3.46 and 7.32 percent, respectively. In 2D modeling bed load transport, the root mean square error (RMSE) was obtained 7.45, 98.8 and 172.9 for three equations of Duboy, Frijlink and Meyer-Peter and Mueller, respectively which indicates that only Duboy formula has an acceptable accuracy while Frijlink and Meyer-Peter and Mueller equations have large errors. The results also showed that the bed load transport in quasi-two-dimensional model using Duboy equation is more accurate than one-dimensional case.
https://ijswr.ut.ac.ir/article_84038_7aa75cb6b72604c5ef7b597f6a6a8635.pdf
2021-09-23
1853
1868
10.22059/ijswr.2021.322917.668954
Bed Load Experimental Relationships
Quasi two-dimensional Modeling
Shiono and Knight Model
Zaramrood river
morteza
nabizadeh valukolaei
nabizade49@gmail.com
1
PhD. Student of Water Structures Dep. of Water Engineering, water and soil college. Gorgan University of Agricultural Sciences and Natural Resources. iran
AUTHOR
Abdolreza
Zahiri
zahiri.areza@gmail.com
2
Associated Professor, Dep. of Water Engineering, Faculty of Water and Soil, Gorgan University of Agricultural Sciences and Natural Resources, Golestan.
LEAD_AUTHOR
amirahmad
dehghani
amirahmad.dehghani@yahoo.com
3
Dep. of Water Engineering, water and soil college. Gorgan University of Agricultural Sciences and Natural Resources. iran
AUTHOR
mehdi
Meftah halaghi
meftahhalaghi@gmail.com
4
Dep. of Water Engineering, water and soil college. Gorgan University of Agricultural Sciences and Natural Resources. iran
AUTHOR
Ackers, P. (1992). Hydraulic design of two-stage channels. Engrs. Wat. Marit. And Energy, 96: 247-257.
1
Aybar, A. (2012). Computational modeling of free surface flow in intake structures using FLOW-3D software. MSc. Thesis, Civil Engineering, Middle East Technical University, Turkey.
2
Ayyoubzadeh, S. A. (1997).Hydraulic aspects of straight-compound channel flow and bed load sediment transport. Ph. D.dissertation, University of Birmingham, U.K.
3
Bousmar, D. (2002). Flow modelling in compound channels. Momentum transfer between main channel and prismatic or non-prismatic floodplains. Ph.D. dissertation, Univ. Cath. de Louvain, Belgium.
4
Chonwattana, S., Weesakul, S., and Vongvisessomjai, S. (2007). 3D numerical modeling of morphological change between fishtail groins. Proceedings of the 30th Int. Conf. on Coastal Engineering, San Diego, California, USA, 3178-3183.
5
Da Silva, A.M. (2006). On why and how do rivers meander. Journal of HydraulicResearches, IAHR, 44(5), 579-590.
6
Darby, E.S. (1998). Modelling width adjustment in straight alluvial channels. Journal of Hydrological Processes, 12(8), 1299-1321.
7
Ervine, D. A., Babaeyan-Koopaei, K. and Sellin, R. H. J. (2000). Two-dimensional solution for straight and meandering overbank flows. Journal of HydraulicEngineering, ASCE, 126(9), 653-669.
8
Eslami, S., Van Rijn, L.C., Walstra, D.J., Luijendijk, A.J., and Stive, M.J.F. (2010). A numerical study on design of coastal groins. In: Burns, S.E., Bhatia, S.K., Avila, C.M.C., and Hunt, B.E. (Hg.): Proceedings of 5th Int. Conf. on Scour and Erosion (ICSE-5), San Francisco, USA. 501-510.
9
Fenton, J. (2016). Hydraulics: science, knowledge, and culture. Journal of Hydraulic Researches, 54 (5), 485-501.
10
Fernandes, J.N., Leal, J.B., and Cardoso, A.H. (2014). Improvement of the lateral distribution method based on the mixing layer theory. Advances in Water Resources, 69, 159–167.
11
Gessler, D., Hall, B., Spasojevic, M. and Holly, F. (1999). Application of 3D mobile bed, Hydrodynamic Model. Journal of HydraulicEngineering, ASCE, 125(7),
12
Gholinejad, J., Zahiri. A., and Dehghani, A. (2018). Simulation of lateral distribution of total load sediment transport in rivers using a quasi two-dimensional mathematical model (Case Study: Gharehsoo river). Journal of Water Resources Engineering, 11(38), 83-93.
13
Haddadchi, A., Omid, M.H., and Dehghani, A.A. (2013). Bedload equation analysis using bed load-material grain size. Journal of Hydrology and Hydromechanics, 61(3), 241-249.
14
Khosronejad, A., Rennie, C., Salehi Neyshabouri, S.A.A., and Townsend, R.D. (2007). 3D numerical modeling of flow and sediment transport in laboratory channel bends. Journal of HydraulicEngineering, ASCE, 133(10), 11-23.
15
Knight, D.W. (2003). Reducing uncertainty in river flood conveyance. Interim Report 2:Review of Methods for Estimating Conveyance, Environment Agency, UK, 73p.
16
Knight, D.W., Shiono, K., and Pirt, J. (1989). Prediction of depth mean velocity and discharge in natural rivers with overbank flow. Int. Con. on Hydraulics and Environmental Modeling of Coastal, Estuarine and River Waters, England, 419-428.
17
Kordi, H., Amini, R., Zahiri, A., and Kordi, E. (2015). Improved Shiono and Knight method for overflow modeling. Journal of HydraulicEngineering, ASCE, 20(12), 1-10.
18
Lai, Y., and Wu, K. (2019). Three-dimensional flow and sediment transport model for free-surface open channel flows on unstructured flexible meshes. Fluids, 4(18), 1-19.
19
Lambert, M.F., and Sellin, R.H.J. (1996). Discharge prediction in straight compound channels using the mixing length concept. Journal of Hydraulicresearches, IAHR, 34: 381-394.
20
Montaseri, H. and Asiaei, H. (2014). Validating of SSIIM 3D Model for flow field simulation in a U shape channel bend with intake. Journal of Water and Soil Conservation, 21(4), 29-53.
21
Omara, H., Elsayed, S.M., Abdeelaal, G.M., Abd-Elhamid, H.F., and Tawfik, A. (2019). Hydromorphological numerical model of the local scour process around bridge piers. Arabian Journal for Science and Engineering, 44, 4183–4199.
22
Sheikhpoor, H., (2014). Measuring the bed load and suspended load of Zalemarood river in Garmrood station and determining their ratio during the water year, Research project of Mazandaran Regional Water Company. (In Farsi)
23
Shiono, K. and Knight, D. W. (1991).Turbulent open-channel flows with variabledepth across the channel. Journal ofFluidMechanics, 222: 617-646.
24
Singh, C.B., and Ghosh, L.K. (2000). Application of 3D mobile bed, hydrodynamic model. Journal of HydraulicEngineering, 126(11), 858–860.
25
Unal, B., Mamak, M., Seckin, G., and Cobaner, M. (2010). Comparison of an ANN approach with 1-D and 2-D methods for estimating discharge capacity of straight compound channels. Advances in Engineering Software, 41: 120-129.
26
Wark, J.B., Samuels, P.G. and Ervine, D.A. (1990). A practical method of estimating velocity and discharge in compound channels. Int. Conf. on River Flood Hydraulics, London, 163-172.
27
Zahiri, A., Ayyoubzadeh, S. A. and Dahanzadeh, B. (2010). Numerical solution of velocity lateral distribution in rivers (Case study: Karoun river at Molasani station), Journal of Agricultural Sciences and Natural Resources, 16 (2), 273-283.(In Farsi)
28
Zahiri. A. (2018). Simulation of flow and sediment transport in river bends (Case study: Karoun river). Journal ofIrrigation Engineering, 41(2), 1-17.(In Farsi)
29
Zahiri. A., Gholinejad, J., and Dehghani, A. (2019). Prediction of sediment transport capacity in rivers using quasi two- dimensional mathematical model. Journal ofWatershed Management Research, 10(19), 142-153.(In Farsi)
30
ORIGINAL_ARTICLE
Determining Actual Evapotranspiration of Silage Maize using Soil Water Balance Method under Different Drip Irrigation Levels with Pulsed and Continuous Management (Case Study: Varamin Region)
The proper irrigation scheduling reduces deep percolation losses, saves water and increases crop yield and water productivity. For this purpose, the crop water requirement must be determined carefully. In the present study, the water requirement of silage maize (ZP 606 cultivar) was determined using soil moisture monitoring method in the field conditions. An experiment in the form of split-strip plots based on a randomized complete block design with three replications was conducted in 2019 in Varamin region. The main factor included three levels of irrigation, supplying 120, 100 and 80% of maize water requirement (I2, I1 and I3, respectively) and the sub-main factor included two types of irrigation management: pulsed (P) and continuous (C). The actual evapotranspiration of silage maize under pulsed and continuous management in full irrigation treatment was 364-341 mm, in deficit irrigation treatment was 348-336 mm and in over-irrigation treatment, was 383-352 mm, respectively. The estimated evapotranspiration of silage maize using FAO-56 method was 400 mm that was 13.5% higher than the average actual evapotranspiration for full irrigation treatment in pulsed and continuous management determined by water balance method, which indicated the importance of using local crop coefficient to estimate crop water requirement accurately. The results also showed that the amount of deep percolation in over-irrigation treatment under pulsed irrigation management had decreased by 30% compared to the over-irrigation treatment with continuous management. Therefore, it is suggested to use pulsed irrigation management in order to save water consumption and to reduce deep percolation under drip irrigation system in the study area.
