Global convergence in terrestrial gross primary production response to atmospheric vapor pressure deficit

Atmospheric vapor pressure deficit (VPD) increases with climate warming and may limit plant growth. However, gross primary production (GPP) responses to VPD remain a mystery, offering a significant source of uncertainty in the estimation of global terrestrial ecosystems carbon dynamics. In this study, in-situ measurements, satellite-derived data, and Earth System Models (ESMs) simulations were analysed to show that the GPP of most ecosystems has a similar threshold in response to VPD: first increasing and then declining. When VPD exceeds these thresholds, atmospheric drought stress reduces soil moisture and stomatal conductance, thereby decreasing the productivity of terrestrial ecosystems. Current ESMs underscore CO2 fertilization effects but predict significant GPP decline in low-latitude ecosystems when VPD exceeds the thresholds. These results emphasize the impacts of climate warming on VPD and propose limitations to future ecosystems productivity caused by increased atmospheric water demand. Incorporating VPD, soil moisture, and canopy conductance interactions into ESMs enhances the prediction of terrestrial ecosystem responses to climate change.

Different responses of oxygen and hydrogen isotopes in leaf and tree-ring organic matter to lethal soil drought

The oxygen and hydrogen isotopic composition (δ18O, δ2H) of plant tissues are key tools for the reconstruction of hydrological and plant physiological processes and may therefore be used to disentangle the reasons for tree mortality. However, how both elements respond to soil drought conditions before death has rarely been investigated. To test this, we performed a greenhouse study and determined predisposing fertilization and lethal soil drought effects on δ18O and δ2H values of organic matter in leaves and tree rings of living and dead saplings of five European tree species. For mechanistic insights, we additionally measured isotopic (i.e. δ18O and δ2H values of leaf and twig water), physiological (i.e. leaf water potential and gas-exchange) and metabolic traits (i.e. leaf and stem non-structural carbohydrate concentration, carbon-to-nitrogen ratios). Across all species, lethal soil drought generally caused a homogenous 2H-enrichment in leaf and tree-ring organic matter, but a low and heterogenous δ18O response in the same tissues. Unlike δ18O values, δ2H values of tree-ring organic matter were correlated with those of leaf and twig water and with plant physiological traits across treatments and species. The 2H-enrichment in plant organic matter also went along with a decrease in stem starch concentrations under soil drought compared with well-watered conditions. In contrast, the predisposing fertilization had generally no significant effect on any tested isotopic, physiological and metabolic traits. We propose that the 2H-enrichment in the dead trees is related to (i) the plant water isotopic composition, (ii) metabolic processes shaping leaf non-structural carbohydrates, (iii) the use of carbon reserves for growth and (iv) species-specific physiological adjustments. The homogenous stress imprint on δ2H but not on δ18O suggests that the former could be used as a proxy to reconstruct soil droughts and underlying processes of tree mortality.

Predicting Soil Organic Matter, Available Nitrogen, Available Phosphorus and Available Potassium in a Black Soil Using a Nearby Hyperspectral Sensor System

Black soils, which play an important role in agricultural production and food security, are well known for their relatively high content of soil organic matter (SOM). SOM has a significant impact on the sustainability of farmland and provides nutrients for plants. Hyperspectral imaging (HSI) in the visible and near-infrared region has shown the potential to detect soil nutrient levels in the laboratory. However, using portable spectrometers directly in the field remains challenging due to variations in soil moisture (SM). The current study used spectral data captured by a handheld spectrometer outdoors to predict SOM, available nitrogen (AN), available phosphorus (AP) and available potassium (AK) with different SM levels. Partial least squares regression (PLSR) models were established to compare the predictive performance of air-dried soil samples with SMs around 20%, 30% and 40%. The results showed that the model established using dry sample data had the best performance (RMSE = 4.47 g/kg) for the prediction of SOM, followed by AN (RMSE = 20.92 mg/kg) and AK (RMSE = 22.67 mg/kg). The AP was better predicted by the model based on 30% SM (RMSE = 8.04 mg/kg). In general, model performance deteriorated with an increase in SM, except for the case of AP. Feature wavelengths for predicting four kinds of soil properties were recommended based on variable importance in the projection (VIP), which offered useful guidance for the development of portable hyperspectral sensors based on discrete wavebands to reduce cost and save time for on-site data collection.

Nitrogen addition alleviates the adverse effects of drought on plant productivity in a temperate steppe

Drought and nitrogen enrichment could profoundly affect the productivity of semiarid ecosystems. However, how ecosystem productivity will respond to different drought scenarios, especially with a concurrent increase in nitrogen availability, is still poorly understood. Using data from a 4-year field experiment conducted in a semiarid temperate steppe, we explored the responses of aboveground net primary productivity (ANPP) to different drought scenarios and nitrogen addition, and the underlying mechanisms linking soil properties, plant species richness, functional diversity (community-weighted means of plant traits, functional dispersion) and phylogenetic diversity (net relatedness index) to ANPP. Our results showed that completely excluding precipitation in June (1-month intense drought) and reducing half the precipitation amount from June to August (season-long chronic drought) both significantly reduced ANPP, with the latter having a more negative impact on ANPP. However, reducing half of the precipitation frequency from June to August (precipitation redistribution) had no significant effect on ANPP. Nitrogen addition increased ANPP irrespective of drought scenarios. ANPP was primarily determined by soil moisture and nitrogen availability by regulating the community-weighted means of plant height, rather than other aspects of plant diversity. Our findings suggest that precipitation amount is more important than precipitation redistribution in influencing the productivity of temperate steppe, and nitrogen supply could alleviate the adverse impacts of drought on grassland productivity. Our study advances the mechanistic understanding of how the temperate grassland responds to drought stress, and implies that management strategies to protect tall species in the community would be beneficial for maintaining the productivity and carbon sequestration of grassland ecosystems under climate drought.

Strong plastic responses in aerenchyma formation in F1 hybrids of Imperata cylindrica under different soil moisture conditions

Hybrids can express traits plastically, enabling them to occupy environments that differ from parental environments. However, there is insufficient evidence demonstrating how phenotypic plasticity in specific traits mediates hybrid performance. Two parental ecotypes of Imperata cylindrica produce F1 hybrids. The E-type in wet habitats has larger internal aerenchyma than the C-type in dry habitats. This study evaluated relationships between habitat utilisation, aerenchyma plasticity, and growth of I. cylindrica accessions. We hypothesize that plasticity in expressing parental traits explains hybrid establishment in habitats with various soil moisture conditions. Aerenchyma formation was examined in the leaf midribs, rhizomes and roots of two parental ecotypes and their F1 hybrids in their natural habitats. In common garden experiments, we examined plastic aerenchyma formation in leaf midribs, rhizomes and roots of natural and artificial F1 hybrids and parental ecotypes and quantified vegetative growth performance. In the natural habitats where soil moisture content varied widely, the F1 hybrids showed larger variation in aerenchyma formation in rhizomes than their parental ecotypes. In the common garden experiments, F1 hybrids showed high plasticity of aerenchyma formation in rhizomes, and their growth was similar to that of C-type and E-type under drained and flooded conditions, respectively. The results demonstrate that F1 hybrids of I. cylindrica exhibit plasticity in aerenchyma development in response to varying local soil moisture content. This characteristic allows the hybrids to thrive in diverse soil moisture conditions.

Integration of Sentinel-1A Radar and SMAP Radiometer for Soil Moisture Retrieval over Vegetated Areas

NASA's Soil Moisture Active Passive (SMAP) was originally designed to combine high-resolution active (radar) and coarse-resolution but highly sensitive passive (radiometer) L-band observations to achieve unprecedented spatial resolution and accuracy for soil moisture retrievals. However, shortly after SMAP was put into orbit, the radar component failed, and the high-resolution capability was lost. In this paper, the integration of an alternative radar sensor with the SMAP radiometer is proposed to enhance soil moisture retrieval capabilities over vegetated areas in the absence of the original high-resolution radar in the SMAP mission. ESA's Sentinel-1A C-band radar was used in this study to enhance the spatial resolution of the SMAP L-band radiometer and to improve soil moisture retrieval accuracy. To achieve this purpose, we downscaled the 9 km radiometer data of the SMAP to 1 km utilizing the Smoothing Filter-based Intensity Modulation (SFIM) method. An Artificial Neural Network (ANN) was then trained to exploit the synergy between the Sentinel-1A radar, SMAP radiometer, and the in situ-measured soil moisture. An analysis of the data obtained for a plant growing season over the Mississippi Delta showed that the VH-polarized Sentinel-1A radar data can yield a coefficient of correlation of 0.81 and serve as a complimentary source to the SMAP radiometer for more accurate and enhanced soil moisture prediction over agricultural fields.

