Matric Potential Measurements Research Articles

Accounting for the impact of tree size and soil spatial variability on leaching from orchards

Commercial agricultural production of orchards is based on water and fertilizer applications. Over-application and orchard spatial variability lead to water and nitrogen (N) losses through leaching and environmental damage. This work introduces a novel advanced method to quantify N and water leaching at the orchard scale by combining soil monitoring, unmanned aerial vehicle (UAV) imagery to estimate tree size, leaf N and canopy content, and water flow and N transport models. Four neighboring commercial orange orchards were selected, and 48 representative trees were instrumented with suction cups and tensiometers at a depth of 90 cm. The physical properties of the soil profiles in the vicinity of each tree were determined. During 2019–2021, UAV structure-from-motion (SfM) photogrammetry was used to classify the tree sizes into small, medium, and big categories. Soil porewater extraction and soil matric potential measurements were conducted every three weeks, while leaf N contents (LNC) were determined through bimonthly leaf tissue sampling. The LNC, N leaching, soil water state, and N use efficiency (NUE) (the ratio between the mass of plant N uptake and the mass of N applied per area) were strongly related to tree size classification. Results of the calibrated hydrological model illustrated that the 'big' trees exhibited minimal N leaching due to higher transpiration compared to the other tree size categories. An NUE map was established using the calibrated model and field measurements, demonstrating that soil spatial variability minimally affected N leaching compared to tree size distribution. The presented holistic approach can be used to identify N-leaching hotspots, improve orchard scale NUE estimates, and promote sustainable agricultural management practices.

Enhancing water use efficiency in precision irrigation: data-driven approaches for addressing data gaps in time series

Real-time soil matric potential measurements for determining potato production's water availability are currently used in precision irrigation. It is well known that managing irrigation based on soil matric potential (SMP) helps increase water use efficiency and reduce crop environmental impact. Yet, SMP monitoring presents challenges and sometimes leads to gaps in the collected data. This research sought to address these data gaps in the SMP time series. Using meteorological and field measurements, we developed a filtering and imputation algorithm by implementing three prominent predictive models in the algorithm to estimate missing values. Over 2 months, we gathered hourly SMP values from a field north of the Péribonka River in Lac-Saint-Jean, Québec, Canada. Our study evaluated various data input combinations, including only meteorological data, SMP measurements, or a mix of both. The Extreme Learning Machine (ELM) model proved the most effective among the tested models. It outperformed the k-Nearest Neighbors (kNN) model and the Evolutionary Optimized Inverse Distance Method (gaIDW). The ELM model, with five inputs comprising SMP measurements, achieved a correlation coefficient of 0.992, a root-mean-square error of 0.164 cm, a mean absolute error of 0.122 cm, and a Nash-Sutcliffe efficiency of 0.983. The ELM model requires at least five inputs to achieve the best results in the study context. These can be meteorological inputs like relative humidity, dew temperature, land inputs, or a combination of both. The results were within 5% of the best-performing input combination we identified earlier. To mitigate the computational demands of these models, a quicker baseline model can be used for initial input filtering. With this method, we expect the output from simpler models such as gaIDW and kNN to vary by no more than 20%. Nevertheless, this discrepancy can be efficiently managed by leveraging more sophisticated models.

Determination of wetting soil water characteristics curve from disk infiltrometer measurements

A wetting soil water characteristic curve (SWCCw) is necessary for understanding and interpreting the re-distribution of infiltrated rainwater, percolation rate, and contaminant transport. Direct determination of SWCCw is tedious and needs destructive sampling and invasive sensor installation. This study demonstrates an indirect method for determining SWCCw based on infiltration measurements using a mini disc infiltrometer (MDI). Under controlled initial conditions, infiltration tests were conducted, coupled with real-time soil moisture and matric potential measurements using sensors. Sensor data facilitated assessment and cross-verification of SWCCw indirectly determined from MDI measurements. The indirect estimation involved inverse analysis and optimization of SWCCw parameters (α and n of the van Genuchten model) based on measured cumulative infiltration (CI) -versus-time response along with the knowledge of final volumetric water content (VWCf). The optimized SWCCw from MDI-infiltration matched the sensor-measured SWCCw reasonably well. The statistical tests using ANOVA proved that the CI measurements from MDI, together with VWCf information, are reliable input for inverse estimation of SWCCw and its parameters. Based on a realistic wetting process in the unsaturated zone beneath the disc infiltrometer, this study demonstrates the utility of a compact MDI for a quick, non-destructive measurement of SWCCw.

