Time Series Analysis of Groundwater Systems

Time series analysis has become integral to the study of groundwater systems by leveraging repeated measurements of hydraulic head, discharge or water quality to characterise aquifer behaviour over time. By decomposing hydrographs into trend, seasonal and residual components, researchers can identify the relative influence of recharge, evapotranspiration, pumping and surface-water interactions. Convolution of recorded stresses with hydrological response functions permits quantification of impulse responses, while transfer function–noise models account for unexplained variability. Advances in clustering, machine learning and comparative regional analysis enable extrapolation of groundwater dynamics to unmonitored locations, facilitating regionalisation and infilling in data-scarce settings. Automated workflows and open-source software now allow practitioners to validate large datasets, detect outliers and simulate groundwater drought development with unprecedented spatial and temporal resolution. Such approaches underpin sustainable resource management, risk assessment for urban tunnelling and drought mitigation strategies, highlighting the global significance of time series methods in adapting to climate variability and growing water demand.

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Time Series Analysis of Groundwater Systems publication trend

The graph below shows the total number of articles in time series analysis of groundwater systems across all publications each year (not limited to Nature Index journals).

Technical terms

Time series analysis: Examination of sequential measurements to discern patterns, trends and system responses in groundwater data.

Impulse response function: Characterisation of an aquifer’s reaction over time to a unit input of recharge, pumping or other stress.

Transfer function–noise model: Statistical framework combining deterministic response functions with stochastic terms to reproduce observed groundwater fluctuations.

Hydrograph: Time-series plot of water table elevation or flow rate, illustrating temporal variations and hydrological events.

Head duration curve: Cumulative frequency distribution of groundwater levels, used to reconstruct hydrographs at ungauged locations.

References

1. GeoTemporal clustering for aquifer delineation: a big data approach to synchronizing and analyzing variable-length groundwater time series. Journal of Big Data (2025).
2. Analysis of nationwide groundwater monitoring networks using lumped-parameter models. Journal of Hydrology (2023).
3. Data‐Driven Estimation of Groundwater Level Time‐Series at Unmonitored Sites Using Comparative Regional Analysis. Water Resources Research (2023).
4. Time series modelling: applications for groundwater control in urban tunnelling. Bulletin of Engineering Geology and the Environment (2023).
5. Pastas: Open Source Software for the Analysis of Groundwater Time Series. Ground Water (2019).
6. Use of seasonal trend decomposition to understand groundwater behaviour in the Permo-Triassic Sandstone aquifer, Eden Valley, UK. Hydrogeology Journal (2015).
7. Estimation of groundwater recharge from groundwater levels using nonlinear transfer function noise models and comparison to lysimeter data. Hydrology and Earth System Sciences (2021).
8. Improved understanding of regional groundwater drought development through time series modelling: the 2018–2019 drought in the Netherlands. Hydrology and Earth System Sciences (2022).