16 Mar 2022
16 Mar 2022
Status: a revised version of this preprint is currently under review for the journal HESS.

Spatio-temporal optimization of groundwater monitoring networks using data-driven sparse sensing methods

Marc Ohmer, Tanja Liesch, and Andreas Wunsch Marc Ohmer et al.
  • Institute of Applied Geosciences, Division of Hydrogeology, Karlsruhe Institute of Technology, Karlsruhe, Germany

Abstract. Groundwater monitoring and specific collection of data on the spatio-temporal dynamics of the aquifer are prerequisites for effective groundwater management and determine nearly all downstream management decisions. An optimally designed groundwater monitoring network will provide the maximum information content at the minimum cost (Pareto optimum). In this study, PySensors, a Python package containing scalable, data-driven algorithms for sparse sensor selection and signal reconstruction with dimensionality reduction is applied to an existing groundwater monitoring network (GMN) in 1D (hydrographs) and 2D (gridded groundwater contour maps). The algorithm first fits a basis object to the training data, then applies a computationally efficient QR algorithm that ranks existing monitoring wells (for 1D) or suitable sites for additional monitoring (for 2D) in order of "importance" based on the state reconstruction to this tailored basis. This procedure enables a network to be reduced or extended along the Pareto front. Moreover, we investigate the effect of basis choice on reconstruction performance by comparing three types typically used for sparse sensor selection (identity, random projection, and singular value decomposition resp. principal component analysis). We define a gridded cost function for the extension case penalizes unsuitable locations. Our results show that this approach is generally better than the best randomly selected wells. The optimized reduction makes it possible to adequately reconstruct the removed hydrographs with a highly reduced subset with low loss. An average absolute reconstruction accuracy of 0.1 m is achieved with a subset of 6 % wells, 0.05 m with 31 %, and 0.01 m with 82 % wells.

Marc Ohmer et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2022-69', Anonymous Referee #1, 28 Mar 2022
  • RC2: 'Comment on hess-2022-69', Anonymous Referee #2, 05 May 2022

Marc Ohmer et al.


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Short summary
We present a data-driven approach to select optimal locations for groundwater monitoring wells. The applied approach can optimize the number of wells and their location for a network reduction (by ranking wells in order of their information content and reducing redundant) and extension (finding sites with great information gain), or both. It allows to include a cost-function to account for more or less suitable areas for new wells and can help to get maximum information content for a budget.