Volumetric Water Content Research Articles

Enhancing streamflow prediction in a mountainous watershed using a convolutional neural network with gridded data.

In this research, we demonstrate the effectiveness of a convolutional neural network (CNN) model, integrated with the ERA5-Land dataset, for accurately simulating daily streamflow in a mountainous watershed. Our methodology harnesses image-based inputs, incorporating spatial distribution maps of key environmental variables, including temperature, snowmelt, snow cover, snow depth, volumetric soil water content, total evaporation, total precipitation, and leaf area index. The proposed CNN architecture, while drawing inspiration from classical designs, is specifically tailored for the task of streamflow prediction. The model's performance, assessed during both the training and testing phases, demonstrates high accuracy, reflected quantitatively in metrics such as RMSE, MAPE, R2, and NSE. Notably, the model exhibits enhanced accuracy in predicting lower flow rates, particularly in autumn and winter, as evidenced by an average RMSE of 2.02 m3/s for flows below 13.8 m3/s. In contrast, the model's precision decreases in high flow rate scenarios, predominantly in spring and early summer. The implementation of forward feature selection (FFS) has further optimized the model, pinpointing total evaporation and volumetric soil water as key parameters, thus enabling a more efficient model with accuracy comparable to the initial, more complex version. This research underscores the practical utility of an image-based approach using CNN models for streamflow prediction. Moreover, the adoption of the freely available and universally accessible ERA5-Land dataset highlights its effectiveness as a valuable and cost-efficient tool for streamflow prediction.

Characterization and prediction of hydraulic properties of traffic-compacted forest soils based on soil information and traffic treatments

Key messageThe hydraulic properties of compacted and rutted soils were evaluated through in-situ infiltration experiments and predicted based on soil texture class and traffic treatments. A significant decrease in saturated soil water content and soil hydraulic conductivity at saturation was observed. The resulting soil hydraulic parameters, when integrated into a soil water transfer model, effectively simulated water dynamics in these impacted forest soils, providing a crucial first step toward developing decision support tools for real-time trafficability. This approach can assist forest managers in minimizing the extent of soil compaction.ContextTo overcome trafficability issues of forest soils induced by heavy logging machinery, planning support tools are needed to determine suitable soil moisture conditions for traffic.AimsThis study aimed to identify the soil properties that differ significantly between undisturbed and compacted soils and to provide several estimation tools to predict the hydraulic properties of compacted soils beneath the skid trails.MethodsFour hundred seventeen water infiltration tests were conducted on 19 forest sites, mostly in North-eastern France, and analysed with the BEST method to estimate the hydraulic properties of the skid trails and undisturbed soils. The hydraulic properties of the skid trails were predicted thanks to linear mixed effect models using a bulk treatment effect, a site effect, or a skid trail degradation score. The predicted hydraulic properties were tested using a water flow model to assess their relevance regarding the prediction of water dynamics in skid trails.ResultsThe compaction effect was only significant for the logarithm of the hydraulic conductivity at saturation (log10(Ksat)) and the soil water content at saturation (θsat). For the skid trails, θsat was reduced by - 0.02 and − 0.11 m3m−3 in the 0 − 10 cm and 15 − 25 cm layers respectively, compared to undisturbed topsoil (0 − 10 cm). log10(Ksat) was reduced by − 0.38 and − 0.85 for skid trails in the 0 − 10 and 15 − 25 cm soil layers respectively, compared to undisturbed topsoil. The use of a pedotransfer function, in replacement of water infiltration tests, and their combination with the same correction coefficients proved to efficiently simulate the difference in water dynamics between skid trails and undisturbed forest soils.ConclusionEstimation of soil hydraulic properties based on in situ water infiltration experiments proved efficient to simulate water dynamics in compacted and rutted forest soils. Yet, further studies are needed to identify the most adapted pedotransfer function to forest soils and to test the generalisation of our findings in different conditions, especially deeply rutted soils (rut depths > 12 cm).