A Bayesian perspective on the information content of soil water measurements for the hydrological characterization of the vadose zone

Transient measurements from soil water monitoring installments are frequently coupled with Richards-based solvers to inversely estimate soil hydraulic parameters (SHPs) and numerically describe vadose zone water fluxes, such as groundwater recharge. To reduce model predictive uncertainty, the experimental setup should be designed to maximize the information content of observations. However, in practice, this is generally done by relying on the a priori expertise of the scientist/user, without exploiting the advantages of model-based experimental design. Thus, the main aim of this study is to demonstrate how model-based experimental design can be used to maximize the information content of observations in multiple synthetic scenarios encompassing different soil textural compositions and climatic conditions. The hydrological model HYDRUS is coupled with a Nested Sampling estimator to calculate the parameters’ posterior distributions and the Kullback-Leibler divergences. Results indicate that the combination of seepage flow, soil water content, and soil matric potential measurements generally leads to highly informative designs especially for fine textured soils, while results from coarse soils are generally affected by higher uncertainty. Additionally, the propagation of parameter uncertainties in a contrasting (dry) climate scenario strongly increased prediction uncertainties for sandy soil, not only in terms of the cumulative amount and magnitude of the peak, but also in the temporal variability of the seepage flow. A complementary real-world application with a sandy soil lysimeter identified a combination of seepage data and matric potential as the most informative design and confirmed findings of the synthetic scenarios, in which matric potential proved to be more informative than soil water content measurements.

Comprehensive in-situ freeze-thaw monitoring under a granular-surfaced road system

Annual freeze-thaw cycles significantly impact the performance of granular-surfaced roadways in cold regions. Various methods are employed to limit the damage caused by these events. A seasonal load restriction (SLR) is one of these methods that is imposed by local roads agencies according to the subgrade condition. Subsurface monitoring is a reliable method to help such agencies determine the status and freeze-thaw behavior of the subgrade in real-time. To help improve the capabilities for predicting the depths and timing of soil freeze-thaw events, a comprehensive subgrade sensor network was installed below a granular-surfaced roadway in Hamilton County, Iowa. The network continuously measured temperature, volumetric water content and matric potential at selected depths below the subgrade surface, and also recorded atmospheric conditions using a weather station installed next to the instrumented roadway section. In this study, the collected dataset for the winter 2019–2020 freeze-thaw period was analyzed using contour mapping over the entire road cross-section. The results of the study revealed unsymmetrical behavior under the road surface. The freezing front was found to be deeper towards the west midpoint and shallower at the shoulders. Volumetric water content and matric potential measurements were also found to be coherent with the temperature data during freezing and thawing periods. In addition, soil water retention curves of matric potential versus water content determined in the laboratory were compared to the curves derived from the field measurements. The two datasets were found to be quite different due to both predictable and unpredictable field conditions.

Effects of irrigation thresholds and temporal distribution on potato yield and water productivity in sandy soil

Water productivity enhancement in farming systems is one of the most critical challenges facing the agricultural sector in the twenty-first century. Precision irrigation based on soil matric potential (SMP) measurements effectively enhances water productivity. However, the temporal effect of an SMP-based comfort zone on potato crops is lacking. This paper evaluates the temporal effect of a range of soil matric potential thresholds (namely, −7, −10, −15, −18, −21, −24, −30, and −45 kPa) in a sandy soil on the marketable yield and irrigation water productivity (WP) of potato grown in containers in a greenhouse with the Partial Least Square algorithm (PLS) and a mixed model. The results of this study suggest that a comfort zone maximizing potato yield is located between − 10 and − 24 kPa. The − 24 kPa threshold generated the highest yield while reducing the irrigation water use up to 47% and generating the highest WP. The average yield in the comfort zone reached 496.4 g per plant, an increase of 88% over the lowest yield achieved at an SMP of –45 kPa. In addition, the leaf expansion and tuber initiation physiological stages are the most important periods where the daily SMP had the most influence on the marketable yield. This information will assist potato growers in maintaining high yields while optimizing their use of irrigation water.

