Sensitivity of groundwater recharge using climatic analogues and HYDRUS-1D

The sensitivity of groundwater recharge to different climate conditions was simulated using the approach of climatic analogue stations, i.e. stations presently experiencing climatic conditions corresponding to a possible future climate state. The study was conducted in the context of a safety assessment of a future near-surface disposal facility for low and intermediate level short-lived radioactive waste in Belgium; this includes estimation of groundwater recharge for the next millennia. Groundwater recharge was simulated using the Richards based soil water balance model HYDRUS-1D and meteorological time series from analogue stations. This study used four analogue stations for a warmer subtropical climate with changes of average annual precipitation and potential evapotranspiration from −42% to +5% and from +8% to +82%, respectively, compared to the present-day climate. Resulting water balance calculations yielded a change in groundwater recharge ranging from a decrease of 72% to an increase of 3% for the four different analogue stations. The Gijon analogue station (Northern Spain), considered as the most representative for the near future climate state in the study area, shows an increase of 3% of groundwater recharge for a 5% increase of annual precipitation. Calculations for a colder (tundra) climate showed a change in groundwater recharge ranging from a decrease of 97% to an increase of 32% for four different analogue stations, with an annual precipitation change from −69% to −14% compared to the present-day climate

Utilisation de l'expérience de drainage à pas de pression multiples pour la détermination des fonctions hydrauliques du sol par la méthode inverse : résultats expérimentaux

La méthode de drainage à pas de pression multiples, combinée avec la méthode inverse, permet la détermination des fonctions hydrauliques du sol (têta(h) et K(h)) simultanément. Cependant, le choix des fonctions décrivant têta(h) et K(h) du sol est d'une importance capitale dans cette méthode. Les résultats expérimentaux montrent que le modèle de CAMPBELL (1974) dans sa version améliorée par HUTSON et CASS (1987) permet une description raisonnable du processus du drainage en fonction du temps et correspondant à des pas de pression croissants. Les courbes de rétention d'eau déduites des paramètres optimisés par la méthode inverse ont les mêmes allures que celles déterminées par la méthode standard (bac de sable et cellules à basses et hautes pressions). Ces résultats sont plus représentatifs de la réalité lorsque les paramètres têta(s) et K(s) sont fixes et égaux aux valeurs expérimentales, avec une précision sensiblement la même dans les cas d'optimisation, où têta(s) est fixe et têta(s) et K(s) fixes et égaux aux valeurs expérimentales; le premier cas où seulement le paramètre têta(s) est fixe est suggéré (il y a moins de paramètres à mesurer). (Résumé d'auteur

Utilisation de l'expérience de drainage à pas de pression multiples pour la détermination des fonctions hydrauliques du sol par la méthode inverse : présentation et évaluation de la méthode

L'optimisation des paramètres des fonctions décrivant les propriétés hydrauliques du sol têta(h) et K(h), basée sur les résultats de l'expérience de drainage interne à pas de pression multiples est une méthode prometteuse. En effet, cette dernière fournit suffisamment d'informations sur les propriétés hydrauliques essentielles d'un sol. Le modèle utilisé pour décrire les relations entre la teneur en eau volumique têta et la pression de l'eau h, d'une part, et entre la conductivité hydraulique K et h, d'autre part, est celui de CAMPBELL (1974) dans sa version améliorée par HUTSON et CASS (1987). Les résultats montrent que ce modèle décrit raisonnablement le processus du drainage en fonction du temps correspondant à des pas de pressions croissantes. La solution de la méthode d'identification des paramètres est unique tant que les valeurs assignées aux paramètres au départ sont proches (plus ou moins 20 %) de celles du sol étudié. L'effet d'une erreur expérimentale allant jusqu'à 10 % n'est pas significatif pour les résultats des paramètres optimisés. (Résumé d'auteur

