Soil Water Content Measurements Research Articles

Customizing pyfao56 for evapotranspiration estimation and irrigation scheduling at the Limited Irrigation Research Farm (LIRF), Greeley, Colorado

Estimation of evapotranspiration (ET, the water used by soil evaporation and plant transpiration) and soil water depletion (Dr, the amount of water to bring the soil water in the root zone to field capacity) are critical in irrigation water management. The open-source Python-based crop ET and water balance modeling package named “pyfao56” was originally developed based on the dual crop coefficient approach as described in the Food and Agricultural Organization of the United Nations, Irrigation and Drainage Paper No. 56 (FAO-56). The package later expanded on the seminal FAO-56 document to consider various options relevant to ET and water balance modeling, including both short (grass) and tall (alfalfa) reference crops, optional discretization of variable soil layers based on field capacity, and interpretation of readily available water (RAW) and Dr based on both a dynamic (growing) root zone and maximum root zone. The package requires two input data objects; the “Parameters” class defines variables affecting soil water balance and ET and the “Weather” class specifies relevant meteorological data. Other optional input data objects include the “Irrigation” class for irrigation events, the “Soil_Profile: class for defining stratified soil layer data, and the “Update” class for assimilation of measured data. Additional tools are available for estimating and forecasting standardized reference ET (ETref), providing seasonal water balance summaries, computing goodness-of-fit statistics between measured and modeled values, and visualizing time series plots of daily Dr, ET, and crop coefficients. The current pyfao56 release (v1.2.1) was incorporated into a customized workflow for specific use at the USDA-ARS Limited Irrigation Research Farm (LIRF) in Greeley, Colorado, and named the LIRF Implementation of Pyfao56 (LIRFIP). Field data from 2023 full and limited irrigation field trials were used to demonstrate the functionality of LIRFIP and its customizations and integration of pyfao56 into the LIRF workflow. Specific customizations included use of pyfao56 “customload” functions to input data from various sources, including weather from an on-site micrometerological station (using the application programming interface (API) developed by the maintainers of the Colorado Agricultural Meteorological (CoAgMet) weather station network), irrigation events (via a shared Google docs sheet), field capacity values by plot and soil layer (via an Excel spreadsheet), and measured soil water content as well as basal crop coefficient (Kcb) calculated from fractional canopy cover (fc) (via an SQLite database). To evaluate multiple research plots, LIRFIP was designed to iterate simulations from multiple instances of pyfao56 and to produce customized output summary files and interactive hypertext markup language (html) graphs as an aid for irrigation management decisions for LIRF field trials. This study demonstrated pyfao56 as a useful, flexible, customizable, and repeatable ET-based water balance model by showcasing its integration within a specific computational workflow for irrigation management field research at LIRF. The approach serves as an example for pyfao56 integration in other water management tools as conceived by water managers, researchers, and practitioners worldwide.

Soil-Specific Calibration Using Plate Compression Filling Technique and Monitoring Soil Biomass Degradation Based on Dielectric Properties

Accurate estimation of soil water content (SWC) is crucial for effective irrigation management and maximizing crop yields. Although dielectric property-based SWC measurements are widely used, their accuracy is still affected by soil variability, soil–sensor contact, and other factors, making the development of convenient and accurate soil-specific calibration methods a major challenge. This study aims to propose a plate compression filling technique for soil-specific calibrations and to monitor the extent of soil biomass degradation using dielectric properties. Before and after biodegradation, dielectric measurements of quartz sand and silt loam were made at seven different water contents with three different filling techniques. A third-order polynomial fitting equation explaining the dependence of the dielectric constant on the volumetric water content was obtained using the least-squares method. The suggested plate compression filling method has a maximum mean bias error (MBE) of less than 0.5%, according to experimental results. Depending on the water content, silt loam’s dielectric characteristics change significantly before and after biodegradation. The best water content, measured in gravimetric units, to encourage the decomposition of biomass was discovered to be 24%. It has been demonstrated that the plate compression filling method serves as a simple, convenient, and accurate alternative to the uniform compaction method, while the dielectric method is a reliable indicator for evaluating biomass degradation. This exploration provides valuable insights into the complex relationship between SWC, biomass degradation, and soil dielectric properties.