https://ijswr.ut.ac.ir/article_84039_72eb840744be02e97d3e8d796dfcd48d.pdf
2021-09-23
1869
1880
10.22059/ijswr.2021.322095.668940
Crop water requirement
Pulsed Irrigation
crop coefficient
Deficit irrigation
Over-Irrigation
iman
hajirad
iman.hajirad@modares.ac.ir
1
4. Graduated Student, Water Management and Engineering Department, Collage of Agriculture, Tarbiat Modares University, Tehran
AUTHOR
Seyed Majid
Mirlatifi
mirlat_m@modares.ac.ir
2
Water Management and Engineering Department, Collage of Agriculture, Tarbiat Modares University, Tehran, Iran
LEAD_AUTHOR
Hossein
Dehghanisanij
dehghanisanij@yahoo.com
3
Associate Researcher, Agricultural Engineering Research Institute, Agricultural Research, Education and Extension Organization, Karaj, Alborz, Iran
AUTHOR
sanaz
mohammadi
sanaz.mohammadi@modares.ac.ir
4
PhD Student, Water Management and Engineering Department, Collage of Agriculture, Tarbiat Modares University, Tehran, Iran
AUTHOR
Alijan, B., Karimi, A., Farhadi, B., and Broumandnasab, S. (2011). Determining Maize Water Requirement and Crop Coefficient using Water Balance Method. 4th Iran Water Resources Management Conference, Tehran, AmirKabir University. https://www.civilica.com/Paper-WRM04-WRM04_468.html.
1
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M. (1998). FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations, 56(97), e156.
2
Azari, A., Broumandnasab, S., Behzad, M., and Moeiri, M. (2007). Assessing Mazie Yield under T-Tape Drip Irrigation System. The Scientific Journal of Agriculture, 30(2), 82-87.
3
Bandyopadhyay, P. K., Mallick, S., and Rana, S. K. (2005). Water balance and crop coefficients of summer-grown peanut (Arachis hypogaea L.) in a humid tropical region of India. Irrigation Science, 23(4), 161-169.
4
Bozkurt, S., and Yazar, A. (2011). Effects of different drip irrigation levels on yield and some agronomic characteristics of raised bed planted corn. African Journal of Agricultural Research, 6(23), 5291-5300.
5
Dehghanisanij, H., Kanani, E., and Akhavan, S. (2018). Evaluation of corn evapotranspiration and its components and relationship between leaf area index and components in surface and subsurface drip irrigation systems. Journal of Water and Soil, 31(6).
6
Gheysari , M. (2006). Effects of Maize fertigation via sprinkler irrigation on nitrate leaching under different levels of fertilizer and water application. Ph.D. dissertation, University of Tarbiat modares, Iran.
7
Gheysari, M., Mirlatifi, S. M., Homaee, M., and Asadi, M. E. (2006). Determination of crop water use and crop coefficient of corn silage based on crop growth stages. Journal of Agricultural Engineering Research, 7(26), 125-142.
8
Gheysari, M., Sadeghi, S. H., Loescher, H. W., Amiri, S., Zareian, M. J., Majidi, M. M., ... and Payero, J. O. (2017). Comparison of deficit irrigation management strategies on root, plant growth and biomass productivity of silage maize. Agricultural Water Management, 182, 126-138.
9
Hao, B., Xue, Q., Marek, T. H., Jessup, K. E., Hou, X., Xu, W., ... and Bean, B. W. (2015). Soil water extraction, water use, and grain yield by drought-tolerant maize on the Texas High Plains. Agricultural Water Management, 155, 11-21.
10
Howell, T. A., Evett, S. R., Tolk, J. A., Copeland, K. S., Colaizzi, P. D., and Gowda, P. H. (2008). Evapotranspiration of corn and forage sorghum for silage. In World Environmental and Water Resources Congress 2008: Ahupua'A (pp. 1-14.
11
Irmak, S., Djaman, K., & Rudnick, D. R. (2016). Effect of full and limited irrigation amount and frequency on subsurface drip-irrigated maize evapotranspiration, yield, water use efficiency and yield response factors. Irrigation Science, 34(4), 271-286.
12
Liu, H., Wang, X., Zhang, X., Zhang, L., Li, Y., & Huang, G. (2017). Evaluation on the responses of maize (Zea mays L.) growth, yield and water use efficiency to drip irrigation water under mulch condition in the Hetao irrigation District of China. Agricultural Water Management, 179, 144-157.
13
Liu, H., Yang, H., Zheng, J., Jia, D., Wang, J., Li, Y., and Huang, G. (2012). Irrigation scheduling strategies based on soil matric potential on yield and fruit quality of mulched-drip irrigated chili pepper in Northwest China. Agricultural water management, 115, 232-241.
14
Oktem, A., Simsek, M., & Oktem, A. G. (2003). Deficit irrigation effects on sweet corn (Zea mays saccharata Sturt) with drip irrigation system in a semi-arid region: I. Water-yield relationship. Agricultural Water Management, 61(1), 63-74.
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Simsek, M., Can, A., Denek, N., & Tonkaz, T. (2011). The effects of different irrigation regimes on yield and silage quality of corn under semi-arid conditions. African Journal of Biotechnology, 10(31), 5869-5877.
17
Zheng, J., Huang, G., Jia, D., Wang, J., Mota, M., Pereira, L. S., Xu, X., and Liu, H. (2013). Responses of drip irrigated tomato (Solanum lycopersicum L.) yield, quality and water productivity to various soil matric potential thresholds in an arid region of Northwest China. Agricultural Water Management, 129, 181-193.
18
Zheng, J., Huang, G., Wang, J., Huang, Q., Pereira, L. S., Xu, X., and Liu, H. (2013). Effects of water deficits on growth, yield and water productivity of drip-irrigated onion (Allium cepa L.) in an arid region of Northwest China. Irrigation Science, 31(5), 995-1008.
19
ORIGINAL_ARTICLE
Screening Rice Varities for Higher Zn Efficiency in Paddy Field
To counteract the widespread negative effects of zinc deficiency on rice yield and the health of the majority of people who depend on this crop for nutrition, it will be necessary and effective to find cultivars resistant to zinc deficiency. For this purpose, field experiments during the crop years of 1396 and 1397 in farm located in the village of Pas-visheh, Rasht city, Gilan province, A two factors split plot experiment was conducted in a completely randomized design with three replications. Experimental factors include soil application of zinc sulfate fertilizer as the main plot in two levels (0 and 20 kg ha-1 zinc sulfate) and cultivar as a sub-plot in 27 levels (including local and improved cultivars and promising lines). The results showed that the Zn application and its interaction with cultivar waere significant for all measured traits except the length and width of the flag leaf. The lowest and highest values of zinc uptake in plant organs in the treatment of non-application of zinc sulfate belonged to line RI18430-2 (Hashemi × Saleh) and Kadous cultivar, respectively. Comparison of the mean of treatments showed that the lowest and highest zinc uptake in plant organs in the treatment of zinc sulfate application belonged to two lines RI18432-2 (Mohammadi × Saleh) and RI18431-1 (Abji Boji × Saleh), respectively and three cultivars or lines that have the highest zinc uptake in plant organs in the application of 20 kg / ha of zinc sulfate are RI18432-2 (Mohammadi × Saleh) and RI18430-22 (Hashemi × Saleh) and Ahlemi-Tarom cultivar, respectively. The results of the GGbiPlot analysis showed that in both levels of zinc (control and application of 20 kg per hectare of zinc sulfate) Gohar, Kadous and Caspian cultivars have been ranked 1 to 3 in terms of Zn efficiency. Saleh, Dilmani, Gilaneh and RI18430-1 (Hashemi × Saleh) cultivars were also ranked as high Zn efficient cultivars and line in both levels. Therefore, for future research works, these cultivars are suitable for cultivattion on Zn deficiency paddy soils or selection of higher Zn uptake cultivar(s) for rice grain quality purpose.
https://ijswr.ut.ac.ir/article_84040_9fbfd42d7f3cbd25f67dd1fde70cff39.pdf
2021-09-23
1881
1901
10.22059/ijswr.2021.324167.668979
rice
Zinc
Varietal Screening
Grain yield
Shahram
MahmoudSoltani
shmsoltani@gmail.com
1
Assistant Professor, Rice Research Institute of Iran, Agricultural Research, Education and Extension, Rasht, Iran
LEAD_AUTHOR
Mehrzad
Allagholipoor
mehrzadallahgholipour@yahoo.com
2
Associate Professor of Rice Research Institute of Iran, Agricultural Research, Education and extension, Rasht, Iran ,
AUTHOR
Amacher, M. C. (1996). Micronutrients. Methods of Soil Analysis Part 3—Chemical Methods, 739-768.