An Analysis of Vertical Infiltration Responses in Unsaturated Soil Columns from Permafrost Regions

Rainfall infiltration affects permafrost-related slope stability by changing the pore water pressure in soil. In this study, the infiltration responses under rainfall conditions were elucidated. The instantaneous profile method and filter paper method were used to obtain the soil–water characteristic curve (SWCC) and hydraulic conductivity function (HCF). During the rainfall infiltration test, the vertical patters of volumetric moisture contents, total hydraulic head or suction and wetting front were recorded. Advancing displacement and rate of the wetting front, the cumulative infiltration, the instantaneous infiltration rate, and the average infiltration rate were determined to comprehensively assess the rainfall infiltration process, along with SWCC and HCF. Additionally, the effects of dry density and runoff on the one-dimensional vertical infiltration process of soil columns were evaluated. The results showed that the variation curve of wetting front displacement versus time obeys a power function relationship. In addition, the infiltration rate–time relationship curve and the unsaturated permeability curve could be roughly divided into three stages, and the SWCC and HCF calculated by volumetric moisture content are more sensitive to changes in dry density than to changes in runoff or hydraulic head height.

Biochar application improves maize yield on the Loess Plateau of China by changing soil pore structure and enhancing root growth

Biochar application emerges as a valuable soil management strategy for enhancing crop yield; however, the mechanisms underlying the relationships between soil and plants remain unclear after biochar application. In this study, soil pore characteristics and maize yield were assessed in a five-year biochar-application experiment on the Loess Plateau of China, including four treatments: Control (no biochar), low-dose biochar application (LB, 3 t ha−1), moderate-dose biochar application (MB, 6 t ha−1), and high-dose biochar application (HB, 9 t ha−1). Root growth traits were evaluated by cultivating maize in intact soil cores collected from field conditions using X-ray computed tomography. Our findings indicate that, compared to the Control, the HB treatment enhanced macroporosity (> 0.1 mm in diameter), porosity of 0.1–0.5 mm pores, and saturated water content, while reducing macropore connectivity and penetration resistance. However, biochar application treatments did not alter the water retention characteristics from field capacity to permanent wilting point or the plant-available water content (PAWC). Furthermore, the mean angle of primary and seminal roots as well as the length and surface area of entire roots increased in the HB treatment, showing a positive correlation with the porosity of 0.1–0.5 mm pores. The mean diameter of primary and seminal roots, leaf fresh and dry weights, and maize yield also increased in the HB treatment compared to the Control. Partial least squares path modeling analysis indicated that biochar application rates positively impacted on root growth and plant productivity through an indirect influence of soil pore size distribution, with 0.1–0.5 mm pores being particularly crucial for facilitating deeper root penetration and root elongation. These findings demonstrate that biochar application primarily augmented 0.1–0.5 mm pores, rather than affecting smaller pores capable of retaining plant-available water or larger macropores, enhancing deeper rooting and root elongation, thus improving plant productivity and crop yield.

Naturally Deposited Charcoal Enhances Water Retention Capacity of Subtropical Forest Soils

Charcoal, a byproduct resulting from incomplete combustion of biomass in fire events, can modify the physical properties of soil due to its high porosity and large surface area. To evaluate the impact of fire-deposited charcoal on soil hydraulic characteristics, soil–charcoal mixtures were analyzed to investigate the effects of different application doses (wt%: 0, 1%, 3%, 5%, 10% and 20%) of charcoal on soil bulk density (BD), porosity (total, capillary, and non-capillary), residual moisture after free drainage (RM), saturated water content (SC), and saturated hydraulic conductivity (Ks) of loamy and sandy soils collected from subtropical forests in south China. The results showed that the impact of charcoal on soil’s physical and hydraulic properties depends on the soil type and the application dose. The incorporation of charcoal significantly decreased the BD of sandy soil (p < 0.001), while a significant decrease in BD in loamy soil was only observed as a result of the higher application doses (10% and 20%) (p < 0.001). Charcoal application doses of 5% or higher led to a significant increase in the total porosity (TP) of sandy soil (p < 0.001) and doses of 3% and 20% resulted in a significant increase in the TP of loamy soil (p < 0.001). The capillary porosity (CP) of both sand and loamy soils significantly increased when charcoal was applied at doses of 3% or higher (p < 0.001). The minimum charcoal application dose that significantly increased the RM in sandy soil was 5%, while for loamy soil, the minimum effective dose was 10%. Charcoal applied at a dose of 3% significantly increased the Ks of sandy soil (p < 0.001), while no significant effect on Ks was observed for loamy soil (p > 0.05). Collectively, our findings suggest that fire-derived charcoal enhances the soil water-retention capacity in subtropical forests, with the effects becoming more pronounced at higher application doses and being particularly notable in sandy soil compared to loamy soil.

