Elevation-dependent dynamics of soil properties in a hilly watershed: a landform-based approach.

Understanding the variation of soil physical properties in relation to land use and elevation is essential for modeling soil-landscape relationships and sustainable land management. Hence, this study investigates the spatio-temporal variability of soil physical properties in a lower Himalayan watershed, where agriculture, forest, and grasslands are dominant. Samples from 104 sites in a 422 km2 watershed were collected using a gridded sampling scheme (2 km × 2 km resolution) over 57 weeks. Spatial patterns were analyzed using the Kriging technique, and Spearman rank correlation was employed to identify landform-dependent correlations between soil properties and elevation. The interdependence of the properties was detected using principal component analysis (PCA), while the random forest (RF) approach explored the factors influencing electrical conductivity (EC), organic content (OC), soil temperature (ST), and soil moisture (SM). The results revealed that forest landforms have higher coarser fractions (40%) compared to other landforms, while grasslands have higher soil fines (66%). A positive correlation was observed for elevation with sand content (0.15*), organic content (0.42*), and specific gravity (0.03), while a negative correlation was observed for silt (0.10), clay (0.21*), bulk density (0.52*), electrical conductivity (0.41*), soil moisture (0.28*), and temperature (0.31*). Elevation, soil texture, and specific gravity were identified as critical controls for EC, OC, ST, and SM, emphasizing the importance of soil properties, especially elevation and texture, in shaping spatial distributions. These findings contribute to creating a high-resolution regional inventory for effective land use management, adaptation to climate change, and improved livelihood, specifically for mountain people.

Why does afforestation policy lead to a drying trend in soil moisture on the Loess Plateau?

Soil moisture is a key factor for vegetation restoration in arid and semi-arid regions. Clarifying the vertical characteristics of soil moisture in artificial forests on a regional scale and its response mechanisms can benefit for land use management in water-deficient areas such as the Loess Plateau. The study targets Robinia pseudoacacia on the Loess Plateau with a meta-analysis based on 790 soil moisture data points abstracted from 35 published papers. The results show that extensive cultivation of R pseudoacacia on the Loess Plateau leads to a significant reduction in soil moisture (P < 0.05). Soil moisture decreases significantly with growth of trees, especially between 400 and 500 cm soil layers. Soil moisture increases with the hydrothermal gradient. The results indicate that intensive afforestation activities in high temperature and rainy areas still significantly consume deep soil moisture. The main reason is that the impact of hydrothermal factors on soil moisture is significant between 0 and 200 cm soil layers and decreases with increasing soil depth. However, the continuous depletion of deep soil moisture leads to insignificant differences in soil moisture responses under different topographical conditions in the region. Therefore, neglecting the impact of forest age and hydrothermal factors on soil moisture in afforestation activities, the excessive water consumption by R pseudoacacia during growth poses potential risks to the ecological environment of the Loess Plateau. This study provides references for knowledge on water relating problems and sustainable management of artificial forests in arid and semi-arid areas.

Integrated UAV and Satellite Multi-Spectral for Agricultural Drought Monitoring of Winter Wheat in the Seedling Stage.

Agricultural droughts are a threat to local economies, as they disrupt crops. The monitoring of agricultural droughts is of practical significance for mitigating loss. Even though satellite data have been extensively used in agricultural studies, realizing wide-range, high-resolution, and high-precision agricultural drought monitoring is still difficult. This study combined the high spatial resolution of unmanned aerial vehicle (UAV) remote sensing with the wide-range monitoring capability of Landsat-8 and employed the local average method for upscaling to match the remote sensing images of the UAVs with satellite images. Based on the measured ground data, this study employed two machine learning algorithms, namely, random forest (RF) and eXtreme Gradient Boosting (XGBoost1.5.1), to establish the inversion models for the relative soil moisture. The results showed that the XGBoost model achieved a higher accuracy for different soil depths. For a soil depth of 0-20 cm, the XGBoost model achieved the optimal result (R2 = 0.6863; root mean square error (RMSE) = 3.882%). Compared with the corresponding model for soil depth before the upscaling correction, the UAV correction can significantly improve the inversion accuracy of the relative soil moisture according to satellite remote sensing. To conclude, a map of the agricultural drought grade of winter wheat in the Huaibei Plain in China was drawn up.