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

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

Optimizing Agroecological Measures for Climate-Resilient Olive Farming in the Mediterranean

In order to evaluate the potential of climate change mitigation measures on soil physiochemical properties, an experiment based on the application of five agroecological practices such as the addition of composted olive-mill wastes, recycling pruning residue, cover crops, organic insect manure, and reduced soil tillage, solely or combined, was conducted over two years (2020 to 2022) in a 48-year-old olive plantation. The results showed significant increases in soil water content during the spring and summer periods for the combined treatment (compost + pruning residue + cover crops) (ALL) compared to the control (CONT) by 41.6% and 51.3%, respectively. Also, ALL expressed the highest soil organic matter (4.33%) compared to CONT (1.65%) at 0-10 cm soil depth. When comparing soil nutrient contents, ALL (37.86 mg kg-1) and cover crops (COVER) (37.21 mg kg-1) had significant increases in soil nitrate compared to CONT (22.90 mg kg-1), the lowest one. Concerning exchangeable potassium, ALL (169.7 mg kg-1) and compost (COMP) (168.7 mg kg-1) were higher than CONT (117.93 mg kg-1) at the 0-10 cm soil depth and had, respectively an increase of 100.9% and 60.7% in calcium content compared to CONT. Over the experimental period, the implementation of the five agroecological management practices resulted in enhanced soil fertility. In a long-term Mediterranean context, this study suggests that these sustainable practices would significantly benefit farmers by improving agroecosystem services, reducing reliance on synthetic fertilizers, optimizing irrigation water use, and ultimately contributing towards a circular economy.

Assessing corn recovery from early season nutrient stress under different soil moisture regimes

Corn (Zea mays) biomass accumulation and nutrient uptake by the six-leaf collar (V6) growth stage are low, and therefore, synchronizing nutrient supply with crop demand could potentially minimize nutrient loss and improve nutrient use efficiency. Knowledge of corn's response to nutrient stress in the early growth stages could inform such nutrient management. Field studies were conducted to assess corn recovery from when no fertilizer application is made until the V6 growth stage, and thereafter, applying fertilizer rates as those in non-stressed conditions. The early season nutrient stress and non-stress conditions received the same amount of nutrients. As the availability of nutrients for plant uptake is largely dependent on soil moisture, corn recovery from the early season nutrient stress was assessed under different soil moisture regimes induced via irrigation scheduling at 50% and 80% field capacity under overhead and subsurface drip irrigation (SSDI) systems. Peanut (Arachis hypogaea) was the previous crop under all conditions, and the fields were under cereal rye (Secale cereale) cover crop prior to planting corn. At the V6 growth stage, the nutrient concentrations of the early season-stressed crops, except for copper, were above the minimum threshold of sufficiency ranges reported for corn. However, the crops showed poor growth, with biomass accumulation being reduced by over 50% compared to non-stressed crops. Also, the uptake of all nutrients was significantly lower under the early season nutrient stress conditions. The recovery of corn from the early season nutrient stress was low. Compared to non-stress conditions, the early season nutrient stress caused 1.58 Mg ha-1 to 3.4 Mg ha-1 yield reduction. The percent yield reduction under the SSDI system was 37.6-38.2% and that under the overhead irrigation system was 11.7-13%. The high yield reduction from the early season nutrient stress under the SSDI system was because of water stress conditions in the topsoil soil layer. The findings of the study suggest ample nutrient supply in the early season growth stage is critical for corn production, and thus, further studies are recommended to determine the optimum nutrient supply for corn at the initial growth stages.

Soil respiration and controls in warmer winter: A snow manipulation study in postfire and undisturbed black pine forests

Climate change impacts drive warmer winters, reduced snowfall, and forest fires. In 2020, a wildfire scorched about 1508 hectares of black pine (Pinus nigra Arnold) forests in Türkiye. Whether the combined effects of lack of snow and forest fires significantly alter winter soil respiration (Rs) and soil temperature remains poorly understood. A field experiment was conducted in the postfire and undisturbed black pine forests during the winter to quantify Rs rates as affected by lack of snow and forest fire. We applied four treatments: snow-exclusion postfire (SEPF), snow postfire (SPF), snow-exclusion-undisturbed forest (SEUF), and snow undisturbed forest (SUF). The SEPF exhibited the significantly lowest mean Rs rates (0.71 μmol m-2 s-1) compared to the SPF (1.02 μmol m-2 s-1), SEUF (1.44 μmol m-2 s-1), and SUF (1.48 μmol m-2 s-1). The Rs also showed significant variations with time (p < .0001). However, treatments and time revealed no statistically significant interaction effects (p = .6801). Total winter Rs (January-March) ranged from 4.47 to 4.59 Mt CO2 ha-1 in the undisturbed forest and 2.20 to 3.16 Mt CO2 ha-2 in the postfire site. The Rs showed a significantly positive relationship (p < .0001) with the soil (0.59) and air (0.46) temperatures and a significantly negative relationship (p = .0017) with the soil moisture (-0.20) at the 5 cm depth. In contrast, the Rs indicated a negative but not statistically significant relationship (p = .0932) with the soil moisture (-0.16) at the 10 cm soil depth. The combined effects of lack of snow and forest fire significantly decreased Rs, thus conserving the soil's organic carbon stocks and reducing the CO2 contribution to the atmosphere. In contrast, a warmer winter significantly increased Rs rates in the undisturbed forest, suggesting an acceleration of soil organic carbon losses and providing positive feedback to climate change.

Cascading effects of earthworm invasion increase graminoid density and rodent grazing intensities

Human-mediated dispersal of non-native earthworms can cause substantial changes to the functioning and composition of ecosystems previously earthworm-free. Some of these earthworm species have the potential to "geoengineer" soils and increase plant nitrogen (N) uptake. Yet the possible consequences of increased plant N concentrations on rodent grazing remains poorly understood. In this study, we present findings from a common garden experiment with two tundra communities, meadow (forb dominated) and heath (shrub dominated), half of them subjected to 4 years of earthworm presence (Lumbricus spp. and Aporrectodea spp.). Within four summers, our earthworm treatment changed plant community composition by increasing graminoid density by, on average, 94% in the heath vegetation and by 49% in the meadow. Rodent winter grazing was more intense on plants growing in soils with earthworms, an effect that coincided with higher N concentrations in plants, indicating a higher palatability. Even though earthworms reduced soil moisture, plant community productivity, as indicated by vegetation greenness (normalized difference vegetation index), was not negatively impacted. We conclude that earthworm-induced changes in plant composition and trophic interactions may fundamentally alter the functioning of tundra ecosystems.

Trajectories and tipping points of piñon-juniper woodlands after fire and thinning

Piñon-juniper (PJ) woodlands are a dominant community type across the Intermountain West, comprising over a million acres and experiencing critical effects from increasing wildfire. Large PJ mortality and regeneration failure after catastrophic wildfire have elevated concerns about the long-term viability of PJ woodlands. Thinning is increasingly used to safeguard forests from fire and in an attempt to increase climate resilience. We have only a limited understanding of how fire and thinning will affect the structure and function of PJ ecosystems. Here, we examined vegetation structure, microclimate conditions, and PJ regeneration dynamics following ~20 years post-fire and thinning treatments. We found that burned areas had undergone a state shift that did not show signs of returning to their previous state. This shift was characterized by (1) distinct plant community composition dominated by grasses; (2) a lack of PJ recruitment; (3) a decrease in the sizes of interspaces in between plants; (4) lower abundance of late successional biological soil crusts; (5) lower mean and minimum daily soil moisture values; (6) lower minimum daily vapor pressure deficit; and (7) higher photosynthetically active radiation. Thinning created distinct plant communities and served as an intermediate between intact and burned communities. More intensive thinning decreased PJ recruitment and late successional biocrust cover. Our results indicate that fire has the potential to create drier and more stressful microsite conditions, and that, in the absence of active management following fire, there may be shifts to persistent ecological states dominated by grasses. Additionally, more intensive thinning had a larger impact on community structure and recruitment than less intensive thinning, suggesting that careful consideration of goals could help avoid unintended consequences. While our results indicate the vulnerability of PJ ecosystems to fire, they also highlight management actions that could be adapted to create conditions that promote PJ re-establishment.

Role of atmospheric resonance and land-atmosphere feedbacks as a precursor to the June 2021 Pacific Northwest Heat Dome event

We demonstrate an indirect, rather than direct, role of quasi-resonant amplification of planetary waves in a summer weather extreme. We find that there was an interplay between a persistent, amplified large-scale atmospheric circulation state and soil moisture feedbacks as a precursor for the June 2021 Pacific Northwest "Heat Dome" event. An extended resonant planetary wave configuration prior to the event created an antecedent soil moisture deficit that amplified lower atmospheric warming through strong nonlinear soil moisture feedbacks, favoring this unprecedented heat event.