Combined 2D- and 3D ERT monitoring as a geophysical tool for investigating spatial and temporal soil moisture fluctuations in a pine-beech forest

As climate change continues, forests are increasingly suffering from drought stress, which is leading to widespread forest dieback, but also to increased mortality of individual trees. In this regard, the impact of small-scale differences in water availability on individual trees has not yet been sufficiently studied to determine possible responses of different tree species to future droughts. Since conventional soil moisture monitoring and sampling methods only consider single points or small volumes, Electrical Resistivity Tomography (ERT) is becoming increasingly important to cover a larger survey area and to detect small-scale heterogeneities with regard to soil properties and soil moisture.The current study describes the application of a combined two- and three-dimensional geoelectrical monitoring approach with daily measurements over two years (May 2021 – April 2023) in a forest ecosystem in Lower Franconia (northwestern Bavaria, Germany), which is strongly affected by climate change. Soil water content, soil matric potential, throughfall, and stem flow are also measured at the forest site as well as precipitation at an adjacent forest clearing.The seasonally (long-term) and precipitation-driven (short-term) temporal change of soil resistivity is correlated strongly with the measured soil water content and matric potential. The applied 3D-ERT approach also allowed a first three-dimensional monitoring of the subsurface below a European beech located in the middle of the measuring grid with a daily resolution. The corresponding results also provide first indications that besides soil moisture changes also chemical processes in the subsurface may influence temporal resistivity changes in the soil of forest sites.The results of this study show that daily 3D-ERT is very suitable for investigating small-scale as well as short- and long-term variations in soil moisture, which is becoming increasingly important for understanding the causal relationships considering tree mortality and the subsurface.

Recent Advances in Dielectric Properties-Based Soil Water Content Measurements

Dielectric properties are crucial in understanding the behavior of water within soil, particularly the soil water content (SWC), as they measure a material’s ability to store an electric charge and are influenced by water and other minerals in the soil. However, a comprehensive review paper is needed that synthesizes the latest developments in this field, identifies the key challenges and limitations, and outlines future research directions. In addition, various factors, such as soil salinity, temperature, texture, probing space, installation gap, density, clay content, sampling volume, and environmental factors, influence the measurement of the dielectric permittivity of the soil. Therefore, this review aims to address the research gap by critically analyzing the current state-of-the-art dielectric properties-based methods for SWC measurements. The motivation for this review is the increasing importance of precise SWC data for various applications such as agriculture, environmental monitoring, and hydrological studies. We examine time domain reflectometry (TDR), frequency domain reflectometry (FDR), ground-penetrating radar (GPR), remote sensing (RS), and capacitance, which are accurate and cost-effective, enabling real-time water resource management and soil health understanding through measuring the travel time of electromagnetic waves in soil and the reflection coefficient of these waves. SWC can be estimated using various approaches, such as TDR, FDR, GPR, and microwave-based techniques. These methods are made possible by increasing the dielectric permittivity and loss factor with SWC. The available dielectric properties are further synthesized on the basis of mathematical models relating apparent permittivity to water content, providing an updated understanding of their development, applications, and monitoring. It also analyzes recent mathematical calibration models, applications, algorithms, challenges, and trends in dielectric permittivity methods for estimating SWC. By consolidating recent advances and highlighting the remaining challenges, this review article aims to guide researchers and practitioners toward more effective strategies for SWC measurements.

Response of the TEROS 12 Soil Moisture Sensor under Different Soils and Variable Electrical Conductivity.

