مجله آب و خاک (Feb 2016)
Simulation of Salinity Distribution in Soil Under Drip Irrigation Tape with Saline Water Using SWAP Model
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.
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