Matric Head Research Articles

Simulations of low impact development designs using the storm water management model

This study assessed the U.S. Environmental Protection Agency's Storm Water Management Model (SWMM) for urban water management challenges. This study conducted a sensitivity analysis to identify the most influential factors in the SWMM. Moreover, the performance of SWMM was evaluated with the HYDRUS-1D module in simulating infiltration rates. The sensitivity results showed field capacity as the most significant factor, highlighting the need for advanced modeling techniques to consider factors like field capacity. The SWMM was evaluated by the HYDRUS-1D that SWMM consistently underestimated peak infiltration rates and commenced infiltration calculations only when soil moisture exceeded field capacity. It reveals its limitations in handling unsaturated soil conditions and highlights the consideration of the matric head of the soil during the infiltration calculation in soil media. Moreover, the evaluation of bioretention areas showed larger areas resulting in more substantial flow reductions but with significant variability under different rainfall conditions. Accordingly, this result emphasizes the importance of careful consideration for environmental factors in bioretention design. This study contributes by enhancing understanding of SWMM's limitations in simulating urban water management challenges. Thus, this research will offer technical assistance to stakeholders focused on challenges such as runoff, hydrologic cycle, and urban flooding in urban areas.

A modified model predicting the hydraulic properties of variably saturated soil accounting for film and capillary flow

Accurate knowledge of hydraulic properties of unsaturated soil is vital to modelling water and solute transport process and addressing soil salinisation problems. Due to the defect that the van Genuchten (VG) model cannot represent soil water characteristics (SWC) during the drying stage, we have introduced a modified model, which combines capillary flow with film flow models to describe the unsaturated hydraulic conductivity. The capillary SWC is obtained by assuming the specific water capacity function with lognormal distribution. The film SWC is modified using the logarithmic nonlinear expression by introducing a modification coefficient into the Campbell equation, and the film relative hydraulic conductivity (RHC) is obtained based on the logarithmic nonlinear relationship. The calculated modification coefficient values range from 0.99 to 2.49. Finally, the proposed model’s rationality and applicability are validated through root mean square error (RMSE) analysis with measured values and comparison with the VG model. While the calculation RMSEs of SWC in the two models are basically consistent, the RMSEs of RHC in the proposed model are around 1–10 times smaller than those of the VG model, indicating its superiority in calculating the hydraulic conductivities over the entire matric head.

A modified SWAT model for mechanistic simulation of soil water‐salt transport and the interactions with shallow groundwater

AbstractCurrent hydrological models still face difficulties in accurately simulating vadose zone (VZ) hydrological processes in arid watersheds with shallow groundwater environments. Therefore, this article introduces a new mechanistic VZ module coupled with an improved groundwater‐balance (GWB) module to enhance the capabilities of the well‐known Soil Water Assessment Tools (SWAT) model. The main improvements and features of the modified SWAT model (M‐SWAT) include: a numerical VZ module to accurately simulate soil water‐salt transport under variably saturated conditions by solving the head‐formed Richards' equation and advection‐dispersion equation; a GWB module (on a sub‐basin scale for shallow aquifer) coupled with VZ module by proposing an effective way of boundary information exchanges; and a more reasonable water‐salt stress functions (i.e., based on matric head and osmotic head) to calculate root water uptake and plant growth. The M‐SWAT was then tested and evaluated with two‐year observation data from the Yangchang canal command area (YCA, an experimental site) and the Jiyuan Irrigation System (Jiyuan, region‐scale), located in the arid upper Yellow River basin of northwest China. The model produced a good agreement between the simulated and observed data in both calibration and validation with sufficiently small root mean square error (RMSE). The determination coefficient (R2) was greater than 0.51, 0.36, and 0.87 in simulating the soil water‐salt dynamics, groundwater depth fluctuations, and leaf area index development, respectively. Case testing also proved that the M‐SWAT had sufficient computational efficiency and stability for regional application. Further comparisons with previous studies indicated that the M‐SWAT could more accurately and rationally simulate the soil water‐salt dynamics and their interactions in arid watersheds with shallow groundwater tables.

