Distribution Of Hydraulic Conductivity Research Articles

Spatiotemporal Variability of Hyporheic Flow in a Losing River Section

AbstractCharacterizing the spatiotemporal variability of water fluxes at the stream‐groundwater interface is extremely challenging due to the lack of methods for estimating hyporheic flows at different scales. To address this, we demonstrate the potential of Active‐Distributed Temperature Sensing (DTS) methods for measuring and mapping hyporheic flow in a lowland stream. Experiments were conducted by burying a few hundred meters of heatable Fiber‐Optic cables within streambed sediments in a large meander, where permanent stream‐losing conditions are observed along the stream reach. We propose a new methodology to filter ambient temperature variations along the heated section of the DTS cable and to extend the application of Active‐DTS to losing streams. After data processing, the results show that, along lateral and longitudinal stream profiles, both thermal conductivity and water flux values follow normal distributions with relatively small standard deviations. Hyporheic fluxes vary by one order of magnitude. The absence of correlation between water fluxes within the hyporheic zone and streambed topography variations suggests that the variability is mainly controlled by local streambed heterogeneities. This means that the spatiotemporal variability of fluxes may be used as a marker of the variability of streambed hydraulic conductivities. The relatively low spatial variability (one order of magnitude) in hyporheic flow suggests a small variability of streambed properties. This is an important result for calibrating models assessing hyporheic processes, in which the hydraulic conductivity distribution is generally assumed. Additionally, measurements made over three years yield similar estimates showing the remarkable stability of hyporheic flows through time.

High-Resolution Estimation of Soil Saturated Hydraulic Conductivity via Upscaling and Karhunen–Loève Expansion within DREAM(ZS)

Quantification of the soil hydraulic conductivity is key to the study of water flow and solute transport in unsaturated soils. Rapid advances in measurement technology have provided a large number of observations at different scales, offering unprecedented opportunities and challenges for the estimation of hydraulic parameters. This paper proposes an inverse estimation method for downscaling of observations on coarse scales to estimate hydraulic parameters on high-resolution scales. Due to the significant spatial heterogeneity, the inversion faces the problems of dynamics-based integration of data at different scales, model uncertainty due to hundreds and thousands of parameters, and computational consumption due to the large number of forward simulations. To overcome these problems, this paper uses an efficient Bayesian optimization DREAM(ZS) as an inverse framework, and incorporates an analytical upscaling method and Karhunen–Loève (KL) expansion to infer finer-scale saturated hydraulic conductivity distribution conditioned on coarse-scale measurements. The efficient upscaling method is used to link measurements and hydraulic parameters at different scales, and Karhunen–Loève (KL) expansion is incorporated to greatly reduce the dimension of the parameter to be estimated. To further improve the efficiency of the inversion, a locally one-dimensional (LOD) algorithm is used to solve the multidimensional water flow model at coarse scales. The proposed inverse model is applied in a series of numerical experiments to demonstrate its applicability and effectiveness under different flow boundary conditions, different levels of ratio between coarse- and fine-scale grids, different densities of observation points, and different degrees of statistic heterogeneity of soil mediums.

Shannon entropy of transport self-organization due to dissolution–precipitation reaction at varying Peclet numbers in initially homogeneous porous media