Soil moisture-dependent tire wear particles aging processes shift soil microbial communities and elevated nitrous oxide emission on drylands.

Tire wear particles (TWPs) have been an emerging threat to the soil ecosystem, while impact of the TWPs aging on soil microbial communities remains poorly understood. This study investigated the dynamic responses of soil microbial communities to the TWPs aging under both wet and flooded conditions. We found that different soil moisture conditions resulted in distinct microbial community structures. Soil bacteria were more sensitive to wet conditions, while soil fungi were more sensitive to flooded conditions. The family Symbiobacteraceae was predominant in the TWP-sphere under both wet and flooded conditions after 60 days, followed by Brevibacillaceae. Notably, we observed that TWPs input significantly increased nitrous oxide (N2O) emission from dryland soil. Several taxa including Cyanobacteriales, Blastocatellaceae and Pyrinomonadaceae were identified as TWP-biomarkers in soils and potentially played significant roles in N2O emissions from drylands. Their responses to the TWPs input correlated closely with changes in the relative abundance of genes involved in ammonia oxidation (amoA/B), nitrite reduction (nirS/K) and N2O reduction (nosZ) in drylands. Our results demonstrate that soil moisture-dependent TWP aging influences N2O emission by altering both the associated microbial communities and the relevant genes.

Response of Seed Germination and Seedling Growth of Six Desert Shrubs to Different Moisture Levels under Greenhouse Conditions.

Moisture is the most important environmental factor limiting seed regeneration of shrubs in desert areas. Therefore, understanding the effects of moisture changes on seed germination, morphological and physiological traits of shrubs is essential for vegetation restoration in desert areas. In March to June 2023, in a greenhouse using the potting method, we tested the effects of soil moisture changes (5%, 10%, 15%, 20% and 25%) on seed germination and seedling growth of six desert shrubs (Zygophyllum xanthoxylum, Nitraria sibirica, Calligonum mongolicum, Corethrodendron scoparium, Caragana korshinskii, and Corethrodendron fruticosu). Results showed that (1) seed germination percent and vigor index were significantly higher at 15 and 20% soil moisture content than at 5 and 10%; (2) shoot length, primary root length, specific leaf area and biomass of seedlings were significantly higher in the 15% and 20% soil moisture content treatments than in the 5% and 10% treatments; (3) superoxide dismutase activity (SOD) and soluble protein content (SP) decreased with decreasing soil water content, while peroxidase activity (POD) and catalase activity (CAT) showed a decreasing and then increasing trend with increasing soil water content; (4) the six seeds and seedling of shrubs were ranked in order of their survivability in response to changes in soil moisture: Caragana korshinskii > Zygophyllum xanthoxylum > Calligonum mongolicum > Corethrodendron scoparium > Corethrodendron fruticosu > Nitraria sibirica. Our study shows that shrub seedlings respond to water changes by regulating morphological and physiological traits together. More importantly, we found that C. korshinskii, Z. xanthoxylum and C. mongolicum were more survivable when coping with water deficit or extreme precipitation. The results of the study may provide a reference for the selection and cultivation of similar shrubs in desert areas under frequent extreme droughts in the future.

The Effect of Soil Type and Moisture on the Development of Forensically Important Megaselia scalaris and Dohrniphora cornuta (Diptera: Phoridae).