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Hazra, G.C., Saha, B., Saha, S., Dasgupta, S., Adhikari, B. and Mandal, B. (2015). Screening of rice cultivars for their zinc biofortification potential in Inceptisols. J. Indian Soc. Soil Sci, 63(3), pp.347-357.
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Mahmoud Soltani, S., Allagholipoor, M., Shakoori Katigari, M., Paykan, M., Shabanzadeh, M., Attar, A., Poorsafar Tabalvandi, A. and Keshtekar, F. (2020). Effect of Soil and Foliar Application of Zinc Sulfate Fertilizer on Zn and Protein Content of Grain, and Zn Content of Rice Tissues at Different Growth Stages. Iranian Journal of Soil Research 43 (3):311-330.
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MahmoudSoltani, S., Allahgholipoor,m.,Shakouri Katigari, m., and Poursafar Tabalvandani, A. (2020). Effect of Basal and Foliar Application of Zinc Sulphate Fertilizer on Zinc Uptake, Yield and Yield Components of Rice (Hashemi Cultivar). J Iranian Journal of Soil and Water Research: 51 (4):1013-1026.
15
MahmoudSoltani, S. (2020). Effect of Foliar Application of Zinc and Phosphorous on Their Dynamic, Biofortification, and on Grain Protein Content of Two Rice Cultivars (Hashemi and Guilaneh). Iranian Iranian Journal of Soil, and Water Research, 51(8), pp.2065-2083.
16
Mahmoudsoltani, S., Mohamed, M.H., Samsuri, A., Syed, M. and Sharifah, K. (2017). Lime and Zn application effects on soil and plant Zn status at different growth stages of rice in tropical acid sulphate paddy soil. Azarian Journal of Agriculture, 4(4), pp.127-138.
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Rahman, K.M., Chowdhury, M.A.K., Sharmeen, F., Sarkar, A., Hye, M.A. and Biswas, G.C. (2011). Effect of zinc and phosphorus on yield of Oryza sativa (cv. br-11). Bangladesh Res. Pub. J, 5(4), pp.315-358.
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Rehman, H.U., Aziz, T., Farooq, M., Wakeel, A. and Rengel, Z. (2012). Zinc nutrition in rice production systems: a review. Plant and Soil, 361(1-2), pp.203-226.
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38
ORIGINAL_ARTICLE
Modeling Soil Wetting Patterns under Pulsed Drip Irrigation by Dimensional Analysis Method and Comparison with HYDRUS-2D Numerical Model
The optimal design, operation and management of drip irrigation systems relies significantly on selection of a suitable combination of emitter discharge rate, emitter and lateral spacing, root depth and soil hydraulic properties that should be in consistent with root growth pattern in the soil for delivering required amount of water and nutrition to the plant. Modeling soil wetting pattern is more practical and easier than the conducting laboratory or field measurements. In this study, an empirical model was developed to predict the dimensions of the wetting pattern under pulsed drip irrigation using dimensional analysis method. The main inputs of the proposed model are emitter discharge rate, saturated hydraulic conductivity, total volume of applied water and pulse ratio. Experimentations included determination of the maximum depth and width of the wetting pattern after water application under different combination of pulses in a clay soil. The treatments were consisted of three pulses (P2, P3, P4) and two Off-Time durations (T1, T2). The predicted values of wetted depth and width by the empirical model and the HYDRUS-2D model were compared with the observations. The coefficient of determination parameter for the measured and estimated wetting pattern dimensions that obtained from empirical model was 0.94 and 0.93 and for numerical model was 0.95 and 0.97, which indicates good accuracy of the models. The results of the T-test analysis indicated that the empirical and numerical model simulated values were not significantly different (with a probability of 99.5%) from the observed ones. Although, on the basis of RMSE, ME and EF parameters the HYDRUS-2D model performance was better than the proposed empirical model but due to the simplicity of use and requiring less number of input parameters, it is recommended to use the developed empirical model to predict the wetting pattern as required in the design of drip irrigation systems.
https://ijswr.ut.ac.ir/article_84047_be306c1bde0cba9468d3f69c6d1f5399.pdf
2021-09-23
1903
1913
10.22059/ijswr.2021.322796.668947
Buckingham’s theorem
Off-Time
Pulsed Management
Wetted Depth
Wetted Zone
sanaz
mohammadi
sanaz.mohammadi@modares.ac.ir
1
PhD Student, Water Management and Engineering Department, Collage of Agriculture, Tarbiat Modares University, Tehran, Iran
AUTHOR
Seyed Majid
Mirlatifi
mirlat_m@modares.ac.ir
2
Water Management and Engineering Department, Collage of Agriculture, Tarbiat Modares University, Tehran, Iran
LEAD_AUTHOR
Hossein
Dehghanisanij
dehghanisanij@yahoo.com
3
Associate Researcher, Agricultural Engineering Research Institute, Agricultural Research, Education and Extension Organization, Karaj, Alborz, Iran
AUTHOR
iman
hajirad
iman.hajirad@modares.ac.ir
4
Graduated Student, Water Management and Engineering Department, Collage of Agriculture, Tarbiat Modares University, Tehran
AUTHOR
Mehdi
Homaee
m.homaee@ut.ac.ir
5
Water Management and Engineering Department, Collage of Agriculture, Tarbiat Modares University, Tehran, Iran
AUTHOR
Al-Ogaidi, A. A., Wayayok, A., Rowshon, M. K., and Abdullah, A. F. (2016). Wetting patterns estimation under drip irrigation systems using an enhanced empirical model. Agricultural Water Management, 176, 203-213.
1
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Dasberg, S., and Or, D. (1999). Practical applications of drip irrigation. In Drip irrigation (pp. 125-138). Springer, Berlin, Heidelberg.
3
Eskandari Tadavani, Z., Delghandi, M., Azhdari, K., Hosseini, S. H., & Dorostkar, V. (2020). Prediction of wetting patterns under surface drip irrigation using numerical and empirical models. Iranian Journal of Irrigation and Drainage, 14(1), 321-330.
4
Hopmans, J. W., and Bristow, K. L. (2002). Current capabilities and future needs of root water and nutrient uptake modeling. Advances in agronomy, 77, 103-183.
5
Ismail, S. M., EL-Abdeen, T. Z., Omara, A. A., and Abdel-Tawab, E. (2014). Modeling the soil wetting pattern under pulse and continuous drip irrigation. American-Eurasian Journal Agricultural & Environment Science, 14(9), 913-922.
6
Kandelous MM, Liaghat A, Abbasi F (2008) Estimation of soil moisture pattern in subsurface drip irrigation using dimensional analysis method. J Agri Sci 39(2):371–378 (in Persian).
7
Kandelous, M. M., and Šimůnek, J. (2010a). Comparison of numerical, analytical, and empirical models to estimate wetting patterns for surface and subsurface drip irrigation. Irrigation Science, 28(5), 435-444.
8
Kandelous, M. M., and Šimůnek, J. (2010b). Numerical simulations of water movement in a subsurface drip irrigation system under field and laboratory conditions using HYDRUS-2D. Agricultural Water Management, 97(7), 1070-1076.
9
Karimi, B., Sohrabi, T., Mirzaei, F., and Ababaei, B. (2015). Developing equations to predict the pattern of soils moisture redistribution in surface and subsurface drip irrigation systems using dimension analysis. Journal of Water and Soil Conservation, 21(6), 223-237.
10
Karimi, B., & Karimi, N. (2019). Simulation of the advance Velocity of the Wetting Front in pulse Drip Irrigation Systems by nonlinear regression model. Iranian Journal of Irrigation and Drainage, 13(5), 1374-1387.
11
Karmeli, D., and Peri, G. (1974). Basic principles of pulse irrigation. Journal of the Irrigation and Drainage Division, 100(3), 309-319.
12
Li, J., Zhang, J., and Rao, M. (2004). Wetting patterns and nitrogen distributions as affected by fertigation strategies from a surface point source. Agricultural Water Management, 67(2), 89-104.
13
Lubana, P. P. S., Narda, N. K., & Brown, L. C. (2004). Application of a hemispherical model to predict radius of wetted soil volume under point source emitters for trickle irrigated tomatoes in Punjab state, India. In 2004 ASAE Annual Meeting (p. 1). American Society of Agricultural and Biological Engineers.
14
Malek, K., and Peters, R. T. (2011). Wetting pattern models for drip irrigation: new empirical model. Journal of Irrigation and Drainage Engineering, 137(8), 530-536.
