Soil Moisture Measurements Research Articles

Climatological Evaluation of Three Assimilation and Reanalysis Datasets on Soil Moisture over the Tibetan Plateau

Soil moisture is critical in the linkage between the land and atmosphere of energy and water exchange, especially over the Tibetan Plateau (TP). However, due to the lack of in situ plateau soil moisture measurements, the reanalyzed and assimilated data are the major supplements for TP climate research. Based on observations from 1992 to 2013, this study provides a comprehensive evaluation of three sets of assimilation and reanalysis products (GLDAS, ERA5-Land, and MERRA-2) on the climatic mean and variability of soil moisture over the Tibetan Plateau (TPSM). For the climatic mean, GLDAS captures the spatial distribution and annual cycle of TPSM better than other datasets in terms of lower spatial RMSE (0.07 m3×m-3) and bias (0.06 m3×m-3). In terms of the climatic variability of TPSM, the multi-data average (MDA) highlights its advantages in reducing the bias relative to any single data product. MDA describes the TPSM anomalies more stably and accurately in terms of temporal trend and variation (r = 0.94), as well as the dipole spatial pattern in EOF1. When considering both the climatic mean and spatial variability, the performance of MDA is more accurate and balanced than that of a single data product. This study overcomes the deficiency of limited time and space in previous evaluations of TPSM and indicates that multi-data averaging may be a more effective approach in the climate investigation of TPSM.

Study of Machine Learning Techniques for the Estimation of Soil Moisture in Agriculture

Soil moisture is crucial in various fields and monitoring it to guide irrigation is challenging. Machine learning has emerged as a promising tool to predict soil moisture levels accurately. This study evaluates machine learning techniques for this task, training models with meteorological variables and direct soil moisture measurements. Four machine learning algorithms were implemented, highlighting the Gradient Boosting Regressor as the most effective. In addition, a processed data set that combines meteorological and soil moisture measurements is presented, hoping it will be helpful for future research. This approach seeks to improve the compression and predictability of soil moisture, which is crucial for agricultural planning and water management in agriculture

Applying minirhizotrons to observe spatiotemporal variations in rooting depth and distribution in agroecosystems to improve the performance of hydrological models

AbstractTo understand and explain soil moisture dynamics, the role of the vegetation is crucial. Hydrological processes in agricultural soils are strongly affected by rooting depth and root distribution. We present a new approach to monitor and model root dynamics and its influence on soil moisture using minirhizotrons combined with phenological observations. Field setups consist of a portable root scanner and acrylic glass tubes installed in the soil at the start of the growing season. 360° scans of soil and roots are taken regularly at different depths in the tubes. Root traits are identified automatically for each soil layer and complemented by observations of aboveground plant phenology. Results from minirhizotron data collected at an agrometeorological observatory in Germany show for both cereal and rapeseed (Brassica napus L.) crops a higher root density in deeper soil layers and a lower density near the soil surface when compared to literature data. An opposite picture emerged for maize (Zea mays L.) and potato (Solanum tuberosum L.), whereas vertical root distribution in sugar beet (Beta vulgaris subsp. vulgaris) had a different seasonal course than expected from literature. Applying the new root distribution data in calculations of the soil water balance resulted in differences of more than 3% in absolute volumetric soil water content. A comparison with in situ measurements of volumetric soil moisture at 20‐ and 50‐cm depth revealed a significant improvement of the model results due to the new parameterization. Thus, we argue that minirhizotrons constitute a useful supplement to hydrological observatories and can help understand and predict soil moisture dynamics in the critical zone.

Temporal variability and predictability predict alpine plant community composition and distribution patterns.