Effects of water table depth variations on canopy transpiration from Mongolian pine plantations in arid ecosystems

Groundwater is a critical moisture source for the growth and survival of dryland forests. Spatiotemporal variations in water table depth (WTD) affect soil water availability in the root zone, which influences canopy transpiration (Ec). However, to date, relatively few studies have been conducted on how changes in WTD regulate Ec. We measured the meteorological variables, volumetric water content (VWC), sap flow, and WTD in 2022 and 2023 from April to October in three Mongolian pine plantations located in Mu Us Sandy Land, China. The initial WTDs were 4 m (WTD4), 9 m (WTD9), and 13 m (WTD13), respectively. The mean VWC did not differ significantly between WTD4, WTD9, and WTD13 in the 0–50 cm layer. However, there was a significant decrease in the mean VWC with increasing WTD in the 50–200 cm layer (p < 0.05). Ec decreased significantly with increasing WTD (p < 0.01), with mean daily Ec of 1.25, 0.75, and 0.28 mm day−1 for WTD4, WTD9, and WTD13, respectively. Under soil drought conditions, Ec decreased significantly at WTD9 and WTD4 (p < 0.05). Meanwhile, Ec at WTD13 did not vary significantly under the different soil moisture conditions. At WTD9 and WTD13, Ec was mainly influenced by VWC. Meanwhile, at WTD4, Ec was influenced by the vapor pressure deficit and solar radiation. The diameter at breast height and leaf area index at WTD13 were significantly lower than those at WTD9 and WTD4 (p < 0.05). Although Mongolian pine could alleviate soil drought by adjusting Ec through morphological and physiological adaptations, it still suffered from the adverse effects of reduced water availability at deep WTD sites. This study establishes a basis for predicting the response of forests in semi-arid areas to changes in WTD. It contributes to the optimization of silvicultural design in similar areas and reduces ecological risks from the over-exploitation of groundwater.

Distribution of evapotranspiration components along vertical layers and their controls in dry days of larch plantation in the Liupan Mountains of northwest China

Precise quantification of forest evapotranspiration (ETf) including transpiration from tree (Tt), shrub (Ts), and herb (Th) layers, as well as evaporation from litter (El) and soil (Es) layers, and elucidating their responses to environmental conditions and stand structures are crucial for forest water management in water-limited forests. In this study, we observed the Tt, Ts, Th, El, and Es, reference evapotranspiration (ETo), soil volumetric water content (SWC), litter water content (LWC), leaf area index (LAI) in tree, shrub, and herb layers, and canopy shade (Ksc) from tree layer of the Larix principis-rupprechtii plantation during the dry days from May to October of 2021 and 2022 to elucidate the distribution of evapotranspiration along vertical layers and their environmental and structural determinants. The results indicated that the contributions of Tt, Ts, Th, El, and Es to ETf during the dry days in 2021 (2022) were 42.1 % (44.7 %), 9.2 % (8.1 %), 8.2 % (8.6 %), 15.0 % (13.1 %), and 25.5 % (25.5 %), respectively. Although Tt and Ts demonstrated quadratic relationships with ETo, Th, El, and Es exhibited linear relationships. All ETcs demonstrated a saturated exponential relationship with either SWC or LWC. Furthermore, Tt, Ts, and Th showed a saturated exponential relationship with their respective LAI, whereas El and Es exhibited a cubic relationship with LAI in tree layers. All understory ETcs (Ts, Th, El, and Es) decreased exponentially with the Ksc. The multi-factor models of evapotranspiration component (ETc) from different vertical layers, which coupled the impacts of environmental conditions and vertical structure, were developed, and provided superior accuracy (R2 = 0.78–0.88, NSE = 0.78–0.86, RSME = 0.03–0.16). Such insights deepened the understanding of vertical structural distribution and multi-factor responses of ETcs in forest ecosystems and hold the potential to inform and optimise forest water management strategies.