15
Mirzaee, F., Alkasir, Z., and Moini, A.R. (2020). Modeling for Estimating Soil Moisture Dimensions in Drip Irrigation in Layer Soil Using Dimensional Analysis Method. Iranian Journal of Irrigation and Drainage, 14(2), 570-578.
16
Mohammadbeigi, A., Mirzaei, F., and Ashraf, N. (2017). Simulation of soil moisture distribution under drip irrigation pulsed and continuous in dimensional analysis method. Journal of Water and Soil Conservation, 23(6), 163-180.
17
Schwartzman, M., and Zur, B. (1986). Emitter spacing and geometry of wetted soil volume. Journal of Irrigation and Drainage Engineering, 112(3), 242-253.
18
Singh, D. K., Rajput, T. B. S., Sikarwar, H. S., Sahoo, R. N., and Ahmad, T. (2006). Simulation of soil wetting pattern with subsurface drip irrigation from line source. Agricultural water management, 83(1-2), 130-134.
19
Subbaiah, R. (2013). A review of models for predicting soil water dynamics during trickle irrigation. Irrigation Science, 31(3), 225-258.
20
Thorburn, P. J., Cook, F. J., and Bristow, K. L. (2003). Soil-dependent wetting from trickle emitters: implications for system design and management. Irrigation Science, 22(3), 121-127.
21
zandi, S., Boroomand Nasab, S., Ainechee, G. (2020). Estimating soil moisture pattern under subsurface drip irrigation using dimensional analysis method. Iranian Journal of Irrigation and Drainage, 14(2), 626-636.
22
ORIGINAL_ARTICLE
Digital Modeling of Three-Dimensional Soil Salinity Variation Using Machine Learning Algorithms in Arid and Semi-Arid lands of Qazvin Plain
Soil salinity, as one of the most important indicators of soil quality, has crucial roles in land use planning and land management in arid and semi-arid regions. The aim of this study was to model soil salinity at five standard depth (0-5, 5-15, 15-30, 30-60, and 60-100 cm) of global digital soil mapping project in 60,000 hectares of Qazvin plain with spatial resolution of 15m. Field studies included a sampling of 278 soil profiles and then the EC was measured in the laboratory. The recursive feature elimination (RFE) method was employed to select environmental covariates including parameters extracted from Landsat 8 image (OLI/TIRS) data, topography, and climatic parameters. Four machine learning algorithms as random forest (RF), cubist (CB), decision tree regression (DTr), and k-nearest neighbors (k-NN) were applied for predicting and mapping soil salinity. According to RFE, 10 covariates were chosen for each standardized depth. The results of modeling showed that the CB model at the depth of 0-5 and 15-30 cm with R2 values of 0.92 and 0.85 and RMSE 4.77 and 7.90 dS/m and the RF model at depths of 5-15, 30-60, and 60-100 cm with R2 values of 0.93, 0.94, 0.96 and RMSE 6.65, 5.10 and 3.20 dS/m, respectively, had the highest accuracy compared to two other models i.e., DTr and k-NN. Furthermore, the covariates extracted from RS data had more impact on topsoil salinity prediction while the climate and topographic attributes influence subsurface soil salinity. Generally, The RF and CB models along with appropriate environmental covariates were able to present salinity variation of study standard depths.
https://ijswr.ut.ac.ir/article_82296_cda5800574aff5d468a98a213fc230be.pdf
2021-09-23
1915
1929
10.22059/ijswr.2021.323030.668957
Soil Salinity
Environmental Covariates
digital soil mapping
Machine learning
Sayed Roholla
Mousavi
r_mousavi@ut.ac.ir
1
Ph.D. Student of Soil Resources Management,, ,Science and soil Engineering Department,, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University, of Tehran. Karaj, Iran.
AUTHOR
Fereydoon
Sarmadian
fsarmad@ut.ac.ir
2
Professor of Soil and Science Engineering Department,, Faculty of Agricultural Engineering and Technology, University College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
LEAD_AUTHOR
Mahmoud
Omid
omid@ut.ac.ir
3
Professor of Agricultural Machinery Engineering Department, Faculty of Agricultural Engineering and Technology, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran
AUTHOR
Patrick
Bogaert
patrick.bogaert@uclouvain.be
4
Professor of Earth and Life Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium
AUTHOR
Abedi, F., Amirian‐Chakan, A., Faraji, M., Taghizadeh‐Mehrjardi, R., Kerry, R., Razmjoue, D., & Scholten, T. (2021). Salt dome related soil salinity in southern Iran: Prediction and mapping with averaging machine learning models. Land Degradation & Development, 32(3), 1540-1554.
1
Allbed, A., & Kumar, L. (2013). Soil salinity mapping and monitoring in arid and semi-arid regions using remote sensing technology: a review. Advances in remote sensing, 2013.
2
Arrouays, D., Grundy, M. G., Hartemink, A. E., Hempel, J. W., Heuvelink, G. B., Hong, S. Y., ... & Zhang, G. L. (2014). GlobalSoilMap: Toward a fine-resolution global grid of soil properties. Advances in agronomy, 125, 93-134.
3
Azabdaftari, A., & Sunarb, F. (2016). Soil salinity mapping using multitemporal Landsat data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 7, 3-9.
4
Belgiu, M., & Drăguţ, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS journal of photogrammetry and remote sensing, 114, 24-31.
5
Chen, Y., Qiu, Y., Zhang, Z., Zhang, J., Chen, C., Han, J., & Liu, D. (2020). Estimating salt content of vegetated soil at different depths with Sentinel-2 data. PeerJ, 8, e10585.
6
Da Silva Chagas, C., de Carvalho Junior, W., Bhering, S. B., & Calderano Filho, B. (2016). Spatial prediction of soil surface texture in a semiarid region using random forest and multiple linear regressions. Catena, 139, 232-240.
7
Daliakopoulos, I. N., Tsanis, I. K., Koutroulis, A., Kourgialas, N. N., Varouchakis, A. E., Karatzas, G. P., & Ritsema, C. J. (2016). The threat of soil salinity: A European scale review. Science of the Total Environment, 573, 727-739.
8
El Hafyani, M., Essahlaoui, A., El Baghdadi, M., Teodoro, A. C., Mohajane, M., El Hmaidi, A., & El Ouali, A. (2019). Modeling and mapping of soil salinity in Tafilalet plain (Morocco). Arabian journal of geosciences, 12(2).
9
Eswaran, H., Lal, R., & Reich, P. F. (2019). Land degradation: an overview. Response to land degradation, 20-35.
10
Forkuor, G., Hounkpatin, O. K., Welp, G., & Thiel, M. (2017). High resolution mapping of soil properties using remote sensing variables in south-western Burkina Faso: a comparison of machine learning and multiple linear regression models. PloS one, 12(1), e0170478.
11
Guo, B., Zang, W., Luo, W., Wen, Y., Yang, F., Han, B., ... & Yang, X. (2020). Detection model of soil salinization information in the Yellow River Delta based on feature space models with typical surface parameters derived from Landsat8 OLI image. Geomatics, Natural Hazards and Risk, 11(1), 288-300.
12
Hengl, T., Heuvelink, G. B., Kempen, B., Leenaars, J. G., Walsh, M. G., Shepherd, K. D., ... & Tondoh, J. E. (2015). Mapping soil properties of Africa at 250 m resolution: Random forests significantly improve current predictions. PloS one, 10(6), e0125814.
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14
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15
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Mohammadifar, A., Gholami, H., Golzari, S., & Collins, A. L. (2021). Spatial modelling of soil salinity: deep or shallow learning models? Environmental Science and Pollution Research, 1-19.
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Mousavi, S. R., Sarmadian, F., & Rahmani, A. (2020). Modelling and Prediction of Soil Classes Using Boosting Regression Tree and Random Forests Machine Learning Algorithms in Some Part of Qazvin Plain. Iranian Journal of Soil and Water Research, 50(10), 2525-2538. (In Farsi).
24
Mousavi, S., Sarmadian, F., Alijani, Z., & Taati, A. (2017). Land suitability evaluation for irrigating wheat by geopedological approach and geographic information system: A case study of Qazvin plain, Iran. Eurasian Journal of Soil Science, 6(3), 275-284
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Naumann, J. C., Young, D. R., & Anderson, J. E. (2009). Spatial variations in salinity stress across a coastal landscape using vegetation indices derived from hyperspectral imagery. Plant Ecology, 202(2), 285-297.
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Nazari, S., Rostaminia, M., Ayoubi, S., Rahmani, A., & Mousavi, S. R. (2020). Efficiency of Different Feature Selection Methods in Digital Mapping of Subgroup and Soil Family Classes with Data Mining Algorithms. Water and Soil journal, 34(4), 973-987. (In Farsi).