In this work, the performance of the TEROS 12 electromagnetic sensor, which measures volumetric soil water content (θ), bulk soil electrical conductivity (σb), and temperature, is examined for a number of different soils, different θ and different levels of the electrical conductivity of the soil solution (ECW) under laboratory conditions. For the above reason, a prototype device was developed including a low-cost microcontroller and suitable adaptation circuits for the aforementioned sensor. Six characteristic porous media were examined in a θ range from air drying to saturation, while four different solutions of increasing Electrical Conductivity (ECw) from 0.28 dS/m to approximately 10 dS/m were used in four of these porous media. It was found that TEROS 12 apparent dielectric permittivity (εa) readings were lower than that of Topp's permittivity-water content relationship, especially at higher soil water content values in the coarse porous bodies. The differences are observed in sand (S), sandy loam (SL) and loam (L), at this order. The results suggested that the relationship between experimentally measured soil water content (θm) and εa0.5 was strongly linear (0.869 < R2 < 0.989), but the linearity of the relation θm-εa0.5 decreases with the increase in bulk EC (σb) of the soil. The most accurate results were provided by the multipoint calibration method (CAL), as evaluated with the root mean square error (RMSE). Also, it was found that εa degrades substantially at values of σb less than 2.5 dS/m while εa returns to near 80 at higher values. Regarding the relation εa-σb, it seems that it is strongly linear and that its slope depends on the pore water electrical conductivity (σp) and the soil type.

Spatial and Temporal Distribution of Soil Moisture in Maize Using Frequency Domain Reflectometry Sensor

Field experiment was conducted during kharif 2018, rabi 2018-19 and kharif 2019 at Department of Soil Water Engineering, Dr. NTR College of Agricultural Engineering, Bapatla, Andhra Pradesh to study the spatial and temporal distribution of soil moisture in maize using FDR sensor.The experiment was laid out in split plot design consisting of three irrigation levels (0.6 ETc (I1), 0.8 ETc (I2) and 1.0 ETc (I3)) and four nitrogen levels consisting of fertigation levels (80% RDN (N1), 100% RDN (N2), 120% RDN (N3) and Manual application (N4) on sandy clay loam soil. Water requirement of the maize crop was calculated on the daily basis by using CROPWAT 8.0. Frequency Domain Reflectometry (FDR) Sensor was used to measure soil moisture data by installing 34 access tubes up to a depth of 100 cm with an interval of 10 cm from the soil surface and measured soil water content during crop period. Soil moisture was evaluated for 2.0 lph emitter discharges at 3h after irrigation, 6h after irrigation, 24h after irrigation at emitter, 15 cm from emitter, 30 cm from emitter and 45 cm from emitter for soil moisture in the soil for arriving the best irrigation strategy. The soil water distribution under all drip irrigation treatments indicated that it was relatively high near the dripper and decreased as the distance from the dripper point increased.

Effects of hillslope aspect on erosion rate of alpine meadows in the Three-River Headwater Region, China

Soil erosion causes serious grassland degradation in the Three-River Headwater Region (TRHR) of the Qinghai-Tibet Plateau. Identifying source areas of erosion is important, including understanding the influences of hillslope aspect on soil erosion. However, the effect of hillslope aspect on soil erosion is a much-debated process, wherein contrasting results were previously reported. We hypothesized that aspect-induced differences in environmental variables will be related to differences in erosion magnitude and spatial pattern, and that these differences will impact soil physiochemical characteristics as well. To test this hypothesis, soil samples were collected at conjoined south- and north-facing hillslopes along elevational profiles in a small watershed in Yushu County, with measurements of soil water content (SWC), soil organic carbon (SOC), soil organic matter (SOM), soil particle size distribution, soil total nitrogen (STN) and phosphorus (STP). Soil erosion rate was estimated using 137Cs technology. The results showed that soil erosion rates of the two hillslopes ranged from 0.073 to 10.83 t ha-1 yr-1, and were 3 times greater on the south-facing hillslope. Soil water content ranged from 7.6% to 76.8% on the two hillslopes with lower values on the south-facing hillslope due to the greater solar radiation. By Spearman correlation analysis and path analysis, we concluded that lower soil water content on the south-facing hillslope restricts vegetation growth, and hence resulting in greater soil erosion under the case where vegetation growth is dominantly controlled by water availability. Greater soil erosion on the south-facing hillslope caused greater surficial accumulation of coarser materials and greater loss of soil nutrients, which are expected to complicate the hydrological erosion process. Moreover, the downslope increased soil erosion rate should be fully considered in conservation practices. These results will improve our understanding of the interaction and feedback mechanisms between soil characteristics, vegetation function, and soil erosion in the TRHR.