Abstract. Dissolution and precipitation processes in reactive transport in porous media are ubiquitous in a multitude of contexts within the field of Earth sciences. In particular, the dynamic interaction between the reactive dissolution and precipitation processes and the solute transport is of interest as it is capable of giving rise to the emergence of preferential flow paths in the porous host matrix. It has been shown that the emergence of preferential flow paths can be considered to be a manifestation of transport self-organization in porous media as these create spatial gradients that distance the system from the state of perfect mixing and allow for a faster and more efficient fluid transport through the host matrix. To investigate the dynamic feedback between the transport and the reactive processes in the field and its influence on the emergence of transport self-organization, we consider a two-dimensional Darcy-scale formulation of a reactive-transport setup, where the precipitation and dissolution of the host matrix are driven by the injection of an acid compound, establishing local equilibrium with the resident fluid and an initially homogeneous porous matrix, composed of a calcite mineral. The coupled reactive process is simulated in a series of computational analyses employing the Lagrangian particle-tracking (LPT) approach, capable of capturing the subtleties of the multiple-scale heterogeneity phenomena. We employ the Shannon entropy to quantify the emergence of self-organization in the field, which we define as a relative reduction in entropy compared to its maximum value. Scalability of the parameters, which characterize the evolution of the reactive process, with the Peclet number in an initially homogeneous field is derived using a simple one-dimensional ADRE model with a linear adsorption reaction term and is then confirmed through numerical simulations, with the global reaction rate, the mean value, and the variance of the hydraulic-conductivity distribution in the field all exhibiting dependency on the reciprocal of the Peclet number. Our findings show that transport self-organization in an initially homogeneous field increases with time, along with the emergence of the field heterogeneity due to the interaction between the transport and reactive processes. By studying the influence of the Peclet number on the reactive process, we arrive at a conclusion that self-organization is more pronounced in diffusion-dominated flows, characterized by small Peclet values. The self-organization of the breakthrough curve exhibits the opposite tendencies, which are observed from the perspective of a thermodynamic analogy. The hydraulic power, required to maintain the driving head pressure difference between the inlet and outlet of the field, was shown to increase with the increasing variance, as well as with the increasing mean value of the hydraulic-conductivity distribution in the field, using a simple analytic model. This was confirmed by numerical experiments. This increase in power, supplied to the flow in the field, results in an increase in the level of transport self-organization. Employing a thermodynamic framework to investigate the dynamic reaction–transport interaction in porous media may prove to be beneficial whenever the need exists to establish relations between the intensification of the preferential flow path phenomenon, represented by a decline in the Shannon entropy of the transport, with the amount of reaction that occurred in the porous medium and the change in its heterogeneity.

Geospatial modeling for groundwater potential zoning using a multi-parameter analytical hierarchy process supported by geophysical data

Groundwater plays a crucial role in Hungary sustaining ecosystems and meeting the growing demand for freshwater to fulfill domestic and agricultural needs. This study employs the analytical hierarchy process (AHP) methodology to delineate groundwater potential zones in the Debrecen area, Hungary. To ensure the robustness and reliability of the potential zoning, geophysical data are utilized for validation purposes. In the AHP modeling seven groundwater conditioning factors are integrated, including geology, topography, slope, land use/land cover, precipitation, drainage density, and lineament density. The integration of the normalized weights for each factor identified three groundwater potential zones (GWPZs) assigned as moderate, high, and very high potential. The result of the AHP model is further validated with geophysical data of gravity and wireline logging. Gravity data is subjected to spectral analysis and forward modeling to map the lineaments and detect the thickness of the sedimentary sequences. The forward modeling indicated that the thickness of these sequences varies between 1.25 and 2.7 km, with a deep local basin delimited by normal faults situated in the eastern part of the study area. Additionally, the analysis of the well-logging data using the Csókás method provided a continuous estimation of petrophysical and hydrogeological parameters along the main hydrostratigraphical units. Accordingly, a high and uniform distribution of hydraulic conductivity is observed in the eastern part of the study area due to the presence of coarse-grained incised valley deposits. The results of geophysical modeling showed a close agreement with that of AHP models. This interdisciplinary approach advanced the mapping of potential groundwater zones and provided valuable insights into the hydrogeological characteristics of the groundwater aquifers in the Debrecen area.

