Measurements Of Soil Moisture Content Research Articles

Feature Variable Selection Based on VIS-NIR Spectra and Soil Moisture Content Prediction Model Construction

The hydrological cycle, surface energy balance, and the management of water resources are all significantly impacted by soil moisture. Because it governs the physical processes of evapotranspiration and rainfall penetration, surface soil moisture is a significant climatic variable. In this work, visible-near infrared (VIS-NIR) bands were used to compare and analyze the spectra of loess samples with varying moisture concentrations. The investigation looked at how changes in the soil moisture content impacted the response of the soil spectra. The researchers used a genetic algorithm (GA), interval combination optimization (ICO), and competitive adaptive reweighted sampling (CARS) to filter feature variables from full-band spectral data. To forecast the moisture content of loess on the soil surface, models like partial least squares regression (PLSR), support vector machine (SVM), and random forest (RF) were created. The findings indicate that: (1) the most reliable spectrum preprocessing technique is the first derivative (FD), which can significantly enhance the model’s prediction power and spectral characteristic information. (2) The feature band selection method’s prediction effect of soil moisture content is typically superior to that of full-spectrum data. (3) The random forest (RF) prediction model for soil moisture content with the highest accuracy was built by combining the genetic algorithm (GA) with the FD preprocessed spectra. The results may provide a new understanding on how to use VIS-NIR to measure soil moisture content.

Soil Moisture Detector

Abstract: Soil Moisture is directly related to the amount of irrigation in agriculture and influences the yield of crops. Accordingly, a soil moisture sensor is an important tool for measuring soil moisture content. In this study, the previous research conducted in recent 2-3 decades on soil moisture sensors was reviewed and the principles of commonly used soil moisture sensors and their various applications were summarized. Furthermore, the advantages, disadvantages, and influencing factors of various measurement methods employed were compared and analyzed. The improvements presented by several scholars have established the major applications and performance levels of soil moisture sensors, thereby setting the course for future development. These studies indicated that soil moisture sensors in the future should be developed to achieve high-precision, lowcost, non-destructive, automated, and highly integrated systems. Also, it was indicated that future studies should involve the development of specialized sensors for different applications and scenarios. This review research aimed to provide a certain reference for application departments and scientific researchers in the process of selecting soil moisture sensor products and measuring soil moisture.

GRU–Transformer: A Novel Hybrid Model for Predicting Soil Moisture Content in Root Zones

The accurate measurement of soil moisture content emerges as a critical parameter within the ambit of agricultural irrigation management, wherein the precise prediction of this variable plays an instrumental role in enhancing the efficiency and conservation of agricultural water resources. This study introduces an innovative, cutting-edge hybrid model that ingeniously integrates Gated Recirculation Unit (GRU) and Transformer technologies, meticulously crafted to amplify the precision and reliability of soil moisture content forecasts. Leveraging meteorological and soil moisture datasets amassed from eight monitoring stations in Hebei Province, China, over the period from 2011 to 2018, this investigation thoroughly assesses the model’s efficacy against a diverse array of input variables and forecast durations. This assessment is concurrently contrasted with a range of conventional machine learning and deep learning frameworks. The results demonstrate that (1) the GRU–Transformer model exhibits remarkable superiority across various aspects, particularly in short-term projections (1- to 2-day latency). The model’s mean square error (MSE) for a 1-day forecast is notably low at 5.22%, reducing further to a significant 2.71%, while the mean coefficient of determination (R2) reaches a high of 89.92%. Despite a gradual increase in predictive error over extended forecast periods, the model consistently maintains robust performance. Moreover, the model shows exceptional versatility in managing different soil depths, notably excelling in predicting moisture levels at greater depths, thereby surpassing its performance in shallower soils. (2) The model’s predictive error inversely correlates with the reduction in parameters. Remarkably, with a streamlined set of just six soil moisture content parameters, the model predicts an average MSE of 0.59% and an R2 of 98.86% for a three-day forecast, highlighting its resilience to varied parameter configurations. (3) In juxtaposition with prevalent models such as Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Gradient Boosting Decision Tree (GBDT), XGBoost, Random Forest, and deep learning models like Deep Neural Network (DNN), Convolutional Neural Network (CNN), and standalone GRU-branch and Transformer-branch models, the GRU–Transformer framework demonstrates a significant advantage in predicting soil moisture content with enhanced precision for a five-day forecast. This underscores its exceptional capacity to navigate the intricacies of soil moisture data. This research not only provides a potent decision-support tool for agricultural irrigation planning but also makes a substantial contribution to the field of water resource conservation and optimization in agriculture, while concurrently imparting novel insights into the application of deep learning techniques in the spheres of agricultural and environmental sciences.