Technical Note: Revisiting the general calibration of cosmic-ray neutron sensors to estimate soil water content

Abstract. Cosmic-ray neutron sensing (CRNS) is becoming increasingly popular for monitoring soil water content (SWC). To retrieve SWC from observed neutron intensities, local measurements of SWC are typically required to calibrate a location-specific parameter, N0, in the corresponding transfer function. In this study, we develop a generalized conversion function that explicitly takes into account the different factors that govern local neutron intensity. Thus, the parameter N0 becomes location independent, i.e. generally applicable. We demonstrate the feasibility of such a “general calibration function” by analysing 75 CRNS sites from four recently published datasets. Given the choice between the two calibration strategies – local or general – users will wonder which one is preferable. To answer this question, we estimated the resulting uncertainty in the SWC by means of error propagation. While the uncertainty in the local calibration depends on both the local reference SWC itself and its error, the uncertainty in the general calibration is mainly governed by the errors in vegetation biomass and soil bulk density. Our results suggest that a local calibration – generally considered best practice – might often not be the best option. In order to support the decision which calibration strategy – local or general – is actually preferable in the user-specific application context, we provide an interactive online tool that assesses the uncertainty in both options (https://cosmic-sense.github.io/local-or-global, last access: 23 February 2024).

Water Uptake Characteristics of Stipa bungeana Trin: Affected by Subsidence in the Coal Mining Areas of Northwest China

Revealing the water use pattern of plants influenced by coal-mining-caused land subsidence is crucial to understand plant–water interactions and guide ecological restoration. However, available information on herbaceous plants, the dominant species in most arid and semi-arid regions with abundant coal resources, remains inadequate. We investigated the water use patterns of Stipa bungeana Trin. by measuring soil water content, root distribution, and stable isotopes of hydrogen (δ2H) and oxygen (δ18O) of soil water and plant stem water both before and after a rainfall event. The results revealed that prior to rainfall, both areas exhibited a low soil water content with no discernible difference in soil drought. However, the soil waters δ2H and δ18O were found to be more enriched at varying depths within the subsidence area, indicating a heightened level of soil evaporation. Both soil water content and soil water isotopic composition responded sensitively to rainfall, with rainfall primarily replenishing the shallow layer (0–20 cm), thereby reflecting an infiltration mode dominated by piston flow. More water seeped into deeper soil layers in the subsidence area compared to the non-subsidence area, with more preferential flow. Before rainfall, the sources of plant water uptake were consistent both at shallow and deep soil layers, implying that the proportion of water uptake gradually decreased with increasing depth. After rainfall, the sources of plant water uptake differed slightly between the two soil layers. The plants in non-subsidence and subsidence areas dominantly extracted soil water at depths greater than 10 cm and 20 cm, respectively. The root system in the subsidence area was more developed than that in the non-subsidence area. Plant water uptake was primarily influenced by the spatial distribution of roots, as well as the post-rainfall water distribution, regardless of whether they were in the subsidence area or not. Although land subsidence affected soil water transport, the water uptake pattern of Stipa bungeana Trin. was similar before and after rainfall, indicating the adaptive growth of plants through their roots in the subsidence area. The high adaptability of herbs such as Stipa bungeana Trin. makes them a viable option for vegetation restoration in subsidence areas. This study has significant implications for evaluating plant–water relationships in subsidence areas due to coal mining, thereby providing a fundamental basis and valuable reference for ecological restoration and management strategies within such affected regions.

