Groundwater is considered a reliable resource, relatively insensitive to seasonal or even multi-year climatic variation; however, quantifying aquifer-scale estimates of stress in diverse hydrologic environments is particularly difficult due to data scarcity and the limited methods for deriving stress parameters, such as groundwater use and availability, which can be applied over a large spatial area. On a global scale, most methods focus on one major sector, such as irrigated agriculture which accounts for a substantial portion of groundwater use on a global-scale. However, this may misrepresent groundwater abstractions in regions significantly impacted by other sectors on a local-scale. The objective of this paper is to quantify annual average groundwater use through a multi-method sectoral approach for regions where groundwater abstraction data are scarce. Sectoral methods are developed for the annual volumetric quantification and spatial distribution of groundwater use for municipal water distribution systems, private domestic well users in municipal and rural regions, industrial use for manufacturing, mining, and oil and gas industries, irrigated agriculture, and finfish aquaculture. Results suggest that British Columbia (BC) uses a total of ∼562 million cubic metres of groundwater annually. The largest annual groundwater use by major sector is agriculture (38%), finfish aquaculture (21%), industrial (16%), municipal water distribution systems (15%), and domestic private well users (11%). This paper highlights the implications of using downscaled values of groundwater use from global datasets for aquifer-scale estimates, which can be misrepresentative in regions where groundwater use is unregulated or newly regulated, as is the case in BC. The sectoral methods developed in this paper provide a framework for estimating groundwater-specific use estimates in data scarce regions critical for groundwater management plans and aquifer-scale groundwater stress studies which depend on spatially-distributed groundwater use data.