Tillage Effects on Soil Moisture and Sulfur use Efficiency by Sesame (Sesamum indicum L.) on Vertisol of North Ethiopia

Sesame, produced in Humera is among the highest quality seeds grown in Ethiopia and even in the world. However, its productivity is by far less than the production potential due to nutrient depletion, moisture stress and lack of crop response to applied fertilizer. An experiment was conducted to evaluate the effects of tillage practices and sulfur fertilizer rates on moisture conservation and agronomic sulfur use efficiency of sesame crop in 2015. The treatments were tied ridges (M1) and flatbed tillage practices combined with five rates of sulfur fertilizer at a split plot design with three replications. M1 and flatbed treatments were assigned to the main plots, while sulfur fertilizer rates were assigned to the sub-plots. Measurements of soil moisture content at 0-20 cm, 20–40 cm and 40–60 cm depth were conducted throughout the growing season at an interval of 13 days using the gravimetric method. The results showed that M1 increased the soil moisture content up to 44% compared to the flatbed. The highest amount of agronomic sulfur use efficiency 14.4 kg kg-1 was also obtained from M1 tillage at 10 kg S ha-1. On the other hand, the highest amount of agronomic sulfur use efficiencies under flatbed tillage was 9.5 kg kg-1 at 10 kg S ha-1. M1 increased agronomic sulfur use efficiency by 51.6%. Tied ridging is the best option to significantly increased soil moisture availability in the root zone and as a consequence it increased agronomic sulfur use efficiency of the sesame. Therefore, in-situ moisture conservation using tillage practices like M1 at farm level should be demonstrated at farmer’s field in the semi-arid Humera areas for improving sulfur fertilizer use efficiency.

Tillage and Crop Residue Effects on Soil Carbon and Moisture for Wheat (Triticum aestivum L.) Productivity in Semiarid Regions of Tigray, Ethiopia

Soil tillage is one of the basic agriculture operations. However, the appropriate tillage type, tillage time, and tillage frequency for effective moisture harvesting and sustainable soil fertility were not investigated for Tigray. A field experiment was conducted during 2016 to 2018 in Enderta district. It was done with the objective of evaluating the effect of different tillage practices on wheat productivity. The treatments were (I) permanent bed+ crop residue,(II) three times tillage with furrow, (III) two times tillage with furrow, and (IV) farmer’s practice tillage. A randomized complete block design with three replications was set up. Measurement of soil moisture content was conducted using the gravimetric method. Agronomic parameters were collected and analyzed using GenStat. Marginal rate of return was also estimated from the total revenue and total variable cost. Positive effect was found on soil fertility, soil moisture content, and grain yield due to the tillage practice and crop residue. Permanent bed+ crop residues and three tillage furrow increased soil moisture content, organic carbon, and total nitrogen, as well as the yield and yield components at the second and third year of experimentation. The highest grain yield 2952 kg ha-1 and biomass yield 8582 kg ha-1 were recorded at the three tillage furrow in the third year of experimentation. The 59,056 ETB was the highest net revenue recorded at three times tillage with furrows. From this result, it can be concluded that without adding any additional input instead of changing agricultural operation techniques, the economic benefit of farmers’ could be improved by 48%. Therefore, three tillage practices with furrow is the most economically feasible technology for farmers to increase wheat productivity in the semiarid area of Enderta district.