Determination of Relative Density and Degree of Saturation in Mineral Soils Based on In Situ Tests.

Based on the results of dynamic probing (DP), time-domain reflectometry (TDR/MUX/MPTS), resistivity cone penetration tests (RCPT), Marchetti dilatometer tests (DMT), and seismic dilatometer tests (SDMT), it is possible to develop a relationship to calculate the relative density (Dr) and degree of saturation (Sr) of selected sandy soils. Compiled databases from documented research points for selected sandy soils were used to construct and develop direct correlations between the measured pressures p0 and p1 from DMT and shear wave velocity (Vs) from SDMT, along with pore water pressures (u0) and atmospheric pressure (Pa). The results allowed us to make a preliminary prediction when evaluating the parameters. Further, they allowed limiting the use of an additional device, especially in the case of multilayer heavy preconsolidated subsoils. Moreover, soil physical and mechanical characteristics (temperature, humidity, pressure, swelling, salinity) measured from TDR/MUX/MPTS (laboratory/field-operated meter for simultaneous measurements of soil moisture, matric potential, temperature, and salinity—bulk electrical conductivity) were assessed. The main achievement of this paper is the original proposal of using a new nomogram chart to determine the relative density and degree of saturation based on DMT and SDMT tests.

Comparative Study of Statistical, Numerical and Machine Learning-based Pedotransfer Functions of Water Retention Curve with Particle Size Distribution Data

The water retention curve (WRC) describes the nonlinear relation of soil water content (SWC) and matric potential. Since direct measurement of SWC and matric potential is difficult and time consuming, indirect approaches including statistical, numerical, and pattern recognition-based pedo-transfer functions (PTFs) that relate basic soil properties to the WRC have been developed during the last few decades. Although several studies reporting the performance of these models can be found in literature, it seems that an extensive investigation which compares the available models and introduces a reliable method to soil hydrologists can be useful. Therefore, in this study, the performance of multiple linear regressions (MLR) models, scaled numerical models and machine learning methods including artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) are compared using 98 UNSODA codes with various soil textures to estimate WRC. Results showed that regardless of the soil texture, ANN (RMSE = 0.029) predicts the WRC more accurately than ANFIS (RMSE = 0.035), scaled model (RMSE = 0.060) and MLR (RMSE = 0.071), respectively. Considering the soil texture, ANFIS performance is the best in the moderate and fine textured soils, while scaled numerical model predicts with acceptable performance in sandy soils. WRC prediction using easily available soil characteristics particularly when there is a lack of data, shows that newly developed machine learning methods are capable of predicting WRC considerably accurate for sustainable water flow and solute transport management.

Comparison of devices for measuring soil matric potential and effects on soil hydraulic functions and related parameters

The tensiometer represents an excellent instrument for measuring in situ soil water status. However, measuring soil matric potential requires a tensiometer reading system sensitive enough to accurately record the matric potential. Hence, an instantaneous profile-type experiment was conducted in the field, to measure the soil matric potentials within a moisture range from saturation to field capacity. After that, matric potential, soil moisture, total potential gradient, flow density and hydraulic conductivity were calculated and van Genuchten equation parameters were estimated through inverse modeling. This study aimed to test the Bourdon pressure gauge and digital tensiometer compared with the mercury manometer to measure the soil matric potential and to examine the differences related to the estimations of soil water content and to the associated variables. In addition, the study also aimed to evaluate soil hydraulic parameters by inverse modeling, based on the matric potentials from each reading system. Bourdon pressure gauge replaces the Hg manometer in the measurement of soil water matric potential within the moisture range from saturation to field capacity; The use of digital tensiometer and Bourdon pressure gauge reduced hydraulic conductivity by four and three times and flow density by approximately three and two times, respectively, at 6 kPa tension and, therefore, are not recommended for the estimation of these hydraulic parameters; Regardless of reading system used in the tensiometer, inverse modeling estimates well van Genuchten equation parameters and, consequently, soil water matric potential.