27
Noroozi, A. A., Homaee, M., & ABBASI, F. (2011). Integrated application of remote sensing and spatial statistical models to the identification of soil salinity: A case study from Garmsar Plain, Iran. Journal of Environmental Sciences. (In Farsi).
28
Parsaie, F., Firouzi, A. F., Mousavi, S. R., Rahmani, A., Sedri, M. H., & Homaee, M. (2021). Large-scale digital mapping of topsoil total nitrogen using machine learning models and associated uncertainty map. Environmental Monitoring and Assessment, 193(4), 1-15.
29
Peng, J., Biswas, A., Jiang, Q., Zhao, R., Hu, J., Hu, B., & Shi, Z. (2019). Estimating soil salinity from remote sensing and terrain data in southern Xinjiang Province, China. Geoderma, 337, 1309-1319.
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Rahmani, A., Sarmadian, F., Mousavi, S. R., & Khamoshi, S. E. (2020). Application of Geomorphometric attributes in digital soil mapping by using of machine learning and fuzzy logic approaches. Journal of Range and Watershed Management, 73(1), 105-124. (In Farsi).
32
Schoeneberger, P.J., Wysocki, D.A. and Benham, E.C. (2012) Soil Survey Staff. Field book for describing and sampling soils, 3nd version. Natural Resources Conservation Service. National Soil Survey Center, Lincoln.
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Soil survey manual. (2018). Soil Science Division Staff. United States Department of Agriculture Handbook No. 18.
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Taghizadeh-Mehrjardi, R., Minasny, B., Sarmadian, F., & Malone, B. P. (2014). Digital mapping of soil salinity in Ardakan region, central Iran. Geoderma, 213, 15-28.
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46
ORIGINAL_ARTICLE
The Role of Irrigation Management on Wheat Water Productivity (Case Study: Arzooieh City of Kerman)
Due to the recent water shortages and droughts, the importance of water productivity is becoming more apparent. The main objective of this study is to examine water productivity of wheat according to its water requirement and proper irrigation management. This research was performed in Orzouieh city of Kerman province located in an arid region. In this study by using meteorological data of the region, wheat evapotranspiration was calculated by CROPWAT. Then, potential evapotranspiration was also calculated by Hargreaves-Samani and Jensen-Haise methods to verify the values resulted by CROPWAT package. Irrigation Hydromodule was calculated too. The Volume of water consumed by wheat per hectare during growing season was also calculated using irrigation hydromodule. For performing this study, three wheat farms with proper irrigation management were selected. For calculating physical and economical productivity of wheat farms in the region, one of the farms was examined. The results of this study showed that the physical productivity (CPD) is equal to 1.6 kg/m3 and economical productivity (NBPD) is equal to 13000 Rls./m3. The calculated productivities demonstrate that supplying actual water requirement of wheat along with proper management of the fields, results high economical efficiency.
https://ijswr.ut.ac.ir/article_84106_2f7b069acad61646432410976143ebce.pdf
2021-09-23
1931
1940
10.22059/ijswr.2021.320517.668915
Water requirement
Wheat
Hydromodule
Physical Productivity
Economical productivity
Rahimeh
Dehghani Dashtabi
rahimeh.dehghany@yahoo.com
1
Department of Water Engineering, Faculty of Agriculture, Shiraz University, Shiraz, Iran
AUTHOR
SeyedHassan
Mirhashemi
hassan.mirhashemi@yahoo.com
2
Department of Water Engineering, Faculty of Water and Soil, Zabol University, Zabol, Iran
AUTHOR
Milad
Jahani
milad.jahani.m@gmail.com
3
Department of Water Engineering, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran
AUTHOR
Parviz
Haghighat jou
parvizhjou@uoz.ac.ir
4
Department of Water Engineering, Faculty of Water and Soil, Zabol University, Zabol, Iran
LEAD_AUTHOR
Abolpour., B. (2010). Cultivation pattern in proportion to the capacity of water resources in the face of drought., Drought coping strategies workshop. P: 120. (In Farsi)
1
Ahmadi Adli, R. )2012(. Use of Class A evaporator for irrigation planning, an effective step in optimal management of water at the farm scale (Case study: Ardabil, Iran). Eleventh Irrigation Congress: Evaporation Reduction. Kerman, Iran. 123-436. (In Farsi)
2
Erfanian, M., Alizadeh , A., & Mohammadian, A. (2011). An Investigation on the Possible Differences between Present Crops Water Requirements and National Documents of Irrigation. Irrigation and Drainage of Iran 3 (3): 478-492. (In Farsi)
3
Allen RG., Jensen ME., Wright JL. and Burman RD. )1989(. Operational estimates of reference evapotranspiration. Agronomy Journal 81: 650-662.
4
Babazadeh, H. & Iftikhar, Sh.. (2011). Optimal Irrigation Planning in Low Irrigation Conditions of Wheat, Barley and Corn Crops in Cold and Moderate Climates, The First National Conference on Meteorology and Agricultural Water Management, Karaj. (In Farsi)
5
Chouhan, S. S., Awasthi, M. K., & Nema, R. K. (2015). Studies on water productivity and yields responses of wheat based on drip irrigation systems in clay loam soil. Indian Journal of science and Technology, 8(7): 650-654.
6
Dehghani Sanij, H. , Nakhjavani Moghaddam, M. & Akbari, M. (2007). Evaluation of water use efficiency based on regional regulations and scarcity irrigation. Irrigation and Drainage of Iran 2 (1): 77-91. (In Farsi)
7
Hargreaves, G.H. )1994(. Defining and using reference evapotranspiration. J. of Irrig. and Drain. Eng., ASCE, 120 (6): 1132-1139.
8
Howell TA., Evett SR., Schneider AD. and Duesek DA, Copelland KS. )2000(. Irrigated fescue grass ET compared with calculated reference grass ET. Proceedings of 4th National Irrigation Symposium, American Society of Agricultural Engineers: St. Joseph, MI; 228-242.
9
Karbasi, M., Ismaili, M., Taheri, M & Bazargan, J. (2011). Study and estimation of water needs of crops and orchards using different methods to provide a suitable cultivation pattern in the irrigation and drainage network of Qara Daragh Dam, 11th National Seminar Irrigation and evaporation reduction, Kerman. (In Farsi)
10
Lecina, S., E. Playan, and D. Isidoro. )2005(. Irrigation evaluation and simulation at the irrigation district V of Bardenas (Spain). Agricultural Water Management, 73: 223-245.
11
Maroofpour, A. , Watankhah, F. And Behzadi Nasab, M. )2016(, Evaluation of efficiency of drip irrigation system in some farms of Zarrineh River Miandoab. Irrigation and Water Engineering 7 (25): 83-96. (In Farsi)
12
Piri, H. (2012) Technical evaluation of drip irrigation systems (Case study: Sarbaz city). 5: 19-36. (In Farsi)
13
Rao, K. V. R., Bajpai, A., Gangwar, S., Chourasia, L., and Soni, K. )2016). Maximizing water productivity of wheat crop by adopting drip irrigation. Research on Crop, 17(1): 163-168.
14
Soleimani, H. And Hasanli, A. M. (2009), Estimation of unit cost of water, water efficiency (WUE) and water added value for major products of Darab as a dry area. Dynamic Agriculture Quarterly 5 (1): 45-60. (In Farsi)
15
Wright JL., Allen, RG., and Howell TA. (2000). Conversion between evapotranspiration references and methods. Proceedings of 4th references and methods. Proceeding of 4th National Irrigation Symposium, American Society of Agricultural Engineers: St. Joseph, MI.; 251-259.
16
ORIGINAL_ARTICLE
Classification of Croplands Using Sentinel-2 Satellite Images and a Novel Deep 3D Convolutional Neural Network (Case Study: Shahrekord)
Agriculture has been recognized as the main motive for economic growth and development in different countries of the world. In the meantime, mapping croplands through the classification of remote sensing images is one of the effective solutions in decision making and providing food security to the community. In this research, croplands are classified into different classes of agricultural products (including wheat, barley, corn, alfalfa, potatoes, and Sugar beets) using multi-temporal optical (Sentinel-2) and synthetic aperture radar (Sentinel-1) satellite images. All the steps related to the preparation of satellite images, have been conducted in the Google Earth Engine online processing platform. A novel three-dimensional deep convolutional neural network is used as the classifier. The designed network, in addition to three-dimensional kernels with the ability to extract spatial and temporal information of each pixel simultaneously, uses some escape connections of the previous layers. These connections, contrary to the feed-forward convolutional networks, feed the output of the previous layers to the new layers. After dividing the ground truth data into two categories of training and evaluation and assessing the performance of the network with 50 different training and evaluation data, the network’s overall accuracy was calculated 91.6% on average. According to the final results, the designed escape connections increased the overall accuracy of classification by 2%. The proposed network was also compared with temporal and spatial-temporal Random Forests and Support Vector Machines which showed a better performance with a difference of at least 2.4%.
https://ijswr.ut.ac.ir/article_84107_2c19eecf82886979c2825fd57018d8e2.pdf
2021-09-23
1941
1953
10.22059/ijswr.2021.320850.668956
remote sensing
Convolutional Neural Network
Deep learning
Cropland Classification
Sentinel satellite images
Alireza
Taheri Dehkordi
alireza.tahery@email.kntu.ac.ir
1
Department of photogrammetry and remote sensing, Faculty of Geodesy and Geomatics Engineering, Khaje Nasir Toosi university of technology, Tehran
LEAD_AUTHOR
Mohammad Javad
Valadan Zoej
valadanzoej@kntu.ac.ir
2
Professor in the Department of Photogrammetry and Remote Sensing, K.N.Toosi university of technology
AUTHOR
Boulze, H., Korosov, A., & Brajard, J. (2020). Classification of sea ice types in Sentinel-1 SAR data using convolutional neural networks. Remote Sensing, 12(13), 2165.