Effect of soil moisture content on thermal performance of ground source heat exchangers: An electromagnetism topology-based analysis

Ground source heat pump (GSHP) technology has gained significant attention as a viable solution for the heating, ventilation, and air conditioning (HVAC) of buildings. One of the factors that can impact the performance of GSHP systems is the soil moisture content. In this study, an innovative approach, referred to as soil impedance topology, is introduced as an alternative to traditional methods for measuring soil moisture content in geothermal heat pump systems. This method is inspired by pipeline grid utilizes impedance measurement, which provides a direct measurement of soil moisture content without the need for sensors. The study also proposes a changing rule of impedance with varying moisture content, where capacitance increases and resistance decreases when the soil moisture content is less than 20% but stabilizes beyond a water content of 20%. The accuracy of the topological mechanism prediction method is high, as demonstrated through the verification of the Smith circle and measured data. However, it is noted that the performance may be insufficient in the high frequency band and high soil moisture content. The use of impedance topology provides a more accurate and reliable method for measuring soil moisture content as well as for modeling and predicting heat transfer rates and system performance. This technology has strong potential to improve efficiency, reduce energy costs, and enhance the environmental sustainability of geothermal heat pump systems.

Research on robust inversion model of soil moisture content based on GF-1 satellite remote sensing

This study aims to improve the accuracy and applicability of traditional linear regression and machine learning algorithms for monitoring soil moisture content by satellite remote sensing. Shahaoqu Experimental Station within Jiefangzha Irrigation District of Hetao Irrigation Area is selected as the study area. The dataset includes GF-1 satellite remote sensing images and measured soil moisture content. The study employs the best subset selection to determine the combination of sensitive variables for soil moisture content at different soil depths. Additionally, robust regression theory is introduced to traditional linear regression and machine learning algorithms to construct soil moisture content inversion models at different soil depths. The results show that all three improved robust algorithms effectively reduce the influence of outliers, improve the accuracy and stability of these inversion models. The improved robust nonlinear algorithm outperforms the improved robust linear algorithm. Compared with the unprocessed algorithm, at the soil depth of 0–20 cm, Robust Extreme Learning Machine (RELM) algorithm is the most optimal, with the validation set's R2adj increasing from 0.555 to 0.696 (approximately 25.33%) and the RMSE decreasing from 0.025 to 0.018 (approximately 26.54%). Robust Least Squares Support Vector Machine (RLSSVM) algorithm is the second best, with the validation set's R2adj increasing from 0.502 to 0.641 (approximately 27.75%) and the RMSE decreasing from 0.026 to 0.020 (approximately 24.59%). Robust Linear Regression (LMS-RLS) algorithm is relatively the worst, with the validation set's R2adj increasing from 0.491 to 0.659 (approximately 34.05%) and the RMSE decreasing from 0.026 to 0.019 (approximately 27.21%). The sensitivity of the five soil depths to soil moisture content follows the order of 0–20 cm, 20–40 cm, 0–60 cm, 0–40 cm, and 40–60 cm. The improved robust regression algorithms can improve the accuracy and applicability of soil moisture content estimation and provide a reference for monitoring soil moisture content using GF-1 satellite.

Development of frequency measurement circuit for soil moisture content and electric conductivity measurement system with detection of soil transient response characteristics for on‐site measurement

AbstractIn agricultural field, compact sensors that can measure both water content and electric conductivity are needed. Focusing on the fact that water content and conductivity depend on soil impedance, our group has been developing a new compact and noise‐resistant driving circuit that measured soil impedance by detecting transient characteristics of the voltage obtained by injecting current through the soil and converting it to frequency information. In order to realize agricultural field measurements with sensors using this new driving circuit, it had been necessary to miniaturize the part that detected the output from the driving circuit, but it hadn't been achieved in our previous work. In this research, a microcomputer‐based frequency counter was developed to downsize the system for on‐site measurement. In general, there are two methods of frequency measurement: direct counting and reciprocal. The direct‐counting method is suited for high‐frequency measurements, while the reciprocal method is suited for low‐frequency measurements. The developed frequency counter could measure with an error of 0.005% over the assumed frequency range by switching between those two measurement methods according to the measured frequency. Using this driving circuit and frequency counter, we measured the model soil and succeeded in capturing changes in water content as changes in impedance.

