Soil Moisture Research Articles

Decadal trends and climatic influences on flash droughts and flash floods in Indian cities

Flash droughts/floods are extreme weather phenomena that are expected to become increasingly frequent and severe with the changing climate. Flash droughts result from a rapid decline in soil moisture, while flash floods occur due to a high extreme rainfall intensity over a short duration. This study analyzes the ERA5 reanalysis data (hourly temperature, soil moisture, and precipitation) from 1992 to 2022 to assess flash drought/flood attribute variations across fourteen Indian cities. Flash drought events are identified based on specific conditions using the obtained Soil Moisture Index (SMI) values. At the same time, we propose a novel approach to attribute flash floods by setting thresholds for precipitation and soil moisture.This study examines the frequency and trends of flash drought and flood events across India's various Köppen-Geiger climatic zones from 1992 to 2022. Jaipur and Dehradun show a statistically significant decrease in flash drought events with magnitudes of −0.0833 events/year and −0.0769 events/year, respectively. Conversely, Hyderabad exhibits a highly significant increase in flash flood events with a magnitude of 1.1851 events/year. Similarly, Bengaluru, Varanasi, and Vishakhapatnam also show substantial increases in flash flood events. These findings underscore the impact of climate change on flash droughts/floods, highlighting the necessity for sustainable strategies.

Multivariate Evaluation of Flash Drought Across the United States

AbstractThis study uses the flash drought intensity index (FDII) to develop a multivariate flash drought climatology for the contiguous U.S. using data from 2001 to 2021. The FDII method uses the rate of intensification (FD‐INT) and subsequent drought severity (DRO‐SEV) to determine when a flash drought occurred and the strength of the event. Overall, the results showed that flash drought occurrence and severity varied with season and region and were sensitive to the drought indicator used to compute the FDII. Precipitation‐based indicators identified more flash droughts across the western U.S. whereas soil moisture (SM) and evapotranspiration indicators identified more flash droughts across the central and eastern U.S. When assessed over the entire U.S., the most flash droughts were found when using an evaporative demand indicator. Though FD‐INT was larger than DRO‐SEV across the U.S. for most indicators, regional patterns were also evident in their relative importance. For example, a distinct east‐west gradient was present in the SM and evapotranspiration FD‐INT, with relatively large values in the central and eastern U.S. A combined data set synthesizing information from multiple indicators showed that the strongest flash droughts from a multivariate perspective were located in the central and southeastern U.S. A seasonal analysis revealed a distinct seasonal cycle in flash drought onset across the western and central U.S. Together, the results illustrate the need to use a multivariate framework to identify and characterize the occurrence and severity of flash droughts.

Impacts of early spring soil moisture over the Greater Mekong Subregion on the interannual variation of South China Sea summer monsoon onset

This study investigates the influence of early spring (March–April) soil moisture (SM) over the Greater Mekong Subregion (GMS) on the interannual variation of South China Sea summer monsoon (SCSSM) onset, using observational analyses and numerical experiments. It is found that when early spring SM over the GMS is wetter, westerly anomalies dominate the South China Sea, corresponding to an early onset of the SCSSM, and vice versa. The analyses of physical mechanism show that the positive anomalies of early spring SM decrease local surface air temperature by adjusting the surface latent heat flux and sensible heat flux. The persistence of anomalous ground cooling contributes to negative geopotential height anomalies in the middle troposphere, and further induces the eastward retreat of western North Pacific subtropical high in May. The anomalous cyclone over the South China Sea favors the early onset of SCSSM. The key physical processes linking the variability of early spring SM over the GMS and the following SCSSM onset are confirmed by the sensitivity experiments using a coupled atmosphere–land model (CAM6–CLM5). Specifically, the onset date of the SCSSM in the drier experiment is 11 days later than that in the wetter experiment. The present results have implications for the forecast of SCSSM onset.