Drought thresholds that impact vegetation reveal the divergent responses of vegetation growth to drought across China

Identifying droughts and accurately evaluating drought impacts on vegetation growth are crucial to understanding the terrestrial carbon balance across China. However, few studies have identified the critical drought thresholds that impact China's vegetation growth, leading to large uncertainty in assessing the ecological consequences of droughts. In this study, we utilize gridded surface soil moisture data and satellite-observed normalized difference vegetation index (NDVI) to assess vegetation response to droughts in China during 2001-2018. Based on the nonlinear relationship between changing drought stress and the coincident anomalies of NDVI during the growing season, we derive the spatial patterns of satellite-based drought thresholds (T SM ) that impact vegetation growth in China via a framework for detecting drought thresholds combining the methods of feature extraction, coincidence analysis, and piecewise linear regression. The T SM values represent percentile-based drought threshold levels, with smaller T SM values corresponding to more negative anomalies of soil moisture. On average, T SM is at the 8.7th percentile and detectable in 64.4% of China's vegetated lands, with lower values in North China and Jianghan Plain and higher values in the Inner Mongolia Plateau. Furthermore, T SM for forests is commonly lower than that for grasslands. We also find that agricultural irrigation modifies the drought thresholds for croplands in the Sichuan Basin. For future projections, Earth System Models predict that more regions in China will face an increasing risk for ecological drought, and the Hexi Corridor-Hetao Plain and Shandong Peninsula will become hotspots of ecological drought. This study has important implications for accurately evaluating the impacts of drought on vegetation growth in China and provides a scientific reference for the effective ecomanagement of China's terrestrial ecosystems.

Basin Scale Soil Moisture Estimation with Grid SWAT and LESTKF Based on WSN

This research utilized in situ soil moisture observations in a coupled grid Soil and Water Assessment Tool (SWAT) and Parallel Data Assimilation Framework (PDAF) data assimilation system, resulting in significant enhancements in soil moisture estimation. By incorporating Wireless Sensor Network (WSN) data (WATERNET), the method captured and integrated local soil moisture characteristics, thereby improving regional model state estimations. The use of varying observation search radii with the Local Error-subspace Transform Kalman Filter (LESTKF) resulted in improved spatial and temporal assimilation performance, while also considering the impact of observation data uncertainties. The best performance (improvement of 0.006 m3/m3) of LESTKF was achieved with a 20 km observation search radii and 0.01 m3/m3 observation standard error. This study assimilated wireless sensor network data into a distributed model, presenting a departure from traditional methods. The high accuracy and resolution capabilities of WATERNET's regional soil moisture observations were crucial, and its provision of multi-layered soil temperature and moisture observations presented new opportunities for integration into the data assimilation framework, further enhancing hydrological state estimations. This study's implications are broad and relevant to regional-scale water resource research and management, particularly for freshwater resource scheduling at small basin scales.

Vapour pressure deficit was not a primary limiting factor for gas exchange in an irrigated, mature dryland Aleppo pine forest

Climate change is often associated with increasing vapour pressure deficit (VPD) and changes in soil moisture (SM). While atmospheric and soil drying often co-occur, their differential effects on plant functioning and productivity remain uncertain. We investigated the divergent effects and underlying mechanisms of soil and atmospheric drought based on continuous, in situ measurements of branch gas exchange with automated chambers in a mature semiarid Aleppo pine forest. We investigated the response of control trees exposed to combined soil-atmospheric drought (low SM, high VPD) during the rainless Mediterranean summer and that of trees experimentally unconstrained by soil dryness (high SM; using supplementary dry season water supply) but subjected to atmospheric drought (high VPD). During the seasonal dry period, branch conductance (gbr ), transpiration rate (E) and net photosynthesis (Anet ) decreased in low-SM trees but greatly increased in high-SM trees. The response of E and gbr to the massive rise in VPD (to 7 kPa) was negative in low-SM trees and positive in high-SM trees. These observations were consistent with predictions based on a simple plant hydraulic model showing the importance of plant water potential in the gbr and E response to VPD. These results demonstrate that avoiding drought on the supply side (SM) and relying on plant hydraulic regulation constrains the effects of atmospheric drought (VPD) as a stressor on canopy gas exchange in mature pine trees under field conditions.

Daily Soil Moisture Retrieval by Fusing CYGNSS and Multi-Source Auxiliary Data Using Machine Learning Methods

The Cyclone Global Navigation Satellite System (CYGNSS), a publicly accessible spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) data, provides a new alternative opportunity for large-scale soil moisture (SM) retrieval, but with interference from complex environmental conditions (i.e., vegetation cover and ground roughness). This study aims to develop a high-accuracy model for CYGNSS SM retrieval. The normalized surface reflectivity calculated by CYGNSS is fused with variables that are highly related to the SM obtained from optical/microwave remote sensing to solve the problem of the influence of complicated environmental conditions. The Gradient Boost Regression Tree (GBRT) model aided by land-type data is then used to construct a multi-variables SM retrieval model with six different land types of multiple models. The methodology is tested in southeastern China, and the results correlate very well with the existing satellite remote sensing products and in situ SM data (R = 0.765, ubRMSE = 0.054 m3m-3 vs. SMAP; R = 0.653, ubRMSE = 0.057 m3 m-3 vs. ERA5 SM; R = 0.691, ubRMSE = 0.057 m3m-3 vs. in situ SM). This study makes contributions from two aspects: (1) improves the accuracy of the CYGNSS retrieval of SM based on fusion with other auxiliary data; (2) constructs the SM retrieval model with multi-layer multiple models, which is suitable for different land properties.

[Effect of soil moisture on efficacy to eliminate dampness and relieve jaundice and flavonoid content of Sedum sarmentosum]

The growth environment of medicinal plants plays an important role in the formation of their medicinal quality. However, there is a lack of combined analysis studying the close relationship between the growth environment, chemical components, and related biological activities of medicinal plants. Therefore, this study investigated the effect of different soil moisture treatments on the efficacy to eliminate dampness and relieve jaundice and the flavonoid content of Sedum sarmentosum, and explored their correlation. The flavonoid content in the decoction of S. sarmentosum growing under field conditions with soil moisture levels of 35%-40%(T1), 55%-60%(T2), 75%-80%(T3), and 95%-100%(T4) was compared. The effects of these treatments on liver function parameters, liver inflammation, and oxidative damage in mice with dampness-heat jaundice were evaluated, and the correlation between pharmacological indicators and flavonoid content was analyzed. The results showed that the total flavonoid and total phenolic acid content in the decoction of S. sarmentosum were highest in the T1 treatment, followed by the T3 treatment. The content of quercetin, kaempferol, and isorhamnetin was highest in the T2, T1, and T3 treatments, respectively. Among the different moisture treatments, the T3 group of S. sarmentosum effectively reduced the levels of serum ALT, AKP, TBIL, DBIL, TBA, as well as hepatic TNF-α and IL-6 in mice with jaundice, followed by T2 treatment, especially in reducing AST level. The T4 treatment had the poorest effect. Correlation analysis showed a significant negative correlation between AST, ALT, AKP levels in mice and the total content of quercetin and the three flavonoids. MDA showed a significant negative correlation with the total flavonoid content and kaempferol. TNF-α exhibited a significant negative correlation with the content of isorhamnetin. In conclusion, S. sarmentosum growing under field conditions with a soil moisture level of 75%-80% exhibited the best efficacy to eliminate dampness and relieve jaundice. This study provides insights for optimizing the cultivation mode of medicinal plants guided by pharmacological experiments.

Diagnosing evapotranspiration responses to water deficit across biomes using deep learning

Accounting for water limitation is key to determining vegetation sensitivity to drought. Quantifying water limitation effects on evapotranspiration (ET) is challenged by the heterogeneity of vegetation types, climate zones and vertically along the rooting zone. Here, we train deep neural networks using flux measurements to study ET responses to progressing drought conditions. We determine a water stress factor (fET) that isolates ET reductions from effects of atmospheric aridity and other covarying drivers. We regress fET against the cumulative water deficit, which reveals the control of whole-column moisture availability. We find a variety of ET responses to water stress. Responses range from rapid declines of fET to 10% of its water-unlimited rate at several savannah and grassland sites, to mild fET reductions in most forests, despite substantial water deficits. Most sensitive responses are found at the most arid and warm sites. A combination of regulation of stomatal and hydraulic conductance and access to belowground water reservoirs, whether in groundwater or deep soil moisture, could explain the different behaviors observed across sites. This variety of responses is not captured by a standard land surface model, likely reflecting simplifications in its representation of belowground water storage.