Evaluating the Crop Coefficient for Cherries Plants in Michigan State

Crop coefficient for cherries was evaluated by measure the water consumption in Michigan State to find its variation with time as the plant growth. Crop coefficients value (Kc) for cherries were predicated by Michigan State University (MSU) and also by Food and Agriculture Organization (FAO) according to consume of water through the season. In this paper crop coefficients for cherries are modified accordingly to the actual measurements of soil moisture content. Actual evapotranspiration (consumptive use) were measured by the soil moisture readings using Time Domain Reflectometers (TDR), and compared with the actual potential evapotranspiration that calculated by using modified Penman-Monteith equation which depends on metrological station and by using pan evaporation method. Absolut error techniques show that the predicated crop coefficient by MSU should be modified and changed from 1.0 to 1.20 during June, and from 1.02 during July and August to 1.2 to reduce the crop water stress and give better water management and perfect schedule for irrigation process.

Development of Frequency Measurement Circuit for Soil Moisture Content and Electric Conductivity Measurement System with Detection of Soil Transient Response Characteristics for On-Site Measurement

In agricultural field, compact sensors that can measure both water content and electric conductivity are needed. Focusing on the fact that water content and conductivity depend on soil impedance, our group has been developing a new compact and noise-resistant driving circuit that measured soil impedance by detecting transient characteristics of the voltage obtained by injecting current through the soil and converting it to frequency information. In order to realize agricultural field measurements with sensors using this new driving circuit, it had been necessary to miniaturize the part that detected the output from the driving circuit, but it hadn't been achieved in our previous work. In this research, a microcomputer-based frequency counter was developed to downsize the system for on-site measurement. In general, there are two methods of frequency measurement: direct counting and reciprocal. The direct-counting method is suited for high-frequency measurements, while the reciprocal method is suited for low-frequency measurements. The developed frequency counter could measure with an error of 0.005% over the assumed frequency range by switching between those two measurement methods according to the measured frequency. Using this driving circuit and frequency counter, we measured the model soil and succeeded in capturing changes in water content as changes in impedance.

Soil water depletion patterns in rainfed apple orchards and wheat fields.

Agricultural production in the Weibei rainfed highland, Northwest China, is challenged by severe drought and water shortages. While the land use pattern has shifted gradually from crop production to orchard farming in Weibei, little is known about the influence of fruit industry development on regional water resources and the rationality of planting orchards. Here, we characterized soil water depletion patterns in rainfed orchards and farmlands to evaluate the occurrence of soil desiccation under land use conversion from farmlands to orchards in Weibei. Soil moisture dynamics were monitored in the 0-150cm soil profiles of different aged Red Fuji apple orchards (young: 7 years, mature: 13 years, old: 22 years) and long-term cultivated winter wheat fields. We measured soil moisture content by oven-drying method in the middle of each month during the growing season of apple trees (March-September 2019). The over-depletion and depletion of soil water were analyzed to evaluate water stress and differential water depletion by distinct vegetation, respectively. The soil desiccation index was used to determine the occurrence of dry soil layers. Water stress was only observed at the 0-70-cm soil depths in the old orchards (mid-June) and farmlands (mid-May-mid-July). Water depletion took place at deeper depths for longer periods in the older orchards than in the younger orchards. Soil desiccation was absent in the young orchards, with mild desiccation at the 0-80-cm soil depths in the mature and old orchards in mid-June. The desiccation intensity was mild at the 0-60-cm soil depths in mid-April-mid-May, intense at the 0-150-cm soil depths in mid-June, and moderate at the 20-150-cm soil depths in mid-July. Results of this study demonstrate the mitigation of water stress and soil desiccation following conversion from wheat fields to apple orchards, which verifies the rationality of planting orchards in the rainfed highland area. Our findings provide strong support for developing a novel model of agro-industrial development, ecological construction, and sustainable economy in the vast arid and semi-arid areas of Northwest China.

Effects of post oak (Quercus stellata) and smooth brome (Bromus inermis) competition on water uptake and root partitioning of eastern redcedar (Juniperus virginiana).