Assessing capacitance soil moisture sensor probes’ ability to sense nitrogen, phosphorus, and potassium using volumetric ion content

Accurate and near real-time volumetric soil water and volumetric ion content (VIC) measurements can both inform precise irrigation scheduling and aid in fertilizer management applications in cropping systems. To assist in the monitoring of these measurements, capacitance-based soil moisture probes are used in agricultural best management practice (BMP) programs. However, the ability of these sensors to detect nutrients in the soil sourced from fertilizers is not well understood. The objective of this study was to evaluate the sensitivity of a capacitance-based soil moisture probe in detecting Nitrogen (N), Phosphorous (P), and Potassium (K) movement in the soil. To achieve this, a laboratory-based setup was established using pure sand soil cores. Raw soil moisture and VIC probe readings from the cores were contrasted across multiple N, P, and K rates. The N treatments applied were rates of 0, 112, 168, and 224 kg/ha; for P, were 0, 3.76, and 37.6 kg/ha, and for K were 0, 1.02, 1.53, and 2.04 kg/ha. Each nutrient was evaluated separately using a randomized complete block design experiment with three replications for N and K, and 5 replications for P. The impact of each nutrient rate on the sensitivity of VIC readings was determined by evaluating differences in three points of the time series, including the observed maximum point, inflection point, and convergence value as well as the time of occurrence of those points over a 24-hour period. These points were assessed at depths 5, 15, 25, 35, 45, and 55 cm. The findings of this study highlight the capacitance-based soil moisture probes’ responsiveness to changes in all K rates at most depths. However, its sensitivity to changes in N and P rates is comparatively lower. The results obtained in this study can be used to develop fertilizer management protocols that utilize K movement as the baseline to indirectly assess N and P, while helping to inform those who currently use the probe which nutrients the probe may be detecting. The probes’ readings could be incorporated into decision support systems for irrigation and nutrient management and improve control systems for precision water and nutrient management.

SOIL PHYSICAL QUALITY INDICES OF MINING-INDUCED DISTURBANCES IN SOIL WITHIN THE LOESS REGION OF WESTERN CHINA

Soil sampling and in situ measurements were conducted at 24 locations at three time points from May 2015 to April 2016. The statistical analysis showed that the variabilities of soil water content and soil penetration were moderate, while particle size and soil saturated hydraulic conductivity varied considerably. Rainfall before measurements contributed positively to the mean soil water content and negatively to particle size. This was mainly due to the soil aggregates and large soil particles being broken into smaller particles from rain splash. The detached small-sized soil particles could coalesce into larger-sized ones and even soil aggregates. Stressors in zones differ, resulting in variations between soil physical quality indices. The point-to-point comparisons indicated that the mean measured soil water content and soil saturated hydraulic conductivity were similar, if the measurements for these two indices were conducted under similar weather conditions during the same period between years. The investigation on the relationships among soil physical quality indices showed a negative relationship between the measured soil water content and soil saturated hydraulic conductivity. A positive correlation was also found between soil particle size and soil saturated hydraulic conductivity. Lower soil strength resulted in higher soil saturated hydraulic conductivity.

A method for measuring soil water content based on principal component analysis.

Principal component analysis (PCA) has been applied in many aspects. To address the problem of measuring water content in soil, this paper proposes a method to measure soil water content based on the PCA. We used PCA to reduce the dimension of the data and processed the soil amplitude ratio frequency response spectra. First, we designed the measuring device and measured the soil amplitude ratio frequency response data of different water content, then we used the PCA to extract features from the frequency response spectra of different water contents, established a relationship model of soil water content, and finally, we solved the model, the maximum error between the calculation results and the actual water content was no more than 0.85%. Subsequently, we carried out experimental verification, and we measured six kinds of soil with known water content by this method. The experimental results showed that the maximum error did not exceed 1.16%, and the average error was 0.71%. Thus, the proposed method can provide a useful way of measuring soil water content.