Abrupt permafrost thaw drives spatially heterogeneous soil moisture and carbon dioxide fluxes in upland tundra

Permafrost thaw causes the seasonally thawed active layer to deepen, causing the Arctic to shift toward carbon release as soil organic matter becomes susceptible to decomposition. Ground subsidence initiated by ice loss can cause these soils to collapse abruptly, rapidly shifting soil moisture as microtopography changes and also accelerating carbon and nutrient mobilization. The uncertainty of soil moisture trajectories during thaw makes it difficult to predict the role of abrupt thaw in suppressing or exacerbating carbon losses. In this study, we investigated the role of shifting soil moisture conditions on carbon dioxide fluxes during a 13-year permafrost warming experiment that exhibited abrupt thaw. Warming deepened the active layer differentially across treatments, leading to variable rates of subsidence and formation of thermokarst depressions. In turn, differential subsidence caused a gradient of moisture conditions, with some plots becoming consistently inundated with water within thermokarst depressions and others exhibiting generally dry, but more variable soil moisture conditions outside of thermokarst depressions. Experimentally induced permafrost thaw initially drove increasing rates of growing season gross primary productivity (GPP), ecosystem respiration (Reco ), and net ecosystem exchange (NEE) (higher carbon uptake), but the formation of thermokarst depressions began to reverse this trend with a high level of spatial heterogeneity. Plots that subsided at the slowest rate stayed relatively dry and supported higher CO2 fluxes throughout the 13-year experiment, while plots that subsided very rapidly into the center of a thermokarst feature became consistently wet and experienced a rapid decline in growing season GPP, Reco , and NEE (lower carbon uptake or carbon release). These findings indicate that Earth system models, which do not simulate subsidence and often predict drier active layer conditions, likely overestimate net growing season carbon uptake in abruptly thawing landscapes.

Investigating Correlations and the Validation of SMAP-Sentinel L2 and In Situ Soil Moisture in Thailand

Soil moisture plays a crucial role in various hydrological processes and energy partitioning of the global surface. The Soil Moisture Active Passive-Sentinel (SMAP-Sentinel) remote-sensing technology has demonstrated great potential for monitoring soil moisture with a maximum spatial resolution of 1 km. This capability can be applied to improve the weather forecast accuracy, enhance water management for agriculture, and managing climate-related disasters. Despite the techniques being increasingly used worldwide, their accuracy still requires field validation in specific regions like Thailand. In this paper, we report on the extensive in situ monitoring of soil moisture (from surface up to 1 m depth) at 10 stations across Thailand, spanning the years 2021 to 2023. The aim was to validate the SMAP surface-soil moisture (SSM) Level 2 product over a period of two years. Using a one-month averaging approach, the study revealed linear relationships between the two measurement types, with the coefficient of determination (R-squared) varying from 0.13 to 0.58. Notably, areas with more uniform land use and topography such as croplands tended to have a better coefficient of determination. We also conducted detailed soil core characterization, including soil-water retention curves, permeability, porosity, and other physical properties. The basic soil properties were used for estimating the correlation constants between SMAP and in situ soil moistures using multiple linear regression. The results produced R-squared values between 0.933 and 0.847. An upscaling approach to SMAP was proposed that showed promising results when a 3-month average of all measurements in cropland was used together. The finding also suggests that the SMAP-Sentinel remote-sensing technology exhibits significant potential for soil-moisture monitoring in certain applications. Further validation efforts and research, particularly in terms of root-zone depths and area-based assessments, especially in the agricultural sector, can greatly improve the technology's effectiveness and usefulness in the region.

Soil Water Deficit Reduced Root Hydraulic Conductivity of Common Reed (Phragmites australis)

Alterations in root hydraulics in response to varying moisture conditions remain a subject of debate. In our investigation, we subjected common reeds (Phragmites australis) to a 45-day treatment with four distinct soil moisture levels. The findings unveiled that, in response to drought stress, the total root length, surface area, volume, and average diameter exhibited varying degrees of reduction. Anatomically, drought caused a reduction in root diameter (RD), cortex thickness (CT), vessel diameter (VD), and root cross-sectional area (RCA). A decrease in soil moisture significantly reduced both whole- and single-root hydraulic conductivity (Lpwr, Lpsr). The total length, surface area, volume, and average diameter of the reed root system were significantly correlated with Lpwr, while RD, CT, and RCA were significantly correlated with Lpsr. A decrease in soil moisture content significantly influenced root morphological and anatomical characteristics, which, in turn, altered Lpr, and the transcriptome results suggest that this may be associated with the variation in the expression of abscisic acid (ABA) and aquaporins (AQPs) genes. Our initial findings address a gap in our understanding of reed hydraulics, offering fresh theoretical insights into how herbaceous plants respond to external stressors.

Multi-Source Soil Moisture Data Fusion Based on Spherical Cap Harmonic Analysis and Helmert Variance Component Estimation in the Western U.S

Soil moisture (SM) is a vital climate variable in the interaction process between the Earth's atmosphere and land. However, global soil moisture products from various satellite missions and land surface models are affected by inherently discontinuous observations and coarse spatial resolution, which limits their application at fine spatial scales. To address this problem, this paper integrates three diverse types of datasets from in situ, satellites, and models through Spherical cap harmonic analysis (SCHA) and Helmert variance component estimation (HVCE) to produce 1 km of spatio-temporally continuous SM products with high accuracy. First, this paper eliminates the bias between different datasets and in situ sites and resamples the datasets before data fusion. Then, multi-source SM data fusion is performed based on the SCHA and HVCE methods. Finally, this paper evaluates the fused products from three aspects, including the performance of representative sites under different climate types, the overall performance of validation sites, and the comparison with other products. The results show that the fused products have better performance than other SM products. In the representative sites, the minimal correlation coefficient (R) of the fused products is above 0.85, and the largest root mean square error (RMSE) is below 0.040 m3 m-3. For all validation sites, the R and RMSE of the fused products are 0.889 and 0.036 m3 m-3, respectively, while the R for other products is below 0.75 and the RMSE is above 0.06 m3 m-3. In comparison to other SM products, the fused products exhibit superior performance, generally align more closely with in situ measurements, and possess the ability to accurately and finely capture the spatial and temporal variability of surface SM.

Research on Soil Moisture Estimation of Multiple-Track-GNSS Dual-Frequency Combination Observations Considering the Detection and Correction of Phase Outliers

Soil moisture (SM), as one of the crucial environmental factors, has traditionally been estimated using global navigation satellite system interferometric reflectometry (GNSS-IR) microwave remote sensing technology. This approach relies on the signal-to-noise ratio (SNR) reflection component, and its accuracy hinges on the successful separation of the reflection component from the direct component. In contrast, the presence of carrier phase and pseudorange multipath errors enables soil moisture retrieval without the requirement for separating the direct component of the signal. To acquire high-quality combined multipath errors and diversify GNSS-IR data sources, this study establishes the dual-frequency pseudorange combination (DFPC) and dual-frequency carrier phase combination (L4) that exclude geometrical factors, ionospheric delay, and tropospheric delay. Simultaneously, we propose two methods for estimating soil moisture: the DFPC method and the L4 method. Initially, the equal-weight least squares method is employed to calculate the initial delay phase. Subsequently, anomalous delay phases are detected and corrected through a combination of the minimum covariance determinant robust estimation (MCD) and the moving average filter (MAF). Finally, we utilize the multivariate linear regression (MLR) and extreme learning machine (ELM) to construct multi-satellite linear regression models (MSLRs) and multi-satellite nonlinear regression models (MSNRs) for soil moisture prediction, and compare the accuracy of each model. To validate the feasibility of these methods, data from site P031 of the Plate Boundary Observatory (PBO) H2O project are utilized. Experimental results demonstrate that combining MCD and MAF can effectively detect and correct outliers, yielding single-satellite delay phase sequences with a high quality. This improvement contributes to varying degrees of enhanced correlation between the single-satellite delay phase and soil moisture. When fusing the corrected delay phases from multiple satellite orbits using the DFPC method for soil moisture estimation, the correlations between the true soil moisture values and the predicted values obtained through MLR and ELM reach 0.81 and 0.88, respectively, while the correlations of the L4 method can reach 0.84 and 0.90, respectively. These findings indicate a substantial achievement in high-precision soil moisture estimation within a small satellite-elevation angle range.

Retrieving soil moisture using cosmic-ray neutron technology based on COSMIC model in the desert-oasis region

Cosmic-ray neutron technology could estimate average soil moisture on scale of hectometers by monitoring the neutron intensity near the ground, which has been successfully applied in forest, grassland, farmland, and other ecosystems. To verify the reliability of Cosmic-ray Soil Moisture Interaction Code (COSMIC) model for retrieving mesoscale soil moisture in arid regions, we carried out soil moisture observation experiment by using the cosmic-ray neutron rover in the desert-oasis region of the middle reaches of Heihe River. The results showed that the fast neutron intensity in the desert-oasis region were 350-715 counts·(30 s)-1, and the calibrated high energy neutron intensity (Ncosmic) were (38.5±2.2) counts·(30 s)-1, which was affected by land surface characteristics. Both COSMIC model (root mean square error=0.019 g·g-1) and N0 equation (root mean square error=0.018 g·g-1) could well assess the mesoscale soil moisture, with the accuracy of soil moisture being higher considering soil lattice water. The average penetration depth was 19 cm in the oasis region and 36 cm in the desert region during the experiment. COSMIC model could be used to retrieve soil moisture by cosmic ray neutron in the desert-oasis regions, which had great potential to realize data assimilation of surface meteorological-hydrological-ecological variables by combining with land surface models.