Eastern redcedar Juniperus virginiana is encroaching into new habitats, which will affect native ecosystems as this species competes with other plants for available resources, including water. We designed a greenhouse experiment to investigate changes in soil moisture content and rooting depths of two-year-old J. virginiana saplings growing with or without competition. We had four competition treatments: 1) none, 2) with a native tree (Quercus stellata), 3) with an invasive grass (Bromus inermis), and 4) with both Q. stellata and B. inermis. We measured soil moisture content over two years as well as root length, total biomass, relative water content, midday water potential, and mortality at the end of the experiment. When J. virginiana and B. inermis grew together, water depletion occurred at both 30-40 cm and 10-20 cm. Combined with root length results, we can infer that J. virginiana most likely took up water from the deeper layers whereas B. inermis used water from the top layers. We found a similar pattern of water depletion and uptake when J. virginiana grew with Q. stellata, indicating that J. virginiana took up water from the deeper layers and Q. stellata used water mostly from the top soil layers. When the three species grew together, we found root overlap between J. virginiana and Q. stellata. Despite the root overlap, our relative water content and water potential indicate that J. virginiana was not water stressed in any of the plant combinations. Regardless, J. virginiana saplings had less total biomass in treatments with B. inermis and we recorded a significantly higher mortality when J. virginiana grew with both competitors. Root overlap and partitioning can affect how J. virginiana perform and adapt to new competitors and can allow their co-existence with grasses and other woody species, which can facilitate J. virginiana encroachment into grasslands and woodlands. Our data also show that competition with both Q. stellata and B. inermis could limit establishment, regardless of water availability.

Research on vehicle-mounted soil electrical conductivity and moisture content detection system based on current–voltage six-terminal method and spectroscopy

To provide real-time, online, and accurate measurements of soil electrical conductivity (EC) and moisture content (SM) in large-scale agricultural fields and guide field management, vehicle-mounted online EC and SM measurement system based on the fusion of current–voltage six-terminal method and near-infrared spectral information was developed and the predictive accuracy of the system was tested in field and laboratory experiments. The traditional current–voltage six-terminal method is used to fuse the spectral information at the moisture-sensitive wavelength of 1450 nm to predict SM and then a correction factor Q was proposed to eliminate the influence of SM on EC. In addition, the linear relationship between soil apparent electrical conductivity (or bulk soil electrical conductivity, ECa) and SM, the exponential relationship between SM and spectral reflectance (R) were used to indirectly obtain the exponential relationship between ECa and R. And then, the binary model of EC is established using the electrical conductivity values of the univariate model (ECeq) obtained from the current–voltage six-terminal method and the univariate model (EClq) obtained from the relationship between the conductivity of the ECa and the R index as an input variable. The experimental data of the univariate model EClq and ECeq and the binary model were analyzed. The field electrical conductivity detection accuracy R2 of the univariate model EClq and ECeq is 0.790 and 0.829 respectively, and the RMSE is 10.113 and 8.078 respectively. While the field electrical conductivity detection accuracy R2 and RMSE of the binary model are 0.899 and 6.535 respectively. The laboratory electrical conductivity detection accuracy of R2 for the univariate models EClq and ECeq were 0.874 and 0.893, and the RMSE were 7.129 and 6.663 respectively. The laboratory electrical conductivity detection accuracy of R2 and RMSE for the binary model were 0.942 and 5.346 respectively. From the above result, it can be seen that R2 for the binary model has improved and the RMSE of the model has decreased. Analysis result of the SM prediction model shows that the accuracy of the SM prediction model R2 in the field and laboratory is 0.888 and 0.938 respectively, and the RMSE is 0.339 and 0.239 respectively, which indicates that the accuracy of the SM prediction model can meet the system requirements. The overall experimental results show that the electrical conductivity detection accuracy of the binary model of the detection system fused with spectral data is significantly improved, and the SM prediction accuracy can meet the needs of farmland detection, which can guide scientific and precise management of farmland.

Study on Optimal Sampling Analysis of Soil Moisture at Field Scale for Remote Sensing Applications

With the rapid development of soil moisture estimation techniques involving remote sensing technology, the sampling designs used in soil moisture research are very important. To estimate the rational sample number for measuring near-surface soil moisture (0–20 cm), a random combination method was used to study the relationship between the average measured soil moisture contents and the true values at given scales. Compared to classic statistics and stratified sampling, the random combination method easily obtained precision estimates from a small number of samples. Moreover, the random combination method was upscaled to further discuss the influence of the coefficient of variation and study-region scale on the rational sample numbers at different scales (2, 10, 20, 40, 80, and 160 m). The results showed that the rational sample numbers for measuring near-surface soil moisture at the 2, 10, 20, 40, 80, and 160 m scales were 2, 5, 5, 8, 20, and 42, respectively, under the relative error of 10% at the 95% confidence level. The rational sample numbers at different scales were proportional to the coefficient of variation and the regional scale.