One of the most reliable features of natural systems is that they change through time. Theory predicts that temporally fluctuating conditions shape community composition, species distribution patterns, and life history variation, yet features of temporal variability are rarely incorporated into studies of species-environment associations. In this study, we evaluated how two components of temporal environmental variation-variability and predictability-impact plant community composition and species distribution patterns in the alpine tundra of the Southern Rocky Mountains in Colorado (USA). Using the Sensor Network Array at the Niwot Ridge Long-Term Ecological Research site, we used in situ, high-resolution temporal measurements of soil moisture and temperature from 13 locations ("nodes") distributed throughout an alpine catchment to characterize the annual mean, variability, and predictability in these variables in each of four consecutive years. We combined these data with annual vegetation surveys at each node to evaluate whether variability over short (within-day) and seasonal (2- to 4-month) timescales could predict patterns in plant community composition, species distributions, and species abundances better than models that considered average annual conditions alone. We found that metrics for variability and predictability in soil moisture and soil temperature, at both daily and seasonal timescales, improved our ability to explain spatial variation in alpine plant community composition. Daily variability in soil moisture and temperature, along with seasonal predictability in soil moisture, was particularly important in predicting community composition and species occurrences. These results indicate that the magnitude and patterns of fluctuations in soil moisture and temperature are important predictors of community composition and plant distribution patterns in alpine plant communities. More broadly, these results highlight that components of temporal change provide important niche axes that can partition species with different growth and life history strategies along environmental gradients in heterogeneous landscapes.

Enhancing spatial resolution of satellite soil moisture data through stacking ensemble learning techniques

Soil moisture (SM) is a critical variable influencing various environmental processes, but traditional microwave sensors often lack the spatial resolution needed for local-scale studies. This study develops a novel stacking ensemble learning framework to enhance the spatial resolution of satellite-derived SM data to 1 km in the Urmia basin, a region facing significant water scarcity. We integrated in-situ SM measurements (obtained using time-domain reflectometry [TDR]), Soil Moisture Active Passive (SMAP) and Advanced Microwave Scanning Radiometer 2 (AMSR2) SM products, Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature and vegetation indices, precipitation records, and topography data. Ten base machine-learning models were evaluated using the Complex Proportional Assessment (COPRAS) method, and the top-performing models were selected as base learners for the stacking ensemble. The ensemble model, incorporating Random Forest, Gradient Boosting, and XGBoost, significantly improved SM estimation accuracy and resolution compared to individual models. The XGBoost and Gradient Boosting meta-models achieved the highest accuracy, with an unbiased root mean square error (ubRMSE) of 1.23% m3/m3 and a coefficient of determination (R2) of 0.97 during testing, demonstrating the exceptional predictive capabilities of our approach. SHapley Additive exPlanations (SHAP) analysis revealed the influence of each base model on the ensemble’s predictions, highlighting the synergistic benefits of combining diverse models. This study establishes new benchmarks for soil moisture monitoring by showcasing the potential of ensemble learning to improve the spatial resolution and accuracy of satellite-derived SM data, providing crucial insights for environmental science and agricultural planning, particularly in water-stressed regions.

Spatial downscaling of SMAP soil moisture to high resolution using machine learning over China’s Loess Plateau

Soil moisture (SM) is a critical physical parameter in land surface processes that affects the atmospheric and hydrological cycles. Soil Moisture Active Passive (SMAP) mission produces high-quality global SM data, but its low spatial resolution limits the applications at a regional or local scale. In this study, we developed a novel method to select optimal factors from 34 candidate downscaling factors for each month using three machine learning methods (Back Propagation Neural Network (BPNN), Support Vector Machine (SVM) and Random Forest (RF)) to produce monthly time series SM products at a spatial resolution of 1 km for the entire Loess Plateau in China from 2015 to 2023. 11 in-situ SM measurements distributed in Loess Plateau and precipitation data were used to evaluate the downscaling performances of the three machine learning approaches. The results show that the spatial trends of the three downscaled SM were essentially the same as the SMAP SM, with similar spatial patterns, and all three downscaled SM can provide more spatial information and texture features than SMAP SM. Among the three types of downscaled SM, the RF downscaled SM reached the highest R (0.654) and the smallest unbiased Root Mean Square Error (ubRMSE) (0.044 m3m−3), better than the BPNN and SVM downscaled SM, and most closely matched the in-situ measurements. The dynamics of SM were successfully captured by RF downscaled SM, which exhibited strong temporal consistency with the in-situ SM and responded well to precipitation events, with significant increases in SM values in the month of high precipitation and subsequent months. In conclusion, the RF model has the best downscaling effect and it can be used to provide high spatial resolution SM data for applications at a regional or local scale across the Loess Plateau.