IoT-Based Agro-Toolbox for Soil Analysis and Environmental Monitoring

The agricultural sector faces numerous challenges in ensuring optimal soil health and environmental conditions for sustainable crop production. Traditional soil analysis methods are often time-consuming and labor-intensive, and provide limited real-time data, making it challenging for farmers to make informed decisions. In recent years, Internet of Things (IoT) technology has emerged as a promising solution to address these challenges by enabling efficient and automated soil analysis and environmental monitoring. This paper presents a 3D-printed IoT-based Agro-toolbox, designed for comprehensive soil analysis and environmental monitoring in the agricultural domain. The toolbox integrates various sensors for both soil and environmental measurements. By deploying this tool across fields, farmers can continuously monitor key soil parameters, including pH levels, moisture content, and temperature. Additionally, environmental factors such as ambient temperature, humidity, intensity of visible light, and barometric pressure can be monitored to assess the overall health of agricultural ecosystems. To evaluate the effectiveness of the Agro-toolbox, a case study was conducted in an aquaponics floating system with rocket, and benchmarking was performed using commercial tools that integrate sensors for soil temperature, moisture, and pH levels, as well as for air temperature, humidity, and intensity of visible light. The results showed that the Agro-toolbox had an acceptable error percentage, and it can be useful for agricultural applications.

Evaluating the Hydrological Components Contributions to Terrestrial Water Storage Changes in Inner Mongolia with Multiple Datasets

In this study, multiple remote sensing data were used to quantitatively evaluate the contributions of surface water, soil moisture and groundwater to terrestrial water storage (TWS) changes in five groundwater resources zones of Inner Mongolia (GW_I, GW_II, GW_III, GW_IV and GW_V), China. The results showed that TWS increased at the rate of 2.14 mm/a for GW_I, while it decreased at the rate of 4.62 mm/a, 5.89 mm/a, 2.79 mm/a and 2.62 mm/a for GW_II, GW_III, GW_IV and GW_V during 2003-2021. Inner Mongolia experienced a widespread soil moisture increase with the rate of 4.17 mm/a, 2.13 mm/a, 1.20 mm/a, 0.25 mm/a and 1.36 mm/a for the five regions, respectively. Significant decreases were detected for regional groundwater storage (GWS) with the rate of 2.21 mm/a, 6.76 mm/a, 6.87 mm/a, 3.01 mm/a, and 4.14 mm/a, respectively. Soil moisture was the major contributor to TWS changes in GW_I, which accounted 58% of the total TWS changes. Groundwater was the greatest contributor to TWS changes in other four regions, especially GWS changes, which accounted for 76% TWS changes in GW_IV. In addition, this study found that the role of surface water was notable for calculating regional GWS changes.

Soil moisture determines the recovery time of ecosystems from drought

Recovery time, the time it takes for ecosystems to return to normal states after experiencing droughts, is critical for assessing the response of ecosystems to droughts; however, the spatial dominant factors determining recovery time are poorly understood. We identify the global patterns of terrestrial ecosystem recovery time based on remote sensed vegetation indices, analyse the affecting factors of recovery time using random forest regression model, and determine the spatial distribution of the dominant factors of recovery time based on partial correlation. The results show that the global average recovery time is approximately 3.3 months, and that the longest recovery time occurs in mid-latitude drylands. Analysis of affecting factors of recovery time suggests that the most important environmental factor affecting recovery time is soil moisture during the recovery period, followed by temperature and vapour pressure deficit (VPD). Recovery time shortens with increasing soil moisture and prolongs with increasing VPD; however, the response of recovery time to temperature is nonmonotonic, with colder or hotter temperatures leading to longer recovery time. Soil moisture dominates the drought recovery time over 58.4% of the assessed land area, mostly in the mid-latitudes. The concern is that soil moisture is projected to decline in more than 65% regions in the future, which will lengthen the drought recovery time and exacerbate drought impacts on terrestrial ecosystems, especially in southwestern United States, the Mediterranean region and southern Africa. Our research provides methodological insights for quantifying recovery time and spatially identifies dominant factors of recovery time, improving our understanding of ecosystem response to drought.

Biocrusts intensify water redistribution and improve water availability to dryland vegetation: insights from a spatially-explicit ecohydrological model

Biocrusts are ecosystem engineers in drylands and structure the landscape through their ecohydrological effects. They regulate soil infiltration and evaporation but also surface water redistribution, providing important resources for vascular vegetation. Spatially-explicit ecohydrological models are useful tools to explore such ecohydrological mechanisms, but biocrusts have rarely been included in them. We contribute to closing this gap and assess how biocrusts shape spatio-temporal water fluxes and availability in a dryland landscape and how landscape hydrology is affected by climate-change induced shifts in the biocrust community. We extended the spatially-explicit, process-based ecohydrological dryland model EcoHyD by a biocrust layer which modifies water in- and outputs from the soil and affects surface runoff. The model was parameterized for a dryland hillslope in South-East Spain using field and literature data. We assessed the effect of biocrusts on landscape-scale soil moisture distribution, plant-available water and the hydrological processes behind it. To quantify the biocrust effects, we ran the model with and without biocrusts for a wet and dry year. Finally, we compared the effect of incipient and well-developed cyanobacteria- and lichen biocrusts on surface hydrology to evaluate possible paths forward if biocrust communities change due to climate change. Our model reproduced the runoff source-sink patterns typical of the landscape. The spatial differentiation of soil moisture in deeper layers matched the observed distribution of vascular vegetation. Biocrusts in the model led to higher water availability overall and in vegetated areas of the landscape and that this positive effect in part also held for a dry year. Compared to bare soil and incipient biocrusts, well-developed biocrusts protected the soil from evaporation thus preserving soil moisture despite lower infiltration while at the same time redistributing water toward downhill vegetation. Biocrust cover is vital for water redistribution and plant-available water but potential changes of biocrust composition and cover can reduce their ability of being a water source and sustaining dryland vegetation. The process-based model used in this study is a promising tool to further quantify and assess long-term scenarios of climate change and how it affects ecohydrological feedbacks that shape and stabilize dryland landscapes.

An Illustration of FY-3E GNOS-R for Global Soil Moisture Monitoring

An effective soil moisture retrieval method for FY-3E (Fengyun-3E) GNOS-R (GNSS occultation sounder II-reflectometry) is developed in this paper. Here, the LAGRS model, which is totally oriented for GNOS-R, is employed to estimate vegetation and surface roughness effects on surface reflectivity. Since the LAGRS (land surface GNSS reflection simulator) model is a space-borne GNSS-R (GNSS reflectometry) simulator based on the microwave radiative transfer equation model, the method presented in this paper takes more consideration on the physical scattering properties for retrieval. Ancillary information from SMAP (soil moisture active passive) such as the vegetation water content and the roughness coefficient are investigated for the final algorithm's development. At first, the SR (surface reflectivity) data calculated from GNOS-R is calculated and then calibrated, and then the vegetation roughness factor is achieved and used to eliminate the effects on both factors. After receiving the Fresnel reflectivity, the corresponding soil moisture estimated from this method is retrieved. The results demonstrate good consistency between soil moisture derived from GNOS-R data and SMAP soil moisture, with a correlation coefficient of 0.9599 and a root mean square error of 0.0483 cm³/cm³. This method succeeds in providing soil moisture on a global scale and is based on the previously developed physical LAGRS model. In this way, the great potential of GNOS-R for soil moisture estimation is presented.

A Data-Driven Approach for Building the Profile of Water Storage Capacity of Soils

The soil water storage capacity is critical for soil management as it drives crop production, soil carbon sequestration, and soil quality and health. It depends on soil textural class, depth, land-use and soil management practices; therefore, the complexity strongly limits its estimation on a large scale with conventional-process-based approaches. In this paper, a machine learning approach is proposed to build the profile of the soil water storage capacity. A neural network is designed to estimate the soil moisture from the meteorology data input. By taking the soil moisture as a proxy in the modelling, the training captures those impact factors of soil water storage capacity and their nonlinear interaction implicitly without knowing the underlying soil hydrologic processes. An internal vector of the proposed neural network assimilates the soil moisture response to meteorological conditions and is regulated as the profile of the soil water storage capacity. The proposed approach is data-driven. Since the low-cost soil moisture sensors have made soil moisture monitoring simple and the meteorology data are easy to obtain, the proposed approach enables a convenient way of estimating soil water storage capacity in a high sampling resolution and at a large scale. Moreover, an average root mean squared deviation at 0.0307m3/m3 can be achieved in the soil moisture estimation; hence, the trained model can be deployed as an alternative to the expensive sensor networks for continuous soil moisture monitoring. The proposed approach innovatively represents the soil water storage capacity as a vector profile rather than a single value indicator. Compared with the single value indicator, which is common in hydrology, a multidimensional vector can encode more information and thus has a more powerful representation. This can be seen in the anomaly detection demonstrated in the paper, where subtle differences in soil water storage capacity among the sensor sites can be captured even though these sensors are installed on the same grassland. Another merit of vector representation is that advanced numeric methods can be applied to soil analysis. This paper demonstrates such an advantage by clustering sensor sites into groups with the unsupervised K-means clustering on the profile vectors which encapsulate soil characteristics and land properties of each sensor site implicitly.