Empirical equations for estimating field capacity in dryland cropping soils of southeastern Australia

The root zone soil water content at field capacity, θFC, is often crucial for irrigation scheduling and soil-plant-atmosphere modelling. Although θFC is traditionally determined using methodologies based on matric head or when water flux at the bottom of the root-zone equal to a prescribed negligible flux, these approaches can be problematic for some soils and applications. In this study, a novel water storage approach was used to estimate θFC for 11 soil texture classes in southeastern Australia (northwest Victoria) by employing the Soil-Water-Atmosphere-Plant (SWAP) model to simulate gravity drainage from saturation in the active root zone (top 60 cm). Field capacity was specified as the average root zone water content corresponding to a 1% relative change in daily soil water storage. We have also estimated flux-based θFC when prescribed flux at the bottom of the root-zone was 0.1 cm d-1. Three new empirical equations were developed to estimate storage-based θFC as a function of SWAP-simulated drainage flux out of the root zone, the n parameter of the van Genuchten function, and the saturated soil hydraulic conductivity, Ks, or the soil water content at − 100 cm matric head. We have evaluated these three new equations in addition to nine published equations for estimating θFC. The three new equations were found to be better predictors of θFC than most of the nine popular equations reported in the literature when compared to flux-based θFC values. Based on findings from this study the new equations are considered to be effective for irrigation scheduling and crop/climate modelling on the dryland soils of Victoria. Future studies will assess the applicability of the new equations to other parts of Australia.

Modeling Water Flow and Solute Transport in Unsaturated Soils Using Physics‐Informed Neural Networks Trained With Geoelectrical Data

AbstractAccurate modeling of water flow and solute transport in unsaturated soils is of significant importance for precision agriculture, environmental protection and aquifer management. Traditional modeling approaches are considerably challenging since they require well‐defined boundaries and initial conditions. Physics‐informed neural networks (PINNs) have recently been developed to learn and solve forward and inverse problems also constrained to a set of partial differential equations and are more flexible than traditional modeling approaches. However, hydrological applications of PINNs used so far spatial measurements of hydraulic head, water content and/or solute concentrations, which were well distributed in the subsurface for training the system. Such measurements are hard to obtain in real‐world applications. Here, we propose to train PINNs with non‐invasive geoelectrical tools for simulating two‐dimensional water flow and solute transport during infiltration and redistribution processes with unknown initial conditions. Two‐dimensional flow and transport numerical simulations were used as benchmarks to examine the suitability of the described approach. Results have shown that the trained PINNs system was able to reproduce the spatiotemporal distribution of both water content and pore‐water salinity during both processes with high accuracy, using five time‐lapse geoelectrical measurements and matric head measurements at a single location. The trained PINNs system reconstructed the initial conditions of both state parameters at both processes. It was also able to separate the measured electrical signal into its two components, that is, water content and pore‐water salinity. The subsurface geoelectrical tomograms were significantly improved compared to those obtained from a classical inversion of the raw geoelectrical data.

Development of the Green-Ampt Infiltration Rate Model and Relationship of the GA Model Parameters with Soil Hydraulic Parameters

In the Green-Ampt model, the infiltration rate is indirectly related to time via the intermediate term of cumulative infiltration depth. In the present study, the direct theoretical relationship between the infiltration rate of the GA infiltration model with time is derived and elaborated. A new laboratory method for separate measurement of the GA model parameters is developed and tested via the constant-head infiltration tests on two sandy soil columns considering different values of initial soil water content. Hysteretic soil water characteristic (SWC) curves of studied soils are also measured. Results indicate that the GA model parameters are constant values and independent from the initial soil water content. Hydraulic conductivity K, wetting front matric head Ψf, and water content of wetted zone θwz of GA model are separately estimated from proposed method and are found to be respectively equal to saturated hydraulic conductivity Ks, water-entry value hw−e, and saturated water content of the wetting SWC curve θsw. By setting K=Ks, Ψf=hw−e, and θwz=θsw, the results of the GA model matched reasonably well with the numerical solution of Richards’ equation using HYDRUS-1D and measured data.

