the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Towards a community-wide effort for benchmarking in subsurface hydrological inversion: benchmarking cases, high-fidelity reference solutions, procedure and a first comparison
Abstract. Inversion in subsurface hydrology refers to estimating spatial distributions of (typically hydraulic) properties, often associated with quantified uncertainty. Many methods are available, each characterized by a set of assumptions, approximations, and numerical implementations. Only a few intercomparison studies have been performed (in the remote past) amongst different approaches (e.g., Zimmerman et al., 1998; Hendricks Franssen et al., 2009). These intercomparisons guarantee broad participation to push forward research efforts of the entire subsurface hydrological inversion community. However, in past studies until now, comparisons were made among approximate methods without firm reference solutions. Without reference solutions, one can only compare competing best estimates and their associated uncertainties in an intercomparison sense, and absolute statements on accuracy are unreachable.
Our current initiative defines benchmarking scenarios for groundwater model inversion. These are targeted for community-wide use as test cases in intercomparison scenarios. Here, we develop five synthetic, open-source benchmarking scenarios for the inversion of hydraulic conductivity from pressure data. We also provide highly accurate reference solutions produced with massive high-performance computing and with a high-fidelity MCMC-type solution algorithm. Our high-end reference solutions are publicly available, as well as the benchmarking scenarios, the reference algorithm, and suggested benchmarking metrics. Thus, in comparison studies, one can test against high-fidelity reference solutions rather than discussing different approximations.
To demonstrate how to use these benchmarking scenarios, reference solutions, and suggested metrics, we provide a blueprint comparison of a specific ensemble Kalman filter version. We invite the community to use our benchmarking scenarios and reference solutions now and into the far future in a community-wide effort towards clean and conclusive benchmarking. For now, we aim at an article collection in an appropriate journal, where such clean comparison studies can be submitted together with an editorial summary that provides an overview.
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Status: closed
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RC1: 'Comment on hess-2024-60', Anonymous Referee #1, 18 Mar 2024
This is a much-needed work for the benchmarking of stochastic inversion techniques. My only comment would be that the choice of log multi-Gaussian random fields with a small variance makes the problem relatively simple, yet it could provide a way to benchmark different inverse methods.
Will the benchmarking data be published alongside this manuscript?
Citation: https://doi.org/10.5194/hess-2024-60-RC1 -
AC1: 'Reply on RC1', Teng Xu, 27 Mar 2024
Thank you so much for your valuable comments and recommendations on our manuscript. As depicted in Figure 2 and Table 4, we have designed two lnK fields with different variances: one with a variance of 4 and the other with a variance of 1. These two lnK fields are indeed meant to be used as benchmarks for different inverse methods. We believe that having a small variance is an important scenario where linearized and quasi-linear methods can also be compared, such as Successive linear estimator (SLE) by Yeh et others, the quasi-linear approach by Kitanidis, and implicitly linearized methods like the EnKF.
Yes, the reference and benchmarking data, as well as the benchmarking codes, are (now) publicly available at https://doi.org/10.18419/darus-2382 and https://github.com/LS3-university-of-stuttgart/hydrological-inversion-benchmarking, as noted in the manuscript in lines 743-744.
Citation: https://doi.org/10.5194/hess-2024-60-AC1
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AC1: 'Reply on RC1', Teng Xu, 27 Mar 2024
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RC2: 'Comment on hess-2024-60', Peter K. Kitanidis, 08 May 2024
The comment was uploaded in the form of a supplement: https://hess.copernicus.org/preprints/hess-2024-60/hess-2024-60-RC2-supplement.pdf
- AC2: 'Reply on RC2', Teng Xu, 02 Jul 2024
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RC3: 'comment on hess-2024-60 June 9, 2024', Anonymous Referee #3, 09 Jun 2024
- AC3: 'Reply on RC3', Teng Xu, 02 Jul 2024
Status: closed
-
RC1: 'Comment on hess-2024-60', Anonymous Referee #1, 18 Mar 2024
This is a much-needed work for the benchmarking of stochastic inversion techniques. My only comment would be that the choice of log multi-Gaussian random fields with a small variance makes the problem relatively simple, yet it could provide a way to benchmark different inverse methods.
Will the benchmarking data be published alongside this manuscript?
Citation: https://doi.org/10.5194/hess-2024-60-RC1 -
AC1: 'Reply on RC1', Teng Xu, 27 Mar 2024
Thank you so much for your valuable comments and recommendations on our manuscript. As depicted in Figure 2 and Table 4, we have designed two lnK fields with different variances: one with a variance of 4 and the other with a variance of 1. These two lnK fields are indeed meant to be used as benchmarks for different inverse methods. We believe that having a small variance is an important scenario where linearized and quasi-linear methods can also be compared, such as Successive linear estimator (SLE) by Yeh et others, the quasi-linear approach by Kitanidis, and implicitly linearized methods like the EnKF.
Yes, the reference and benchmarking data, as well as the benchmarking codes, are (now) publicly available at https://doi.org/10.18419/darus-2382 and https://github.com/LS3-university-of-stuttgart/hydrological-inversion-benchmarking, as noted in the manuscript in lines 743-744.
Citation: https://doi.org/10.5194/hess-2024-60-AC1
-
AC1: 'Reply on RC1', Teng Xu, 27 Mar 2024
-
RC2: 'Comment on hess-2024-60', Peter K. Kitanidis, 08 May 2024
The comment was uploaded in the form of a supplement: https://hess.copernicus.org/preprints/hess-2024-60/hess-2024-60-RC2-supplement.pdf
- AC2: 'Reply on RC2', Teng Xu, 02 Jul 2024
-
RC3: 'comment on hess-2024-60 June 9, 2024', Anonymous Referee #3, 09 Jun 2024
- AC3: 'Reply on RC3', Teng Xu, 02 Jul 2024
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