Identification of high-permeability and water-rich zones in a fractured karst water source area based on the hydraulic tomography method

Determining the spatial hydraulic parameters of aquifers is of paramount importance for the development and utilization of groundwater in water source areas. However, most methods are unable to accurately characterize the heterogeneity of aquifers because of the influences of complex geology, hydrogeology, structure and insufficient information. Hydraulic tomography (HT) is an effective and robust method for hydraulic parameter inversion to characterize heterogeneity. In this study, a two-dimensional HT model was established to simulate hydraulic parameter fields for a fractured karst aquifer in the Zhangji water source area (Xuzhou city, China). Steady-state hydraulic tomography (SSHT) and transient hydraulic tomography (THT) with different prior information conditions were applied to determine the high-permeability area and water-rich zones, and to infer possible locations of karst conduits and strong runoff paths in the water source area. The results indicate that: (1) THT can better reveal larger-scale heterogeneous characteristics of hydraulic properties compared to SSHT. The hydraulic conductivity distribution estimated by THT correlates well with the geological conditions of the area, especially in identifying water-rich zones formed by faults and other formations. (2) When combined with geological conditions, HT possesses the ability to identify strong runoff paths in fractured karst aquifers. (3) Accurate prior information, which is more relevant to the head response information, is conducive to obtaining more acceptable HT results. (4) THT can simultaneously identify high-permeability and high-storage zones, and the analysis of the overlap of these two regions has the potential to predict underground water reservoirs, which is of the utmost importance for the development and utilization of groundwater resources in water source areas.

Multi-well clustering and inverse modeling-based approaches for exploring geometry, petrophysical, and hydrogeological parameters of the Quaternary aquifer system around Debrecen area, Hungary.

This research aims to explore the application of an unsupervised machine learning and inverse modeling-based methods to map the aquifers geometry and investigate the petrophysical and hydrogeological parameters of the Quaternary aquifer system around the Debrecen area, Eastern Hungary. The study utilized a limited well-logs, including spontaneous potential, natural gamma ray, and normal resistivity logs. The k-means clustering technique is applied to identify the distribution of lithological facies within the formerly identified basin-scale hydrostratigraphical units of coarsening upward, alluvial, valley incision, and Late Miocene deposits. Based on the mathematical and geological considerations, the analysis revealed three main clusters (C1, C2, and C3), representing different lithofacies of shale, shaly sand, and sand and gravel. The result of the cluster analysis is further validated with a surface geophysical survey using the vertical electrical sounding (VES) technique. Furthermore, an inverse-modeling-based approach using Csókás method is employed to detect the vertical and horizontal distribution of the hydraulic conductivity along these units. Csókás model is empirically modified from the Kozeny-Carman equation that suites the unconsolidated freshwater-bearing units. The results indicated that the petrophysical and hydrogeological parameters widely ranged with the hydraulic conductivity between almost zero in the shaly layers to more than 21.5 m/d in the sandy and gravely layers. However, the valley incision unit aquifers showed a more uniform distribution of hydraulic conductivity. Based on these results, the Quaternary aquifers in the Debrecen area are classified as moderate to highly productive and ideal for groundwater development. The applied methodology contributed to understanding the complexity of the hydrogeological conditions, providing a robust approach to characterize the heterogeneous groundwater systems.

Sequential Direct and Inverse Modeling of Underground Flows in the Upper Cheliff Alluvial Aquifer, Algeria

The Upper Cheliff plain lies 125 km south-west of Algiers, capital city of Algeria. It is characterized by its unconfined aquifer contained in a heterogeneous filling of Mio-Plio-Quaternary age. Nowadays, this aquifer is severely exploited to meet the domestic, industrial and agricultural needs in the region. Groundwater resources management by means of numerical modeling is highly required in order to improve our knowledge of the hydrogeological operation of this aquifer. In this work we implemented an approach based on a sequential solving of the direct and inverse problems. In the case of the direct problem, steadystate flow simulations revealed the persistence of large discrepancies between the measured and calculated piezometric levels, which reflect the heterogeneity of the aquifer. The zonation provided by the direct method, based on the conceptual model, is then fed in the inverse method. The latter, in addition to its speed and its convergence, made it possible to reduce the disparities with measurements as noted in the direct method. The main results highlight a heterogeneous hydraulic conductivity distribution which reaches its highest values in the central and north-western sectors of the aquifer, whereas the lowest values are found in the eastern, northern and southern sectors of the aquifer. Furthermore, calculation of the groundwater flow budget by the inverse model showed that rainfall recharge is the main input into the aquifer with 462.5x103 m3/day, whereas the most important outlet of the aquifer is the Cheliff Wadi with a flow rate of 627x103 m3/day.

