Inferring regional aquifer parameters by spectral analysis of groundwater head fluctuations – A feasibility study

Abstract:

Climate change is projected to have severe impacts on the amount and dynamics of groundwater recharge and consequently on water resources stored in aquifers. Projecting the response of regional aquifer systems to changing climate conditions using regional groundwater models is paramount. However, parameterizing the large-scale GW models are challenging due to scarcity of observational records as well as mismatch between scales of modeling and measurements.

In this work, we propose to bridge the scale gap and derive regional scale hydraulic parameters by spectral analysis of groundwater head fluctuations.

While the response of groundwater to external perturbations depends on local properties and local boundary conditions, it also contains signals of regional components due to the diffusive character of Darcy’s law. We hypothesize that specific locations in aquifers can reveal regional parameters of the hydraulic system (e.g. aquifer transmissivity, storativity and characteristic time).

In order to proof our hypothesis, we first generate ensembles of synthetic but realistic aquifers which systematically differ in complexity with respect to their hydraulic heterogeneity. Applying Liang and Zhang’s (2013) semi-analytical solution for the power spectrum of head data, we identify for each ensemble member and at different locations effective aquifer parameters. Next, we extend our study to investigate the use of spectral analysis in more complex numerical models and in real settings.

Our analyses indicate that the variance of inferred transmissivity and storativity values for the stochastic aquifer ensembles is small for observations points which are far away from the river boundary. Moreover, the head time series have to cover a period which is roughly ten times as long as the characteristic time of the aquifer. In deterministic aquifer models we infer effective regionally valid parameters which cannot be identified with classical pumping tests. Furthermore, the derived transmissivity and storativity values can be mapped to the distributed aquifer parameters of complex model domains. A sensitivity analysis further reveals that as long as aquifer length and position of the groundwater location is roughly known, transmissivity, storativity as well as the characteristic time can be robustly estimated.