Using phenology to unravel differential soil water use and productivity in a semiarid savanna

AbstractSavannas are water‐limited ecosystems characterized by two dominant plant types: trees and an understory primarily made up grass. Different phenology and root structures of these plant types complicate how savanna primary productivity responds to changes in water availability. We tested the hypothesis that productivity in savannas is controlled by the temporal and vertical distribution of soil water content (SWC) and differences in growing season length of understory and tree plant functional types. To quantify the relationship between tree, understory, and savanna‐wide phenology and productivity, we used PhenoCam and satellite observations surrounding an eddy covariance tower at a semiarid savanna site in Arizona, USA. We distinguished between SWC across two different depth intervals (shallow, &lt;0–30 cm and deep, &gt;30–100 cm). We found that tree greenness increased with SWC at both depths, while understory greenness was only sensitive to the shallower SWC measurements. Onset of ecosystem dormancy, estimated from satellite observations close to the eddy covariance tower, explained more variability in annual gross primary productivity (GPP) than in other phenometrics. Higher SWC led to an extended growing season, caused by delayed dormancy in trees, but the understory showed no evidence of delayed dormancy in wetter periods. We infer that the timing of ecosystem scale dormancy, driven by trees, is important in understanding changes in a savanna's GPP. These findings highlight the important effects of rainfall during the winter. These findings suggest that savanna GPP is conditional on different responses to moisture availability in each of the dominant vegetation components.

Evaluation of canopy fraction-based vegetation indices, derived from multispectral UAV imagery, to map water status variability in a commercial vineyard

AbstractWater stress is a major factor affecting grapevine yield and quality. Standard methods for measuring water stress, such as midday stem water potential (ΨSWP), are laborious and time-consuming for intra-block variability mapping. In this study, we investigate water status variability within a 2.42-ha commercial Cabernet Sauvignon block with a standard vertical trellis system, using remote sensing (RS) tools, specifically canopy fraction-based vegetation indices (VIs) derived from multispectral unmanned aerial vehicle (UAV) imagery, as well as standard reference methods to evaluate soil and plant water status. A total of 31 target vines were monitored for ΨSWP during the whole growing season. The highest variability was at véraison when the highest atmospheric demand occurred. The ΨSWP variability present in the block was contrasted with soil water content (SWC) measurements, showing similar patterns. With spatial and temporal water stress variability confirmed for the block, the relationship between the ΨSWP measured in the field and fraction-based VIs obtained from multispectral UAV data was analysed. Four UAV flights were obtained, and five different VIs were evaluated per target vine across the vineyard. The VI correlation to ΨSWP was further evaluated by comparing VI obtained from canopy fraction (VIcanopy) versus the mean (VImean). It was found that using canopy fraction-based VIs did not significantly improve the correlation with ΨSWP (NDVIcanopyr = 0.57 and NDVImeanr = 0.53), however fractional cover (fcover) did seem to show a similar trend to plant water stress with decreasing canopy size corresponding with water stress classes. A subset of 14 target vines were further evaluated to evaluate if additional parameters (maximum temperature, relative humidity (RH), vapour pressure deficit, SWC and fractional cover) could serve as potential water stress indicators for future mapping. Results showed that the integration of NDVIcanopy and NDREmean with additional information could be used as an indicator for mapping water stress variability within a block.

Assessing the Potential of UAV-Based Multispectral and Thermal Data to Estimate Soil Water Content Using Geophysical Methods

Knowledge of the soil water content (SWC) is important for many aspects of agriculture and must be monitored to maximize crop yield, efficiently use limited supplies of irrigation water, and ensure optimal nutrient management with minimal environmental impact. Single-location sensors are often used to monitor SWC, but a limited number of point measurements is insufficient to measure SWC across most fields since SWC is typically very heterogeneous. To overcome this difficulty, several researchers have used data acquired from unmanned aerial vehicles (UAVs) to predict the SWC by using machine learning on a limited number of point measurements acquired across a field. While useful, these methods are limited by the relatively small number of SWC measurements that can be acquired with conventional measurement techniques. This study uses UAV-based data and thousands of SWC measurements acquired using geophysical methods at two different depths and before and after precipitation to predict the SWC using the random forest method across a vineyard in the central United States. Both multispectral data (five reflectance bands and eleven vegetation indices calculated from these bands) and thermal UAV-based data were acquired, and the importance of different reflectance data and vegetation indices in the prediction of SWC was analyzed. Results showed that when both thermal and multispectral data were used to estimate SWC, the thermal data contributed the most to prediction accuracy, although multispectral data were also important. Reflectance data contributed as much or more to prediction accuracy than most vegetation indices. SWC measurements that had a larger sample size and greater penetration depth (~30 cm sampling depth) were more accurately predicted than smaller and shallower SWC estimates (~18 cm sampling depth). The timing of SWC estimation was also important; higher accuracy predictions were achieved in wetter soils than in drier soils, and a light precipitation event also improved prediction accuracy.

