Decomposing groundwater head variations into meteorological and pumping components: a synthetic study

V. Shapoori, T. J. Peterson, A. W. Western, J. F. Costelloe

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Time-series modeling is often used to decompose groundwater hydrographs into individual drivers such as pumping and meteorological factors. To date, there has been an assumption that a simulation fitting the total hydrograph produces reliable estimates of the impact from each driver. That is, assessment of the decomposition has not used an independent estimate of each decomposition result. To begin to address this, a synthetic study is undertaken so that the impact of each driver is known. In this study, 500 MODFLOW groundwater models of a one-layer unconfined aquifer were constructed. For each model, three hydrogeological properties (saturated hydraulic conductivity, storativity and depth to aquifer basement), the distance between observation and pumping bores, and extraction rate were set randomly and synthetic groundwater hydrographs were derived. For each hydrograph, the influence of individual drivers was estimated using six different time-series models. These estimates were then compared to the known meteorological and pumping influences derived from the MODFLOW models. The results demonstrate that hydrograph separations obtained from time-series models do not always result in reliable estimation of pumping and meteorological influences even when the overall hydrograph fit is good. However, when the time-series model represents the important processes (e.g. phreatic evaporation is included for shallow water tables) and the (head) variance of the pumping signal to the meteorological signal is between 0.1 and 10, the time-series model has the potential to adequately separate the influence of pumping and climate.

Original languageEnglish
Pages (from-to)1431-1448
Number of pages18
JournalHydrogeology Journal
Issue number7
Publication statusPublished - Nov 2015
Externally publishedYes


  • Australia
  • Groundwater pumping
  • Statistical modeling
  • Time series modeling

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