Soil moisture estimation in the unsaturated zone using surface wave measurements and hybrid modeling framework

Abstract Soil moisture plays a critical role in influencing various facets of ecosystem dynamics. The preference for measuring soil moisture without physical intrusion has been desirable for precise assessments while minimizing disruptions to soil structural, hydraulic, and biological characteristics. In this study, we explored the potential of surface elastic waves as a proxy to estimate soil moisture profiles to a depth of 1.05 m at intervals of 0.1 m. We conducted a multichannel analysis of surface waves (MASW) survey and measured soil moisture at depths of 0.15 m and 0.35 m. To address the limited availability of soil moisture measurements, we developed a mechanistic soil moisture model as a substitute for measured soil moisture profiles. Our results showed that as soil moisture increased, the propagation of surface waves became more pronounced due to reduced frictional resistance. However, it was not straightforward to link measured surface wave responses and subsurface soil moisture profile. To address these challenges, we developed a convolutional neural network (CNN) with the inputs of the frequency-velocity and frequency-wavenumber images obtained from the measured surface waves. We found that the integration of MASW and CNN proved effective in estimating soil moisture profiles to a depth of 1.05 m at intervals of 0.1 m without causing disturbances to the soil (MAE = 0.0035 m3 m−3). This study suggested that the combined use of surface waves and CNN hold promise in measuring soil moisture profiles without physical disruptions. As such, the proposed approach could serve as a viable alternative to noninvasive soil moisture sensors.

Identification of plant soil water and soil aeration corequisites: A management tool

AbstractPlant‐available soil‐water and adequate soil aeration within the root zone are essential corequisites for successful plant growth. Characterization of these two requirements for plant growth is generally done by independent measurements of water and air phase properties and functions in soil, with limited emphasis on their combined effect. This study investigated soil‐water characteristic (SWC) measurements in vadose soil profiles (up to 117‐cm depth) in six pasture soils to examine the combined behavior of soil‐water and diffusion‐controlled aeration within the root zone. The soil moisture measurements were made over matric potentials ranging from −1 to −1500 kPa using tension table and pressure plate apparatus. The van Genuchten model was used to parameterize the measured SWC curve, while the Millington–Quick model was used to derive soil‐gas diffusivity from measured soil physical properties. Based on two simple indices, derived to represent plant‐available soil‐water and soil aeration status at a given moisture content, we propose an assessment matrix, which illustrates how a soil satisfies the corequisites for successful plant growth, as it drains to different matric potentials upon irrigation. Results show a pronounced effect of soil texture and, to a lesser extent, soil structure on satisfying these corequisites. With the aid of the assessment matrix, we observed that loam, sandy loam textures exhibited a good overall performance, while sand, clay loam, and clay soils struggled to meet these corequisites during drainage.

Field investigation of soil moisture profile in the seedbeds of clayey paddy fields composed of large clods (aggregates)

ABSTRACT Unstable germination and emergence of upland crops in clayey paddy fields is a pressing issue. As tillage leads to the formation of large clods measuring a few centimeters, the soil moisture content in the seedbed markedly decreases, potentially inhibiting germination of seeds. Although the effect of the clod size on the soil moisture profile was confirmed experimentally, the validity of this effect has not been tested in the field. In this study, we aimed to clarify the effect of clod size on the moisture profile and water balance in the seedbeds of clayey paddy field. We developed an apparatus for measuring soil moisture content in the soil layer with clods. This apparatus used a laser displacement sensor and large transparent plate horizontally fixed above the soil surface. Temporal changes in the soil moisture profile (0–10 cm depth) and evaporation rate of clayey paddy fields with different clod sizes were monitored during the sowing period of soybeans. We found that the larger the mean clod diameter (MCD), the greater the decrease in soil moisture content near the soil surface. In addition, soil moisture loss at the sowing depth (5 cm) was positively correlated with MCD. In terms of the water balance, the estimated upward water flux at a depth of 5 cm was less than 1 mm d−1 across all the plots and periods. By contrast, the estimated ratio of the apparent to molecular diffusion coefficient of water vapor in the dry soil layer of clayey paddy field with an MCD of 2.5 and 1.8 cm were 5.1 and 2.0, respectively. These results suggest that more significant soil moisture loss causing reduction of germination and emergence rate can occur in a clayey paddy field with large clods, likely owing to the increased enhancement of water vapor movement. To increase the germination and emergence rate, therefore, a numerical model that can evaluate the moisture profile of soil layer with clods should be developed and the sowing depth should be optimized based on the MCD.