Comparison of three stream tube models predicting field-scale solute transport

International audienceIn this paper the relation between local- and field-scale solute transport parameters in an unsaturated soil profile is investigated. At two experimental sites, local-scale steady-state solute transport was measured in-situ using 120 horizontally installed TDR probes at 5 depths. Local-scale solute transport parameters determined from BTCs were used to predict field-scale solute transport using stochastic stream tube models (STM). Local-scale solute transport was described by two transport models: (1) the convection-dispersion transport model (CDE), and (2) the stochastic convective lognormat transfer model (CLT). The parameters of the CDE-model were found to be lognormally distributed, whereas the parameters of the CLT model were normally distributed. Local-scale solute transport heterogeneity within the measurement volume of a TDR-probe was an important factor causing field-scale solute dispersion. The study of the horizontal scale-dependency revealed that the variability in the solute transport parameters contributes more to the field-scale dispersion at deeper depths than at depths near the surface. Three STMs were used to upscale the local transport parameters: (i) the stochastic piston flow STM-I assuming local piston flow transport, (ii) the convective-dispersive STM-II assuming local CDE transport, and (iii) the stochastic convective lognormal STM-III assuming local CLT. The STM-I considerably underpredicted the field-scale solute dispersion indicating that local-scale dispersion processes, which are captured within the measurement volume of the TDR-probe, are important to predict field-scale solute transport. STM-II and STM-III both described the field-scale breakthrough curves (BTC) accurately if depth dependent parameters were used. In addition, a reasonable description of the horizontal variance of the local BTCs was found. STM-III was (more) superior to STM-II if only one set of parameters from one depth is used to predict the field-scale solute BTCs at several depths. This indicates that the local-scale solute transport process, as measured with TDR in this study, is in agreement with the CLT-hypothesis

Estimation of hydraulic conductivity and its uncertainty from grain-size data using GLUE and artificial neural networks

peer reviewedaudience: researcher, professionalVarious approaches exist to relate saturated hydraulic conductivity (Ks) to grain-size data. Most methods use a single grain-size parameter and hence omit the information encompassed by the entire grain-size distribution. This study compares two data-driven modelling methods, i.e.multiple linear regression and artificial neural networks, that use the entire grain-size distribution data as input for Ks prediction. Besides the predictive capacity of the methods, the uncertainty associated with the model predictions is also evaluated, since such information is important for stochastic groundwater flow and contaminant transport modelling. Artificial neural networks (ANNs) are combined with a generalized likelihood uncertainty estimation (GLUE) approach to predict Ks from grain-size data. The resulting GLUE-ANN hydraulic conductivity predictions and associated uncertainty estimates are compared with those obtained from the multiple linear regression models by a leave-one-out cross-validation. The GLUE-ANN ensemble prediction proved to be slightly better than multiple linear regression. The prediction uncertainty, however, was reduced by half an order of magnitude on average, and decreased at most by an order of magnitude. This demonstrates that the proposed method outperforms classical data-driven modelling techniques. Moreover, a comparison with methods from literature demonstrates the importance of site specific calibration. The dataset used for this purpose originates mainly from unconsolidated sandy sediments of the Neogene aquifer, northern Belgium. The proposed predictive models are developed for 173 grain-size -Ks pairs. Finally, an application with the optimized models is presented for a borehole lacking Ks data

Development and analysis of the Soil Water Infiltration Global database.

In this paper, we present and analyze a novel global database of soil infiltration measurements, the Soil Water Infiltration Global (SWIG) database. 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 the USA. In addition to its extensive geographical coverage, the collected infiltration curves cover research from 1976 to late 2017. Basic information on measurement location and method, soil properties, and land use was gathered along with the infiltration data, making the database valuable for the development of pedotransfer functions (PTFs) 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 land use is available for 76% of the 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 public domain use only and can be copied freely by referencing it. Supplementary data are available at https://doi.org/10.1594/PANGAEA.885492 (Rahmati et al., 2018). Data quality assessment is strongly advised prior to any use of this database. Finally, we would like to encourage scientists to extend and update the SWIG database by uploading new data to it