Hydrus-1D for Simulating Potassium Transport in Flooded Paddy Soils

ABSTRACT Understanding the transport of potassium (K) in flooded rice fields is important for the efficient management of K fertilizers. In this study, the data from lysimeter experiments were used for simulating the water flow and K transport using Hydrus-1D and the influence of split doses of recommended K fertilizer on its availability to paddy crop was investigated. Paddy crop was grown in non-weighing-type lysimeters. The depth of ponding water, soil matric potential heads, K concentration of ponding water and soil solution were measured during three paddy growing seasons. The Hydrus-1D model was calibrated and validated using the lysimeter data and simulated the water flow and K concentrations well. The deep percolation was 51–56% of the applied water and the K load in deep percolation was 0.3–0.7% of the total available K. About 56–62% of K is utilized by rice crop. The applied K was observed to be remained mostly within the top 20 cm soil depth and did not transport to deeper layers. The recommended dose of K fertilizer with four equal splits applied at four crop growth stages could increase the K availability to paddy throughout its growing period, and it may be the best K management practice for paddy.

A fractal hydraulic model for water retention and hydraulic conductivity considering adsorption and capillarity

Commonly used hydraulic models that describe the water retention and transport properties of variably saturated soils mainly account for the capillary component of matric potential but ignore the adsorption component. Experimental evidence suggests that soil hydraulic conductivity may be underestimated by several orders of magnitude in the low saturation range for models ignoring the adsorbed film flow. Thus, this paper introduces a fractal method to characterise the effects of capillary and adsorption components of matric potential on soil hydraulic properties considering a simplified pore water transport process. Furthermore, the concept of specific thickness for the adsorbed film was defined to describe the adsorption and adsorption capacity of the soil. Based on these, a complete hydraulic model, including the soil water retention curve (SWRC) and relative hydraulic conductivity curve (RHCC), was derived to describe the water retention and transport properties of the soil in the entire matric head range. The performance of the proposed model was then evaluated using 13 sets of test data, from clean sand to bentonite, showing that it can capture the characteristics of water retention and transport properties well. The model parameters calibrated from the SWRC dataset could predict RHCC over the entire suction range without introducing any additional parameters. Additionally, the influences of the model parameters on soil hydraulic behaviours are also discussed in the paper. It was found that the pore size distribution of the soils mainly controlled the transport characteristics of capillary water, but had a slight influence on the hydraulic properties of adsorbed water, which was dominated by the type and content of minerals and specific surface area of the soil.

Hydraulic properties of unsaturated calcarenites by means of a new integrated approach

Most infiltration and water retention models for unsaturated porous media proposed in the literature often are based on simplified representations of the pore system and use the matric head as the main state variable.An integrated study on the hydraulic properties of homogeneous calcarenites with a complex pore network is presented, aimed at assessing water infiltration and retention mechanisms, starting from dry to wet conditions under high water inflow.Medium-grained grainstones and fine-grained packstones were used as material for laboratory testing and model set up. Petrophysical characterization and bimodal distribution of pore volume was obtained by standard and unconventional procedures. Permeability test under saturated conditions was performed with constant and falling head tests. Falling head infiltration tests at bench scale were carried out to estimate the hydraulic properties of the materials under unsaturated conditions.Unsaturated hydraulic relationships such as the soil water retention curve (SWRC) and hydraulic conductivity function were examined. The advection–diffusion form of Richard’s equation, together with the pore bundle model and dual porosity concept under equilibrium conditions represent the theoretical framework used to investigate the unsaturated hydraulic behavior of the calcarenites in the falling head infiltration test. The SWRC, relative hydraulic conductivity and celerity were determined through the capillary theory and pore bundle concept considering the bi-modal nature of the pore-size distribution. Theoretical solutions and model predictions were compared with experimental data, which demonstrated the dominant control of macro-porosity on water flow and infiltration rate. In particular, our experimental results indicate that the wetting front propagates mainly through the macropores with a gravity driven flow velocity higher than the diffusive flow through the micropores. The effect of texture, degree of saturation and boundary conditions on infiltration dynamics is discussed.