1
Brinkhoff, J., Vardanega, J., & Robson, A. J. (2020). Land cover classification of nine perennial crops using sentinel-1 and-2 data. Remote Sensing, 12(1), 96.
2
Carranza-García, M., García-Gutiérrez, J., & Riquelme, J. C. (2019). A framework for evaluating land use and land cover classification using convolutional neural networks. Remote Sensing, 11(3), 274.
3
Carrasco, L., O’Neil, A.W., Morton, R.D., & Rowland, C.S. (2019). Evaluating combinations of temporally aggregated Sentinel-1, Sentinel-2 and Landsat 8 for land cover mapping with Google Earth Engine. Remote Sensing, 11(3), 288.
4
Chakhar, A., Hernández-López, D., Ballesteros, R., & Moreno, M. A. (2021). Improving the Accuracy of Multiple Algorithms for Crop Classification by Integrating Sentinel-1 Observations with Sentinel-2 Data. Remote Sensing, 13(2), 243.
5
Chakhar, A., Ortega-Terol, D., Hernández-López, D., Ballesteros, R., Ortega, J. F., & Moreno, M. A. (2020). Assessing the accuracy of multiple classification algorithms for crop classification using Landsat-8 and Sentinel-2 data. Remote Sensing, 12(11), 1735.
6
Chang, L., Chen, Y. T., Wang, J. H., & Chang, Y. L. (2021). Rice-Field Mapping with Sentinel-1A SAR Time-Series Data. Remote Sensing, 13(1), 103.
7
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote sensing of Environment, 202, 18-27.
8
Guidici, D., & Clark, M. L. (2017). One-Dimensional convolutional neural network land-cover classification of multi-seasonal hyperspectral imagery in the San Francisco Bay Area, California. Remote Sensing, 9(6), 629.
9
Ioffe, S., & Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning, 448-456.
10
Jin, X., Kumar, L., Li, Z., Feng, H., Xu, X., Yang, G., & Wang, J. (2018). A review of data assimilation of remote sensing and crop models. European Journal of Agronomy, 92, 141-152.
11
Joshi, N., Baumann, M., Ehammer, A., Fensholt, R., Grogan, K., Hostert, P., & Waske, B. (2016). A review of the application of optical and radar remote sensing data fusion to land use mapping and monitoring. Remote Sensing, 8(1), 70.
12
Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). A review of the use of convolutional neural networks in agriculture. The Journal of Agricultural Science, 156(3), 312-322.
13
Karthikeyan, L., Chawla, I., & Mishra, A. K. (2020). A review of remote sensing applications in agriculture for food security: Crop growth and yield, irrigation, and crop losses. Journal of Hydrology, 586, 124905.
14
Kattenborn, T., Leitloff, J., Schiefer, F., & Hinz, S. (2021). Review on Convolutional Neural Networks (CNN) in vegetation remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 173, 24-49.
15
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25, 1097-1105.
16
Li, Y., Zhang, H., & Shen, Q. (2017). Spectral–spatial classification of hyperspectral imagery with 3D convolutional neural network. Remote Sensing, 9(1), 67.
17
Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., & Johnson, B. A. (2019). Deep learning in remote sensing applications: A meta-analysis and review. ISPRS journal of photogrammetry and remote sensing, 152, 166-177.
18
Mandal, D., Kumar, V., Bhattacharya, A., Rao, Y. S., Siqueira, P., & Bera, S. (2018). Sen4Rice: A processing chain for differentiating early and late transplanted rice using time-series Sentinel-1 SAR data with Google Earth engine. IEEE Geoscience and Remote Sensing Letters, 15(12), 1947-1951.
19
Mazzia, V., Khaliq, A., & Chiaberge, M. (2020). Improvement in land cover and crop classification based on temporal features learning from Sentinel-2 data using recurrent-convolutional neural network (R-CNN). Applied Sciences, 10(1), 238.
20
Rezaee, M., Mahdianpari, M., Zhang, Y., & Salehi, B. (2018). Deep convolutional neural network for complex wetland classification using optical remote sensing imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 3030-3039.
21
Sharma, A., Liu, X., Yang, X., & Shi, D. (2017). A patch-based convolutional neural network for remote sensing image classification. Neural Networks, 95, 19-28.
22
Singha, M., Dong, J., Sarmah, S., You, N., Zhou, Y., Zhang, G., & Xiao, X. (2020). Identifying floods and flood-affected paddy rice fields in Bangladesh based on Sentinel-1 imagery and Google Earth Engine. ISPRS Journal of Photogrammetry and Remote Sensing, 166, 278-293.
23
Van Tricht, K., Gobin, A., Gilliams, S., & Piccard, I. (2018). Synergistic use of radar Sentinel-1 and optical Sentinel-2 imagery for crop mapping: a case study for Belgium. Remote Sensing, 1642.
24
Xiao, J., Wu, H., Wang, C., & Xia, H. (2018). Land cover classification using features generated from annual time-series Landsat data. IEEE Geoscience and Remote Sensing Letters, 15(5), 739-743.
25
Xu, L., Zhang, H., Wang, C., Zhang, B., & Liu, M. (2019). Crop classification based on temporal information using sentinel-1 SAR time-series data. Remote Sensing, 11(1), 53.
26
Zhai, Y., Wang, N., Zhang, L., Hao, L., & Hao, C. (2020). Automatic crop classification in northeastern China by improved nonlinear dimensionality reduction for satellite image time series. Remote Sensing, 12(17), 2726.
27
Zhao, H., Chen, Z., Jiang, H., Jing, W., Sun, L., & Feng, M. (2019). Evaluation of three deep learning models for early crop classification using sentinel-1A imagery time series—A case study in Zhanjiang, China. Remote Sensing, 11(22), 2673.
28
Zhong, L., Hu, L., & Zhou, H. (2019). Deep learning based multi-temporal crop classification. Remote sensing of environment, 221, 430-443.
29
ORIGINAL_ARTICLE
Assessment of Escherichia coli Leaching in two Acidic Soils
Manures are used to increase pH and fertility of acidic soils in Gilan province. Although they are useful, but contain coliform bacteria that can reach the groundwater resources and lead contamination. This study aimed to investigate an indicator bacterium; Escherichia coli transport in two acidic soils. Two soil samples with pH vlues of 5.88 and 3.99 were taken from Amlash and Lahijan area respectively. For leaching experiment, air dried soil was freely packed in Polyvinyl chloride sylinders (with diameter of 4.8 and height of 14.92 cm). A 0.1 pore volume (PV) of bacteria (1× 108 CFU mL-1) and bromide (0.008 mol L-1) as a pulse flow was applied on the top of the soil columns after water flow rate reached steady state condition and leaching experiment was followed with distilled water. Leachate sampling was carried out in regular time intervals till 4.5 PV and E. coli and bromide concentrations were measured in the leachate. Resident E. coli number were also determined in each cutted 3 cm section of soil after leaching experiment endup. C/C0 peak of E. coli in the leachate of Amlash and Lahijan soil columns was observed at 0.7 and 0.9 PV repectively, while the C/C0 peak of bromide was occurred at 0.8 and 1.8 PV respectively. Early occurance of E. coli bacteria rather than bromide in the leachate of both soils was attributed to preferential water flow path which was dominant in the Lahijan soil column due to more clay and organic carbon content. The most resident E. coli number was determined in the surface layer of both soils which was greater in Amlash soil and decreased by 0.9 and 1.44 (log unit) in Amlash and Lahijan soil columns respectively. Overall, not only the cumulative number of E. coli bacteria was higher in the leachate of Amlash soil column, but also it contained more resident E. coli bacteria rather than Lahijan soil column due to greater pH value.
https://ijswr.ut.ac.ir/article_81918_e2c8ad6ceff5cac45346808ed96d322a.pdf
2021-09-23
1955
1970
10.22059/ijswr.2021.321673.668932
Bromide
Filtration coefficient
Preferential flow
Relative adsorption ratio
Zahra
Ramezani
zahra1993.ramezani@gmail.com
1
Soil Science Department, Faculty of agricultural Science, University of Guilan, Rasht, Iran
AUTHOR
Mohammad Bagher
Farhangi
m.farhangi@guilan.ac.ir
2
Soil Science Department, Faculty of agricultural Science, University of Guilan, Rasht, Iran
LEAD_AUTHOR
Nasrin
Ghorbanzadeh
nghorbanzadeh@guilan.ac.ir
3
Soil Science Department, Faculty of agricultural Science, University of Guilan, Rasht, Iran
AUTHOR
Mahmoud
Shabanpour
shabanpour@guilan.ac.ir
4
Soil Science Department, Faculty of agricultural Science, University of Guilan, Rasht, Iran
AUTHOR
Bitton, G. and Harvey R. W. (1992). Transport of pathogens through soils and aquifers. PP: 103-124. In: R. Mitchell, (Ed.) Environmental Microbiology. Wiley-Liss, New York.