Extreme rainfall events eliminate the response of greenhouse gas fluxes to hydrological alterations and fertilization in a riparian ecosystem

Riparian ecosystems are essential carbon dioxide (CO2) sources, which considerably promotes climate warming. However, the other greenhouse gas fluxes (GHGs), such as methane (CH4) and nitrous oxide (N2O), in the riparian ecosystems have not been well studied, and it remains unclear whether and how these GHG fluxes respond to extreme weather, fertilization and hydrological alterations associated with reservoir management. Here, we assessed the impacts of hydrological alterations (i.e., flooding frequency) and fertilization (nitrogen and/or phosphorus) induced by human activities (hydroengineering construction and agricultural activities) on GHG fluxes, and further investigated the underlying mechanisms in two contrasting years (normal vs. extreme rainfall years) in a reservoir riparian zone dominated by grasses. The significant combined effects of extreme rainfall events and human activities (hydrological alterations and fertilization) on the GHGs were observed. Continuous flooding reduced CO2 emissions by 24% but increased CH4 emissions by ∼4 times in a normal rainfall year. In addition, nitrogen fertilization promoted CO2 emissions by 37%. However, these phenomena were not observed in the year with extreme rainfall events, which made the flooding levels homogeneous across the treatments. Furthermore, we found that CO2 fluxes were driven by the soil moisture, nutrient content, aboveground biomass, and root carbon content, while CH4 and N2O fluxes were merely driven by the soil properties (pH, moisture, and nutrient content). This study provides valuable insights into the crucial role of extreme rainfall events, hydrological alteration, and fertilization in regulating GHG fluxes in riparian ecosystems, as well as supports the integration of these changes in GHG emission models.

A novel multivariate multiscale index for drought-flood abrupt alternations: Considering precipitation, evapotranspiration, and soil moisture

Drought-flood abrupt alternation (DFAA), or the rapid shift between droughts and floods, is a complex phenomenon that cannot be fully captured by individual meteorological or hydrological factors alone. However, previous studies have rarely taken into account multiple factors simultaneously, and have identified DFAA events using either precipitation or runoff. This study proposed a novel index, the Multi-factor Standardized DFAA Index (MSDFI), which integrated multiple factors (i.e., precipitation, evapotranspiration, and soil moisture) and multiple timescales using copula functions to detect DFAA events. The proposed index was applied to the Pearl River Basin (PRB), containing the largest river in southern China, to examine the applicability of the index and explore the spatial and temporal evolution patterns of DFAA from 1979 to 2018. The potential driving mechanisms behind these patterns were also explored. The results demonstrated that the proposed MSDFI, considering multiple variables and time scales, can effectively identify DFAA events. The identified drought to flood (DTF) and flood to drought (FTD) events aligned with the hydro-meteorological transition months in the PRB. There were more than 32 DTF and FTD events during 1979–2018 in the PRB and these events were uniformly distributed spatially. The DTF events were mainly observed during spring and winter, while FTD events mainly occurred in summer and autumn. The DTF intensity ranged from 0.97 to 1.75, whereas the FTD intensity ranged from 0.87 to 1.75. Both DTF and FTD showed spatial patterns with slightly higher intensities in the west than that in the east. The potential drivers of DTF and FTD differed, with precipitation, surface pressure, and NDVI being key factors for DTF, and precipitation and evapotranspiration being key factors for FTD. These findings suggested that the proposed index and approach are effective and can be applied to any region globally.

Multifunctional effects of nitrification and urease inhibitors: decreasing soil herbicide residues and reducing nitrous oxide emissions simultaneously

Glyphosate pollution and greenhouse gas emissions are major problem in achieving sustainable soil management. It is necessary to develop effective strategies to simultaneously reduce herbicide residues and nitrous oxide (N2O) emissions in soil. This study aimed to: (1) quantitative analyze the effects of nitrogen (N) cycle inhibitors (nitrification inhibitors 3,4 dimethylpyrazole phosphate (DMPP) and dicyandiamide (DCD) and urease inhibitor N-(n-butyl) thiophosphoric triamide (NBPT)) on glyphosate degradation and reduction of N2O under different soil moistures; (2) identify the functional microbes and genes associated with glyphosate degradation and N2O emissions; and (3) decipher the main mechanisms of N cycle inhibitors affecting glyphosate degradation at different soil water contents. Compared to the control, the application of DMPP, DCD and NBPT reduced glyphosate residues in soil by 33.0%, 60.3% and 35.7%, respectively, under 90% water holding capacity (WHC). The application of DCD stimulated Acidobacteria and the phnX gene to degrade soil glyphosate. Further, soil glyphosate residues were significantly and negatively related to soil N2O emissions at both 60% and 90% WHC. Compared to the control, NBPT application decreased cumulative N2O emissions by 91.4% at 90% WHC by decreasing soil nitrate N (NO3--N) and inhibiting amoC and narG genes at 90%. The application of N cycle inhibitors could be a potential strategy to simultaneously reduce glyphosate residues and soil N2O emissions. Our study could provide technical support to reduce the risks of herbicide exposure and reduce greenhouse gas emissions.

