Simulation of Salinity Distribution in Soil Under Drip Irrigation Tape with Saline Water Using SWAP Model

Document Type : Research Article

Authors

• M. Tabei ¹
• Saeid Boroomand Nasab ²
• A. Soltani Mohamadi ¹
• A. H. Nasrollahi ³

¹ ShahidChamran University of Ahvaz

² Professor, Faculty of Water Sciences Engineering, Shahid Chamran University of Ahvaz, Iran

³ Lorestan University

https://doi.org/10.22067/jsw.v0i0.32175

Abstract

Introduction: The to be limited available water amount from one side and to be increased needs of world population from the other side have caused increase of cultivation for products. For this reason, employing new irrigation ways and using new water resources like using the uncommon water (salty water, water drainage) are two main strategies for regulating water shortage conditions. On the other side, accumulation of salts on the soil surface in dry regions having low rainfall and much evaporation, i.e. an avoidable case. As doing experiment for determining moisture distribution form demands needs a lot of time and conducting desert experiments are costly, stimulator models are suitable alternatives in answering the problem concerning moving and saltiness distribution.
Materials and Methods: In this research, simulation of soil saltiness under drip irrigation was done by the SWAP model and potency of the above model was done in comparison with evaluated relevant results. SWAP model was performed based on measured data in a corn field equipped with drip irrigation system in the farming year 1391-92 in the number one research field in the engineering faculty of water science, ShahidChamran university of Ahvaz and hydraulic parameters of soil obtained from RETC . Statistical model in the form of a random full base plan with four attendants for irrigating water saltiness including salinity S1 (Karoon River water with salinity 3 ds/m as a control treatment), S2 (S1 +0/5), S3 (S1 +1) and S4 (S1 +1/5) dS/m, in 3 repetition and in 3 intervals of 10 cm emitter, 20 cm emitters on the stack, at a depth of 0-90 cm (instead of each 30 cm) from soil surface and intervals of 30, 60 and 90 days after modeling cultiviation was done. The cultivation way was done handheld in plots including four rows of 3 m in distance of 75 cm rows and with denseness of 80 bushes in a hectar. Drip irrigation system was of type strip with space of 20 cm pores.
Results and Discussion: The results of this section of work have shown in the form of chart drawing and calculating identity indices or recognition (R2), maximum error (ME), normalized root mean second error (NRMSE) and coefficient of residual mass (CRM) in the distances on the stack, 10 and 20 cm dropper. The amount of R2, ME, NRMSE and CRM in 10 cm dripper were calculated to be 0/81, 0/46, 11/77 and 0/018 mg/cm3, in 20 cmdripper 0/78, 0/48, 16/44 and 0/1172 mg/cm3 and on the stack 0/75, 2/8, 18/19 and 0/07 mg/cm3. The highest recognition factor was a distance of 10 cm dripper (81 percent) and then reduces to keep distance from dripper recognition factor . This subject is the highest potency close to the dripper. This can happen for less saltiness in the spaces close to the dripper according to drip irrigation features. The high ME amount shows the less attendance computing of the model, it comes to it’s maximum on the stack, however (2/8 mg/cm3), the distances near to the dripper the obtained ME amount shows the good care in estimating soil saltiness. Also, based on being positive CRM parameter amount was seen. It is less in the amount observed in anticipating of saltiness in the anticipated amount. By considering NRMSE factor, higher amount of anticipating is based on observations.
Conclusion: Generally, the results obtained from stimulating of SWAP show that this model can stimulate saltiness distribution in soil under drip irrigation with salty water. This model can be used as useful tools for evaluation of saltiness distribution around the dripper.

Keywords

• Drip irrigation
• Salinity Simulation
• Salt Water
• SWAP model

20.1001.1.20084757.1394.29.3.8.5

References

1- Alizadeh A. 2007. Designing of irrigation systems. ThePublishers University Ferdowsi Mashhad, 1(7): 131.