Interaction of a UWB impulse with a layer of sandy soil, having a rough upper boundary

ABSTRACT In this paper, the changes in the character of time shapes, amplitudes, frequency spectra, and time delay of ultra-wideband (UWB) impulses (duration of 0.33 ns) reflected from the layer of wet sand with a rough upper boundary were investigated. It was shown that the attenuation of UWB impulse amplitude, which is reflected from the upper rough boundary of the sand layer, can be described by the model of Fresnel reflection coefficient for a smooth surface using the Gaussian correction factor (FRGC). At this root-mean-square (RMS), height of soil surface roughness was in the range of ~0–21 mm, but the determination coefficient and RMS error (RMSE) were found to be equal to 0.94 and 0.02, respectively. An experiment close to linear increase/decrease in the propagation time of impulses reflected from the upper/lower boundary of the layer, respectively, was found with increasing the RMS heights of upper boundary. It is shown that the propagation time of such impulses can be described by the modified FRGC model with an error from RMSE = 0.09 ns to RMSE = 0.15 ns. The results of these findings are of practical importance for the interpretation of remote sensing data for precise soil moisture measurements from unmanned aerial vehicle platforms using UWB impulses.

In situ soil moisture and thermal properties estimated using a dual-probe heat-pulse

In situ monitoring of the temporal and spatial distribution of soil moisture and thermal properties are important for studying the water and energy transport in the vadose zone. The single-probe heat-pulse method based on fiber Bragg grating technology (SPHP-FBG) has become a research focus in field monitoring because of its capability to realize quasi-distributed and real-time monitoring. However, the SPHP-FBG method can only obtain thermal conductivity. This study developed a dual-probe heat-pulse method based on FBG (DPHP-FBG). The DPHP-FBG method can measure thermal conductivity (λ), volumetric heat capacity (Cv), and thermal diffusivity (k). Consequently, volumetric soil water content (θ) can be estimated from its linear relationship with Cv. The accuracy of the DPHP-FBG method in the estimation of Cv, λ, and θ was tested under different heating duration and various soil moisture conditions. In addition, Monte Carlo simulation was performed to investigate the impact of FBG measurement errors on accuracy. Finally, a field test was conducted to verify the effectiveness of the developed DPHP-FBG system. The results show that the DPHP-FBG method allows accurate soil moisture and thermal properties estimation without soil-specific calibration. The mean errors of the Cv and θ decrease with the extended heating duration. When the heating lasts 20 s, the measured Cv and θ have mean errors of 0.02 MJ m−3 K−1 and 0.01 m3/m3, respectively, for various moisture conditions. In the field test, the spatio-temporal distribution of soil moisture and thermal properties can be obtained in real time. Thereby, the proposed DPHP-FBG monitoring system is potential to conduct in situ coupled heat and soil moisture measurements at a large scale.

Rancang Bangun Alat Otomatisasi Pengomposan dari Sampah Organik Berbasis Internet of Things

Organic waste management in Indonesia is currently suboptimal. Approximately 33.7% of waste is not properly managed, and 56% of the total waste consists of organic waste, most of which comes from households. Composting is an effective method for managing organic waste. However, many people are still reluctant to convert organic waste into compost due to the lengthy process, which typically takes 2 to 3 months. This study proposes an IoT-based composting automation system with the goal of accelerating the composting process without the use of additional microorganisms, producing both dry and liquid compost. The system method involves integrating Internet of Things (IoT) technology with sensors: DHT22 for temperature monitoring, soil moisture sensors for measuring soil moisture, MQ-4 sensors for detecting methane gas, pH sensors for measuring soil acidity, and water level sensors for detecting the height of liquid compost. Data collected from these sensors is used to automatically control the organic waste mixing process until all composting specifications are met. The results indicate that this system effectively accelerates the composting process to 24 days for dry compost and 19 days for liquid compost without the need for additional microorganisms, while producing compost of high quality according to the SNI-19-7030-2004 specifications.