Divergent responses of forest canopy height to environmental conditions across China

As major components of terrestrial ecosystems, forest ecosystems play an important role in sequestering carbon and hence mitigating climate change. Canopy height is a crucial factor characterizing the structure and function of forest ecosystems, yet the driving mechanism of forest canopy height receives less attentions in China. Here, we utilize the satellite-based forest canopy height product with several environmental and climate factors (e.g. forest age, temperature, etc.) to delineate the spatial distributions of forest canopy height and its drivers in China at 1 km spatial resolution during the period of 2014 to 2018. The random forest is employed for identifying the dominant factors at province level, while Shapley additive explanations (SHAP) analysis is further incorporated at pixel-level to dig into the specific contributions of each driver. The results show that forest age primarily dominates the spatial distributions of forest canopy height across different forest ecosystems of China, followed by mean annual precipitation, soil type, and aspect. SHAP analysis further indicates that other factors, such as soil moisture and wind speed, also play critical roles to shape the spatial patterns of forest canopy height in China, which could not be revealed from province-level random forest analyses. Such results emphasize the priority of incorporating SHAP analysis with random forest to advance our understanding of forest canopy height distributions and benefit future projections. Our study highlights the necessity to characterize the spatial heterogeneity of forest canopy height, which is critical for accurate estimations of forest and even terrestrial carbon sink in China, facilitating the achievement of the goal of “carbon peak in 2030 and carbon neutrality in 2060”.

Differentiated growth of the most widely planted conifer in response to extreme droughts across semi-arid regions in Northern China

Global warming is leading to more frequent and intense drought events, exerting unprecedented pressure on forest growth. Although post-drought recovery in plantation growth has been studied enormously, the variation of planted populations across the whole distribution range of a species is not well understood. In this study, the growth suitability of the most widely planted conifer species in dry lands of northern China, Mongolian Scots pine (Pinus sylvestris var. mongolica), was estimated, based on tree-ring data sampled in 289 plots of afforestation across most of its planting range. According to changes in the hydrothermal condition during the last forty years (1980–2018), the distribution range was divided into three parts: warming, warming-wetting, and non-significant trends (NT) regions. Two severe droughts in 2000 and 2007 were identified using the Standardized Precipitation Evapotranspiration Index (SPEI). Then, the resistance (Rt), recovery (Rc), and resilience (Rs) growth indices were calculated and compared among these three parts. The growth of planted Mongolian Scots pine has overall significantly increased from 1980 to 2018 (p < 0.001). The pine plantations in warming regions were sensitive to annual precipitation, while warming-wetting regions were more influenced by the growing season climate. Pines reverted to pre-drought growth levels within 3 years after the drought event in 2000 but failed in 2007, and populations in the warming region showed significantly lower post-drought recovery. Furthermore, high pre-drought growth levels were linked to low Rs in both drought events (p < 0.001). Post-drought precipitation enhanced the Rc of plantations, compensating for the growth reduction caused by droughts. Soil moisture during the drought events enhanced Rt and Rs. The Rt and Rs indices decreased significantly with the age of plantations (p < 0.05). Overall, our results revealed that pre-drought growth and post-drought precipitation were key factors influencing the resilience of planted Mongolian Scots pine. Besides, the plantations experiencing wetting trends revealed favorable drought resilience and stable radial growth, which indicated promising potential for planted Mongolian Scots pine in those areas. Controlling the age of plantations seems to be crucial for maintaining drought resilience in large-scale plantations and maximizing ecological benefits.