Possible remediation of impact-loading debris avalanches via fine long rooted grass: an experimental and material point method (MPM) analysis

Debris avalanches often originate along steep unsaturated slopes and have catastrophic consequences. However, their forecast and mitigation still pose relevant scientific challenges. This is also due to the variety of mechanisms observed near high sub-vertical bedrock outcrops, such as the impact loading of soil failed upslope the outcrop, the build-up of pore water pressures in the inception zone, and the bed entrainment along the landslide propagation path. At the University of Salerno, an experimental and numerical investigation campaign started some years ago to explore the feasibility of using long-root grass to mitigate or even inhibit the inception of debris avalanches. Previous laboratory results were achieved through two twin 2-m-long columns (one bare, one vegetated), where the change in soil retention curve and soil mechanical response was assessed. As follow-up, an experimental field setup was installed in 2020 first, and in an improved configuration in 2021. Here, three different species of long-root grass were grown. In situ soil suction and water content measurements were periodically collected in the vegetated and in the original soils. In both cases, soil specimens were also collected, and laboratory geotechnical tests were performed to individuate the changes in both the water retention and strength response. Increased values of soil suction and shear strength were outlined, despite some differences, for all the grown species compared to the original soil. Using these novel experimental data, advanced large-deformation stress–strain hydro-mechanically coupled analyses were recently performed through a material point method (MPM) approach. The original slope conditions were compared to various slope configurations engineered via long-root grass. The benefits and the open issues related to this novel green technology for landslide mitigation are discussed. Some insights are outlined for the possible reduction of the soil volumes mobilized inside the inception zone of debris avalanches.

Gamma radiation for the estimation of mineral soil water content in a boreal forest

Continuous monitoring of water quantities in different soil horizons is necessary to understand the behavior of infiltrated water in the soil. Under certain conditions, using measurements of natural ground gamma radiation can help us estimate soil water content measurements over a 100 m2 surface within a 15 cm depth. A CS725 sensor can provide up to four daily estimates of soil water content by detecting the natural emission of gamma radiation. However, in boreal forest environments, gamma radiation mitigated by the water in the thick humus layers (litter, fermented, and humic horizon) can bias the underlying mineral soil water content measurements. The objective of this research was to evaluate the accuracy of methods that incorporate variables describing the surface humus layer into calculations of the underlying mineral soil water content, by measuring the soil’s natural gamma emission with the CS725. Using raw gamma radiation values obtained by CS725 sensors deployed over various boreal soils, we tested two functions. The first one included variables describing the humus layer and the other excluded these variables (manufacturer’s method). The function that included the descriptive humus layer variables showed superior results compared to the function without. The results of this study suggest that the CS725 sensor can adequately estimate mineral soil water content within ±10% absolute of the reference water content when examined with the following humus variables: humus layer thickness, fractioned composition, bulk density, and linear gamma radiation attenuation.

