## 1 Introduction  
Groundwater in the Lower Indus Basin (LIB) originates from the river system, which has flowed through the valley since late Tertiary times. The fresh water occurs in lenses near the Indus River or near the major canals. These freshwater lenses are underlain by dense saline water. This is due to the hydrogeological conditions in the LIB, which cause rapid mineralization of the groundwater. The largest freshwater lens occurs along the Indus River, and its quality deteriorates away from the river [1]. Inequity in surface water distribution and increasing irrigation intensities have led to an increase in groundwater dependence on fresh groundwater lenses. Government subsidies for installation of solar tube wells are exacerbating this trend and undermining farmer incentives to avoid unsustainable use of groundwater. Groundwater level declines will become a critical issue for farmers in the future, as the overexploitation of freshwater lenses will cause up-coning of saline water into the freshwater zone. There is a crucial need to monitor groundwater levels and maintain pumping at sustainable rates that preserve the integrity of fresh groundwater lenses. The overall aim of the current study was to simulate the groundwater hydraulics, delineate critical areas, and propose monitoring networks that can be continuously monitored to maintain water levels in the freshwater zone. Monitoring network design is considered to be a complex and iterative process. This is due to the fact that there are multiple factors to consider, including: uncertainties regarding the hydrology and geology of the system; spatial and temporal variation of the parameters; competing objectives of the monitoring; and consideration of the most suitable approach. There are different approaches to designing a monitoring network, which can generally be categorized as hydrogeological or statistical in nature [2]. Hydrogeologic approaches utilize the qualitative and quantitative description of the hydrology and geology of the area to find suitable locations for monitoring, while statistical approaches utilize the historical data and statistical methods to find suitable locations. Previously, different approaches have been used for groundwater level monitoring (GWLM) network design and optimization. GWLM network design has been done through variance-based approaches [3; 4], regression techniques [5], geostatistical approaches [6; 7; 8; 9; 10], information theory [11], and simulation-optimization approaches [12; 13]. For example, [PERSON] et al. [3] evaluated an existing groundwater monitoring network in the Bangkok metropolitan area using principal component analysis (PCA) to rank the observation wells in order of their importance in the monitoring network. Similarly, [PERSON] et al. [4] analyzed an existing groundwater level monitoring network in an irrigation area of New South Wales, Australia using PCA. [PERSON] and [PERSON] [5] used a correlation coefficient to prioritize observation wells to rationalize an existing groundwater level monitoring network in Erbil, Iraq. [PERSON] et al. [10] modified the existing monitoring network using ordinary kriging. Generally, while applying statistical approaches, a dense network of observation wells is installed and temporal observations are recorded; then the number of observation locations is reduced rationally by applying statistical techniques to historical water level data. There are limitations associated with designing an optimal network using only statistical approaches, which include: the requirement for large temporal and spatial datasets; the consideration of one aspect of groundwater dynamics at a time (either of the groundwater table fluctuations or spatial coverage of the monitoring area); and a lack of representation of hydrogeology in the monitoring area. Significant improvement in the design of networks can be achieved if all the aspects of the groundwater system are addressed in a single approach.