1
Bradford, S.A. and Bettahar, M. (2006). Concentration dependent colloid transport in saturated porous media. Journal of Contaminant Hydrology, 82, 99-117.
2
Bradford, S.A., Bettahar, M., Simunek, J., Genuchten, M. T. V. (2004). Straining and attachment of colloids in physically heterogeneous porous media. Vadose Zone Journal, 3, 384-394.
3
Brady, N.C. and Weil, R.R., (2008). The Nature and Properties of Soils. Upper Saddle River, NJ: Prentice hall.
4
Brovelli, A., Cassiani, G., Dalla, E., Bergamini, F., Pitea, D., Binley A. M. (2005). Electrical properties of partially saturated sandstones: Novel computational approach with hydrogeophysical applications, Water Resources Research, 41(8), 853-858.
5
Carter, M. R. and Gregorich, E. G. (2007). Soil Sampling and Methods of Analysis. CRC press.
6
Choo, H., Kim, J., Lee, W., Lee, C. (2016). Relationship between hydraulic conductivity and formation factor of coarse-grained soils as a function of particle size. Journal of Applied Geophysics, 127, 91-101.
7
Crawford, T. W., Singh, U. J., Breman, H. (2008). Solving Agricultural Problems Related to Soil Acidity in Central Africa’s Great Lakes Region. International Center for Soil Fertility and Agricultural Development, Muscle Shoals, Alabama, U.S.A.
8
Davatgar, N., Zare, A., Shakoori Katigari, M., Rezaei, L., Kavousi, M., Sheikhalaslam, H., Shahnazari, M., Kohneh, E., Shirinfekr, A., Bonyadi, I., Adibi, SH., Moshirtalesh, I., Khodashenas, A., Shokri Vahed, H., Darygh Speech, F., Rahimi Moghadam, A., Ajili Lahiji, A. (2015). Investigation of fertility status of paddy soils in Guilan province. Journal of Land Management, 3(1), 1-13. (In Persian)
9
Dexter, A.R., Richard, G., Arrouays, D., Czy, EA., Jolivet, C., Duval, O. (2008). Complexed organic matter controls soil physical properties. Geoderma, 144(3-4), 620-627.
10
Elimelech, M., Gregory J., Jia, X., Williams, R.A. (1995). Particle Deposition and Aggregation. Measurement, Modelling and Simulation. Butterworth-Heinemann: Woburn.
11
FAOSTAT. Database collection of the Food and Agriculture Organization of the United Nations; (2019). Food and Agriculture Organization of the United Nations. Rome, Italy: FAO.
12
Farhangi, M. B., M. R. Mosaddeghi, M, R., Safari-Sinegani, A. A., Mahboubi, A. A. (2011). Unsaturated transport of cow manure-borne Escherichia Coli through the field soil. Journal of Water and Soil Science, 16(59), 127-140. (In Persian)
13
Farrokhian Firouzi, A., Homaei, M., Clumpp, E., Kasteel, R., Satari, M. (2010). Bacteria transport and deposition in calcareous soils under saturated flow condition. Journal of Water and Soil (Agricultural Science and Technology), 24(3), 439-459. (In Persian)
14
Foppen, J. and Schijven, J.F. (2006). Evaluation of data from the literature on the transport and survival of Escherichia coli and thermotolerant coliforms in aquifers under saturated conditions. Water Research, 40(3), 401-426.
15
Franz, E., Semenov, A.V., Termorshuizen, A.J., De Vos, O. J., Bokhorst, J.G. (2008). Manure-amended soil characteristics affecting the survival of E. coli O157:H7 in 36 Dutch soils. Environmental Microbiology, 10(2), 313-327.
16
Frazier C. S., Graham R. C., Shouse P. J., Yates M. V., Anderson, M. A. (2002). A field study of water flow and virus transport in weathered granitic bedrock. Vadose Zone Journal, 1(1), 113–124.
17
Gargiulo, G., Bradford, S., Šimunek, J., Ustohal, P., Vereecken, H., Klumpp, E. (2008). Bacteria transport and deposition under unsaturated flow conditions: The role of water content and bacteria surface hydrophobicity. Vadose Zone Journal, 7, 406-419.
18
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ORIGINAL_ARTICLE
Time-dependent Stochastic Hedging Rules to Reservoir Operation: A Case Study of the Bukan Dam Reservoir
In operation of dam reservoir, due to the possibility of severe water shortages in the future, supplying total demand of current step is not rational, and the use of hedging rules can provide insurance for water supply in the future. In the reservoir long-term operation to supply the irrigation water demand, uncertainty of reservoir inflow and uncertainty of irrigation water demand have a significant effect on release. Crop water stress sensitivity variation at different growth stages varies the crop production function slope, which is not seen in seasonal production functions. In this study, a stochastic planning model with time-dependent production functions and a deterministic planning model with seasonal production function, in operation of the Buchan dam reservoir by using hedging rules are compared. The results show the reservoir operation by hedging rules increases economic benefit by 46.8% compared to the existing operation model. The time-dependent production function can improve the results by 19% over seasonal production functions. Also, the results show using stochastic model with the inflow uncertainty, irrigation water demand uncertainty and both, inflow uncertainty and irrigation water demand uncertainty simultaneously, the economic benefit increase by 0.73, 4.95 and 12.99%, respectively.
https://ijswr.ut.ac.ir/article_82250_34577a4b96440f292c455e3b94523239.pdf
2021-09-23
1971
1985
10.22059/ijswr.2021.322227.668941
Reservoir operation
Hedging rules
Inflow uncertainty
irrigation water demand
Stochastic
Shahram
Zebardast
sh.zebardast59@ut.ac.ir
1
, Department of Irrigation and Reclamation Engineering, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
AUTHOR
Masoud
Parsinejad
parsinejad@ut.ac.ir
2
Irrigation Engineering Department, Campus of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
LEAD_AUTHOR
Aasgård, E. K., Bolkesjø, T. H., Johnsen, R. I., Kristiansen, F., Larsen, T. J., Riddervold, H. O., ... & Skjelbred, H. I. (2015). Validating the SHARM model. SINTEF energy research. Postboks 4761 sluppen. No 7465. Trondheim, Norway. TR A7521- Unrestricted.
1
Amerian, M., Mohammadi, K. and Eslami, H. R. (2003). Optimal operation model of Buchan dam reservoir by dynamic programming method (artificial neural network), the first national conference of hydropower plants, Tehran, Iran Water and Power Resources Development Company. (In Farsi)
2
Belsnes, M. M., Wolfgang, O., Follestad, T., & Aasgård, E. K. (2016). Applying successive linear programming for stochastic short-term hydropower optimization. Electric Power Systems Research, 130, 167-180.
3
Bzorg haddad, O. (2014). Optimization of water resources systems, University of Tehran Publishing Institute, Publication No. 3561, Second Edition, 412 pages. (In Farsi)
4
Claxton, K., Sculpher, M., & Drummond, M. (2002). A rational framework for decision making by the National Institute for Clinical Excellence (NICE). The Lancet, 360(9334), 711-715.
5
Draper, A. J., & Lund, J. R. (2004). Optimal hedging and carryover storage value. Journal of water resources planning and management, 130(1), 83-87.
6
Emami, F., & Koch, M. (2017). Evaluating the water resources and operation of the Boukan Dam in Iran under climate change. Eur. Water, 59, 17-24.
7
Emami, F., & Koch, M. (2019). Modeling the impact of climate change on water availability in the Zarrine River Basin and inflow to the Boukan Dam, Iran. Climate, 7(4), 51.
8
Gavahi, K., Mousavi, S. J., and Ponnambalam, K. (2018). Comparison of Two Streamflow Forecast Approaches in an Adaptive Optimal Reservoir Operation Model.
9
Gavahi, K., Mousavi, S. J., & Ponnambalam, K. (2019). Adaptive forecast-based real-time optimal reservoir operations: application to Lake Urmia. Journal of Hydroinformatics, 21(5), 908-924.