The impact of formation heterogeneity on water discharge and groundwater depletion of an excavated tunnel

The mountainous tectonic zone exhibits high formation heterogeneity, which significantly affects groundwater seepage around the excavated tunnel. Traditional modeling methods that assume the surrounding rock to be a homogeneous field face challenges in accurately predicting and evaluating water discharge within the tunnel. To gain a better understanding of how heterogeneity influences tunnel water discharge, synthetic heterogeneous fields are conducted based on the conceptualization and parameters of the Daxuecheng Tunnel in Chongqing, southwest China. In the heterogeneous modeling, we consider four standard deviations of hydraulic conductivity distribution (0.21, 0.37, 0.55, 0.72) and four dip angles (0°, 30°, 60°, 90°). The results reveal that formation heterogeneity leads to localized instances of significant water inrush and irregular head drawdown distribution, which closely resemble the actual situation compared to the homogeneous model. Generally, the total water discharge into the tunnels increased with increasing heterogeneity and dip angles. Furthermore, the head drawdown in the tunnel region decreased with heterogeneity and increased with dip angles. These findings emphasize the importance of considering formation heterogeneity in tunnel zones and offer valuable guidance for evaluating engineering and environmental challenges in tunnel construction.

A mixed pressure-velocity formulation to model flow in heterogeneous porous media with physics-informed neural networks

Current implementations of Physics Informed Neural Networks (PINNs) can experience convergence problems in simulating fluid flow in porous media with highly heterogeneous domains. The objective of this work is to develop an accurate implementation of PINNs to model fluid flow in heterogeneous porous media that avoids alternative approaches in the literature that tend to impose unphysical continuity of the hydraulic conductivity field. The proposed implementation is inspired by the mixed formulation of the governing equations, which separates the mass continuity equation and Darcy's law. This methodology has been used with the mixed finite element method and is known to provide accurate pressure and velocity fields in highly heterogeneous domains. In this work, a similar methodology is applied to PINNs, where the training loss function is based on the decoupled continuity equation and Darcy's law. The separation of the continuity equation and Darcy's law allows for calculating the residual term in the learning loss function without evaluating the spatial derivatives of the discontinuous hydraulic conductivity and associated non-differentiable functions. This approach provides more accurate automatic differentiation of the neural networks for pressure and velocity fields. The new implementation of PINNs can be applied to simulate flow in porous media, regardless of the type and level of heterogeneity with a discontinuous hydraulic conductivity distribution. A variety of structures of the new implementation are investigated. Numerical experiments show that the structure based on different neural networks for the pressure and each component of the velocity field provides the highest accuracy with equivalent training time as other structures. The proposed methodology presents a novel approach to broaden the scope of PINNs in modeling fluid flow in porous media, while preserving the precise representation of the domain's discontinuous features.

Stochastic Analysis of the Drawdown Time of Infiltration Basins in the Presence of Heterogeneous Soils

AbstractThe drawdown time is important for the design and assessment of infiltration basins. This paper investigates its dependence on soil heterogeneity, and simple analytical solutions for the mean and the variance are given. The solutions are tested through Monte Carlo simulations in various realistic scenarios, using a modified Green Ampt model for layered soils and a model based on the integration of the 1‐D Richards equation. The impact of the employed approximations is assessed, and the leading role of the spatial distribution of the saturated hydraulic conductivity is emphasized. The effects of other relevant design and natural factors, including the initial water level and water content distribution, are also investigated.