Examination of the Dry Density Importances in TDR Use and of the ASTM D6780 Standard Based on a New Calibration Procedure

Abstract The use of time domain reflectometry (TDR) to monitor soil water content at either field or laboratory scales is highly common. Regarding the soil dry density, ρd, effect on the TDR application, three main approaches are found in the literature: (1) Ignore the density issue and use calibration to evaluate the water content regardless of the density. This approach is basic, yet common. (2) Include soil density in the calibration equation and use calibration for the water content as a function of both the TDR output and soil density. In this approach, the soil density should be provided from an independent source. (3) Determined ρd from the TDR waveform. In this case both water content and ρd are set from the TDR analysis. In cases where the ρd is not available, in order to use TDR for water content measurement, a decision has to be made whether to address the density issue although it requires resources, or to neglect it. This article reviews this issue and validates the density effect using an efficient TDR calibration methodology. A calibration scheme that controls ρd of the specimen has been developed and applied. The methodology creates continuous calibration curves over a range of volumetric water content from a single test specimen. Based on the results, it has been demonstrated that the soil density has a major effect on the TDR output and it cannot be ignored. In addition, this article presents a unique examination of ASTM D6780, Standard Test Methods for Water Content and Density of Soil In Situ by Time Domain Reflectometry (TDR). The ASTM dry density estimation was found to provide reliable results; however, the gravimetric water contents estimation gives unsatisfactory results, especially for low densities.

Peanut production in saline-alkali land of Yellow River Delta: influence of spatiotemporal changes of meteorological conditions and soil properties

BackgroundThis study clarified the synergistic relationship among annual changes to specify the changes in agro-meteorological factors, soil characteristics and peanut growth in saline-alkali land near the estuary of the Yellow River Delta. We aimed to find the key factors affecting peanut production to optimize and regulate peanut planting mode in saline alkali soil.ResultsThe daily average temperature from early May to late September in Lijin and Kenli was above 24 °C, with 470–600 mm of precipitation. The sunshine duration was 7.9 h/day and 7.3 h/day and the accumulated temperature was 3742 °C and 3809 °C, in Lijin and Kenli, respectively. Agro-meteorological conditions were suitable for peanut growth and development with the consistent main developmental period in the two experiment regions. The best sowing period was when the soil temperature stabilized above 18 °C in early May, and the best harvest was in mid-September. The soil volumetric water content in Lijin concentrated among 25–40%. Salt was mainly distributed in the 40–60 cm soil layers, and increased rapidly to 2.5 g kg− 1 in 0–20 cm cultivation layer in mid-May due to lack of precipitation. In Kenli experiment region, the soil volumetric water content ranged from 10 to 35%. Soil salinity was mainly distributed in the 20 cm soil layer, and the changes in salinity was little affected by precipitation. From mid-July to mid-August, the effective accumulated temperature of 5 cm soil layer was above 520 °C in both regions, which could ensure the normal pod development. The slow dynamic growth of kernel, high unfilled pod rate (26.99%) and low shelling rate (66.0%) might be the main reasons for low peanut yield in Lijin.ConclusionSoil salinity was the main factor affecting pod development and yield. It was also a key point in optimizing the peanut planting mode in the saline alkali land of the Yellow River Delta.

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.

Dynamic Characteristics of Soil Respiration in Park Green Spaces in Qingdao City

Urban green spaces play an essential role in maintaining the carbon cycle and mitigating climate change in urban ecosystems. In order to gain more carbon sinks from urban green ecosystems, it is essential to determine the carbon sequestration statuses and soil respiration rates of dominant green spaces, especially park green spaces. However, in comparison to natural ecosystems, the dynamic characteristics of soil respiration in artificial park green spaces remain unclear. This study investigated the soil respiration rates for three forest communities (dominated by Prunus serrulata var. lannesiana, Cedrus deodara, Ginkgo biloba, respectively), a shrub community (dominated by Aucuba japonica var. variegata) and a lawn community (dominated by Poa pratensis) in the Qingdao Olympic Sculpture and Culture Park. We used the CRIAS-3 portable photosynthesis system in combination with the SRC-1 soil respiration chamber to measure the soil respiration rate from July 2022 to June 2023 and analyzed the dynamic variations in the soil respiration rate for these specific plant communities. Our results showed that the diurnal variation in soil respiration presented a unimodal curve for the five plant communities, and it peaked at midday or in the early afternoon. They also exhibited a significant seasonal difference in the soil respiration rate, which was characterized by higher rates in summer and lower rates in winter. The lawn community exhibited significantly higher soil respiration rates compared to the woody plant community. The mean annual soil respiration rate (RS) was, respectively, 2.88 ± 0.49 µmol·m−2·s−1, 1.94 ± 0.31 µmol·m−2·s−1, 1.43 ± 0.21 µmol·m−2·s−1, 1.24 ± 0.14 µmol·m−2·s−1 and 1.05 ± 0.11 µmol·m−2·s−1 for the lawn community, Ginkgo biloba community, Prunus serrulata var. lannesiana community, shrub community and Cedrus deodara community. The soil temperature at a 10 cm depth (T10) accounted for 67.39–86.76% of the variation in the soil respiration rate, while the soil volumetric water content at a 5 cm depth (W5) accounted for 9.29–44.01% of the variation for the five plant communities. The explained variance for both T10 and W5 ranged from 67.8% to 87.6% for the five plant communities. The Q10 values for the five different communities ranged from 1.97 to 2.75. Based on these findings, this paper concludes that the factors influencing the soil respiration process in urban green spaces are more complicated in comparison to natural ecosystems, and it is essential to comprehensively analyze these driving factors and key controlling factors of soil respiration across urban green spaces in future studies.