Water Flow Path Characterization in Shallow Vadose Zone Using Tensiometers

In this project, we will present the findings of a study using Tensiometer systems designed to investigate the water flow path pattern in shallow vadose zone. The purpose of this paper is to evaluate water flow path in shallow vadose zone and to calculate the infiltration rate and hydraulic conductivity of a soil using Tensiometer. We have measured the subsurface water flow paths in sandy clay loam soil following infiltration experiment using Tensiometers. The matric potential and hydraulic conductivity measurements show that subsequent infiltration and water movement in unsaturated (vadose) zone are vertical, but it can have large lateral component under steady condition. This shows that water moves generally from high water content to lower water content region. Average pressure head for the percolation test conduction locations EB and HB was -30 and -80 cm respectively. Hysteresis produces another interesting situation when the soil is drained. We found that the wetter portion of the soil in vadose zone could be at a lower potential (head) than the dryer portions, resulting in lateral driving force for a preferential flow of water from the dryer to the wet soil. The infiltration rate for the 5 cm ponded water was calculated at 5.45 cm/hr. The infiltration rate curve shows that the rate of infiltration decreases with the time. When infiltration first starts, the wetting front is steep and very close to the surface. Similarly, due to the pressure head gradient, large value for infiltration is recorded. Under these conditions, we believe that the gradient in pressure head is responsible for the rapid movement of water into the dry soil. The effect of gravity is less on water during the initial stages of infiltration; however, it is more effective for preferential flow pattern. In the latter infiltration event, the wetting front has moved deeper into the soil. As a result, the pressure head gradient at the surface is much smaller and consequently has little effect. When the pressure head is equal zero, infiltration rate approaches almost to the lowest level. We also observed that even a minor change in soil-water pressure due to slope could change both direction and magnitude of water flux.

Estimating groundwater evapotranspiration by a subtropical pine plantation using diurnal water table fluctuations: Implications from night-time water use

Exotic pine plantations have replaced large areas of the native forests for timber production in the subtropical coastal Australia. To evaluate potential impacts of changes in vegetation on local groundwater discharge, we estimated groundwater evapotranspiration (ETg) by the pine plantation using diurnal water table fluctuations for the dry season of 2012 from August 1st to December 31st. The modified White method was used to estimate the ETg, considering the night-time water use by pine trees (Tn). Depth-dependent specific yields were also determined both experimentally and numerically for estimation of ETg. Night-time water use by pine trees was comprehensively investigated using a combination of groundwater level, sap flow, tree growth, specific yield, soil matric potential and climatic variables measurements. Results reveal a constant average transpiration flux of 0.02mmh−1 at the plot scale from 23:00 to 05:00 during the study period, which verified the presence of night-time water use. The total ETg for the period investigated was 259.0mm with an accumulated Tn of 64.5mm, resulting in an error of 25% on accumulated evapotranspiration from the groundwater if night-time water use was neglected. The results indicate that the development of commercial pine plantations may result in groundwater losses in these areas. It is also recommended that any future application of diurnal water table fluctuation based methods investigate the validity of the zero night-time water use assumption prior to use.

Optimizing drip irrigation for eggplant crops in semi-arid zones using evolving thresholds