Estimation of soil water retention in conservation agriculture using published and new pedotransfer functions

Conservation agriculture has been developed as a means to improve the sustainability of agricultural systems and reduce drawbacks of conventional agricultural practices. Cropping practices can have a large influence on soil properties such as water retention. Proper tools are needed to assess and model effects of conservation agriculture on soil properties. As measuring soil water retention is expensive and time consuming, pedotransfer functions (PTFs) are now commonly used to predict them. The objectives of this study were to (i) present a new dataset of conservation agriculture data, (ii) assess performances of existing PTFs in predicting soil water retention of soils under conservation agriculture and (iii) develop new specific PTFs to predict water retention in conservation agriculture more accurately. We used data collected only in fields under conservation agriculture in France to evaluate several published PTFs with three evaluation criteria (RMSE, prediction bias (ME) and Nash-Sutcliffe Efficiency (EF)). We then developed new PTFs using three methods ― multiple linear regression, regression tree and random forest ― to predict soil water content at matric heads of -100 (θ100, field capacity for sandy soils), -330 (θ330, field capacity for other soils) and -15 000 cm (θ15 000, wilting point). Soil tillage, presence of a cover crop, rotation length and previous reduced/no tillage were used as predictors in addition to basic soil properties for regression trees and random forests. The quality of prediction (RMSE, ME and EF) was calculated for each new PTF using a cross-validation procedure. Generally, predictions of wilting point had lower absolute error than those of sandy-soil field capacity (RMSE = 0.044 and 0.066 cm3/cm3, respectively). EF was usually negative for all water contents. The cross-validation performance of the new PTFs was similar for multiple linear regression (RMSE: 0.028, ME: 0.000, EF: 0.34 for θ100) and random forest (RMSE: 0.027, ME: 0.000, EF: 0.36 for θ100), and generally worse for regression tree (especially EF). Multiple linear regression that did not consider cropping practices performed as well as random forest and thus did not identify any major influence of agricultural management on predicted water content. Future research on developing PTFs should focus on identifying more relevant predictors.

Simulation of vadose zone flow processes via inverse modeling of modified multistep outflow for fine‐grained soils

AbstractCharacterization and measurement of the hydraulic properties of unsaturated porous media is still a challenge in natural environments, although exact knowledge of the soil's hydraulic properties [unsaturated soil hydraulic conductivity (K), volumetric water content (θ), soil matric head (h)] is crucial for solving many soil, hydrological, and environmental issues. The main purpose of this study was to establish a modified multistep outflow technique that facilitates laboratory operations and reduces time and costs. We also focused on inverse parameter optimization of the Mualem–van Genuchten (MVG) model with laboratory‐provided data for three fine‐grained soils. This modified version was assessed by inverse modeling of the MVG model and the generalized reduced gradient algorithm. The results represent the best estimate of the adjusted parameters and the very low uncertainty associated with the fitted values. The soil hydraulic functions were simulated well up to pressures of 1,500 kPa and their uncertainties were extremely low. The R2 values were 88 to 97 and 81 to 96% for the soil water characteristics and the unsaturated hydraulic conductivity functions, respectively. The simultaneous optimization of the nonlinear hydraulic functions from a single transient flow experiment agreed well with their observed data for each soil examined. Besides, by eliminating the use of tensiometers and having no independent measurements of saturated soil hydraulic conductivity and saturated volumetric water content, the modified version was faster and easier than the conventional method. The proposed protocol appears to be suited for the vadose zone flow simulation of fine‐grained soils.

Methodology to simulate unsaturated zone hydrology in Storm Water Management Model (SWMM) for green infrastructure design and evaluation.

