Stochastic Modeling of Groundwater Extractions over a Data-Sparse Region of Australia.

Abstract

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.