Remote sensing retrieval of surface soil moisture in Mu Us Sandy Land based on optical trapezoid model

Abstract Maowuosu sandland ecosystem is fragile, and moisture is a key factor restricting the growth and development of sandland plants and ecological construction in semi-arid areas. Therefore, this study constructed the eigenspace based on the data of the 4th period of 2016, fitted the dry and wet edge equations of the eigenspace, calculated the OPTRAM, and utilized the measured in situ soil moisture data from various depths during the corresponding period. The OPTRAM was regressed to construct a model for estimating soil moisture. This model was then subjected to a series of validation procedures to ensure its accuracy. The coefficients for the shortwave infrarouges conversion reflection coefficient (STR) - standardized differential vegetation index (NDVI) feature space dry and wet edge fits increased with the increase of the month, where the dry and the R2 for the wet side in September was 0.88 and 0.87, respectively. OPTRAM and the measured soil moisture content data in the surface layer were all correlated to some extent, with correlation coefficients above 0.45. The OPTRAM model is more accurate than other models in inverting in situ moisture content at the 0∼10 cm depths within this area.

Performance of Soil Moisture Sensors at Different Salinity Levels: Comparative Analysis and Calibration.

Soil dielectric sensors have been widely used to obtain real-time soil moisture data, which are important for water resource management. However, soluble salts in the soil significantly affect the accuracy of these sensor measurements. Therefore, it is crucial to select suitable soil dielectric sensors for soil moisture measurements at different salinity levels. Eight mainstream sensors (EC-5, 5TE, Teros12, Hydra-probe II, TDR315L, TDR315H, TDR305H, and CS655) were selected and tested at four different soil salinity levels (EC1:5 = 3.0, 1.5, 1.0, and 0.75 dS·m-1). The measured values using the factory calibration formulas were compared at six soil moisture levels. The results showed that the measured soil moisture values from various sensors exhibited varying degrees of overestimation, which increased with increasing salinity. Only EC-5 did not exhibit distortion at high-salinity levels, with the measured values showing a good linear trend compared to the standard values. Mutational distortion of the measured apparent dielectric permittivity occurred in TDR315L, TDR315H, Hydra-probe II, and 5TE at EC1:5 = 3.0 dS·m-1. Insensitive distortion of the measured apparent dielectric permittivity occurred in Teros12 and TDR305H at EC1:5 = 3.0 dS·m-1 as well as in Teros12, TDR305H, 5TE and Hydra-probe II at EC1:5 = 1.5 dS·m-1. All tested sensors performed reasonably well at EC1:5 ≤ 1.0 dS·m-1. Seven sensors (excluding CS655) were calibrated within the distortion threshold. The soil moisture accuracy using the calibrated formulas could reach ±0.02 cm3·cm-3. At EC1:5 ≤ 1.0 dS·m-1, most sensors in this study could be applied with the factory calibration formulas. TDR series, EC-5, 5TE and Teros12 were recommended after calibration for EC1:5 > 1.0 dS·m-1. For extremely high soil salinity levels, the TDR series and EC-5 may be the best choices.

Mesoscale soil moisture measurements along the rover route using the mobile cosmic-ray neutron sensing in the eastern Tibetan Plateau

Water resources in the soil play an essential role in hydrological processes and ecosystem functions on the Tibetan Plateau. However, accurately measuring soil moisture distribution in this region presents challenges due to the diverse ecosystem types, complex terrain, and harsh environmental conditions. In this study, we introduce an approach for estimating mesoscale soil moisture in the Qilian Mountains (QLM) region of the eastern Tibetan Plateau using a cosmic-ray neutron rover. Soil moisture estimates derived from neutron count rates, newly adjusted by vegetation effects, demonstrated good agreement with soil moisture measurements obtained through soil sampling at 26 calibration sites across the region (RMSE = 0.025 g g−1). The calibration parameter N0_NDVI was 443 cpm in the QLM. Utilizing NDVI as vegetation correction method showed potential improvements in the accuracy of converting neutron counts to soil moisture across the diverse mountainous ecosystem types. The newly developed calibration equation provided a high-precision, high spatial resolution soil moisture transect across various landscapes measured by the rover. The average mesoscale soil moisture along the rover route varied by ecosystem types, with values of 0.10 g/g in deserts, 0.17 g/g in grasslands, 0.13 g/g in forests, 0.18 g/g in subalpine shrublands, and 0.20 g/g in croplands. Land cover types emerged as crucial determinants of mesoscale soil moisture variability in the QLM region. These findings offer valuable mesoscale soil moisture data and new insights into soil water information at the transect scale across diverse ecosystem types in the Tibetan Plateau.