Assessing the accuracy indicators of the ground-based spectral estimation of soil moisture

Contact point field measurements of soil moisture are characterized by low productivity. High efficiency of determining and monitoring soil moisture can be achieved remote sensing. The known microwave methods for remote determination of soil moisture have low spatial resolution, are expensive and hence unsuitable for the use in agriculture. For estimating soil moisture content, the article proposes the ground-based on-board complex, which includes an on-board passive meter of the optical signal reflected from the soil and a ground contact meter. Operation of the latter can be performed either by an automatic measuring network or by an operator conducting contact measurements. The authors formulated and solved the problem related to assessing the accuracy of validation of the on-board measurement of soil moisture content. As an on-board meter, a passive radiation spectroradiometer of the soil illuminated by the Sun is used, and as a ground meter, a contact meter is used for estimating soil moisture at different spatial increments. The accuracy indicators of ground measurements and their relationship with the estimated values were analyzed. Comparison of the results obtained by the ground and on-board measurements allowed to determine the relationship between the moisture content measured from the board and the error of ground measurements as a validation error.

Perspective methods of monitoring soil moisture under irrigation in the near-Ob area in the Novosibirsk region

The aim of the study. Analyzing the known methods and technologies of soil moisture monitoring for irrigation management in the agricultural production areas in the Near-Ob region, Russia. Location and time of the study. Novosibirsk region, Russia, 2023. Methods. Comparative analysis of irrigation regime management technologies described in the literature and currently used in irrigation practice. Results. Methods of quick operational control over soil moisture using as an example a large irrigation system (Verkh-Irmenskaya irrigation system in the Novosibirsk region) were analyzed and compared. The methods were evaluated from the point of view of their applicability in large grain production areas. Of many methods of soil moisture content measurement, the neutron method and the method of soil dielectric constant measurement are described in detail, and their advantages and disadvantages identified. Electrometric methods are proposed for grain production areas. Trends in the development of irrigation technologies are outlined, primarily digitalization and automation, which allow processing large data amounts. Conclusions. At present, irrigation management technologies are mostly represented by experimental developments; the use of artificial intelligence remains mainly the prerogative of research projects. For agricultural production, determination of soil moisture reserves using soil moisture sensors is still the most popular method. From our point of view, electrometric methods are the most promising, as they are mobile, not costly and have high measurement speed.

Mapping of soil moisture by time domain reflectometry and electrical resistivity tomography at Velika Gorica well field, Zagreb aquifer

Knowing the soil moisture distribution in the unsaturated zone can improve understanding the water flow through the unsaturated zone and thereby enable the calculation of aquifer recharge, which occurs through precipitation. One part of the Zagreb aquifer recharge occurs through infiltration from precipitation. In order to observe and model infiltration from precipitation through the unsaturated zone, the research polygon was constructed at the Velika Gorica well field, located in the southern part of the Zagreb aquifer, Croatia, where hourly measurements of electric conductivity (EC) and soil moisture content were carried out. EC and soil moisture data are measured by Time Domain Reflectometry (TDR) probes which are placed at different depths in the unsaturated zone. Furthermore, electrical resistivity tomography (ERT) measurements were conducted. Geophysical data, along with moisture and EC data from TDR probes, were used as input data for MoisturEC software, in order to obtain soil moisture distribution along a 2D profile. MoisturEC program offers three options for translating EC data to moisture content data which are all tested in this research. We obtained eight moisture content distributions along the observed profile and concluded that MoisturEC provides reasonable results with input data from geophysical measurements and TDR probe measurements. Soil moisture distribution in the unsaturated zone represents the initial conditions for further unsaturated flow modeling. Understanding the flow in the unsaturated zone enables the quantification of effective infiltration and can improve groundwater management.