Effects of organic carbon and clay contents on structure‐related properties of arable soils with high clay content

AbstractUnderstanding of factors governing soil structural features is necessary for managing key processes affecting crop productivity and environmental impacts of agriculture, for example, soil water balance, aeration, and root penetration. Organic matter is known to act as a major binding agent in soil aggregation and thus constitutes a central pillar in soil structure formation. However, knowledge of the structural role of organic matter or carbon (OC) in soils highly rich in clay‐sized particles (<0.002 mm) is limited. In this study, the effects of clay and OC contents on aggregate stability, water holding capacity, near‐saturated hydraulic conductivity, total porosity, and pore size distribution were assessed in cultivated fields with high clay content located in private crop production farms in southern Finland. Significant positive correlations were found between OC content and proportion of water stable aggregates and specific pore sizes from the range of 30 μm up to 1 mm diameter determined by image analysis. Porosities on a smaller size range derived from water retention measurements likewise showed a positive correlation with OC in <0.2 μm sizes. On the range of 0.2–1 μm, a negative relationship was observed, which induced a negative effect of OC on soil plant available water reserves. In line with the positive correlation between OC and larger soil pores, free water, representing the amount of water that can be drained by gravity, exhibited a positive relationship with OC suggesting that OC content can enhance aeration of soils with high clay content. Compared to OC, clay content tended to have an adverse effect on soil structural properties. Clay correlated negatively with pores larger than 30 μm, free water content, and extrapolated field saturated hydraulic conductivity. Further, our imaging results showed how saturated hydraulic conductivity was controlled by pore morphology, and there was a power law relationship between the conductivity and critical pore diameter. in agreement with the percolation theory. Overall, the structural impacts and hydrological implications of OC and clay in heavy clay soils vary by pore size ranges and their emergent practical impacts are thus not straightforward.

Early steps toward practical subsurface computations with quantum computing

Quantum computing exploits quantum mechanics to perform certain computations more efficiently than classical computers. Current quantum computers have performed carefully tailored computational tasks that would be difficult or impossible for even the fastest supercomputers in the world. This “quantum supremacy” result demonstrates that quantum computing is more powerful than classical computing in some computational regimes. At present, it is unknown if any computational problems related to the Earth's subsurface fall within these regimes. Here, we describe an approach to performing seismic inverse analysis that combines a type of quantum computer called a quantum annealer with classical computing. This approach improves upon past work on applying quantum computing to the subsurface (via subsurface hydrology) in two ways. First, the seismic inverse problem enables better performance from the quantum annealer because of the Earth's relatively narrow distribution of P-wave velocities compared to the broad distribution of hydraulic conductivities. Second, we develop an iterative approach to quantum-computational inverse analysis, which works with a realistic set of observations. By contrast, the previous method used an inverse method that depended on an impractically dense set of observations. In combination, these two advances significantly narrow the gap a quantum-computational advantage for a practical subsurface geoscience problem. Closing the gap completely requires more work, but has the potential to dramatically accelerate inverse analyses for subsurface geoscience.

Assessing Contaminant Mass Discharge Uncertainty With Application of Hydraulic Conductivities Derived From Geoelectrical Cross‐Borehole Induced Polarization and Other Methods

