Quantification of long-term hydrologic change in groundwater often requires the comparison of states pre- and post-change. The assessment of these changes in ungauged catchments using numerical models and other quantitative methods is particularly difficult from a conceptual point of view and due to parameter non-uniqueness and associated uncertainty of quantitative frameworks. In these contexts, the use of data assimilation, sensitivity analysis and uncertainty quantification techniques are critical to maximize the use of available data both in terms of conceptualization and quantification. This paper summarizes findings of a study undertaken in the Lake Muir-Unicup Natural Diversity Recovery Catchment (MUNDRC), a small-scale endorheic basin located in southwestern Australia that has been subject to a systematic decline in rainfall rates since 1970s. A combination of data assimilation techniques was applied to conceptual and numerical frameworks in order to understand and quantify impacts of rainfall decline on the catchment using a variety of metrics involving groundwater and lake levels, as well as fluxes between these compartments and mass balance components. Conceptualization was facilitated with the use of a novel data-driven method relating rainfall and groundwater responses running backwards in time, allowing the establishment of the likely baseline conditions prior to rainfall decline, estimation of net recharge rates and providing initial heads for the forward numerical modelling. Numerical model parameter and predictive uncertainties associated with data gaps were then minimized and quantified utilizing an Iterative Ensemble Smoother algorithm, while further refinement of conceptual model was made possible following results from sensitivity analysis, where major parameter controls on groundwater levels and other predictions of interest were quantified. The combination of methods can be considered as a template for other long-term catchment modelling studies that seek to constrain uncertainty in situations with sparse data availability.