Combining thermal imaging and soil water content sensors to assess tree water status in pear trees

Volumetric soil water content is commonly used for irrigation management in fruit trees. By integrating direct information on tree water status into measurements of soil water content, we can improve detection of water stress and irrigation scheduling. Thermal-based indicators can be an alternative to traditional measurements of midday stem water potential and stomatal conductance for irrigation management of pear trees (Pyrus communis L.). These indicators are easy, quick, and cost-effective. The soil and tree water status of two cultivars of pear trees 'D'Anjou' and 'Bartlett' submitted to regulated deficit irrigation was measured regularly in a pear orchard in Rock Island, WA (USA) for two seasons, 2021 and 2022. These assessments were compared to the canopy temperature (Tc), the difference between the canopy and air temperature (Tc-Ta) and the crop water stress index (CWSI). Trees under deficit irrigation had lower midday stem water potential and stomatal conductance but higher Tc, Tc-Ta, and CWSI. Tc was not a robust method to assess tree water status since it was strongly related to air temperature (R = 0.99). However, Tc-Ta and CWSI were greater than 0°C or 0.5, respectively, and were less dependent on the environmental conditions when trees were under water deficits (midday stem water potential values< -1.2 MPa). Moreover, values of Tc-Ta = 2°C and CWSI = 0.8 occurred when midday stem water potential was close to -1.5 MPa and stomatal conductance was lower than 200 mmol m^-2s^-1. Soil water content (SWC) was the first indicator in detecting the deficit irrigation applied, however, it was not as strongly related to the tree water status as the thermal-based indicators. Thus, the relation between the indicators studied with the stem water potential followed the order: CWSI > Tc-Ta > SWC = Tc. A multiple regression analysis is proposed that combines both soil water content and thermal-based indices to overcome limitations of individual use of each indicator.

Cultivar-dependent differences in tuber growth cause increased soil resistance in potato fields

Since soil compaction of potato fields delays shoot emergence and decreases total yield, the causes and effects of this compaction need to be better understood. In a controlled environment trial with young (before tuber initiation) plants, roots of cv. Inca Bella (a phureja group cultivar) were more sensitive to increased soil resistance (3.0 MPa) than cv. Maris Piper (a tuberosum group cultivar). Such variation was hypothesized to cause yield differences in two field trials, in which compaction treatments were applied after tuber planting. Trial 1 increased initial soil resistance from 0.15 MPa to 0.3 MPa. By the end of the growing season, soil resistance increased three-fold in the upper 20 cm of the soil, but resistance in Maris Piper plots was up to twice that of Inca Bella plots. Maris Piper yield was 60% higher than Inca Bella and independent of soil compaction treatment, whilst compacted soil reduced Inca Bella yield by 30%. Trial 2 increased initial soil resistance from 0.2 MPa to 1.0 MPa. Soil resistance in the compacted treatments increased to similar, cultivar-dependent resistances as trial 1. Maris Piper yield was 12% higher than Inca Bella, but cultivar variation in yield response to compacted soil did not occur. Soil water content, root growth and tuber growth were measured to determine whether these factors could explain cultivar differences in soil resistance. Soil water content was similar between cultivars, thus did not cause soil resistance to vary between cultivars. Root density was insufficient to cause observed increases soil resistance. Finally, differences in soil resistance between cultivars became significant during tuber initiation, and became more pronounced until harvest. Increased tuber biomass volume (yield) of Maris Piper increased estimated mean soil density (and thus soil resistance) more than Inca Bella. This increase seems to depend on initial compaction, as soil resistance did not significantly increase in uncompacted soil. While increased soil resistance caused cultivar-dependent restriction of root density of young plants that was consistent with cultivar variation in yield, tuber growth likely caused cultivar-dependent increases in soil resistance in field trials, which may have further limited Inca Bella yield.

Effects of grazing on soil water recharge of rehabilitated grassland under natural rainfall on the Loess Pla-teau, Northwest China

Rainfall is critical to the regulation of slope runoff and soil water recharge. Grazing affects land cover and soil structure, with consequence on slope runoff generation and soil water recharge. Little attention has been paid to the effects of rainfall on soil water recharge caused by grazing. In this study, we examined land covers and soil water contents under different grazing intensities (G1-G5: 2.2, 3.0, 4.2, 6.7, 16.7 sheep·hm-2) and no grazing sites (NG), aiming to analyze soil water recharge under natural rainfall conditions after grazing. The results showed that grazing exerted significant effects on vegetation and biocrust coverage. The vegetation coverage was decreased by 8.3%-16.4% under G1-G5 grazing, while the biocrust coverage was increased by 106.9% under G2 grazing compared to NG. The soil surface roughness under G1-G5 grazing was increased by 53.1%-152.5%, and the thickness of biocrust was decreased by 24.1% under G5. Soil wetting front velocity decreased with increasing rainfall intensity, and that of 0-5 cm layer under the G2 grazing intensity decreased by 60.0% to 83.3% under rainfall between 18.0 mm and 70.3 mm compared to NG. The effect of grazing on soil wetting front velocity was significantly related to biocrust coverage and soil bulk density of 0-5 cm soil layer. Generally, grazing did not affect soil water recharge rates of the slope grassland on the Loess Plateau. G2 grazing may prolong the migration time of soil water in the surface layer by increasing the coverage of cyanobacteria biocrusts, which may be beneficial to the restoration of soil microenvironment. Our results provided scientific basis for water management in the enclosure grassland of the Loess Plateau in the "post-conversion era".

Compound droughts slow down the greening of the Earth

Vegetation response to soil and atmospheric drought has raised extensively controversy, however, the relative contributions of soil drought, atmospheric drought, and their compound droughts on global vegetation growth remain unclear. Combining the changes in soil moisture (SM), vapor pressure deficit (VPD), and vegetation growth (normalized difference vegetation index [NDVI]) during 1982-2015, here we evaluated the trends of these three drought types and quantified their impacts on global NDVI. We found that global VPD has increased 0.22 ± 0.05 kPa·decade^-1 during 1982-2015, and this trend was doubled after 1996 (0.32 ± 0.16 kPa·decade^-1 ) than before 1996 (0.16 ± 0.15 kPa·decade^-1 ). Regions with large increase in VPD trend generally accompanied with decreasing trend in SM, leading to a widespread increasing trend in compound droughts across 37.62% land areas. We further found compound droughts dominated the vegetation browning since late 1990s, contributing to a declined NDVI of 64.56%. Earth system models agree with the dominant role of compound droughts on vegetation growth, but their negative magnitudes are considerably underestimated, with half of the observed results (34.48%). Our results provided the evidence of compound droughts-induced global vegetation browning, highlighting the importance of correctly simulating the ecosystem-scale response to the under-appreciated exposure to compound droughts as it will increase with climate change.

Effects of intra-annual precipitation patterns on grassland productivity moderated by the dominant species phenology

Phenology and productivity are important functional indicators of grassland ecosystems. However, our understanding of how intra-annual precipitation patterns affect plant phenology and productivity in grasslands is still limited. Here, we conducted a two-year precipitation manipulation experiment to explore the responses of plant phenology and productivity to intra-annual precipitation patterns at the community and dominant species levels in a temperate grassland. We found that increased early growing season precipitation enhanced the above-ground biomass of the dominant rhizome grass, Leymus chinensis, by advancing its flowering date, while increased late growing season precipitation increased the above-ground biomass of the dominant bunchgrass, Stipa grandis, by delaying senescence. The complementary effects in phenology and biomass of the dominant species, L. chinensis and S. grandis, maintained stable dynamics of the community above-ground biomass under intra-annual precipitation pattern variations. Our results highlight the critical role that intra-annual precipitation and soil moisture patterns play in the phenology of temperate grasslands. By understanding the response of phenology to intra-annual precipitation patterns, we can more accurately predict the productivity of temperate grasslands under future climate change.

Artificial Grassland Had Higher Water Use Efficiency in Year with Less Precipitation in the Agro-Pastoral Ecotone

Improving plant water use efficiency is a key strategy for the utilization of regional limited water resources as well as the sustainable development of agriculture industry. To investigate the effects of different land use types on plant water use efficiency and their mechanisms, a randomized block experiment was designed in the agro-pastoral ecotone of northern China during 2020-2021. The differences in dry matter accumulation, evapotranspiration, soil physical and chemical properties, soil water storage and water use efficiency and their relationships among cropland, natural grassland and artificial grassland were studied. The results show that: In 2020, the dry matter accumulation and water use efficiency of cropland were significantly higher than those of artificial and natural grassland. In 2021, dry matter accumulation and water use efficiency of artificial grassland increased significantly from 364.79 g·m^-2 and 24.92 kg·ha^-1·mm^-1 to 1037.14 g·m^-2 and 50.82 kg·ha^-1·mm^-1, respectively, which were significantly higher than cropland and natural grassland. The evapotranspiration of three land use types showed an increasing trend in two years. The main reason affecting the difference of water use efficiency was that land use type affected soil moisture and soil nutrients, and then changed the dry matter accumulation and evapotranspiration of plants. During the study period, the water use efficiency of artificial grassland was higher in years with less precipitation. Therefore, expanding the planted area of artificial grassland may be one of the effective ways to promote the full utilization of regional water resources.