AbstractA new methodology was developed to support contaminant mass discharge (CMD)‐based risk assessment of groundwater contamination downgradient of point source zones. Geoelectrical cross‐borehole induced polarization (IP) data were collected at a site undergoing in situ remediation of chlorinated solvents for determining 2D hydraulic conductivity (K) distributions with an inversion model resolution of 0.15 m (vertically) x 0.50 m (horizontally) in three control planes from 10 to 20 m depth. Additionally, 18 slug tests and 31 grain size distribution analyses (GSA) from the control planes, were used for K‐estimation. The geometric means and variance of the IP, slug test, and GSA derived K‐estimates were consistent with previously studied sandy aquifers. Furthermore, the vertical variation in K between two geological settings, a sandy till and a meltwater sand formation, was clearly identified by the IP K‐estimates. The vertical variation was backed up by hydraulic profiling tool (HPT) measurements. Random realizations of CMD were simulated based on the cross‐borehole IP derived K‐values. For comparison, the CMD was also estimated with a geostatistical conditional simulation approach, using the data from slug tests and GSAs. The high IP resolution captured the small scale variations in K across the transects and led to CMD predictions with a narrow uncertainty interval, whereas slug test and GSA either under‐ or overestimated the magnitude of the areas with the highest CMD. Applying the geophysical cross‐borehole method for estimating K‐distributions in addition to traditional methods would improve CMD‐based risk assessment and evaluation of remediation performance at contaminated sites.

Effect of injection water ionic strength on estimating hydraulic parameters in a 3D sand tank using silica encapsulated magnetic DNA particles

We investigated the applicability of Silica encapsulated, superparamagnetic DNA particles (SiDNAmag) in determining aquifer hydraulic parameters at different ionic strengths (1 mM, 5 mM, and 20 mM phosphate buffer) of injection suspension. Thereto, in a homogeneous, unconsolidated sand tank we pulse - injected two uniquely sequenced SiDNAmag at two injection points. At 0.5 m and 0.8 m downstream from the injection points, we measured the concentration of SiDNAmags at three vertically distributed and two horizontally distributed sampling locations. We estimated the hydraulic parameter distributions from the SiDNAmag breakthrough curves through a Monte – Carlo approach and compared the parameter distributions with salt tracer breakthrough curves. Our results indicated that at all the ionic strengths, the times of peak concentrations, and the shapes of the breakthrough curves were similar to the salt tracer. As compared to the salt, a 1 – 3 log units reduction in the maximum effluent concentration of SiDNAmag was due to kinetic attachment. The attachment rate reduced from 1 mM to 5 mM phosphate buffer possibly due to competitive adsorption of phosphate onto the favourable attachment sites. SiDNAmag attachment rate further increased in 20 mM buffer suspension, possibly due to the compression of electric double layer and reduction in energy barrier for attachment. The parameter distributions of hydraulic conductivity (k), effective porosity (ne), longitudinal dispersivity (αL), vertical transverse dispersivity (αTV /αL) and horizontal transverse dispersivity (αTH /αL) estimated from the SiDNAmag and the salt tracer breakthrough curves were statistically similar. Our work contributes to the applicability of colloidal SiDNAmags for determining hydraulic parameters at different ionic strength conditions.

Innovative hydrogeophysical approach for detecting the spatial distribution of hydraulic conductivity in Bahri city, Sudan: A comparative study of Csókás and Heigold methods

Hydraulic conductivity is one of the crucial parameters used to identify the potentiality and productivity of groundwater aquifers. This research employs an integrated approach using geophysical well logging, exploratory factor analysis and surface electrical resistivity methods to detect the vertical and horizontal variation of hydraulic conductivity in Bahri city, Sudan. Based on the geophysical well logs of Spontaneous potential (SP), natural gamma ray (GR), and electrical resistivity (RS), Csókás method is used to determine the continuous variation of hydraulic conductivity along the aquifer. Csókás method is an experimentally modified version of the Kozeny–Carman equation and is based on the formation factor of the groundwater aquifer and the effective grain size. This approach is performed in three groundwater boreholes, and the obtained hydraulic conductivities showed a close agreement with that of the pumping test analysis. Furthermore, the hydraulic conductivity is measured using multivariate statistical factor analysis. This statistical approach relies on the correlation between the extracted factors and petrophysical and hydrogeological parameters. In this research, a strong negative linear correlation between the first factor and hydraulic conductivity is indicated. Consequently, a site-specific equation is suggested for continuous estimation of hydraulic conductivity along the aquifer. In the last stage, the results obtained from the Csókás method are interpolated with vertical electrical sounding (VES) measurements using to detect the horizontal variation of hydraulic conductivity throughout the studied area. This was achieved by combining the hydraulic conductivities of geophysical well logging and vertical electrical soundings to obtain a consistent estimation. As a result, the variation of hydraulic conductivity is obtained, and the average was 1.9 m/day which shows a close agreement with the average of the previous investigations (1.5 m/day). This approach is highly recommended since it can enhance data coverage, cutting down the expense of hydrogeological investigations and lowering the uncertainty of the hydrogeological models.