Widespread and systematic effects of fire on plant–soil water relations

Wildfire activity and the hydrological cycle are strongly interlinked. While it is well known that wildfire occurrence and intensity are controlled by water availability, less is known about the effects of wildfire on plant and soil water cycling, especially at large scales. Here we investigate this by analysing fire impacts on the coupling between plant and soil water content, at the global scale, using remote sensing of soil moisture, vegetation water content and burned area. We find a strong effect of fire on plant–soil water relations, accelerating soil moisture loss by 17% and leading to faster gains in vegetation water content by 62%, both of which are positively related to fire severity and largest in forests. This effect is spatially extensive, with accelerated soil moisture loss found in 67%, and increased vegetation water content gain found in 67% of all analysed burned areas. After fire, plants also tended to have less control on their water content (that is, were more anisohydric). In summary, fire changes ecosystem functioning by increasing ecosystem water losses and shifting the relationship between soil and vegetation water budgets. With climate change, wildfire is likely to play an increasingly important role in ecosystem water cycling and subsequent ecosystem recovery.

Impact of Summer Air Temperature on Water and Solute Transport on a Permafrost‐Affected Slope in West Greenland

AbstractIn Arctic landscapes, the active layer forms a near‐surface aquifer on top of the permafrost where water and nutrients are available for plants or subject to downslope transport. Warmer summer air temperatures can increase the thickness of the active layer and alter the partitioning of water into evapotranspiration and discharge by increasing the potential evapotranspiration, the depth to the water table, and changing the flow paths but the interacting processes are poorly understood. In this study, a numerical model for surface‐ and subsurface cryo‐hydrology is calibrated based on field observations from a discontinue permafrost area in West Greenland considered sensitive to future climate changes. The validated model is used to simulate the effect of three summers with contrasting temperature regimes to quantify the variations in the active layer thickness, the resulting changes in the water balance, and the implications on solute transport. We find that an increase of summer air temperature by1.6°C, under similar precipitation can increase the active layer thickness by 0.25 m, increase evapotranspiration by 5%, and reduce the total discharge compared to a colder summer by 9%. Differences in soil moisture and evapotranspiration between upslope and downslope were amplified in a warm summer. These hydrological differences impact solute transport which is 1.6 times faster in a cold summer. Surprisingly, we note that future warmer summer with increase in permafrost thaw may not necessary lead to an increase in discharge along a hill slope with underlying permafrost.

Factors that influence the migration of sorbed pesticides in polyethylene and biodegradable mesoplastics

Trifluralin, Chlorpyrifos, and Procymidone migration performance from polyethylene (PE) and biodegradable (Mater-Bi: M-B) mulching films was examined. Desorption of pesticides from PE and M-B was studied using soil-plastic microcosms, considering temperature, soil humidity, and mulching film type as experimental variables. Trifluralin and Chlorpyrifos desorption was higher for PE than for M-B under all experimental conditions. In both cases, as the temperature increased from 25º C to 40º C, pesticide migration also increased, whereas as the soil humidity raised from 30% to 60%, pesticide desorption decreased. In the case of Procymidone, migration from PE and M-B at 25º C was similar under both soil moisture conditions. Migration percentages were similar for both mulch films at 40ºC and 30% soil humidity. However, at higher soil moisture (60%), migration from M-B was greater than from PE. A linear relationship was observed between the percentage of migration and the vapor pressure of the pesticides. In all cases, migration increased with higher vapor pressure, indicating a possible migration mechanism in the vapor phase. Pesticide migration increased at high temperatures (40º C). The effect of soil humidity in reducing pesticide migration was more significant at lower levels (30%).In addition, the mesoplastic sorption of pesticides in soil columns was studied using PE and M-B films. While the recoveries for Trifluralin, Chlorpyrifos, and Procymidone in the PE films were 0.05% ± 0.01%, 0.13% ± 0.03%, and non-detectable, the recoveries for M-B were: 0.49% ± 0.07%, 0.31% ± 0.09%, and 0.17% ± 0.10%, respectively, indicating that M-B was a better adsorbent than PE in all cases. This behavior should be considered in combination with the lower migration percentages observed for this type of mulching film in the microcosm experiments. These results could indicate a potential carrier effect of pesticide on biomesoplastic in the environment.

