عنوان مقاله [English]
Introduction: Agriculture consumes a large portion of groundwater resources. In order to understand the status of groundwater resources in a basin and to optimize its management, it is necessary to carry out an accurate examination of the fluctuations in the groundwater levels. Recharging groundwater aquifers is one of the main strategies for water resources management which its accurate estimation plays a crucial role in the proper management of ground water resources. That portion of the excess irrigation water which becomes in the form of deep percolation should not be considered as wasted water, if its quality is not adversely reduced and it enters and recharges groundwater aquifers. The question is whether deep percolations resulting from irrigating farms with low application efficiencies and poor irrigation management in the Urmia basin would finally recharge ground water aquifers or not. In order to provide a solution to the aforementioned question, after calibrating HYDRUS-1D model, it was used to estimate the fluctuations of the levels of the water tables as a results of irrigations or rainfalls in a number of wheat, barley and sugar beet fields located in Miandoab and Mahabad regions where all agricultural practices were managed and carried out by the local farmers.
Materials and Methods: In order to ascertain the effects of irrigation on the groundwater recharge, the required field data was collected from nine agricultural fields including one wheat farm, three barley farms, and three sugar beet farms, all located in the Miandoab region and two wheat fields located in the Mahabad region. All the water balance parameters for each one of the fields were measured in the studied fields, including the depth of irrigation at each irrigation event by using WSC flumes. The Surface runoff from the studied farms was considered as negligible, since all the fields were irrigated using closed end borders. The evapotranspiration of wheat, barley and sugar beet were calculated in the regions using the CROPWAT8.0 model.
The soil texture of each of the study fields were determined by hydrometric method in the laboratory and then soil hydraulic parameters were estimated by ROSETTA model. The soil moisture of all the fields during the growing season were measured using a PR2 moisture meter instrument measuring soil moisture at various depths up to 105 cm below the soil surface. The amount of deep percolation occurring during the growing season was simulated by the HYDRUS-1D model. The soil water content measured by PR2 (Delta-T Device) probe were used for HYDRUS-1D model calibration and validation using the inverse solution method. Because of the occurrence of rainfall, irrigation and evapotranspiration, the atmospheric boundary condition was selected as the upper boundary condition and free drainage was considered as the lower boundary condition in order to estimate the groundwater recharge, assuming that water passes through and below the root zone. In areas with shallow ground water depth, constant flow with zero flux was chosen as the lower boundary condition in order to determine the fluctuations of the ground water level. Since the groundwater level in this case study was shallow, zero flux was considered as the lower boundary condition. The soil moisture content before irrigation was selected as the modelling initial condition.
Result and Discussion: The HYDRUS-1D model was calibrated by comparing the model estimated soil moisture contents with the corresponding measured values which indicated the coefficient of determination (R2) and root mean square error (RMSE) values ranging from 0.6 to 0.85 and 0.17 to 0.033 (), respectively. Another set of measured soil moisture data which was collected by using PR2 instrument and was not used for calibrating the model, was applied to verify the model simulation of the soil moisture content. Comparing the measured and simulated soil moisture contents at this verification stage resulted in coefficient of determination (R2) and root mean square error (RMSE) values ranging from 0.62 to 0.88 and 0.002 to 0.023 (), respectively. There was no significant difference between the predicted and measured soil moisture data in all the fields (P-value> 0.05). The minimum and the maximum coefficient of determinations in the validation stage were obtained in the T5 field with a silty loam soil and in the H3 field having a sandy loam soil. The accuracy of the model performance after it was calibrated and verified using the collected field data, was appropriate for estimating the soil water content during the growing season. The model was used to simulate the soil water contents from the soil surface to the depth of the water table during the growing season to evaluate the degree of aquifer recharge if any happened. The soil moisture front advanced to a depth of 0.7 m below the soil surface in the M1 field and to 4.7 m in the T1field. The amount of groundwater recharge varied from field to field depending on each field’s soil type, cultivation and irrigation management including the depth and the time of the irrigations. The amount of groundwater recharge increased by decreasing crop evapotranspiration. The percentage of ground water recharge in N1, M1 and M2 fields due to limited availability of water resources which resulted in deficit irrigation was very low. The irrigation water requirements estimated by the CROPWAT model for the aforementioned fields were more than the depths of the irrigation water applied by the farmers. The CROPWAT model estimated the irrigation water requirements during the growing season for wheat, barley and sugar beet in the Miandoab region to be 308, 273 and 736 mm, respectively. However, the depths of irrigation applied to such farms ranged from 306 to 500 mm.
Conclusion: This research was conducted to ascertain the effects of local farmer’s irrigation management practices considered as poor management in some areas with plenty of water resources available and rainfall on the amount of the groundwater recharge occurring in the regions studied located in the Lake Urmia basin. The simulated groundwater recharge by the HYDRUS-1D model indicated that the amount of recharge varied from field to field depending on soil type, cultivation and irrigation management practices. In all the fields, the highest amount of groundwater recharge occurred when the crop evapotranspiration was low and therefore, enhancing deep percolation to take place. The highest percentage of groundwater recharge was 28% of the sum of the irrigation and rainfall depths which occurred in the barley field (H3).