Earlier leaf senescence dates are constrained by soil moisture

The unprecedented warming that has occurred in recent decades has led to later autumn leaf senescence dates (LSD) throughout the Northern Hemisphere. Yet, great uncertainties still exist regarding the strength of these delaying trends, especially in terms of how soil moisture affects them. Here we show that changes in soil moisture in 1982-2015 had a substantial impact on autumn LSD in one-fifth of the vegetated areas in the Northern Hemisphere (>30° N), and how it contributed more to LSD variability than either temperature, precipitation or radiation. We developed a new model based on soil-moisture-constrained cooling degree days (CDD_SM ) to characterize the effects of soil moisture on LSD and compared its performance with the CDD, Delpierre and spring-influenced autumn models. We show that the CDD_SM model with inputs of temperature and soil moisture outperformed the three other models for LSD modelling and had an overall higher correlation coefficient (R), a lower root mean square error and lower Akaike information criterion (AIC) between observations and model predictions. These improvements were particularly evident in arid and semi-arid regions. We studied future LSD using the CDD_SM model under two scenarios (SSP126 and SSP585) and found that predicted LSD was 4.1 ± 1.4 days and 5.8 ± 2.8 days earlier under SSP126 and SSP585, respectively, than other models for the end of this century. Our study therefore reveals the importance of soil moisture in regulating autumn LSD and, in particular, highlights how coupling this effect with LSD models can improve simulations of the response of vegetation phenology to future climate change.

Extreme drought disrupts plant phenology: Insights from 35 years of cloud forest data in Venezuela

The potential effects of climate change on plant reproductive phenology include asynchronies with pollinators and reductions in plant fitness, leading to extinction and loss of ecosystem function. In particular, plant phenology is sensitive to extreme weather events, which are occurring with increasing severity and frequency in recent decades and are linked to anthropogenic climate change and shifts in atmospheric circulation. For 15 plant species in a Venezuelan cloud forest, we documented dramatic changes in monthly flower and fruit community composition over a 35-year time series, from 1983 to 2017, and these changes were linked directly to higher temperatures, lower precipitation, and decreased soil water availability. The patterns documented here do not mirror trends in temperate zones but corroborate results from the Asian tropics. More intense droughts are predicted to occur in the region, which will cause dramatic changes in flower and fruit availability.

Automated Low-Cost Soil Moisture Sensors: Trade-Off between Cost and Accuracy

Automated soil moisture systems are commonly used in precision agriculture. Using low-cost sensors, the spatial extension can be maximized, but the accuracy might be reduced. In this paper, we address the trade-off between cost and accuracy comparing low-cost and commercial soil moisture sensors. The analysis is based on the capacitive sensor SKU:SEN0193 tested under lab and field conditions. In addition to individual calibration, two simplified calibration techniques are proposed: universal calibration, based on all 63 sensors, and a single-point calibration using the sensor response in dry soil. During the second stage of testing, the sensors were coupled to a low-cost monitoring station and installed in the field. The sensors were capable of measuring daily and seasonal oscillations in soil moisture resulting from solar radiation and precipitation. The low-cost sensor performance was compared to commercial sensors based on five variables: (1) cost, (2) accuracy, (3) qualified labor demand, (4) sample volume, and (5) life expectancy. Commercial sensors provide single-point information with high reliability but at a high acquisition cost, while low-cost sensors can be acquired in larger numbers at a lower cost, allowing for more detailed spatial and temporal observations, but with medium accuracy. The use of SKU sensors is then indicated for short-term and limited-budget projects in which high accuracy of the collected data is not required.

Biochar with Inorganic Nitrogen Fertilizer Reduces Direct Greenhouse Gas Emission Flux from Soil

Agricultural waste can have a catastrophic impact on climate change, as it contributes significantly to greenhouse gas (GHG) emissions if not managed sustainably. Swine-digestate-manure-derived biochar may be one sustainable way to manage waste and tackle GHG emissions in temperate climatic conditions. The purpose of this study was to ascertain how such biochar could be used to reduce soil GHG emissions. Spring barley (Hordeum vulgare L.) and pea crops in 2020 and 2021, respectively, were treated with 25 t ha^-1 of swine-digestate-manure-derived biochar (B₁) and 120 kg ha^-1 (N₁) and 160 kg ha^-1 (N₂) of synthetic nitrogen fertilizer (ammonium nitrate). Biochar with or without nitrogen fertilizer substantially lowered GHG emissions compared to the control treatment (without any treatment) or treatments without biochar application. Carbon dioxide (CO₂), nitrous oxide (N₂O), and methane (CH₄) emissions were directly measured using static chamber technology. Cumulative emissions and global warming potential (GWP) followed the same trend and were significantly lowered in biochar-treated soils. The influences of soil and environmental parameters on GHG emissions were, therefore, investigated. A positive correlation was found between both moisture and temperature and GHG emissions. Thus, biochar made from swine digestate manure may be an effective organic amendment to reduce GHG emissions and address climate change challenges.

Ecophysiology of Endangered Plant Species Saussurea esthonica: Effect of Mineral Nutrient Availability and Soil Moisture

Saussurea esthonica is an endangered plant species typical for wet inland habitats such as calcareous fens. Due to its limited population size and distribution, non-invasive sampling of is important in the research of S. esthonica. The aim of the present study was to assess the effect of mineral nutrient availability and substrate moisture on the growth, physiological status, and mineral nutrition of S. esthonica. The non-destructive measurement of physiological parameters was performed in native habitats during three vegetative seasons, followed by two experiments in controlled conditions. Soil at the two Estonian sites had a relatively larger similarity in the composition of plant-available mineral nutrients in comparison to the two Latvian sites. The chlorophyll a fluorescence parameter Performance Index correlated with the total precipitation in the respective month before measurement, but no significant relationship with other environmental variables was found. For mineral nutrient experiments, plants were grown in four substrates with different mineral nutrient composition, resembling that of soil at different S. esthonica sites. Plant growth and physiological indices were significantly affected by the mineral composition of the substrate. Differences in leaf and root mineral nutrient concentrations of S. esthonica plants in part reflected differences in substrate mineral concentration. To evaluate the effect of soil moisture on growth and photosynthesis-associated parameters of S. esthonica, plants were cultivated in "Pope+" substrate at four different moisture treatments (dry, normal, wet, and waterlodged). The most intense growth of S. esthonica plants was evident in waterlodged conditions, which decreased with a decrease in soil moisture. The biomass of leaves increased by 106% and that of the roots increased by 72% as soil moisture increased from dry to normal. For waterlodged plants, leaf biomass increased by 263% and root biomass increased by 566%, in comparison to that for plants cultivated in dry substrate. Substrate drying had a more negative effect on the growth of S. esthonica plants in comparison to that of waterlodging, and this can be directly linked to prevalent hydrological conditions of an alkaline fen habitat native to the species. Therefore, the preservation of the natural water regime in natural habitats is critical to the conservation of the species.

Development of a Soil Moisture Prediction Model Based on Recurrent Neural Network Long Short-Term Memory (RNN-LSTM) in Soybean Cultivation

Due to climate change, soil moisture may increase, and outflows could become more frequent, which will have a considerable impact on crop growth. Crops are affected by soil moisture; thus, soil moisture prediction is necessary for irrigating at an appropriate time according to weather changes. Therefore, the aim of this study is to develop a future soil moisture (SM) prediction model to determine whether to conduct irrigation according to changes in soil moisture due to weather conditions. Sensors were used to measure soil moisture and soil temperature at a depth of 10 cm, 20 cm, and 30 cm from the topsoil. The combination of optimal variables was investigated using soil moisture and soil temperature at depths between 10 cm and 30 cm and weather data as input variables. The recurrent neural network long short-term memory (RNN-LSTM) models for predicting SM was developed using time series data. The loss and the coefficient of determination (R²) values were used as indicators for evaluating the model performance and two verification datasets were used to test various conditions. The best model performance for 10 cm depth was an R² of 0.999, a loss of 0.022, and a validation loss of 0.105, and the best results for 20 cm and 30 cm depths were an R² of 0.999, a loss of 0.016, and a validation loss of 0.098 and an R² of 0.956, a loss of 0.057, and a validation loss of 2.883, respectively. The RNN-LSTM model was used to confirm the SM predictability in soybean arable land and could be applied to supply the appropriate moisture needed for crop growth. The results of this study show that a soil moisture prediction model based on time-series weather data can help determine the appropriate amount of irrigation required for crop cultivation.