Field experiments were combined with a numerical model to optimize drip irrigation management based on soil matric potential (SMP) measurements. An experimental crop of eggplant was grown in Burkina Faso from December 2014 to March 2015 and plant response to water stress was investigated by applying four different irrigation treatments. Treatments consisted in using two different irrigation depths (low or high), combined with a water provision of 150%, 100% or 66% (150/100/66) of the maximum crop evapotranspiration (T150low, T66low, T100high, T66high). Soil matric potential measurements at 5, 10 and 15cm depth were taken using a wireless sensor network and were compared with measurements of plant and root biomass and crop yields. Field data were used to calibrate a numerical model to simulate triggered drip irrigation. Different simulations were built using the software HYDRUS 2D/3D to analyze the impact of the irrigation depth and frequency, the irrigation threshold and the soil texture on plant transpiration and water losses. Numerical results highlighted the great impact of the root distribution on the soil water dynamics and the importance of the sensor location to define thresholds. A fixed optimal sensor depth of 10 cm was found to manage irrigation from the vegetative state to the end of fruit development. Thresholds were defined to minimize water losses while allowing a sufficient soil water availability for optimal crop production. A threshold at 10cm depth of −15kPa is recommended for the early growth stage and −40kPa during the fruit formation and maturation phase. Simulations showed that those thresholds resulted in optimal transpiration regardless of the soil texture so that this management system can constitute the basis of an irrigation schedule for eggplant crops and possibly other vegetable crops in semi-arid regions.

Nonstationarity of the electrical resistivity and soil moisture relationship in a heterogeneous soil system: a case study

Abstract. Understanding the role of vegetation in the interface between the atmosphere and groundwater is the most decisive key in analyzing the processes involved in water transfer. The main effect of vegetation is its root water uptake, which significantly modifies the processes involved in water transfer in the vadose zone. This paper focuses on mapping temporal and spatial changes in soil moisture using electrical resistivity tomography (ERT). The main objective is to assess how electrical resistivity (ER) is useful for mapping water distribution along a heterogeneous toposequence crossed by a hedgerow. Ten ERT were performed over the studied period for a 28 m long toposequence and compared to matric potential and groundwater level measurements. Soil volumetric water content (VWC) was predicted with two methods: (i) from ER using the Waxman and Smits model (ii) and from matric potential using an experimental retention curve fitted by a Van Genuchten model. Probability density functions (PDFs) of our set of data show that the largest change in mean ER and matric potential was observed in the topsoil layer. We then analyzed the consistency between ER and point measurements in this layer by extracting the arrays at the junction of ER grids and point measurements. PDFs of ER maps at each monitoring time (from T01 to T10) were also calculated to select the most contrasting distributions, corresponding to the wettest (T06) and driest states (T10). Results of ER were consistent with matric-potential measurements, with two different behaviors for locations inside and outside the root zone. A consistent correlation between VWC values from the Waxman and Smits model and those obtained from the retention curve was observed outside the root zone. The heterogeneous soil system inside the root zone shows a different pattern in this relationship. A shift in the relationship between ER and soil moisture for the locations outside and inside the root zone highlights the nonstationarity between wet and dry periods inside the root zone. The equivocal behavior of this relationship shows the limitation of using ER to predict soil moisture in a heterogeneous soil system. Such systems were actually related to the high hedgerow root density and also to a particular topographical context (ditch and bank) that is encountered in Brittany and throughout northwestern Europe.

An Alternative Tensiometer Design for Deep Vadose Zone Monitoring

The conventional tensiometer is among the most accurate devices for soil water matric potential measurements, as well as for estimations of soil water flux from soil water potential gradients. Uncertainties associated with conventional tensiometers such as caused by ambient temperature effects and the draining of the tensiometer tube, as well as their limitation for deep soil monitoring, has prevented their widespread use for vadose zone monitoring, despite their superior accuracy in general. We introduce an alternative tensiometer design that offers the accuracy of the conventional tensiometer, while minimizing the aforementioned uncertainties and limitations. The proposed alternative tensiometer largely eliminates temperature-induced diurnal fluctuations and uncertainties associated with draining of the tensiometer tube and removes the limitation in installation depth. In addition, the manufacturing costs of this alternative tensiometer design are close to those of the conventional tensiometer, while it is especially suited for monitoring of soil water potential gradients as required for soil water flux measurements.

