Changing the amount of exploitation in order to increase productivity and reduce groundwater extraction in the plains of Iran has had a positive effect on water balance. Some management strategies, such as well relocation, have been able to reduce the effects of water table drawdown. Simulation of water resources systems has an effective role in studying the effects of drought and determining the temporal and spatial constraints of water management. Joint functions are tools that join or “couple” multivariate distribution functions to their one-dimensional univariate (marginal) distribution functions. The copula function is capable of exhibiting the structure of dependence between two or more random variables and has recently emerged as a practical and efficient method for modeling the general dependence in multivariate data. In addition, in the field of hydrogeological studies in recent years, a lot of research has been done to investigate some specific aspects of groundwater simulation such as exploitation optimization, artificial recharge, underground dam, quantitative and qualitative simulation of groundwater, economic studies of water supply and demand. In this paper, the framework of a probabilistic multivariate decision model based on long-term rainfall information and extraction of effective variables in aquifer recharge are considered. The relationships and constraints governing the severity and depth of precipitation with emphasis on the hydrodynamic characteristics of the aquifer and also the use of real-time information updating system, a decision model for the design of surface artificial recharge structure is developed and simulated. The main difference considered in this study in probabilistic rainfall planning as a long-term goal for the sustainability of water resources management is the description of special conditions and constraints of the aquifer.

Rafsanjan plain located 110 km from Kerman has an unlimited aquifer with an area of 4108 km2. This plain is located between 252 ° 54 to ´34 ° 56 east longitude and 151 ° 29 to ´31 ° 31 north latitude at an altitude of 1400 to 3443 m above sea level and in the northeast of Kerman province. The three plains of Rafsanjan, Noogh and Anar are located in this area, which does not have a permanent river and is a source of water from the aquifer. About 620 million cubic meters (MCM) of groundwater is discharged annually by 1289 existing wells. According to long-term statistics, the volume of groundwater resources in the Rafsanjan area is estimated at about 430 MCM, with an annual overexploitation of 190 MCM. 8 locations were selected for the construction of surface artificial recharge structures that can collect rainfall and upstream runoff. It should be noted that the surface slope and hydraulic gradient are influential components in the movement and infiltration of water, which are estimated using the topographic map and water table. The volume of water demand based on groundwater exploitation annually has been estimated in the region of each point. Moreover, the priority of water supply is determined based on the population living in each area. Groundwater level simulation in Rafsanjan plain aquifer from 1997 to 2020 was performed using 35 active piezometer data. The first 17 years were used for calibration and the last 4 years for verification. Seasonal stress periods and daily time steps were considered for the calculations.

The results showed that this method can be a managerial strategy for groundwater balancing in the event of seasonal and scattered rainfall. The aquifer transmissivity which reaches more than 400 m2/day, indicates the storage capacity and water movement. Bivariate frequency analysis showed that in a 100-year return period, there is a possibility of rainfall with a depth of about 115 mm or an intensity higher than 70 mm/day. This rainfall can increase the groundwater level by an average of 2 m in the center of the aquifer. Moreover, beneficiaries are able to reduce drought stresses and control their management significantly by performing optimal planning.