Root phenotyping and root water uptake calculation using soil water contents measured in a winter wheat field

Non-destructive phenotyping of roots and measurement of root water uptake from different soil layers in the field are vital for improving water management and facilitating the development of drought-resistant crop varieties, but difficult because of their opaqueness. As a result, indirect methods using easy-to-measure variables such as soil water content have been used as alternatives. However, the inherent measurement errors could undermine the robustness and reliability of these methods. This paper proposes a new method to bridge this knowledge gap by using soil water content profiles measured at two time points to calculate root uptake and root-length density. It is based on the Richards' equation by treating root uptake from different soil layers between the two time points as random unknown numbers; their distributions are calculated using the Bayesian framework, solved by the Markov Chain Monte Carlo method. We applied the method to 39 winter wheat lines grown in a silt-clay loam field. Soil water content profile measured at the first time point from each plot served as the initial condition, and water content measured at the second time point was the target to match the model for calculating average root water uptake and root-length density between the two time points. The results show that the measured soil water contents fall within the 95% confidence interval of the calculated soil water contents. The inherent soil water measurement errors lead to uncertainties in the calculated root water uptake for all lines, but such uncertainties decrease with soil depth. Although the soil types and agronomic management were the same for all 39 lines, their root water uptake from different soil layers varies considerably, with some lines more capable of using subsoil water than others. Generally, the calculated and measured root-length densities agree well, albeit the degree of the agreement varies with lines. While this paper focuses on methodology and applies the method to one growth stage spanning one month only, the consistent results for all 39 lines indicates the method is robust and can be applied to other crops cultivated in different conditions. Given the growing interest in improving root traits to enhance water use efficiency, the proposed method has important implications as phenotyping roots and understanding their water uptake from different soil layers in the field is a prerequisite to achieve this crucial target.

Exploring recharge mechanisms of soil water in the thick unsaturated zone using water isotopes in the North China Plain

Exploring the groundwater recharge mechanisms in regions with thick unsaturated zones can enhance our understanding of intricate groundwater processes, especially in intensive agricultural regions such as the North China Plain (NCP). For example, to better answer the question of how deep soil water can be recharged, it is of utmost importance to illustrate the recharge mechanisms of groundwater under different land use types in the NCP. This study collected soil samples from four boreholes up to 18 m deep covering farmland and orchards with different stand ages and measured soil water content (SWC), stable and radioactive isotopes (δ18O, δ2H and 3H) of both soil water and groundwater. Results indicated that the movement of water through the deep vadose zone of soils was primarily piston flow based on a soil tritium profile. The infiltration rate was estimated to be 32.2 mm yr−1 based on the tritium peak method. The variations of soil water δ18O and line control excess (lc-excess) indicated that the orchards had lower evaporation effects and a higher precipitation offset than those in farmland soils. Furthermore, the deep soil water was replenished by intensive precipitation events (from July to September). The great soil water deficit and decreased deep drainage under pear orchards is usually closely related to huge water consumption by evapotranspiration (ET) via root uptake, which explains the increase of ET in pear orchards with increasing stand age. These findings have provided substantial insights into groundwater resource management in similar regions with limited water resources and recharge modeling for regions covered with thick unsaturated zones.

Investigation of Non-Equilibrium and Dynamic Behavior of Soil Water Characteristic Curves through Field Monitoring

In this paper, soil water characteristic curves (SWCCs) were reconstructed through simultaneous field measurement of soil water content and suction value. The objective of this study was to evaluate whether field-based monitoring could capture the different SWCC paths in various seasons. Moreover, field measurement of SWCC makes it possible to quantify the hysteresis phenomenon, analyzes the effect of various temporal resolutions on SWCC, and highlights the differences in reconstructed SWCC for the same soil during various weather conditions. Field data from one expansive subgrade site in Texas, USA were collected through moisture and suction sensors over a 3-year monitoring period. Analyzing the rainfall and evapotranspiration data, three distinct wetting and drying cycles were identified which allowed correct discrimination of the cycles. The pattern of changes in SWCC was identified in the various wetting and drying cycles. While the field data was fitted through the Van Genuchten equation, variations of the unsaturated soil parameters ( α, n, m) were identified in various cycles. Air entry value indicator parameter α ranged from 0.05 to 0.11 kPa−1 while the slope of the SWCC curve parameter n value varied from 1.1 to 1.45. Comparing these results with previous studies, it was concluded that wetting and drying cycle parameters do not have any predefined correlation. However, these unsaturated parameters required for numerical modeling are dependent on the climatic events happening year-round.