Hydrologic models such as the USEPA Stormwater Management Model (SWMM) are commonly used to assess the design and performance of green infrastructure (GI). To accurately represent GI performance models used in design need to be able to address both the hydrology/hydraulics of the catchment and the GI unsaturated (vadose) zone hydrology. While hydrologic models, such as SWMM, address the need for catchment hydrology/hydraulics, they often simplify the unsaturated zone hydrology. This paper presents a methodology utilizing existing components of SWMM to represent unsaturated zone hydrology in an accessible format that does not require adjustments to the SWMM source code. The methodology simulated the unsaturated soil water movement by considering flow caused by differences of soil matric head and flow caused by gravity between soil layers with finite depth/length. The flow flux related to the soil matric head is a function of soil water diffusivity (D) and the soil moisture gradient, where D can be represented by a pump curve in SWMM. The flow flux related to gravity was controlled by unsaturated hydraulic conductivity (K) only and was also simulated by a pump. The methodology was compared to another variably saturated model, HYDRUS, with theoretical soils (with single layers of sand, loam, silt, and clay, as well as dual-layer scenarios). Field data was used to compare the methodology to HYDRUS and the SWMM LID (Low Impact Development) module. In all comparisons the presented methodology and HYDRUS delivered similar results for the vadose zone response to a storm event, while the LID module of SWMM exhibited slower water movement. The results showed that under natural conditions, the approximation of the presented methodology yielded satisfactory results to simulate flow through the unsaturated vadose zone.

Evaluation of Assouline–Or Adjusted Model to Express Soil Drainage Curve

The objectives of this research are prediction of soil drainage curve using Assouline and Or (2014) adjusted soil drainage model and comparison of the predicted results with experimental data on small and large scales. This research is conducted for 18 small soil columns under different salinity level of irrigation water using different scenarios. In scenario A, water from saturated water and water of 0.1 (A-1) and 15 (A-2) bar matric head assumed as the mobile water, D∞, and input to Assouline and Or model to estimate weighted average of hydraulic conductivity, Km with fitting. In scenario B, Km is computed using water characteristic curve and D∞ is obtained using fitted model. Finally, in scenario C, D∞ and Km are predicted using fitted model. All experiments were repeated in the presence of plant for small soil columns and columns with 2.7 m depth as well. Results showed that the model prediction is influenced by irrigation water salinity, study time and depth scales. However, soil and water physical properties and their interactions can partially explain the results and the flexibility of Assouline and Or model is appropriate, we suspected to the validity of physical meaning of model parameters and, the certainty of model predictions for the field capacity, FC, concept.

The relevance of measuring saturated hydraulic conductivity: Sensitivity analysis and functional evaluation

The saturated hydraulic conductivity (Ks) is considered to be one of the main soil hydraulic properties to simulate water transport in the vadose zone, yet it is notoriously difficult to assess due to its large field-scale variability and sensitivity to preferential flow paths. With the increasing availability of convenient methods to measure the unsaturated hydraulic conductivity (Ku), the additional value of measuring Ks could be questioned. This study investigates the relevance of measuring Ks in two ways. Firstly, a sensitivity analysis was performed to evaluate which part of the hydraulic conductivity curve impacts the simulation of unsaturated water transport most. It was found that hydraulic conductivity curve between −10 cm and −1000 cm matric head was affecting the simulation more than at saturation. Secondly, a functional evaluation was performed by simulating water flow in six test fields according to three scenarios, in which the hydraulic properties were determined with the measured Ks and/or Ku. The simulation of saturation degree at three depths was the least accurate in the scenario where only Ks was measured. Remarkably, the most accurate simulation was found in the scenario with only the measured Ku. According to these results, measuring Ku would be of greater importance than measuring Ks for the simulation of water flow in the vadose zone.

