Investigating the effect of appropriate levels of artificial recharge and flood management in Shahriar Plain on groundwater resources

In arid and semi-arid regions, groundwater is the main source of development of sectors (industrial, agricultural, domestic). And with the increase in demand for water due to population growth, irrigation of agricultural lands and industrial development, exploitation of groundwater resources has also increased. This issue, combined with droughts and the consequences of climate change, has reduced the groundwater level in arid and semi-arid regions. In order to supply the lack of surface water, as well as to overcome drought in emergency situations and sustainable management of groundwater resources, installing a managed aquifer recharge system (MAR) to recharge groundwater using flood and surface water in a long-term is important and necessary. The methods of artificial recharge are diverse and depend on the need, facilities and many factors that affect on artificial recharge. Due to this issue, tools such as geographic information system (GIS) are very useful for finding the best places to recharge groundwater. Identifying the groundwater recharge area is an important factor for achieving success and managing water resources in any region. The purpose of this research is the optimal management of surface and groundwater resources using a hybrid modeling pattern in order to increase the stability of the groundwater resources system and reduce its quantitative and environmental issues in the study area. Therefore, in this study, the identification and management of groundwater recharge was carried out in the Shahriar Plain located in the western outskirts of Tehran, Iran.

In this research, a simulation pattern was presented, according to which, first, the best artificial recharge place was determined by fuzzy and weight method using GIS model in Shahriar plain. Then, HEC-RAS hydraulic model was used to calculate the parameter (the ratio of infiltration debi to flood debi) in the Karaj river. In the next step, two independent models for flood routng in the river and reservoirs of the artificial recharge system were defined by the HEC-HMS model. In the first model, the parameter of the ratio of infiltration debi to flood debi was used in the Massingham-Kange method to determine the flood routng in the river, and in this way, the amount of flood entering to the artificial recharge plan was estimated. In the second model, flood routng was carried out in the reservoirs of the recharge plan and the amount of infiltration, storage and recharge was calculated by the plan. And finally, the amount of recharge in the artificial recharge plan was simulated using the artificial neural network (ANN) model, and the performance of the artificial recharge system and the groundwater level changes resulting from it were estimated.

According to the fuzzy and weight analysis, the best place for artificial recharge in the entire studied time horizon was considered to be alluvial fan in the north of the plain. The area of the suitable (good) region obtained by the fuzzy method was calculated to be 8.62 percent more than the weighted method, and the performance of the fuzzy method in estimating the suitable locations was evaluated better than the weighted method. The maximum volume of flood entering the recharge plan from the HEC-RAS model and the first HEC-HMS model in the studied period is 8.56 and 8 (MCM), respectively, and the minimum value of this variable from the HEC-HMS simulator is 0.87 (MCM) and in the case of the HEC-RAS model, the same amount was estimated. The results indicated that the maximum and minimum flood volumes entering to plan are related to the maximum and minimum floods occurred. The total amount of artificial recharge resulting from the HEC-HMS model, in the entire time period studied, without considering the flood output from the recharge plan, was computed as 37.6 (MCM), and the amount of total recharge for the neural network model was computed as 36.3 (MCM). Also, the total level changes due to recharge of the plan resulting from the estimation of the simulator and the neural network model in the entire study period are equal to 1.59 and 1.53 (m), respectively, which indicates the appropriate accuracy of the neural network model. Also, the level changes caused by recharge in the plan and the river in the entire period of desired time was computed to be 1.88 (m). In total, the reserve performance of the plan in the entire period of desired time was 54 percent and the recharge performance of the plan was 89 percent, and the performance of the entire artificial recharge system including (river section and artificial recharge plan) in recharge applications was computed at 90 percent. Therefore, the results of the simulation pattern and the performance of the artificial recharge plan in recharging were evaluated as suitable and acceptable. Finally, according to the results of the hybrid modeling pattern, at the same time, floods, surface and groundwater resources and artificial recharge system are under control, and based on available water resources, increasing the stability of the groundwater resources system and reducing quantitative and environmental issues related to water resources can be managed and controlled according to the existing conditions.