Laboratory assessment of real-time water infiltration measurement in SPV200 bentonite using TDR for high-level radioactive waste disposal design

The water content movement within compacted bentonite holds significance in appraising the thermo-hydro-mechanical (THM) performance of the engineered barrier system (EBS) for the disposal of high-level radioactive waste (HLRW), but it demands considerable time to monitor water infiltration in a representative model. Therefore, employing bentonite with varying water content is favored in the laboratory. A comprehensive 3-D phase diagram was established as a benchmark for dielectric constant, temperature, and volumetric water content (VWC) using Time Domain Reflectometry (TDR). SPV200 bentonite at different densities (1400, 1500, and 1600 kg/m3) and temperatures (25, 40, and 60 °C) were prepared with TDR, achieving real-time VWC measurements within a 6 % relative error. Furthermore, numerical outcomes exhibited trends like the TDR tests. Comparation between the simulation and test data is within a relative error ±10 %, which showcased the potential for efficient and effective estimations at a reduced cost for HLRW disposal design.

Unirrigated extensive green roofs in humid subtropics – Plant selection and substrate design for low maintenance and climate resilience

Low-maintenance extensive green roofs hold the potential for adapting to climate change, but there is a limited understanding of their effective design in humid subtropical regions. In this study, we aimed to investigate low-maintenance green roof ecosystems capable of thriving under heat stress. Plant growth performance was evaluated in the second year of an experimental green roof in southwest China. This green roof was unirrigated and involved two plant communities (forbs and grasses) cultivated in four substrate types. Metrics of growth performance, including plant survival, green coverage, visual appearance, leaf stomatal conductance, and substrate volumetric water content were used to analyze the impact of substrate on plant growth. ESP substrate, comprising 30 %v (percent of volume) expanded shale, 25 %v perlite, 25 %v vermiculite, and 20 %v compost, featured the highest porosity and optimal growth of forbs, achieving an average green coverage of 66.76 % and average visual appearance value of 3.56 throughout the monitoring period. The high total porosity of this substrate could be critical in facilitating root development and drainage. Moreover, during the summer’s heat and drought, while substrate water content is not a limiting factor for the survival of some established forbs, it remains crucial for the growth of grasses. Additionally, the same substrate impacts different plant communities variably. Four particularly well-performing forb species, namely Lycoris chinensis, Tradescantia pallida, Liriope muscari, and Salvia farinacea, demonstrated survival rates closed to 100 % and an average visual appearance value exceeding 3 was noted, and thus, they are recommended as plant candidates for unirrigated green roofs. The distinguished performance of these species is attributable to their adaptation to heat and drought through specific physiological traits. Our findings provide insights into vegetation selection and substrate optimization for the successful establishment of extensive green roofs that could be feasibly developed without irrigation in the humid subtropical regions.

No-Till and Crop Rotation Are Promising Practices to Enhance Soil Health in Cotton-Producing Semiarid Regions: Insights from Citizen Science

This on-farm study was conducted to assess the impact of six prevalent crop management practices adopted by growers in West Texas on various indicators of soil health. This study is a part of a citizen science project, where we collaborated with cotton growers who helped with standardized sample and data collection from 2017 to 2022. This project aimed to identify soil management practices that increase carbon sequestration, enhance biological activities, and improve overall soil health. We monitored soil moisture, soil organic matter (SOM), inorganic nitrogen (NH4+-N and NO3−-N) and other exchangeable nutrients, and soil microbial abundances as obtained via fatty acid methyl ester (FAME) in 85 fields, incorporating different management practices during the cotton growing season. In our study, volumetric moisture content (VWC) was increased by no-till, irrigation, and crop rotation, but the addition of residue decreased VWC. No-till, irrigation, and crop rotation increased SOM, but a cover crop decreased SOM. No-till and residue retention also increased microbial biomass carbon (MBC). Tillage, irrigation, and crop rotation influenced the abundance of the main microbial groups, including bacterial, fungi, and arbuscular mycorrhizal fungi (AMF). Additionally, water content, SOM, and microbial abundances are correlated with clay percentage. Our results indicate that no-till and crop rotation are the two most crucial soil management approaches for sustainable soil health. As such, implementing both no-till and crop rotation in the cropping systems has the most promising potential to increase the soil resilience in dryland cotton production in semiarid regions, thereby helping growers to maintain cotton production.