Necrophagous phorid flies are common insects found on buried corpses, and their developmental data play a crucial role in estimating the post-burial interval (PBI). This study aimed to investigate the effects of soil type and moisture content on some life cycle parameters of two forensically important insects, Megaselia scalaris (Loew, 1866) and Dohrniphora cornuta (Bigot, 1857) (Diptera: Phoridae). Larval and pupal survival, development time, and larval body length of M. scalaris and D. cornuta were observed in three different soil types (loamy sand, sandy loam A, and sandy loam B) with six moisture contents (0%, 20%, 40%, 60%, 80%, and 100%). The results indicated that soil types, soil moisture, and their interaction significantly influenced the growth and development of both species, with moisture being the most influential factor. In each soil, 20% and 40% moisture contents were more suitable for their growth and development. Both the development time and maximum larval body length were significantly different among soil types and moisture contents. The larval period of both species lasted the longest in all soils with 0% moisture content. Additionally, a regression analysis of the relationship between larval body length and development time was performed at different moisture contents in three soils. This study expanded our knowledge of the factors that influence the development of necrophagous insects and provided some reference data for applications of M. scalaris and D. cornuta in PBI estimation.

Assessment of Low-Cost and Higher-End Soil Moisture Sensors across Various Moisture Ranges and Soil Textures.

The accuracy and unit cost of sensors are important factors for a continuous soil moisture monitoring system. This study compares the accuracy of four soil moisture sensors differing in unit costs in coarse-, fine-, and medium-textured soils. The sensor outputs were recorded for the VWC, ranging from 0% to 50%. Low-cost capacitive and resistive sensors were evaluated with and without the external 16-bit analog-to-digital converter ADS1115 to improve their performances without adding much cost. Without ADS1115, using only Arduino's built-in analog-to-digital converter, the low-cost sensors had a maximum RMSE of 4.79% (v/v) for resistive sensors and 3.78% for capacitive sensors in medium-textured soil. The addition of ADS1115 showed improved performance of the low-cost sensors, with a maximum RMSE of 2.64% for resistive sensors and 1.87% for capacitive sensors. The higher-end sensors had an RMSE of up to 1.8% for VH400 and up to 0.95% for the 5TM sensor. The RMSE differences between higher-end and low-cost sensors with the use of ADS1115 were not statistically significant.

Calibration of Low-Cost Moisture Sensors in a Biochar-Amended Sandy Loam Soil with Different Salinity Levels.

With the increasing focus on irrigation management, it is crucial to consider cost-effective alternatives for soil water monitoring, such as multi-point monitoring with low-cost soil moisture sensors. This study assesses the accuracy and functionality of low-cost sensors in a sandy loam (SL) soil amended with biochar at rates of 15.6 and 31.2 tons/ha by calibrating the sensors in the presence of two nitrogen (N) and potassium (K) commercial fertilizers at three salinity levels (non/slightly/moderately) and six soil water contents. Sensors were calibrated across nine SL-soil combinations with biochar and N and K fertilizers, counting for 21 treatments. The best fit for soil water content calibration was obtained using polynomial equations, demonstrating reliability with R2 values greater than 0.98 for each case. After a second calibration, low-cost soil moisture sensors provide acceptable results concerning previous calibration, especially for non- and slightly saline treatments and at soil moisture levels lower than 0.17 cm3cm-3. The results showed that at low frequencies, biochar and salinity increase the capacitance detected by the sensors, with calibration curves deviating up to 30% from the control sandy loam soil. Due to changes in the physical and chemical properties of soil resulting from biochar amendments and the conductive properties influenced by fertilization practices, it is required to conduct specific and continuous calibrations of soil water content sensor, leading to better agricultural management decisions.

Multi-source machine learning and spaceborne remote sensing data accurately predict three-dimensional soil moisture in an in-service uranium disposal cell.

