No abstract

No abstract

Global probabilistic inversion within the latent space learned by a Generative Adversarial Network (GAN) has been recently demonstrated. Compared to inversion on the original model space, using the latent space of a trained GAN can offer the following benefits: (1) the generated model proposals are geostatistically consistent with the prescribed prior training image (TI), and (2) the parameter space is reduced by orders of magnitude compared to the original model space. Nevertheless, exploring the learned latent space by state-of-the-art Markov chain Monte Carlo (MCMC) methods may still require a large computational effort. As an alternative, parameters in this latent space could possibly be optimized with much less computationally expensive gradient-based methods. This study shows that due to the typically highly nonlinear relationship between the latent space and the associated output space of a GAN, gradient-based deterministic inversion may fail even when considering a linear forward physical model. We tested two deterministic inversion approaches: a quasi-Newton gradient descent using the Adam algorithm and a Gauss-Newton (GN) method that makes use of the Jacobian matrix calculated by finite-differencing. For a channelized binary TI and a synthetic linear crosshole ground penetrating radar (GPR) tomography problem involving 576 measurements with low noise, we observe that when allowing for a total of 10,000 iterations only 13% of the gradient descent trials locate a solution that has the required data misfit. The tested GN inversion was unable to recover a solution with the appropriate data misfit. Our results suggest that deterministic inversion performance strongly depends on the inversion approach, starting model, true reference model, number of iterations and noise realization. In contrast, computationally-expensive probabilistic global optimization based on differential evolution always finds an appropriate solution.

While inverse parameter estimation techniques for determining key parameters affecting water flow and solute transport are becoming increasingly common in saturated and unsaturated zone studies, their application to practical problems, such as irrigation, have received relatively little attention. In this article, we used the Levenberg-Marquardt optimization algorithm in combination with the HYDRUS-2D numerical code to estimate soil hydraulic and solute transport parameters of several soil horizons below experimental furrows. Three experiments were carried out, each of the same duration but with different amounts of water and solutes resulting from 6, 10, and 14 cm water depths in the furrows. Two more experiments were performed with the same amounts of applied water and solute and, consequently, for different durations, on furrows with depths of 6 and 10 cm of water. We first used a scaling method to characterize spatial variability in the soil hydraulic properties, and then simultaneously estimated the saturated hydraulic conductivity (K s) and the longitudinal dispersivity (D L) for the different horizons. Model predictions showed only minor improvements over those previously obtained assuming homogeneous soil profiles. In an effort to improve the predictions, we also carried out a two-step, sequential optimization in which we first estimated the soil hydraulic parameters followed by estimation of the solute transport parameters. This approach allowed us to include additional parameters in the optimization process. A sensitivity analysis was performed to determine the most sensitive hydraulic and solute transport parameters. Soil water contents were found to be most sensitive to the n parameter in van Genuchten's soil hydraulic model, followed by the saturated water content (q s), while solute concentrations were most affected by q s and D L. For these reasons, we estimated q s and n for the various soil horizons of the sequential optimization process during the first step, and only D L during the second step. Sequential estimation somewhat improved predictions of the cumulative infiltration rates during the first irrigation event. It also significantly improved descriptions of the soil water content, particularly of the upper horizons, as compared to those obtained using simultaneous estimation, whereas deep percolation rates of water did not improve. Solute concentrations in the soil profiles were predicted equally well with both optimization approaches.

Abstract. In this paper, we present and analyze a global database of soil infiltration measurements, the Soil Water Infiltration Global (SWIG) database, for the first time. In total, 5023 infiltration curves were collected across all continents in the SWIG database. These data were either provided and quality checked by the scientists who performed the experiments or they were digitized from published articles. Data from 54 different countries were included in the database with major contributions from Iran, China, and USA. In addition to its global spatial coverage, the collected infiltration curves cover a time span of research from 1976 to late 2017. Basic information on measurement location and method, soil properties, and land use were gathered along with the infiltration data, which makes the database valuable for the development of pedo-transfer functions for estimating soil hydraulic properties, for the evaluation of infiltration measurement methods, and for developing and validating infiltration models. Soil textural information (clay, silt, and sand content) is available for 3842 out of 5023 infiltration measurements (~76 %) covering nearly all soil USDA textural classes except for the sandy clay and silt classes. Information on the land use is available for 76 % of experimental sites with agricultural land use as the dominant type (~40 %). We are convinced that the SWIG database will allow for a better parameterization of the infiltration process in land surface models and for testing infiltration models. All collected data and related soil characteristics are provided online in *.xlsx and *.csv formats for reference, and we add a disclaimer that the database is for use by public domain only and can be copied freely by referencing it. Supplementary data are available at doi:10.1594/PANGAEA.885492. Data quality assessment is strongly advised prior to any use of this database. Finally, we would like to encourage scientists to extend/update the SWIG by uploading new data to it.