Exploring Soil–Water Characteristic Curves in Transitional Oil Sands Tailings

Soil–water characteristics curves (SWCC) have proved useful in estimating parameters used in modeling unsaturated geotechnical properties of soils including permeability and strength. Either saturation, gravimetric, and instantaneous and initial volumetric water content designation can be used to develop SWCCs. Studies have shown that any of the designations will give good estimates for soils that do not undergo volume change with suction change whereas, for soils that undergo substantial volume change, only saturation and instantaneous volumetric water content designation obtained by incorporating shrinkage curves can give correct estimates. Transition oil sands tailings have fines content that cannot be categorized as sandy or fine materials, and research on volume change with suction change in these materials is limited. In this study, HyProps, Tempe cells, and a chilled-mirror water potential meter were used to measure suction and corresponding water contents for samples that were prepared by mixing coarse sand and Fluid Tailing by ratios that mimic transition zone tailings. Shrinkage tests were also performed to observe the extent of volume change with suction increase. Air Entry Values (AEV) estimated from SWCCs based on gravimetric water content were found to be lower than those estimated from saturation-based SWCCs due to substantial volume changes in these materials with suction increase. The use of saturation water content designation is recommended in estimating AEV for transitional oil sands tailings. This is useful information in predicting the long term unsaturated geotechnical behavior of these materials for environmental management and safety purposes.

How the vertical gradient of light in the understorey and water seasonality affect leaf traits of Vanilla phaeantha (Orchidaceae), a crassulacean acid metabolism (CAM) hemiephyte.

Structural and physiological leaf traits and their plasticity were compared in the hemiepiphyte Vanilla phaeantha . This species grows along a phorophyte reaching different understorey positions and exhibiting diverse responses to environment changes. We analysed three height strata above the ground, establishing a light gradient, and considering seasonal water fluctuations. The upper leaves had higher area and mass and were less pigmented. The dry season induced a reduction of approximately 2h of stomatal opening over the diel 24h crassulacean acid metabolism (CAM) cycle in the leaves at all understorey positions. The leaves more exposed to sunlight were larger with higher titratable acidity during the rainy season, while the leaves near the ground maintained the same rates of stomatal conductance and nocturnal acidification between seasons, with lowest values of carbon isotopes in the rainy season. Our research showed that some structural leaf traits (such as specific leaf mass, biomass, and saturated water content) are sensitive to variation in understorey position. In contrast, other physiological traits (stomatal conductance, transpiration, and fluorescence parameters) are more sensitive to seasonal variations. The results are a novelty in assessing the variation of CAM along the same plant in a height gradient and under field conditions.

Calibration of TEROS 10 and TEROS 12 electromagnetic soil moisture sensors

AbstractThe TEROS 10 and TEROS 12 are widely used capacitance‐based sensors for measuring volumetric water content (), but few systematic studies of the measurement volume and accuracy of these sensors have been published. The objectives of this study were to (1) calibrate and validate these sensors in mineral soils, (2) determine the sensing volume of each sensor, and (3) evaluate the temperature sensitivity of the sensors. Both sensors were calibrated in the laboratory using columns of packed soil at multiple moisture levels from air‐dry to near saturation. Sensors were validated using additional laboratory and field measurements. The sensing volume was determined by quantifying the response of raw sensor outputs while increasing the level of oven‐dry sand, moist sand (0.100 cm3 cm−3), or water around the sensor in the radial and axial directions. Temperature sensitivity was tested in controlled conditions over a range of temperatures from 2°C to 40°C using encapsulated sensors in packed soil columns at constant . Linear calibration models resulted in mean absolute error ≤0.030 cm3 cm−3 for both sensors during laboratory and field validations. In moist sand, the sensing volume contributing 95% of the maximum sensor response was 439 cm3 for the TEROS 10 and 423 cm3 for the TEROS 12. Both sensors exhibited changes in of ≤0.02 cm3 cm−3 when subjected to a temperature change from 2°C to 40°C. For the soils considered here, both sensors demonstrated accuracy that is likely sufficient for a variety of agricultural and hydrological applications.