One reason arid and semi-arid environments have been used to store waste is due to low groundwater recharge, presumably limiting the potential for meteoric water to mobilize and transport contaminants into groundwater. The U.S. Department of Energy Office of Legacy Management (LM) is evaluating selected uranium mill tailings disposal cell covers to be managed as evapotranspiration (ET) covers, where vegetation is used to naturally remove water from the cover profile via transpiration, further reducing deep percolation. An important parameter in monitoring the performance of ET covers is soil moisture (SM). If SM is too high, water may drain into tailings material, potentially transporting contaminants into groundwater; if SM is too low, radon flux may increase through the cover. However, monitoring SM via traditional instrumentation is invasive, expensive, and may fail to account for spatial heterogeneity, especially over vegetated disposal cells. Here we investigated the potential for non-invasive SM monitoring using radar remote sensing and other geospatial data to see if this approach could provide a practical, accurate, and spatially comprehensive tool to monitor SM. We used theoretical simulations to analyze the sensitivity of multi-frequency radar backscatter to SM at different depths of a field-scale (3 ha) drainage lysimeter embedded within an in-service LM disposal cell. We then evaluated a shallow and deep form of machine learning (ML) using Google Earth Engine to integrate multi-source observations and estimate the SM profile across six soil layers from depths of 0-2 m. The ML models were trained using in situ SM measurements from 2019 and validated using data from 2014 to 2018 and 2020-2021. Model predictors included backscatter observations from satellite synthetic aperture radar, vegetation, temperature products from optical infrared sensors, and accumulated, gridded rainfall data. The radar simulations confirmed that the lower frequencies (L- and P-band) and smaller incidence angles show better sensitivity to deeper soil layers and an overall larger SM dynamic range relative to the higher frequencies (C- and X-band). The ML models produced accurate SM estimates throughout the soil profile (r values from 0.75 to 0.94; RMSE = 0.003-0.017 cm3/cm3; bias = 0.00 cm3/cm3), with the simpler shallow-learning approach outperforming a selected deep-learning model. The ML models we developed provide an accurate, cost-effective tool for monitoring SM within ET covers that could be applied to other vegetated disposal cell covers, potentially including those with rock-armored covers.

Analysis of Tamarix chinensis Forest Characteristics, Salt Ion Distribution, and Non-Structural Carbohydrate Levels in the Yellow River Delta: A Spatial Study Based on Proximity to the Shoreline.

The distribution of vegetation in coastal wetlands is significantly influenced by soil properties. However, the mechanisms of how soil characteristics impact the physiological processes of Tamarix chinensis forests remain underexplored. This study examined changes in the soil physicochemical properties and structural attributes of natural T. chinensis forests in the Yellow River Delta with increasing distance from the shoreline. T. chinensis trees were classified into healthy, intermediate, and dying categories based on growth potential, and dynamic changes in salt ions and non-structural carbohydrates (NSCs) were investigated. Results indicated that increasing distance from the shoreline corresponded to decreased soil salinity and pH, and increased soil moisture. T. chinensis mortality rate decreased, while tree height and ground diameter increased with distance. Soil salt content was positively correlated with T. chinensis mortality, but negatively correlated with tree height and ground diameter. Trees with lower growth potential had higher Na+ but lower K+ and K+/Na+ ratio. Soil salt content was positively correlated with root and stem Na+, while soil moisture was positively correlated with leaf NSCs. These findings suggest that soil salt content and moisture significantly influence T. chinensis ion absorption and NSC accumulation, with sodium toxicity being a key factor in the spatial distribution of T. chinensis forests.

Monitoring Soybean Soil Moisture Content Based on UAV Multispectral and Thermal-Infrared Remote-Sensing Information Fusion.