2- Jiang J., FengSh., Huo Z., Zhao Z., and Bin J. 2011. Application of the SWAP model to simulate watersalt transport under deficit irrigation with saline water. Mathematical and Computer Modelling, 54: 902-911.

3- Jury W.A., Gardner W. R. and Gardner W. H. 1991. Soil Physics. Fifth Edition. Wiley, New York. P. 330.

4- Khaksari V., Mousavi Sa., Chraghi Sm., Kamkar A. and Parsa Sh. 2006. Evaluation of SWAP and LEACHC Computer models the Leaching of field in the soil solute in ChahAfzal Yazd. Journal of Science and Technology of Agriculture and Natural Resources. 10(2): 57-68.

5- Khani Qryhgpy M., Davari K., Alizadeh A., Hasheminia M., and Zulfiqar A. 2007. Evaluation of SWAP model to estimate The quantity and quality of sugar beet yield under different irrigation. Journal Irrigation and Drainage, 2: 107-117.

6- Kiani R. 2007. Use the SWAP model for simulation solute water transfer and the relative performance of wheat. Seminar water and reduce evaporation, 9: 13-30.

7- Liu H.F., Genard M., Guichard S. and Bertin N. 2007.Model-assisted analysis of tomato fruit growth in relation to carbon and water fluxes. Journal of Experimental Botany, 58(13): 3567-3580.

8- Mass E.V., and Hoffman G.J. 1977. Crop salt tlorance current assessment, J.Irrigation and Drainage Division. ASCE, 103(IR2):115-134.

9- Mann L., Su N., Bethune M., and Heuperman H.A. 2005.Simulation of water and salt movement in tiledrained field irrigation with saline water under a serial biological concentration management scenario. department water resource, Wagenningen Agricultural University, Report No. 85.

10- Mostafazadeh Fard B., Mansouri H., Mousavi S.F., and Feizi M. 2008. Application of SWAP model to predict yield and soil salinity for sustainable agriculture in an arid region. International Journal of Sustainable Development and Planning, 3(4): 334-342.

11- Mualem Y. 1976. A new model for predicting the hydraulic conductivity of unsaturated porous media. Water Resourc Research., 12: 513-522.

12- Shahidi A. 2008. Interaction deficit irrigation and salinity on yield and yield components of wheat with salt water production function in Birjand. Irrigation and Drainage Thesis. Water Sciences and Engineering Department. Shahid Chamran Ahwaz University.

13- Singh R. 2003. Simulation on direct and cyclic use of saline waters for sustaining cottonwheat in a semiarid area of north-west India. department of soil and water engineering. college of agricultural engineering, CCS Haryana Agricultural University. Hisar 125004. India.

14- Soltani Mohammadi A.M. 2011. The impact of water stress and salinity at different growth stages. Irrigation and Drainage Thesis. Water Sciences and Engineering Department. Shahid Chamran Ahwaz University.

15- Su N., Bethune M., Mann L. and Heuperman A. 2005. Simulating water and salt movement in tile-drained fields irrigated with saline water under a serial biological concentration management scenario. Agricultural water Management, 78: 165-180.

16- Van Dam J.C., Huygen J., Wesseling J. G., Feddes R. A., Kabat P., Van Walsum P. E. V., Groenendijk P. and Van Diepen C. A. 1997. Theory of SWAP, version 2. Simulation of water flow, solute transported plant growth in the soil water atmosphere plant environment. Report 71, Department of Water Resource, Wageningen Agricultural University, 167pp.

17- Van Genuchten M. Th. 1980. A closed form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Science Society of America Journal, 44: 892-898.

18- Van Genuchten M.T., Leij F.J., and Yates S.R. 1991. The RETC Code for Quantifying the Hydraulic Functions ofUnsaturated Soils. Office of research and developement U.S. environmental protection agency ADA, Oklahoma.

19- Vazifedoust M.,Van Dam J.C Feddes R.A. and Feizi M. 2008.Increasing water productivity of irrigated crops under limited water supply at field scale. Agricultural Water Management 95:89-102.