3D hydrostratigraphic and hydraulic conductivity modelling using supervised machine learning

Accurately modeling highly heterogenous aquifers is one of the big challenges in hydrogeology. There is a pressing need to develop new methods that transform high-resolution data into hydrogeological parameters representative of such aquifers. We use random forest-based machine learning to predict the distribution of hydrostratigraphic units and hydraulic conductivity (K) at a regional scale. We used lithologic logs from >2000 boreholes and resistivity-depth models from 2717 km of Airborne Electromagnetics (AEM). Eighty unique lithologic categories are lumped into 5 hydrostratigraphic units. K data is derived from descriptions of grain size and texture. The input data are resampled into a 200 × 200 × 1m grid and split into 70% training and 30% validation. K prediction had a training F1 score of 95% and 87% testing accuracy. After hyperparameter tuning these scores improved to 99.6% and 92%, respectively. Hydrostratigraphic unit prediction showed a training F1 score of 97% and 91% testing accuracy, improving to 100% and 95% after hyperparameter tuning. This method produces a high-resolution 3D model of K and hydrostratigraphic units that fills gaps between widely spaced boreholes. It is applicable in any setting where boreholes and AEM are available and can be used to build robust groundwater models for heterogeneous aquifers.

Cover crop and biofuel crop effects on hydraulic properties for claypan soils

AbstractPerennial biofuel and cover crops systems are important for enhancing soil health and can provide numerous soil, agricultural, and environmental benefits. The study objective was to investigate the effects of cover crops and biofuel crops on soil hydraulic properties relative to traditional management for claypan soils. The study site included selected management practices: cover crop (CC) and no cover crop (NC) with corn/soybean rotation, switchgrass (SW), and miscanthus (MI). The CC mixture consisted of cereal rye, hairy vetch, and Austrian winter pea. The research site was located at Bradford Research Center in Missouri, USA, and was implemented on a Mexico silt loam. Intact soil cores (76‐mm diam. by 76‐mm long) were taken from the 0–10, 10–20, 20–30, and 30–40 cm depths with three plot replicates and two sub‐samples per plot replicate per depth. Soil hydraulic properties evaluated for each sample included: saturated hydraulic conductivity (Ksat), water retention, bulk density, and pore size distributions. Results showed with the test of Duncan's least significant differences that treatments of MI (1.18 Mg m−3) and SW (1.21 Mg m−3) had lower values of bulk density averaging across soil depth than CC (1.27 Mg m−3) and NC (1.31 Mg m−3). Management systems significantly increased Ksat with the biofuel treatments at 0–10 cm compared to NC system. The MI management showed a significant increase in macroporosity and fine mesoporosity as compared to other management systems. Slight changes have occurred in the measured soil physical properties for CC system compared to NC plots. Overall, increasing soil organic matter from more plant roots from long‐term biofuel cropping systems can improve soil water storage and crop productivity.