By integrating the thermal characteristics from thermal-infrared remote sensing with the physiological and structural information of vegetation revealed by multispectral remote sensing, a more comprehensive assessment of the crop soil-moisture-status response can be achieved. In this study, multispectral and thermal-infrared remote-sensing data, along with soil-moisture-content (SMC) samples (0~20 cm, 20~40 cm, and 40~60 cm soil layers), were collected during the flowering stage of soybean. Data sources included vegetation indices, texture features, texture indices, and thermal-infrared vegetation indices. Spectral parameters with a significant correlation level (p < 0.01) were selected and input into the model as single- and fuse-input variables. Three machine learning methods, eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and Genetic Algorithm-optimized Backpropagation Neural Network (GA-BP), were utilized to construct prediction models for soybean SMC based on the fusion of UAV multispectral and thermal-infrared remote-sensing information. The results indicated that among the single-input variables, the vegetation indices (VIs) derived from multispectral sensors had the optimal accuracy for monitoring SMC in different soil layers under soybean cultivation. The prediction accuracy was the lowest when using single-texture information, while the combination of texture feature values into new texture indices significantly improved the performance of estimating SMC. The fusion of vegetation indices (VIs), texture indices (TIs), and thermal-infrared vegetation indices (TVIs) provided a better prediction of soybean SMC. The optimal prediction model for SMC in different soil layers under soybean cultivation was constructed based on the input combination of VIs + TIs + TVIs, and XGBoost was identified as the preferred method for soybean SMC monitoring and modeling, with its R2 = 0.780, RMSE = 0.437%, and MRE = 1.667% in predicting 0~20 cm SMC. In summary, the fusion of UAV multispectral and thermal-infrared remote-sensing information has good application value in predicting SMC in different soil layers under soybean cultivation. This study can provide technical support for precise management of soybean soil moisture status using the UAV platform.

Spatiotemporal patterns of greenhouse gas fluxes in the subtropical wetland ecosystem of Indian Himalayan foothill.

The study characterized the temporal and spatial variability in greenhouse gas (GHG) fluxes (CO2, CH4, and N2O) between December 2020 and November 2021 and their regulating drivers in the subtropical wetland of the Indian Himalayan foothill. Five distinct habitats (M1-sloppy surface at swamp forest, M2-plain surface at swamp forest, M3-swamp surface with small grasses, M4-marshy land with dense macrophytes, and M5-marshy land with sparse macrophytes) were studied. We conducted in situ measurements of GHG fluxes, microclimate (AT, ST, and SMC(v/v)), and soil properties (pH, EC, N, P, K, and SOC) in triplicates in all the habitat types. Across the habitats, CO2, CH4, and N2O fluxes ranged from 125 to 536 mg m-2 h-1, 0.32 to 28.4 mg m-2 h-1, and 0.16 to 3.14 mg m-2 h-1, respectively. The habitats (M3 and M5) exhibited higher GHG fluxes than the others. The CH4 flux followed the summer > autumn > spring > winter hierarchy. However, CO2 and N2O fluxes followed the summer > spring > autumn > winter. CO2 fluxes were primarily governed by ST and SOC. However, CH4 and N2O fluxes were mainly regulated by ST and SMC(v/v) across the habitats. In the case of N2O fluxes, soil P and EC also played a crucial role across the habitats. AT was a universal driver controlling all GHG fluxes across the habitats. The results emphasize that long-term GHG flux monitoring in sub-tropical Himalayan Wetlands has become imperative to accurately predict the near-future GHG fluxes and their changing nature with the ongoing climate change.

Temporal changes in net ecosystem CO2 exchange and influential factors in an apple orchard in Northeast China.

The apple orchards in Liaoning, one of the four major apple-producing areas in Bohai Bay, Northeast China, play a crucial role in regulating the carbon sink effect. However, there is limited information on the variation in carbon flux and its influential factors in apple orchards in this region. To address this, CO2 flux data were monitored throughout the entire apple growth seasons from April to November in 2017 and 2018 in the apple (Malus pumila Mill. cv Hanfu) orchard in Shenyang, China. The energy closure of the apple orchard was calculated, and variations in net ecosystem exchange (NEE) at different time scales and its response to environmental factors were analyzed. Our results showed that the energy balance ratio of the apple was 0.74 in 2017 and 1.38 in 2018. NEE was generally positive in April and November and negative from May to October, indicating a strong carbon sink throughout the growth season. The daily average NEE ranged from - 0.103 to 0.094 mg m-2 s-1 in 2017 and from - 0.134 to 0.059 mg m-2 s-1 in 2018, with the lowest values observed in June and July. NEE was negatively correlated with net radiation, atmospheric temperature, saturated vapor pressure deficit, and soil temperature. These findings provide valuable insights for predicting carbon flux in orchard ecosystems within the context of global climate change.