Dwarf shrubs may mitigate the negative effects of climate change on spiders by moderating microclimate

Climate change negatively affects arthropod biodiversity worldwide. Mitigating the resulting arthropod decline is a great challenge. Dwarf shrubs in open areas might buffer microclimatic extremities by reducing the solar radiation reaching the ground and weakening air circulation near the soil surface. Forest steppes are mosaics of forests and grasslands covering a vast area in Eurasia. This heterogeneous ecosystem offers the opportunity to study the effect of small habitat features, i.e. dwarf shrubs, in dry grasslands and compare the fauna of rosemary-leaved willow (Salix rosmarinifolia) shrubs with forest patch interiors, open grasslands and their edges. We hypothesized that the dwarf shrub microhabitat has a wetter and cooler microclimate than open grassland and a different spider community composition than other forest-steppe microhabitats. We recorded microclimatic parameters with data loggers, measured soil moisture with TDR and collected ground-dwelling spiders with pitfall traps. We detected the highest soil moisture (6.26 ± 1.21%, mean ± 95% confidence interval) and air humidity (80.19 ± 3.19%) in forests and the lowest in grasslands (4.36 ± 0.65%; 66.59 ± 2.53%, respectively). The warmest microhabitats were grasslands (23.23 ± 0.51°C), whereas the coolest microhabitats were forests (18.92 ± 0.41°C). The distinct microclimate of dwarf shrubs was cooler (21.46 ± 0.41°C) and moister (5.43 ± 0.53%) than the surrounding semi-desert like grassland. Furthermore, we found a different spider community composition and trait state composition of spiders in forests, edges, grasslands and dwarf shrub microhabitats. Forests (9.90 ± 0.95) and edges (11.44 ± 1.27) hosted a higher species richness than grasslands (7.08 ± 4.27) and dwarf shrubs (5.09 ± 1.33). We collected larger spiders on the edges than in dwarf shrub microhabitats. The dwarf shrubs hosted a different microclimate and spider community composition from the grassland. Climate change in the forest-steppe region is assumed to be driven by a combination of warming and drying. In the coming decades, drought frequency and severity are predicted to increase. Woody vegetation, even dwarf shrubs, creates a thermal and moisture heterogeneity that might aid arthropods in buffering macroclimatic warming through behavioural thermoregulation. Therefore, their presence on grasslands can benefit the conservation of specialised grassland arthropods.

A Low-Cost Sensor Network for Monitoring Peatland.

Peatlands across the world are vital carbon stores. However, human activities have caused the degradation of many sites, increasing their greenhouse gas emissions and vulnerability to wildfires. Comprehensive monitoring of peatlands is essential for their protection, tracking degradation and restoration, but current techniques are limited by cost, poor reliability and low spatial or temporal resolution. This paper covers the research, development, deployment and performance of a resilient and modular multi-purpose wireless sensor network as an alternative means of monitoring peatlands. The sensor network consists of four sensor nodes and a gateway and measures temperature, humidity, soil moisture, carbon dioxide and methane. The sensor nodes transmit measured data over LoRaWAN to The Things Network every 30 min. To increase the maximum possible deployment duration, a novel datastring encoder was implemented which reduced the transmitted datastring length by 23%. This system was deployed in a New Forest (Hampshire, UK) peatland site for two months and collected more than 7500 measurements. This deployment demonstrated that low-cost sensor networks have the potential to improve the temporal and spatial resolution of peatland emission monitoring beyond what is achievable with traditional monitoring techniques.