Improved drainage relationships for characterizing the agronomic and environmental performance of field crop production

Simple algebraic drainage equations produce useably accurate depth and time distributions for unsaturated soil water content and drainage flux, but they systematically underestimate matric head. The primary objective of this study was to improve the matric head estimate, and thereby increase the value of algebraic drainage equations in agri-environmental applications. A secondary objective was to illustrate how algebraic drainage equations can be used to inform and characterize the agronomic and environmental performance of field crop production.A quasi-empirical correction factor was developed that reduced matric head error to an acceptable 0–22% relative to numerical simulation results for weak, moderate and strong capillarity soils at profile depths of 0.3 m and 1.0 m, and for drainage from saturation times ranging from 0.01 to 30 days. Without correction, matric head error ranged from a barely acceptable maximum of 35–40% in weak and moderate capillarity soils, to a clearly unacceptable maximum of 141% in strong capillarity soil. The drainage equations, along with corrected matric head and target criteria from the literature, were used to illustrate how various silt loam addition rates impacted the agronomic and environmental functioning of an agricultural sand soil in terms of root zone drainage time, drainage flux, plant-available air and water, readily available water range, and potential for excess loss of water and nutrients through deep percolation and leaching.

A Comparative Study of Water and Bromide Transport in a Bare Loam Soil Using Lysimeters and Field Plots

The purpose of this methodological study was to test whether similar soil hydraulic and solute transport properties could be estimated from field plots and lysimeter measurements. The transport of water and bromide (as an inert conservative solute tracer) in three bare field plots and in six bare soil lysimeters were compared. Daily readings of matric head and volumetric water content in the lysimeters showed a profile that was increasingly humid with depth. The hydrodynamic parameters optimized with HYDRUS-1D provided an accurate description of the experimental data for both the field plots and the lysimeters. However, bromide transport in the lysimeters was influenced by preferential transport, which required the use of the mobile/immobile water (MIM) model to suitably describe the experimental data. Water and solute transport observed in the field plots was not accurately described when using parameters optimized with lysimeter data (cross-simulation), and vice versa. The soil’s return to atmospheric pressure at the bottom of the lysimeter and differences in tillage practices between the two set-ups had a strong impact on soil water dynamics. The preferential flow of bromide observed in the lysimeters prevented an accurate simulation of solute transport in field plots using the mean optimized parameters on lysimeters and vice versa.

Capillary Length and Field Capacity in Draining Soil Profiles

AbstractThe capillary length (λs) and time (ts) are dynamic scalars that emerge routinely in the infiltration problem when gravitational and pressure gradients forces are involved. During drainage, however, capillary gradients oppose gravity and retain soil moisture close to surface. In this case, the pull of capillary gradients increases with drainage time and offsets gravity resulting in a quasi‐hydrostatic pressure distribution and negligibly small drainage flux in the profile. In this paper, it is proposed to anchor the dynamic concept of field capacity—the attainment of a small negligible drainage flux—in the physics of soil moisture redistribution as influenced by gravity and capillary forces. Similar to infiltration, this dynamic approach grounds the concept of field capacity in soil hydrology and allows its estimation from readily measured intrinsic physical characteristics such as λs, ts, and Ks. Finally, we exploit an analytical solution by Broadbridge and White (1988, https://doi.org/10.1029/WR024i001p00145) to track the drainage front as soil water redistributes in an initially saturated soil profile. While initially large, the downward migrating drainage front decelerates with time reaching near steady state condition at t ≈ 1,000ts. Quasi‐hydrostatic pressure matric head and water content profiles develop above the drainage front.