A Simple Nearest-Neighbor Technique to Predict the Soil Water Retention Curve

Abstract. Knowing the soil water retention curve (WRC) is essential for analyzing soil hydraulic behavior within the vadose zone. The van Genuchten (VG) soil hydraulic equation is one of the most frequently adopted models to parameterize the WRC. Some measured water retention points are needed to fit the VG model, but direct measurement of water content versus matric potential is expensive and time consuming. A pedotransfer function (PTF) enables indirect determination of a WRC from basic soil information. The typical method employed to derive a PTF using the VG model (VG-PTF) is to establish a mathematical relationship between the parameters of the VG model and basic soil data. However, both establishing and reusing a VG-PTF for new soils are challenging due to several reasons, such as over-parameterization, low correlation between basic soil data and VG parameters, and interdependency among parameters. In this study, a nonparametric approach based on the k nearest neighbor technique was designed and tested to establish a VG-PTF. A subset of soils from the UNSODA database (n = 554)) and a data set from Belgium (n = 69) were used as the model development and validation data sets, respectively. The proposed PTF showed reasonable accuracy and reliability and was comparable to well-known parametric VG-PTFs available in the literature.

Analysis of Water Stress Prediction Quality as Influenced by the Number and Placement of Temporal Soil-Water Monitoring Sites

In an agricultural field, monitoring the temporal changes in soil conditions can be as important as understanding spatial heterogeneity when it comes to determining the locally-optimized application rates of key agricultural inputs. For example, the monitoring of soil water content is needed to decide on the amount and timing of irrigation. On-the-go soil sensing technology provides a way to rapidly obtain high-resolution, multiple data layers to reveal soil spatial variability, at a relatively low cost. To take advantage of this information, it is important to define the locations, which represent diversified field conditions, in terms of their potential to store and release soil water. Choosing the proper locations and the number of soil monitoring sites is not straightforward. In this project, sensor-based maps of soil apparent electrical conductivity and field elevation were produced for seven agricultural fields in Nebraska, USA. In one of these fields, an eight-node wireless sensor network was used to establish real-time relationships between these maps and the Water Stress Potential (WSP) estimated using soil matric potential measurements. The results were used to model hypothetical WSP maps in the remaining fields. Different placement schemes for temporal soil monitoring sites were evaluated in terms of their ability to predict the hypothetical WSP maps with a different range and magnitude of spatial variability. When a large number of monitoring sites were used, it was shown that the probability for uncertain model predictions was relatively low regardless of the site selection strategy. However, a small number of monitoring sites may be used to reveal the underlying relationship only if these locations are chosen carefully.

New Soil Property Database Improves Oklahoma Mesonet Soil Moisture Estimates*

Abstract Soil moisture data from the Oklahoma Mesonet are widely used in research efforts spanning many disciplines within Earth sciences. These soil moisture estimates are derived by translating measurements of matric potential into volumetric water content through site- and depth-specific water retention curves. The objective of this research was to increase the accuracy of the Oklahoma Mesonet soil moisture data through improved estimates of the water retention curve parameters. A comprehensive field sampling and laboratory measurement effort was conducted that resulted in new measurements of the percent of sand, silt, and clay; bulk density; and volumetric water content at −33 and −1500 kPa. These inputs were provided to the Rosetta pedotransfer function, and parameters for the water retention curve and hydraulic conductivity functions were obtained. The resulting soil property database, MesoSoil, includes 13 soil physical properties for 545 individual soil layers across 117 Oklahoma Mesonet sites. The root-mean-square difference (RMSD) between the resulting soil moisture estimates and those obtained by direct sampling was reduced from 0.078 to 0.053 cm3 cm−3 by use of the new water retention curve parameters, a 32% improvement. A >0.15 cm3 cm−3 high bias on the dry end was also largely eliminated by using the new parameters. Reanalysis of prior studies that used Oklahoma Mesonet soil moisture data may be warranted given these improvements. No other large-scale soil moisture monitoring network has a comparable published soil property database or has undergone such comprehensive in situ validation.

