Solving Groundwater Flow Problems with Time Series Analysis: You May Not Even Need Another Model

Mark Bakker, Frans Schaars

Research output: Contribution to journalArticleScientificpeer-review

14 Citations (Scopus)
56 Downloads (Pure)

Abstract

Time series analysis is a data-driven approach to analyze time series of heads measured in an observation well. Time series models are commonly much simpler and give much better fits than regular groundwater models. Time series analysis with response functions gives insight into why heads vary, while such insight is difficult to gain with black box models out of the artificial intelligence world. An important application is to quantify the contributions to the head variation of different stresses on the aquifer, such as rainfall and evaporation, pumping, and surface water levels. Time series analysis may be applied to answer many groundwater questions without the need for a regular groundwater model, such as what is the drawdown caused by a pumping station? Or, how long will it take before groundwater levels recover after a period of drought? Even when a regular groundwater model is needed to solve a groundwater problem, time series analysis can be of great value. It can be used to clean up the data, identify the major stresses on the aquifer, determine the most important processes that affect flow in the aquifer, and give an indication of the fit that can be expected. In addition, it can be used to determine calibration targets for steady-state models, and it can provide several alternative calibration methods for transient models. In summary, the overarching message of this paper is that it would be wise to do time series analysis for any application that uses measured groundwater heads.
Original languageEnglish
Pages (from-to)826-833
Number of pages8
JournalGroundwater
Volume57
Issue number6
DOIs
Publication statusPublished - 2019

Fingerprint Dive into the research topics of 'Solving Groundwater Flow Problems with Time Series Analysis: You May Not Even Need Another Model'. Together they form a unique fingerprint.

Cite this