Characteristics of an in-situ unsaturated hydraulic conductivity of soil

ABSTRACT A soil hydraulic conductivity (K) indicates the ability of a soil to conduct water and is often assumed to be in one-to-one correspondence with matric head (h) or volumetric water content (θ) for mathematically analyzing field soil water regimes. However, to what extent this assumption represents K in a field is rarely questioned, and how h-K and θ-K relations actually behave under natural field conditions is seldom investigated sufficiently. Therefore, this study aimed at clarifying actual behaviors of h-K and θ-K relations under natural field conditions (in-situ K), and evaluated the degree to which the in-situ K(h) diverted from its equivalent h-K relations that were measured through drainage processes starting from water-saturated conditions (primary-drying K(h)). The in-situ K values were obtained in the range of 4×10-8 cm s-1 < K < 8×10-6 cm s-1 with -55 cm < h < -5 cm during a four-years monitoring in a field. And the variation among the observed in-situ K values for a given h was one to two orders of magnitude, implying that the amount and pattern of water-conducting pores for a given h had changed frequently through years of wetting-drying cycles of the field soil, and that the application of a single-valued K(h) function to quantifying in-situ soil water flow can cause errors in these orders of magnitude. Also the in-situ K was 10 to 100 times lower than the primary-drying K in any h. The in-situ K fluctuated even for a given θ. Thus, it was suggested that neither an h-K relation nor a θ-K relation can sufficiently work for understanding a field soil water regime if it is regarded as merely a single-valued function. And more knowledge about the temporal variability in in-situ K is required for understanding soil water regimes in a field.

Evaluation of Parametric and Nonparametric Machine‐Learning Techniques for Prediction of Saturated and Near‐Saturated Hydraulic Conductivity

Core Ideas Accurate Ks and K10 were obtained using water content data at several matric potentials. Robust Ks and K10 prediction by machine‐learning methods confirmed by bootstrapping. Gaussian process regression predicted Ks and K10 with minimum number of predictors. Parametric and nonparametric supervised machine learning techniques were used to estimate saturated and near‐saturated hydraulic conductivities (Ks and K10, respectively) from easily measurable soil properties including the name of the pedological horizon (HOR), soil texture (sand, silt, and clay), organic matter (OM), bulk density (BD), and water contents (θpF1, θpF2, θpF3, and θpF4.2) measured at four different matric heads (−10, −100, −1000, and −15,848 cm, respectively). Using a stepwise linear model (SWLM) and the Lasso regression as parametric methods with 316 data in training and 135 data in the testing phase, four pedotransfer functions (PTFs) were obtained in which water contents for both methods play an important role compared with other variables. The SWLM showed better performance than Lasso in the testing phase for log(Ks) and log(K10) prediction, with RMSE values of 0.666 and 0.551 cm d−1 and R2 of 0.26 and 0.65. Nonparametric supervised machine learning methods trained and tested with a similar data set significantly improved the accuracy of Ks prediction, with R2 of 0.52, 0.36, and 0.53 for Gaussian process regression (GPR), support vector machine (SVM), and ensemble (ENS) methods in the testing stage. These methods also described 74.9, 66.7, and 72.5% of the variation of log(K10). Bootstrapping validated the strong performance of nonparametric techniques. The feature selection capability of GPR determined that instead of using a model with all predictors, HOR, silt, θpF1, and θpF3 are sufficient for the prediction of log(Ks), while HOR, silt, and OM can predict log(K10) as accurate as the comprehensive model with all variables.

Sampling at high spatial and temporal resolutions with an experimental chamber

SummaryLaboratory‐scale studies of the dynamics of soil biogeochemical processes often require destructive sampling for chemical, biological and genetic analyses. The consequences are either ‘end‐of‐experiment’ sampling or a set‐up with multiple experiments in parallel, of which some are sacrificed over time. We propose an alternative experimental set‐up to enable matrix sampling and monitoring at high spatiotemporal resolution of chemical, biological and physical properties. A customized experimental chamber was constructed, including an array of sensors on one side and hundreds of sampling ports on the other. A vertical unidirectional flow regime was maintained in the chamber, and soil samples were collected through ports that were not adjacent laterally to ones used in an earlier sampling campaign. We give an example of an experiment with such a chamber in which the environmental conditions (matric head and redox) were kept almost uniform in the horizontal dimension. Under these conditions nitrogen cycling was also almost the same in the lateral dimension. This system offers a unique combination of high spatial and temporal resolution sensors in the same system that is destructively sampled.Highlights A new experimental chamber allows high‐resolution sampling In unidirectional flow, sacrificial sampling can be avoided