Multifactorial Analysis of the Effect of Applied Gamma-Polyglutamic Acid on Soil Infiltration Characteristics

To investigate the mechanism and influence of applying gamma-polyglutamic acid (γ-PGA) on soil water infiltration, laboratory experiments and numerical simulations were conducted using Hydrus-1D. These studies assessed the impact of various application rates of γ-PGA on soil water characteristic parameters. Orthogonal simulation experiments on soil bulk density, γ-PGA application rates, and burial depths were performed utilizing predefined soil water characteristic values (twelve groups: nine groups of numerical simulation experiments and three groups of laboratory verification tests), and the soil infiltration characteristics were analyzed. Concurrently, an empirical model was developed to elucidate the relationships between the empirical model parameters and influencing factors, as well as to examine the sensitivity of these factors to changes in soil infiltration rate. The relationship between cumulative infiltration and the distance of wetting front movement, based on the water balance equation, was refined. The results indicated that γ-PGA significantly affected soil water characteristic parameters, where the saturated water content and the reciprocal of soil intake suction increased with rising γ-PGA applications (p &lt; 0.01), while the saturated hydraulic conductivity and the parameter n decreased (p &lt; 0.01), with no notable changes in the retained water content (p &gt; 0.05). The trend in cumulative infiltration influenced by various factors could be modeled by a capacitive charging model function, which yielded a superior fit. A negative correlation existed between the sensitivity index and all the influencing factors (p &lt; 0.05), with the order of influence being soil bulk density, γ-PGA application rate, and γ-PGA burial depth, respectively. Utilizing the modified water balance equation, the ratio of cumulative infiltration to wetting front migration distance corresponded more closely with a power function. These findings provide a theoretical foundation for further studies on the effects of γ-PGA on crop growth characteristics in fields and the optimization of γ-PGA technical element combinations.

MVD and LWC prediction in icing environment based on genetic algorithm optimized neural network

Mean volumetric diameter (MVD) and liquid water content (LWC) are two important meteorological parameters that affect aircraft icing, but they are difficult to measure accurately in practice. If these two parameters can be obtained in real time and accurately, it can provide some guidance for ice accumulation prediction and the establishment of aircraft airworthiness certification standards. This paper proposes a prediction model for icing meteorological parameters based on genetic algorithm optimized neural network. With ice thickness and icing rate of different measurement point combinations, ambient temperature, flight speed and wing angle of attack as input parameters, and icing meteorological parameters MVD and LWC as output parameters, the genetic algorithm optimized icing meteorological parameter prediction model is constructed, and the prediction model is used to predict the icing meteorological parameters of the numerical calculation test group data and the icing wind tunnel experimental data. The results show that the prediction model based on genetic algorithm optimized Elman neural network predicts the icing meteorological parameters of the test group within the relative error of the test group 10%, and the relative error of the experimental data is within the relative error of the experimental data 20%. This method has certain feasibility.

Spatial variability and uncertainty associated with soil moisture content using INLA-SPDE combined with PyMC3 probability programming

Spatial variability and uncertainty associated with soil volumetric moisture content (SVMC) is crucial in moisture prediction accuracy, this paper sets out to address this point of SVMC by developing data-driven model. Grid samples of SVMC covered approximately a 3-ha field during the jointing growth stage of winter wheat, and SVMC were measured by Time Domain Reflectometry (TDR), located in North China Plain, China. Bayesian inference was performed to explore spatial heterogeneity, robustness, transparency, interpretability and uncertainty related to SVMC using python-based PyMC3 combined with Integrated Nested Laplace Approximation with the Stochastic Partial Differential Equation (INLA-SPDE) model. The results showed that the prediction surface of SVMC, the lower and upper limits of 95% credible intervals quantified uncertainty associated with SVMC, cauchy prior of the flexibility and adaptability to obtain state-of-the-art predictive performance is more robust than gaussian prior for SVMC prediction, the transparency and interpretability of SVMC prediction model were revealed by MCMC (Markov-Chain Monte-Carlo) trace plots, KDE (Kernel density estimates), and rank plots. The uncertainty associated with SVMC can explicitly be described using the highest-posterior density interval, the prediction lower and upper limits.