Performance analysis of capacitive soil moisture, temperature sensors and their applications at farmer's field.

This study aims to assess the effectiveness of PCB-based capacitive soil moisture sensors for local field conditions. The electrical scheme of designed sensors has been presented in this study. The PCB-based capacitive soil moisture sensors are calibrated using a linear equation developed between analog values of capacitive sensors and soil moisture content measured from the gravimetric method. The performance of the designed soil moisture sensors was assessed at five different locations at varying depths (i.e., 15 cm, 30 cm, and 45 cm). The calibration results indicated a positive correlation between the soil moisture content and measurement frequency of the sensor for wheat crop, with R2 values of 0.72, 0.83, and 0.83 for 15 cm, 30 cm, and 45 cm depths, respectively. Results reveal that 85% of the sensors accurately detected the patterns in soil moisture fluctuations during the cropping period. The designed capacitive sensors demonstrated a maximum relative error of 5.87% for 45 cm depth. However, the relative error remained below 5% for the 15 cm and 30 cm soil depths. For the sugarcane crop, R2 values vary from 0.66 to 0.82, with the highest relative error of 5.22% at a 15 cm depth. These sensors offer a highly cost-effective solution for farmers, with the entire wireless sensor network system including one sensor node, three soil moisture sensors, and one soil temperature sensor, which is priced at approximately $150, making it a practical and affordable option for widespread adoption.

Impact of native vegetation and soil moisture dynamics on evapotranspiration in green roof systems.

Evapotranspiration (ET) is an important water budget term for understanding the recovery of stormwater retention in green roof systems (GRs). However, ET evaluations, particularly in full-scale GRs, remain challenging. This study investigated ET dynamics within a GR in the City of Pittsburgh, USA, using a water balance based on continuously monitored soil moisture from moisture sensors over 15 months. Results suggest under well-watered soil conditions, daily moisture loss correlated with solar radiation, temperature, and humidity, in decreasing order of correlation strength, while wind speed had limited effects. Compared to sensor-informed moisture loss (using moisture-based water balance), the Hargreaves and FAO-56 Penman-Monteith equations predicted cumulative ET that was 1.8 and 2.1 times higher, respectively. When soil moisture declined and approached the temporary wilting points, a noticeable reduction in daily moisture loss was observed. This suggests the necessity of using a water stress coefficient alongside a crop coefficient to represent actual ET based on FAO-56 Penman-Monteith estimates. Seasonal crop coefficients from dominant native plant species present at our monitored location, eastern bluestar (Amsonia tabernaemontana) and creeping woodsorrel (Oxalis corniculata), had mean values of 0.48, 0.62, and 0.65 for fall, spring, and summer, respectively. The impact of water stress on ET could be characterized by a linear relationship with moisture content. Our results highlight the importance of soil moisture in regulating ET processes and demonstrate the utility of soil moisture data for evaluating ET in GRs and informing irrigation practices.

Harnessing rain hose technology for water-saving sustainable irrigation and enhancing blackgram productivity in garden land.