Evaluation of a Multivariate Calibration Model for the WET Sensor That Incorporates Apparent Dielectric Permittivity and Bulk Soil Electrical Conductivity

The measurement of apparent dielectric permittivity (εs) by low-frequency capacitance sensors and its conversion to the volumetric water content of soil (θ) through a factory calibration is a valuable tool in precision irrigation. Under certain soil conditions, however, εs readings are substantially affected by the bulk soil electrical conductivity (ECb) variability, which is omitted in default calibration, leading to inaccurate θ estimations. This poses a challenge to the reliability of the capacitance sensors that require soil-specific calibrations, considering the ECb impact to ensure the accuracy in θ measurements. In this work, a multivariate calibration equation (multivariate) incorporating both εs and ECb for the determination of θ by the capacitance WET sensor (Delta-T Devices Ltd., Cambridge, UK) is examined. The experiments were conducted in the laboratory using the WET sensor, which measured θ, εs, and ECb simultaneously over a range of soil types with a predetermined actual volumetric water content value (θm) ranging from θ = 0 to saturation, which were obtained by wetting the soils with four water solutions of different electrical conductivities (ECi). The multivariate model’s performance was evaluated against the univariate CAL and the manufacturer’s (Manuf) calibration methods with the Root Mean Square Error (RMSE). According to the results, the multivariate model provided the most accurate θ estimations, (RMSE ≤ 0.022 m3m−3) compared to CAL (RMSE ≤ 0.027 m3m−3) and Manuf (RMSE ≤ 0.042 m3m−3), across all the examined soils. This study validates the effects of ECb on θ for the WET and recommends the multivariate approach for improving the capacitance sensors’ accuracy in soil moisture measurements.

Does afforestation increase soil water buffering? A demonstrator study on soil moisture variability in the Alpine Geroldsbach catchment, Austria

This study employed an operational monitoring network to measure soil moisture and runoff behaviour continuously in the Alpine catchment Geroldsbach-Götzens, Austria. We hypothesize that afforestation can have a positive impact on soil water buffering. To analyse the impact of soil properties and vegetation cover changes on soil water dynamics, four experimental plots were established on grassland and monitoring stations were installed in the forest. The rainfall test site is equipped with an automatic weather station to obtain meteorological observations, and weirs to measure surface runoff of natural occurring precipitation events and artificial rainfall simulations. In the plots, 200 soil moisture sensors were installed at five different depths, aimed to track and visualize infiltration and subsurface flow processes. Another twenty sensors monitored soil moisture at different afforestation stages in the forested part of the catchment. The measurements show that soils covered with young and old-growth forest have a higher and more stable soil moisture content than grassland and soils with a lack of vegetation throughout the seasons. We observed large spatial differences at plot scale, where the spatial variability of soil moisture increases with depth and is highest during convective precipitation. The initial conditions and rainfall characteristics play an important role in infiltration processes and soil water storage. Our rainfall test site demonstrated the challenges of innovative monitoring techniques and that it offers opportunities for more experiments to gather evidence-based data as input for flood models. Overall findings confirm the sponge effect of forest soils and indicate that afforestation as Nature-Based Solution reduces the temporal soil moisture variability, buffering soil water during precipitation events, which can be beneficial for runoff reduction in Alpine catchments.

An Integrated Hydrogeophysical Approach for Unsaturated Zone Monitoring Using Time Domain Reflectometry, Electrical Resistivity Tomography and Ground Penetrating Radar

Continuous measurements of soil moisture in the deeper parts of the unsaturated zone remain an important challenge. This study examines the development of an integrated system for the continuous and 3-D monitoring of the vadose zone processes in a cost- and energy-efficient way. This system comprises TDR, ERT and GPR geophysical techniques. Their capacities to adequately image subsurface moisture changes with continuous and time-lapse measurements are assessed during an artificial infiltration experiment conducted in a characteristic urban site with anthropogenic fills and much compaction. A 3-D array was designed for each method to expand the information of a single TDR probe and obtain a broader image of the subsurface. Custom spatial TDR probes installed in boreholes made with a percussion drilling instrument were used for soil moisture measurements. Moisture profiles along the probes were estimated with a numerical one-dimensional inversion model and a standard calibration equation. High conductivity water used during all infiltration tests led to the detection of the flow by all techniques. Preferential flow was present throughout the experiment and imaged sufficiently by all methods. Overall, the integrated approach conceals each method’s weaknesses and provides a reliable 3-D view of the subsurface. The results suggest that this approach can be used to monitor the unsaturated zone at even greater depths.