Incorporating machine learning models and remote sensing to assess the spatial distribution of saturated hydraulic conductivity in a light-textured soil

Saturated soil hydraulic conductivity (Ksat) is a key component in hydrogeology and water management. This study aimed at evaluating popular tree-based machine learning algorithms (Random forest (RF), Quantile random forest (QRF), Cubist (Cu), and Decision tree regression (DTr)) to assess the spatial distribution of Ksat in a sandy agricultural field. Soil surface reflectance derived from Landsat-8 (OLI/TIRS) (several remotely sensed data including original surface reflectances of spectral bands and 22 remote sensing indices) as well as limited ground measured soil data were used as predictor covariates. Using collected disturbed and undisturbed soil samples, physicochemical properties (Ksat, porosity, organic matter, and texture contents) were determined. Based on novel supervised feature selection, covariates including Landsat-8 spectral Band2, Band4, Band6, and Band7, normalized difference pond index (NDPI), normalized difference vegetation index (NDVI), and silt content were identified as the most important factors in Ksat prediction. Variable importance analysis showed Band7 and Band6 were explaining 54% of the total Ksat variation. The model evaluation, using five statistics cretria, Taylor diagram and the Kruskal–Wallis (KW) test, demonstrates the outperformance of RF being followed by QRF, Cu, and DTr models. Accordingly, the RF method combined with an optimized sampling approach provides the most accurate digital soil Ksat map. The uncertainty analysis showed no significant different trend in Ksat predictions by applying more dense datasets confirming original-limited data (n = 28) was efficient and sufficient to derive a reliable Ksat-map. The developed approach, which can be scaled up at a large scale, seems to be useful in modelling water flow and solute transport for water and nutrient management purposes toward precision agriculture.

A Short‐Term Pumping Strategy for Hydraulic Tomography Based on the Successive Linear Estimator

AbstractIn this study, the random finite element method, a finite element method with random field generation techniques, was applied to investigate the cross correlations between the observed head and hydraulic conductivity and specific storage at different locations and different times in pumping tests. The results show that the two cross correlations between the pumping well and the observation well reach their maximums before pumping reaches a steady state. Specifically, the cross correlation between the observed head and hydraulic conductivity is the greatest when the temporal derivative of the observed head does not change significantly, and that between the observed head and specific storage is the greatest when the temporal derivative (the rate) of the observed head is maximum. Based on the results of cross‐correlation analysis, a short‐term pumping strategy for hydraulic tomography is proposed to obtain the spatial distribution of hydraulic conductivity and specific storage using the successive linear estimator. Furthermore, this strategy was validated by Monte Carlo simulations. This paper points out that the sensitivity and cross‐correlation analyses report the ensemble (averaged) behaviors of any heterogeneous aquifers, which is not necessarily suitable for one realization. Furthermore, Monte Carlo simulation is suggested for validating any groundwater inverse modeling result.

Modelling Plume Development with Annual Pulses of Contaminants Released from an Airport Runway to a Layered Aquifer, Evaluation of an In Situ Monitoring System

In cold climates, the use of de-icing chemicals in the winter can lead to groundwater contamination, especially when used in large quantities, such as at airports. Oslo Airport, Gardermoen, is situated on Norway’s largest rain-fed aquifer. Potassium formate is used to remove ice from runways and propylene glycol from airplanes; the organic parts are degradable. Most of the wells to monitor the spread of de-icing chemicals in the underlying aquifer have well screens near the groundwater level, while the runways and the source of de-icing chemicals are near the groundwater divides, where vertical flow is expected. The objective of this study is to demonstrate the importance of layers and time-varying recharge on the spreading of contaminant plumes in an aquifer near a groundwater divide. This is done with numerical modelling. The model results show increased vertical transport of the added tracer in the presence of horizontal layers, both continuous and discontinuous, in the aquifer. With certain distributions of hydraulic conductivity, Ks, we demonstrate that deeper monitoring wells are required. With the scenarios modelled here, time-varying recharge has a weaker effect on plume distribution. Measured concentrations of potassium and total organic carbon show the cyclic effect of seasonally varying recharge of contaminants, and an asymptotic accumulation of concentration over time, that is consistent with the model runs. In conclusion, groundwater monitoring systems near a groundwater divide should include multi-level samplers to ensure control of the vertical plume movement.