Model reliability is important when groundwater models are used for evaluation of environmental impact and water resource management. Model attributes such as geohydrologic units and parameter values need to be quantified in order to obtain reliable results. A primary objective of sampling design for groundwater models is to increase the reliability of modelling results by selecting effective measurement locations and times. It is advantageous to employ simulation models to guide measurement strategies already in early investigation stages. Normally, optimal design is only possible when model attributes are known prior to constructing a design. This is not a meaningful requirement as the model attributes are the final result of the analysis and are not known beforehand. Thus, robust design methods are required that are effective for ranges of parameter values, measurement error types and for alternative conceptual models. Parameter sensitivity is the fundamental model property that is used in this thesis to create effective designs. For conceptual model uncertainty, large-scale sensitivity analysis is used to devise networks that capture sufficient information to determine which model best describes the system with a minimum of measurement points. In fixed conceptual models, effective parameter- and error-robust designs are based on criteria that minimise the size of the parameter covariance matrix (D-optimality). Optimal designs do not necessarily have observations with the highest parameter sensitivities because D-optimality reduces parameter estimation errors by balancing high sensitivity and low correlation between parameters. Ignoring correlation in sparse designs may result in considerably inefficient designs. Different measurement error assumptions may also give widely different optimal designs. Early stage design often involves simple homogenous models for which the design effectiveness may be seriously offset by significant aquifer heterogeneity. Simple automatic and manual methods are possible for design generation. While none of these guarantee globally optimal designs, they do generate designs that are more effective than those normally used for measurement programs. Effective designs are seldom intuitively obvious, indicating that this methodology is quite useful. A general benefit of this type of analysis, in addition to the actual generation of designs, is insight into the relative importance of model attributes and their relation to different measurement strategies.