Plot-scale modeling of soil water dynamics and impacts of drought conditions beneath rainfed maize in Eastern Nebraska

Intermittent drought periods pose major challenges to the management of rainfed agricultural systems because their productivity is sensitive to water stress during crop development. The objective of this study was to assess soil water dynamics and crop stress patterns during the drought year of 2012, which was among the most severe on record in the US Corn Belt. The study utilized a continuous matric potential profile monitoring record from June 2008–December 2012 beneath a rainfed corn plot in Eastern Nebraska to provide a direct comparison of the 2012 growing season with moderate to relatively wet periods within 2008–2011. The analysis was based on a transient unsaturated zone model that was calibrated with laboratory-measured water retention functions and matric potential measurements from heat dissipation sensors. In each year of the model simulation, soil water storage volumes steadily decreased during the growing season and increased episodically following precipitation events outside of the growing season. Precipitation during the 2012 growing season was <50% of the mean for growing seasons 2009–2012 consequently, soil water storage loss during 2012 was ∼14% of initial storage in comparison to 8–9% in 2009–2011. Model results suggest that the 2012 soil water deficit was partially buffered by upward flux of deep soil moisture, with approximately 17% of root water uptake in 2012 deriving from moisture redistributed from below the root zone. Nevertheless, root zone matric potentials exceeded the crop wilting point (ψ=−8000cm) in 2012 for the first time within the monitoring record. These results highlight the need for heightened attention to drought resilience of rainfed cropping systems given future climate predictions that involve increase rainfall intermittency.

Measurement of the matric potential of soil water in the rhizosphere

The availability of soil water, and the ability of plants to extract it, are important variables in plant research. The matric potential has been a useful way to describe water status in a soil-plant system. In soil it is the potential that is derived from the surface tension of water menisci between soil particles. The magnitude of matric potential depends on the soil water content, the size of the soil pores, the surface properties of the soil particles, and the surface tension of the soil water. Of all the measures of soil water, matric potential is perhaps the most useful for plant scientists. In this review, the relationship between matric potential and soil water content is explored. It is shown that for any given soil type, this relationship is not unique and therefore both soil water content and matric potential need to be measured for the soil water status to be fully described. However, in comparison with water content, approaches for measuring matric potential have received less attention until recently. In this review, a critique of current methods to measure matric potential is presented, together with their limitations as well as underexploited opportunities. The relative merits of both direct and indirect methods to measure matric potential are discussed. The different approaches needed in wet and dry soil are outlined. In the final part of the paper, the emerging technologies are discussed in so far as our current imagination allows. The review draws upon current developments in the field of civil engineering where the measurement of matric potential is also important. The approaches made by civil engineers have been more imaginative than those of plant and soil scientists.

Simultaneous Estimation of Soil Hydraulic and Root Distribution Parameters from Lysimeter Data by Inverse Modeling

Weighable lysimeters are powerful measurement systems for identifying soil hydraulic processes and properties, because the boundary fluxes (precipitation, actual evapotranspiration, and seepage across the bottom) can be determined very precisely. However, root water uptake by plants and the soil water flux are interrelated. Thus, the simultaneous estimation of root water uptake parameters and soil hydraulic parameters from macroscopic state observations is a challenge. In this study we investigated the possibility of simultaneously estimating root water uptake and soil hydraulic parameters by inverse simulation of soil water flow in monolithic lysimeters under atmospheric boundary conditions. We used the Richards equation and a macroscopic root water uptake model to simulate the processes. The amount of information needed for the unique identification of parameters was analyzed and the magnitude of their uncertainties was investigated. To check the principal feasibility of our approach, we first examined synthetic data sets for different scenarios and instrumentation campaigns that differed in their information content and complexity of soil properties. The investigations of synthetic data showed that for homogeneous profiles, cumulative outflow and profile-averaged water content data contained enough system information to allow the simultaneous estimation of soil hydraulic properties and root-distribution parameters. In contrast, for soil profiles consisting of two layers, unique soil hydraulic parameters and the correct rooting depth could only be estimated if matric potential measurements from both layers were included in the objective function. To test the procedure with real data, soil hydraulic properties of the grass-reference lysimeter at Wagna (Austria) were estimated using actual measurements. Water dynamics in the lysimeter could be described well by an effective parameterization assuming a homogeneous soil profile. Furthermore, the system behavior under different boundary conditions could be predicted adequately with the estimated parameters.