Land Surface Model Calibration Using Satellite Remote Sensing Data

Satellite remote sensing provides a unique opportunity for calibrating land surface models due to their direct measurements of various hydrological variables as well as extensive spatial and temporal coverage. This study aims to apply terrestrial water storage (TWS) estimated from the gravity recovery and climate experiment (GRACE) mission as well as soil moisture products from advanced microwave scanning radiometer-earth observing system (AMSR-E) to calibrate a land surface model using multi-objective evolutionary algorithms. For this purpose, the non-dominated sorting genetic algorithm (NSGA) is used to improve the model's parameters. The calibration is carried out for the period of two years 2003 and 2010 (calibration period) in Australia, and the impact is further monitored over 2011 (forecasting period). A new combined objective function based on the observations' uncertainty is developed to efficiently improve the model parameters for a consistent and reliable forecasting skill. According to the evaluation of the results against independent measurements, it is found that the calibrated model parameters lead to better model simulations both in the calibration and forecasting period.

Conifer water-use patterns across temporal and topographic gradients in the southern Sierra Nevada

Climate change is increasing the severity and duration of drought events experienced by forest ecosystems. Because water is essential for tree physiological processes, the ability of trees to survive prolonged droughts will largely depend on whether they have access to reliable water sources. While many woody plant species exhibit the ability to shift water sources between different depths of soil and rock water in response to changes in climate and water availability, it is unclear if Sierra Nevada conifers exhibit this plasticity. Here we analysed the δ18O and δ13C values of annual tree rings to determine the water-use patterns of large Sierra Nevada conifers during the 2012-16 California drought and 4 years before this drought event (2004-07). We analysed four species (Pinus jeffreyi Grev. & Balf. (Jeffrey pine), Pinus lambertiana Dougl. (sugar pine), Abies concolor (Gord. & Glend.) Lindl. Ex Hilderbr (white fir) and Calocedrus decurrens (Torr.) Florin (incense-cedar)) across a range of topographic positions to investigate differences in water-use patterns by species and position on the landscape. We found no significant differences in δ18O and δ13C values for the pre-drought and drought periods. This stability in δ18O values suggests that trees did not shift their water-use patterns in response to the 2012-16 drought. We did find species-specific differences in water-use patterns, with incense-cedar exhibiting more depleted δ18O values than all other species. We also found trends that suggest the water source used by a tree may depend on topographic and growing environment attributes such as topographic wetness and the surrounding basal area. Overall, our results suggest that the water source used by trees varies by the species and topographic position, but that Sierra Nevada conifers do not switch their water-use patterns in response to the drought. This lack of plasticity could make Sierra Nevada conifers particularly vulnerable to drought mortality as their historically reliable water sources begin to dry out with climate change.

Environmental controls on the light use efficiency of terrestrial gross primary production

Gross primary production (GPP) by terrestrial ecosystems is a key quantity in the global carbon cycle. The instantaneous controls of leaf-level photosynthesis are well established, but there is still no consensus on the mechanisms by which canopy-level GPP depends on spatial and temporal variation in the environment. The standard model of photosynthesis provides a robust mechanistic representation for C₃ species; however, additional assumptions are required to "scale up" from leaf to canopy. As a consequence, competing models make inconsistent predictions about how GPP will respond to continuing environmental change. This problem is addressed here by means of an empirical analysis of the light use efficiency (LUE) of GPP inferred from eddy covariance carbon dioxide flux measurements, in situ measurements of photosynthetically active radiation (PAR), and remotely sensed estimates of the fraction of PAR (fAPAR) absorbed by the vegetation canopy. Focusing on LUE allows potential drivers of GPP to be separated from its overriding dependence on light. GPP data from over 100 sites, collated over 20 years and located in a range of biomes and climate zones, were extracted from the FLUXNET2015 database and combined with remotely sensed fAPAR data to estimate daily LUE. Daytime air temperature, vapor pressure deficit, diffuse fraction of solar radiation, and soil moisture were shown to be salient predictors of LUE in a generalized linear mixed-effects model. The same model design was fitted to site-based LUE estimates generated by 16 terrestrial ecosystem models. The published models showed wide variation in the shape, the strength, and even the sign of the environmental effects on modeled LUE. These findings highlight important model deficiencies and suggest a need to progress beyond simple "goodness of fit" comparisons of inferred and predicted carbon fluxes toward an approach focused on the functional responses of the underlying dependencies.

The other side of tropical forest drought: do shallow water table regions of Amazonia act as large-scale hydrological refugia from drought?

Tropical forest function is of global significance to climate change responses, and critically determined by water availability patterns. Groundwater is tightly related to soil water through the water table depth (WT), but historically neglected in ecological studies. Shallow WT forests (WT < 5 m) are underrepresented in forest research networks and absent in eddy flux measurements, although they represent c. 50% of the Amazon and are expected to respond differently to global-change-related droughts. We review WT patterns and consequences for plants, emerging results, and advance a conceptual model integrating environment and trait distributions to predict climate change effects. Shallow WT forests have a distinct species composition, with more resource-acquisitive and hydrologically vulnerable trees, shorter canopies and lower biomass than deep WT forests. During 'normal' climatic years, shallow WT forests have higher mortality and lower productivity than deep WT forests, but during moderate droughts mortality is buffered and productivity increases. However, during severe drought, shallow WT forests may be more sensitive due to shallow roots and drought-intolerant traits. Our evidence supports the hypothesis of neglected shallow WT forests being resilient to moderate drought, challenging the prevailing view of widespread negative effects of climate change on Amazonian forests that ignores WT gradients, but predicts they could collapse under very strong droughts.

Low soil moisture suppresses the thermal compensatory response of microbial respiration

The thermal compensatory response of microbial respiration contributes to a decrease in warming-induced enhancement of soil respiration over time, which could weaken the positive feedback between the carbon cycle and climate warming. Climate warming is also predicted to cause a worldwide decrease in soil moisture, which has an effect on the microbial metabolism of soil carbon. However, whether and how changes in moisture affect the thermal compensatory response of microbial respiration are unexplored. Here, using soils from an 8-year warming experiment in an alpine grassland, we assayed the thermal response of microbial respiration rates at different soil moisture levels. The results showed that relatively low soil moisture suppressed the thermal compensatory response of microbial respiration, leading to an enhanced response to warming. A subsequent moisture incubation experiment involving off-plot soils also showed that the response of microbial respiration to 100 d warming shifted from a slight compensatory response to an enhanced response with decreasing incubation moisture. Further analysis revealed that such respiration regulation by moisture was associated with shifts in enzymatic activities and carbon use efficiency. Our findings suggest that future drought induced by climate warming might weaken the thermal compensatory capacity of microbial respiration, with important consequences for carbon-climate feedback.

Population dynamics of Meloidogyne graminicola in soil in different types of direct-seeded rice agroecosystems in Hunan Province, China

The rice root-knot nematode Meloidogyne graminicola is increasingly widely distributed in China and has had a severe incidence in Hunan Province. It is thus necessary to investigate its population dynamics in paddy fields. This study was conducted to ascertain the effect of direct-seeded rice agroecosystems on the population dynamics of M. graminicola and root gall development in rice. The results indicated that the population density of M. graminicola in soil was markedly influenced by the agroecosystem, rainfall and temperature. The population density of M. graminicola J2, and eggs in the soil and root galls, were significantly larger in the dry aerobic rice agroecosystem and in the rain-fed upland agroecosystem than in the lowland double-rice cropping sequence agroecosystem. As it can affect soil moisture rainfall was the key factor affecting the density of nematodes in both the rain-fed upland agroecosystem and the dry aerobic rice agroecosystem. Field flooding was still an effective way to reduce the population density of M. graminicola. In addition, we observed that M. graminicola can lay eggs outside rice roots under laboratory conditions. Therefore, we propose a hypothesis that M. graminicola lays egg masses within roots when the soil moisture is high, but lays eggs outside when the soil moisture is suitable. By clarifying the population dynamics of M. graminicola in different types of direct-seeded rice agroecosystems, this study is conducive to controlling rice root-knot nematodes.

Sensing Bioavailable Water Content of Granulated Matrices: A Combined Experimental and Computational Study

This paper represents the synthesis, characterization and validation of a cobalt chloride functionalised nano-porous cellulose membrane, a unique sensor for non-contact measurement of water potential in various biomedical and environmentally important matrices. The developed nano sensor, along with associated electronic components, is assembled as a prototype device called "MEGH" (Measuring Essential Good Hydration) to measure essential hydration of matrices of both environmental and biomedical importance, including soil and human skin. The relative humidity above the soil surface in equilibrium with the soil moisture has been studied for both hydrophobic and hydrophilic soil types. Our studies confirm that the percentage of water available to plants is greater in hydrophobic soil rather than in hydrophilic soil, which has also been corroborated using simulation studies. Furthermore, the requirement of hydration in human skin has also been evaluated by measuring the water potential of both dry and moist skin.