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Setting limit on groundwater extractions is important to ensure sustainable groundwater management. Lack of extraction data can affect interpretations of historical pressure changes, predictions of future impacts, accuracy of groundwater model calibration, and identification of sustainable management options. Yet, many groundwater extractions are unmetered. Therefore, there is a need for models that estimate extraction rates and quantify model outputs uncertainties arising due to a lack of data. This paper develops such a model within the Generalized Linear Modeling (GLM) framework, using a case study of stock and domestic (SD) extractions in the Surat Cumulative Management Area, a predominantly cattle farming region in eastern Australia. Various types of extraction observations were used, ranging from metering to analytically‐derived estimates. GLMs were developed and applied to estimate the property‐level extraction amounts, where observation types were weighted by perceived relative accuracy, and well usage status. The primary variables found to affect property‐level extraction rates were: yearly average temperature and rainfall, pasture, property area, and number of active wells; while variables most affecting well usage were well water electrical conductivity, spatial coordinates, and well age. Results were compared with analytical estimates of property‐level extraction, illustrating uncertainties and potential biases across 20 hydrogeological units. Spatial patterns of mean extraction rates (and standard deviations) are presented. It is concluded that GLMs are well suited to the problem of extraction rate estimation and uncertainty analysis, and are ideal when model verification is supported by measurement of a random sample of properties. Article impact statement: We present a new approach that incorporates observations of varying accuracy to estimate spatial groundwater extractions and uncertainties. [ABSTRACT FROM AUTHOR]

The accuracy of hydrological assessments in mountain regions is often hindered by the low density of gauges, coupled with complex spatial variations in climate. Increasingly, spatial data sets (i.e. satellite and gridded products) and new computational tools are used to address this problem, by assisting with the spatial interpolation of ground observations. This paper presents a comparison of approaches of different complexity to spatially interpolate precipitation and temperature time-series in the upper Aconcagua catchment in central Chile. A Generalised Linear Mixed Model whose parameters are estimated through approximate Bayesian inference is compared with three simpler alternatives: Inverse Distance Weighting, Lapse Rates and a method based on WorldClim data. The assessment is based on a leave-one-out cross validation, with the Root Mean Squared Error being the primary performance criterion for both climate variables, while Probability of Detection and False Alarm Ratio are also used for precipitation. Results show that for spatial interpolation of the expected values of temperature and precipitation, the WorldClim approach may be recommended as being the more accurate, easy to apply and relatively more robust to tested reductions in the number of estimation gauges, particularly for temperature. The Generalised Linear Mixed Model has comparable performance when all gauges were included, but is more sensitive to the reduction in the number of gauges used for estimation, which is a constraint in sparsely monitored catchments. [ABSTRACT FROM AUTHOR]