Inverse Estimation of Soil Hydraulic Parameters in a Landslide Deposit Based on a DE-MC Approach

Extreme rainfall is a common triggering factor of landslide disasters, for infiltration and pore water pressure propagation can reduce suction stress and shear strength at the slip surface. The subsurface hydrological model is an essential component in the early-warning system of rainfall-triggered landslides, whereas soil moisture and pore water pressure simulated by the Darcy–Richards equation could be significantly affected by uncertainties in soil hydraulic parameters. This study conducted an inverse analysis of in situ measured soil moisture in an earthquake-induced landslide deposit, and the soil hydraulic parameters were optimized with the Differential Evolution Markov chain Monte Carlo method (DE-MC). The DE-MC approach was initially validated with a synthetic numerical experiment to demonstrate its effectiveness in finding the true soil hydraulic parameters. Besides, the soil water characteristic curve (SWCC) and hydraulic conductivity function (HCF) described with optimized soil hydraulic parameter sets had similar shapes despite the fact that soil hydraulic parameters may be different. Such equifinality phenomenon in inversely estimated soil hydraulic parameters, however, did not affect the performance of simulated soil moisture dynamics in the synthetic numerical experiment. The application of DE-MC to a real case study of a landslide deposit also indicated satisfying model performance in terms of accurate match between the in situ measured soil moisture content and ensemble of simulations. In conclusion, based on the satisfying performance of simulated soil moisture and the posterior probability density function (PDF) of parameter sets, the DE-MC approach can significantly reduce uncertainties in specified prior soil hydraulic parameters. This study suggested the integration of the DE-MC approach with the Darcy–Richards equation for an accurate quantification of unsaturated soil hydrology, which can be an essential modeling strategy to support the early-warning of rainfall-triggered landslides.

In-situ measurements of wall moisture in a historic building in response to the installation of an impermeable floor.

When impermeable ground bearing slabs are installed in old buildings without a damp-proof course, it is a common belief of conservation practitioners that ground moisture will be 'driven' up adjacent walls by capillary action. However, there is limited evidence to test this hypothesis. An experiment was used to determine if the installation of a vapour-proof barrier above a flagstone floor in a historic building would increase moisture content levels in an adjacent stone rubble wall. This was achieved by undertaking measurements of wall, soil and atmospheric moisture content over a 3-year period. Measurements taken using timber dowels showed that the moisture content within the wall did not vary in response to wall evaporation rates and did not increase following the installation of a vapour-proof barrier above the floor. This indicates that the moisture levels in the rubble wall were not influenced by changes in the vapour-permeability of the floor.

Improving moisture content estimation from field resistivity measurements with subsurface structure information

• Smoothness constraint in regularized resistivity inversion is removed at structural boundaries. • Structural unit-specific petrophysical models are used for moisture interpretation. • Moisture content is better estimated from resistivity with the new method. In Earth sciences, the measurement of soil and rock moisture content is essential in improving our understanding of various hydrologic processes. Recently, the electrical resistivity method has been frequently used to estimate the moisture content in the field. The uncertainty associated with resistivity-estimated moisture content is mainly from two sources: regularized inversion and petrophysical interpretation. In this study, to reduce the uncertainty, we propose (1) to use subsurface structural information from seismic refraction measurements to relax the smoothness-based regularization at structural boundaries and (2) to use structural unit-specific petrophysical relationships to translate resistivity into moisture content. The proposed methods are tested on a synthetic subsurface model featuring three distinct layers of a granitic critical zone (CZ). The results of the synthetic example show that both the spatial pattern and the moisture content values estimated with the new method are very close to the true model with low uncertainty. Compared to the traditional method, the estimation is significantly improved, particularly at the CZ boundaries, such as the regolith-fractured bedrock interface. We also apply the new method to a granitic hillslope to estimate the moisture content distribution from field resistivity measurements. Although no ground truth is available for validation, the estimated moisture content distributions exhibit some typical hydrological features in hillslopes, such as the perched water at the soil/rock interface and preferential flow path in fractured rocks. Therefore, it is concluded that incorporating structural information in resistivity inversion and using structural unit-specific petrophysical models can improve moisture content estimation from field resistivity measurements.