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12
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14
Men, B., Wu, Z., Liu, H., Li, Y., & Zhao, Y. (2019). Research on Hedging Rules Based on Water Supply Priority and Benefit Loss of Water Shortage—A Case Study of Tianjin, China. Water, 11(4), 778.
15
Mirhasani, S. A. and Hoshmandkhaligh, F. (2019). Stochastic programming. Published by Amir Kabir University (polytechnic). 305-307. (In Farsi)
16
Moghaddasi, M., Araghinejad, S., & Morid, S. (2010). Long-term operation of irrigation dams considering variable demands: Case study of Zayandeh-rud reservoir, Iran. Journal of irrigation and drainage engineering, 136(5), 309-316.
17
Neelakantan, T. R., & Pundarikanthan, N. V. (1999). Hedging rule optimisation for water supply reservoirs system. Water resources management, 13(6), 409-426.
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19
Seo, S. B., Kim, Y. O., & Kang, S. U. (2019). Time-Varying Discrete Hedging Rules for Drought Contingency Plan Considering Long-Range Dependency in Streamflow. Water Resources Management, 33(8), 2791-2807.
20
Sreekanth, J., Datta, B., & Mohapatra, P. K. (2012). Optimal short-term reservoir operation with integrated long-term goals. Water resources management, 26(10), 2833-2850.
21
Shiau, J. T., & Lee, H. C. (2005). Derivation of optimal hedging rules for a water-supply reservoir through compromise programming. Water resources management, 19(2), 111-132.
22
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23
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24
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25
Tu, M. Y., Hsu, N. S., Tsai, F. T. C., & Yeh, W. W. G. (2008). Optimization of hedging rules for reservoir operations. Journal of Water Resources Planning and Management, 134(1), 3-13.
26
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27
Yeh, W. W. G. (1985). Reservoir management and operations models: A state‐of‐the‐art review. Water resources research, 21(12), 1797-1818.
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29
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30
Zareabyaneh, H., Abdolahzadeh, B. and Palangi, S. (2017). Development of control curves for reservoir operation of Buchan and Mahabad dams with PSO algorithm. Irrigation and Water Engineering of Iran 8 (2). (In Farsi)
31
Zhang, Z., Zhang, Q., Singh, V. P., and Shi, P. (2018). River flow modelling: comparison of performance and evaluation of uncertainty using data-driven models and conceptual hydrological model. Stochastic Environmental Research and Risk Assessment, 32(9), 2667-2682.
32
Zhao, T., Cai, X., and Yang, D. (2011). Effect of streamflow forecast uncertainty on real-time reservoir operation. Advances in water resources, 34(4), 495-504.
33
ORIGINAL_ARTICLE
Modeling and Assessment of Discharge Coefficient of Arc Labyrinth Weir Using Experimental and Meta-model Methods
While having economic advantages, nonlinear labyrinth weirs have more passing flow capacity than linear weirs. Having a high capability of extracting hidden complex relationships among dependent and independent variables besides saving financial and time, intelligent algorithms are economic and time-saving and have dedicated a remarkable role among researchers. In this research, the performance of support vector machine (SVM) and gene expression programming (GEP) algorithms is figured out to predict the discharge coefficient (Cd) of the arched labyrinth weir using 226 experimental data series. Involved geometric and hydraulic parameters are total head (Ht), weir height (P), cycle arc angle (θ), Froud number (Fr), cycle wall length (Lt), the width of a cycle (w), weir nose length (A), an increase of weir height of 15% and change of weir crest shape change to quarter circle (U). Results showed that the maximum values of the Cd belong to arc labyrinth weir of arc angle 40 degrees. Numerical simulation illustrated that combination of (c، u، ، ، ، ) and (c، u، Fr، ، ، ) parameters have optimum performance in the SVM and GEP algorithms of assessment indices as (R2=0.9791, RMSE=0.03, DC=0.9776) and (R2=0.9871, RMSE=0.0231, DC=0.9856), respectively; showing highly accurate performance of two algorithms in the prediction of the Cd.
https://ijswr.ut.ac.ir/article_82131_f938533100aab9ff1bf5f06c31d44182.pdf
2021-09-23
1987
2000
10.22059/ijswr.2021.322432.668943
Dimensinal Analysis
Intelligent Algorithm
Nonlinear Weir
Optimum Performance
Overflow Capacity
Mahdi
Majedi Asl
mehdi.majedi@gmail.com
1
Assistant Professor, Department of Civil Engineering, Faculty of Engineering, University of Maragheh, Maragheh, Iran.
LEAD_AUTHOR
mehdi
fuladipanah
fuladipanah@gmail.com
2
Department of Civil Engineering, Ra,hormoz Branch, Islamic Azad University, Ramhormoz., Iran
AUTHOR
Rasoul
Daneshfaraz
daneshfaraz@yahoo.com
3
Professor , Department of Civil Engineering, Faculty of Engineering, University of Maragheh, Iran.
AUTHOR
khalil
Jannat
khalil.jannat1366@gmail.com
4
M.Sc.Student. Water and hydraulic structhures, Univ. of Maragheh, Iran
AUTHOR
Abbaspour A, and Arovanaghy, (2009). Flow prediction weirs Composite Triangular Regular Using Gene Expression Programming. 10th Iran Hydraulic Conference, November, Iran Hydraulic Association, University of Gilan, Iran.
1
Aydin I., Sakarya A.B., Sisman Cigdem (2011). Discharge formula for rectangular sharp crested weir. Flow Measurement and Instrumentation, 22(2):144-151.
2
Crookston, B.M. (2010). Labyrinth weirs. Ph.D. thesis, Utah State University, Logan, UT.
3
Dabling, M.R. (2014). Nonlinear weir hydraulics. M.Sc. Thesis. Utah State University, Logan, UT.
4
Darvas, L. A. (1971). Performance and design of labyrinth weirs. Journal of Hydraulic Engineering 97(8): 1246-1251.
5
Dizabadi, Sh., SeyedHakim, S. and AzimiA A.H. (2020). Discharge characteristics and structure of flow in labyrinth weirs with a downstream pool. Flow Measurement and Instrumentation, 71, 1-16.
6
Farrokhy, A., Givachy, A,. and Azhdary moghaddam. (2009). Estimating Determining the Discharge Coefficient of lateral weirs with neural network and adaptive neural-inference system. 6th National Congress of Civil Engineering, Semnan University, Iran. (In Farsi)
7
Fuladipanah, M. and Majedi Asl, M. (2020). Soft Computing Application to Amplify Discharge Coefficient Prediction in the Side Rectangular Weirs. Journal Of Irrigation and Water Engineering, DOI: 10.22125/IWE.2020.255601.1438. (In Farsi)
8
Fuladipanah, M., Majedi Asl, M. and Haghgooyi, A. (2020). Application of intelligent algorithm to model head-discharge relationship for submerged labyrinth and linear weirs. Journal of Hydraulics, 15(2): 149-164. (In Farsi)
9
Haghiabi, A.H., Parsaie, A. and Shamsi Z., 2018. Intelligent Modeling of Discharge Coefficient of Lateral Intakes. AUT Journal of Civil Engineering, 2(1): 3-11.
10
Hay, N. and G. Taylor. 1970. Performance and design of labyrinth weirs. Journal of Hydraulic Engineering 96(11): 2337–2357
11
Kabiri-Samani, A.R., Ansari, A., and Borghei, S.M. (2010). Hydraulic behavior of flow over an oblique weir. Journal of Hydraulic Research. 48(5): 669-673.
12
Kumar, M., Sihag, P., Tiwari, N.K. and Ranjan S. (2020). Experimental study and modelling discharge coefficient of trapezoidal and rectangular piano key weirs. Applied Water Science, 10: 43-52.
13
Lux, F. and Hinchcliff, D. (1985). Design and construction of labyrinth spillways. Proceeding of the 15th Congress ICOLD, Lausanne, Switzerland.
14
Majedi Asl, M. and Fuladipanah M. (2018). Application of the Evolutionary Methods in Determining the Discharge Coefficient of Triangular Labyrinth Weirs. Journal of Water and Soil Science (Science and Technology of Agriculture and Natural Resources), 22(4), 279-290 (In Farsi)
15
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16
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17
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18
Parsaie, A. and Haghiabi, A.H. (2017). Support Vector Machine to predict the discharge coefficient of Sharp crested w-planform weirs. AUT Journal of Civil Engineering, 1(2): 195-204.
19
Parsaie, A.,Haghiabi, A.H. and Shamsi Z. (2019). Intelligent mathematical modeling of discharge coefficient of nonlinear weirs with triangular plan. AUT Journal of Civil Engineering, 3(2): 149-156.
20
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21
Seo, I.W., Do, K.Y., Park, Y.S. and Song, C.G. (2016). Spillway discharges by modification of weir shapes and overflow surroundings. Environmental Earth Science, 75(6):496-509.
22
Tullis J.P., Amanian N. and Waldron D. (1995). Design of Labyrinth Spillways. Journal of Hydraulic
23
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24