Blackgram, a protein-rich pulse crop (24%), is crucial for combating food insecurity, particularly in malnourished and economically weak countries. Enhancing blackgram production requires improved, input-saving management practices. Given the challenges of climate change and population growth, efficient water management is vital for increasing pulse productivity and water use efficiency with minimal investment. This study aimed to identify cost-effective irrigation methods to optimise blackgram yields. Experiments were conducted at the National Pulses Research Centre in Vamban, Pudukkottai, and the Agricultural College and Research Institute in Kumulur, Tiruchirappalli, during the kharif season of 2021 and 2022. The study compared different treatments of irrigation methods, such as check basin, raised bed, drip, sprinkler and rain hose irrigation. Results showed that the rain hose system maintained the highest soil moisture (23.93% at 10 cm depth and 19.71% at 20 cm depth). Even though drip irrigation resulted in a higher seed yield (1363 kg ha-1), the rain hose system proved to be more cost-effective, saving 27.09% in costs and achieving a 15.23% higher benefit-cost ratio. These findings suggest that the rain hose method, combined with current agronomic practices, is a viable low-cost technique for sustainable blackgram cultivation, optimising water use and maximising profits. This research provides valuable insights into water-saving irrigation methods for pulse crops.

Response of soil moisture to rainfall following deep soil drying in China's hilly loess lands.

Deep soil drying is a physical soil phenomenon that has become increasingly characteristic to artificial afforestation on China's Loess Plateau. Current research is largely short of conclusive reports on soil moisture recovery following deep soil drying in afforested lands. In this study, a 10-m deep underground column was constructed at Pengyang Experimental Station in Ningxia. The CS650-CR1000 automatic soil moisture monitoring system and BLJW-4 small meteorological observation stations were used to respectively monitor soil moisture and meteorological conditions in the study area for the period 2014-2019. The local rainfall was classified and the characteristics of soil infiltration analyzed at both monthly and annual scales. The results showed that: i) Deep soil moisture recovery in the semi-arid Loess Plateau region depended mainly on 25-49.9 mm and >50 mm types of rainfall; together accounting for 35.44 % of the precipitation. ii) Deep soil moisture replenishment occurred mainly for the period from April to October. While this accounted for 30.13 % of the precipitation, evaporation loss accounted for 69.87 % of it. With increasing monthly rainfall (Pm), the variation in monthly infiltration depth (Zm) was quadratic in shape - where Zm = -0.0094 Pm2 + 3.7702 Pm (R2 = 0.9577). iii) At the annual scale, deep soil moisture replenishment was mainly driven by year-on-year infiltration water accumulation. This is because a single year precipitation infiltration was not enough to replenish deep soil moisture. The cumulative infiltration depth for 2014-2019 was 180, 260, 400, 700, 1000 > 1000 cm. It suggested that soil water infiltration and deep dry soil recovery occurred at different times under rainfed conditions in the semi-arid loess hills in China. This is key for in-depth studies of the hydrological process in dry soil regions.

Source contributions to two super dust storms over Northern China in March 2021 and the impact of soil moisture.

Two extremely devastating super dust storms (SDS) hit Mongolia and Northern China in March 2021, causing many deaths and substantial economic damage. Accurate forecasting of dust storms is of great importance for avoiding or mitigating their effects. One of the most critical factors affecting dust emissions is soil moisture, but its value in desert exhibits significant uncertainty. In this study, model experiments were conducted to simulate dust emissions using four soil moisture datasets. The results were compared with observations to assess the effects of soil moisture on the dust emission strength. The Integrated Source Apportionment Method (ISAM) was used to track the dust sources and quantify the contribution from each source region to the dust load over the North China Plain (NCP), Korea peninsula, and western Japan. The results show large differences in the dust load depending on the soil moisture datasets used. The high soil moisture in the NCEP dataset results in substantial underestimation of the dust emission flux and PM10 concentration. Despite a minor overestimation of PM10 concentrations in many Northern China cities, the ERA5 dataset yields the best simulation performance. During the two SDS events, about 7.5 Mt dust was released from the deserts in Mongolia and 2.8 Mt from the deserts in China. Source apportionment indicates that the Mongolian Gobi Desert is the dominant source of PM10 in the NCP, Korea peninsula, and western Japan, accounting for 60 %-80 %, while Inner Mongolia contributed 10 %-20 %.

