In recent decades, due to reduction in precipitation, groundwater resource management has become one of the most important issues considered to prevent loss of water. Many solutions are concerned with the investigation of groundwater flow behavior. In this regard, development of meshless methods for solving the groundwater flow system equations in both complex and simple aquifers' geometry make them useful tools for such investigations. The independency of these methods to meshing and remeshing, as well as its capability in both reducing the computation requirement and presenting accurate results, make them receive more attention than other numerical methods. In this study, meshless local Petrov–Galerkin (MLPG) is used to simulate groundwater flow in Birjand unconfined aquifer located in Iran in a transient state for 1 year with a monthly time step. Moving least squares and cubic spline are employed as approximation and weight functions respectively and the simulated head from MLPG is compared to the observation results and finite difference solutions. The results clearly reveal the capability and accuracy of MLPG in groundwater simulation as the acquired root mean square error is 0.757. Also, with using this method there is no need to change the geometry of aquifer in order to construct shape function.

This study presents the first attempt to link the multi-algorithm genetically adaptive search method (AMALGAM) with a groundwater model to define pumping rates within a well distributed set of Pareto solutions. The pumping rates along with three minimization objectives, i.e. minimizing shortage affected by the failure to supply, modified shortage index and minimization of extent of drawdown within prespecified regions, were chosen to define an optimal solution for groundwater drawdown and subsidence. Hydraulic conductivity, specific yield parameters of a modular three-dimensional finite-difference (MODFLOW) groundwater model were first optimized using Cuckoo optimization algorithm (COA) by minimizing the sum of absolute deviation between the observed and simulated water table depths. These parameters were then applied in AMALGAM to optimize the pumping rate variables for an arid groundwater system in Iran. The Pareto parameter sets yielded satisfactory results when maximum and minimum drawdowns of the aquifer were defined in a range of −40 to +40 cm/year. Overall, ‘Modelling – Optimization – Simulation’ procedure was capable to compute a set of optimal solutions displayed on a Pareto front. The proposed optimal solution provides sustainable groundwater management alternatives to decision makers in arid region.

Uncertainty assessment of groundwater modeling is important for sustainable groundwater management and planning. The purpose of this study is to assess parameter uncertainty of groundwater modeling in the Birjand plain, Iran. This arid aquifer was modeled using MATLAB-based MODFLOW to avoid propagating uncertainty associated with hydraulic conductivity and recharge parameters. So, the aquifer was divided into 17 hydraulic conductivity homogenous zones; besides, 9 recharge zones were considered separately. Parameter uncertainty was evaluated using the Monte Carlo (MC) sampling technique, namely, the generalized likelihood uncertainty estimation (GLUE). The results indicated that the performance of the GLUE based on the inverse error variance likelihood function was satisfied, because it gave the higher bracketing of observations equal to 86 %. Parameter uncertainty is well defined in the zones where they are not influenced directly by an inflow or outflow stream while hydraulic conductivity parameters of these zones follow approximately a normal distribution. In addition, groundwater modeling leads to a uniform exponential distribution in the zones with inflow or outflow streams.

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