Simultaneous immobilization strategy of anionic metalloids and cationic metals in agricultural systems: A review.

Concurrent heavy metals remediation in natural environments poses significant challenges due to factors like metal speciation and interactions with soil moisture. This review focuses on strategies for immobilizing both anionic and cationic metals simultaneously in soil-crop systems. Key approaches include water management, biochar utilization, stabilizing agents, nanotechnology, fertilization, and bioremediation. Sprinkler or intermittent irrigation combined with soil amendments/biochar effectively immobilizes As/Cd/Pb simultaneously. This immobilization occurs through continuous adsorption-desorption, oxidation-reduction, and precipitation mechanisms influenced by soil pH, redox reactions, and Fe-oxides. Biochar from sources like wine lees, sewage sludge, spent coffee, and Fe-nanoparticles can immobilize As/Cd/Pb/Cr/Co/Cu/Zn together via precipitation. In addition, biochar from rice, wheat, corn straw, rice husk, sawdust, and wood chips, modified with chemicals or nanoparticles, simultaneously immobilizes As and Cd, containing higher Fe3O4, Fe-oxide, and OH groups. Ligand exchange immobilizes As, while ion exchange immobilizes Cd. Furthermore, combining biochar especially with iron, hydroxyapatite, magnetite, goethite, silicon, graphene, alginate, compost, and microbes-can achieve simultaneous immobilization. Other effective amendments are selenium fertilizer, Ge-nanocomposites, Fe-Si materials, ash, hormone, and sterilization. Notably, combining nano-biochar with microbes and/or fertilizers with Fe-containing higher adsorption sites, metal-binding cores, and maintaining a neutral pH could stimulate simultaneous immobilization. The amendments have a positive impact on soil physio-chemical improvement and crop development. Crops enhance production of growth metabolites, hormones, and xylem tissue thickening, forming a protective barrier by root Fe-plaque containing higher Fe-oxide, restricting upward metal movement. Therefore, a holistic immobilization mechanism reduces plant oxidative damage, improves soil and crop quality, and reduces food contamination.

Observed flash drought to persist in future over southern Africa.

Southern Africa has experienced multiple occurrences of drought episodes, which is projected to persist in the future, considering all climate scenarios. Despite the documented change in a meteorological, agricultural, and hydrological drought situation in the region, few studies are yet to explore the changes in flash drought (hereafter; FD), which is characterized by a rapid reduction in root-zone soil moisture and more substantial intensification in few days to weeks. Here, we analyze the observed FD and related underlying drivers during the past 34 years. Also, we estimate the future changes in FD using the severity-duration magnitude matrix under the middle of the road (SSP2-4.5) and business as usual (SSP5-8.5), scenario. Lastly, the study investigates the role of anthropogenic warming using the fraction of attributable risk (FAR) approach and possible bivariate return periods of FD events. Our results demonstrate that the region has experienced multiple occurrences up to 72 pentads from 1980 to 2014. Underlying mechanisms revealed the compounding influence of Vapor Pressure Deficit (VPD), Potential Evapotranspiration (PET), and precipitation deficit that have a significant impact on the abrupt onset and rapid intensification of FD events and other hot extremes over the SAF region. Under a high emission scenario, the region will experience FD duration lasting for 30 days with >40 % severity projected to impact the region. Anthropogenic climate change and land use and land cover changes remain the most dominant drivers altering the FD events over the SAF region. The return period of FD events under the SSP5-8.5 scenario shows that the SAF region will witness multiple FD events of up to 80 pentads in the far future. These findings reinforce the need to limit the emission of greenhouse gases. Sustained warming of the climate will exacerbate the extreme events and other compounding factors, thus affecting the livelihoods of humans and life-supporting strata.