Quantifying the Impacts of Fire‐Related Perturbations in WRF‐Hydro Terrestrial Water Budget Simulations in California's Feather River Basin

ABSTRACTWildfire activity in the western United States (WUS) is increasingly impacting water supply, and land surface models (LSMs) that do not explicitly account for fire disturbances can have critical uncertainties in burned areas. This study quantified responses from the Weather Research and Forecasting Hydrological modelling system (WRF‐Hydro) to a suite of fire‐related perturbations to hydrologic soil and runoff parameters, vegetation area, land cover classifications and associated vegetation properties, and snow albedo across the heavily burned Feather River Basin in California. These experiments were used to quantify the impacts of fire‐related perturbations in model simulations under the observed meteorological conditions during the 2000–2022 water years and determine whether applying these fire‐related perturbations enhanced post‐fire model accuracy across the 11–12 post‐fire months evaluated herein. The most comprehensive fire‐aware simulation consistently modelled enhanced annual catchment streamflow (by 8%–37%), subsurface flow (by 72%–116%), and soil moisture (by 4%–9%), relative to the baseline simulation which neglected fire impacts. Simulated fire‐enhanced streamflow was predominately attributable to fire‐induced vegetation area reductions that reduced transpiration. Simulated streamflow enhancements occurred throughout the water year, excluding early‐summer (e.g., May–June) when the baseline simulation modelled relatively more snowmelt and streamflow because fire perturbations caused earlier model snow depletion. Vegetation area reductions favoured increased model ground snow accumulation and enhanced snow ablation while imposed snow albedo darkening enhanced ablation, ultimately resulting in similar peak SWE and earlier snow disappearance (on average by 8‐days) from the most comprehensive fire‐aware simulation relative to the baseline simulation. The baseline simulation had large degradations in streamflow accuracy following major fire events that were likely partially attributable to neglecting fire disturbances. Applying fire‐related perturbations reduced post‐fire streamflow anomaly biases across the three study catchments. However, remaining large post‐fire streamflow uncertainties in the fire‐perturbed simulation underscores the importance of additional observationally constrained fire‐disturbance model developments.

Spatiotemporal variations of surface albedo in Central Asia and its influencing factors and confirmatory path analysis during the 21st century

Surface albedo (SA) is crucial for understanding land surface processes and climate simulation. This study analyzed SA changes and its influencing factors in Central Asia from 2001 to 2020, with projections 2025 to 2100. Factors analyzed included snow cover fraction, fractional vegetation cover, soil moisture, average state climate indices (temperature and precipitation), and extreme climate indices (heatwave indices and extreme precipitation indices). Pearson correlation coefficient, geographical convergent cross mapping, and geographical detector were used to quantify the correlation, causal relationship strength, and impact degree between SA and the influencing factors. To address multicollinearity, ridge regression (RR), geographically weighted ridge regression (GWRR), and piecewise structural equation modeling (pSEM) were combined to construct RR-pSEM and GWRR-pSEM models. Results indicated that SA in Central Asia increased from 2001 to 2010 and decreased from 2011 to 2020, with a projected future decline. There is a strong correlation and significant causality between SA and each factor. Snow cover fraction was identified as the most critical factor influencing SA. Average temperature and precipitation had a greater impact on SA than extreme climate indices, with a 1 °C temperature increase corresponding to a 0.004 decrease in SA. This study enhances understanding of SA changes under climate change, and provides a methodological framework for analyzing complex systems with multicollinearity. The proposed models offer valuable tools for studying interrelated factors in Earth system science.

Smart farming based on IoT to predict conditions using machine learning

&lt;span&gt;Smart farming is a type of technology that utilizes the internet of things (IoT) to provide information on agricultural and environmental conditions as well as perform automation. Some of these ecological conditions can be used and analyzed in machine learning (ML) data management. This study focuses on utilizing ML algorithms to find the best prediction; typically used methods include linear regression, decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost). In the application of smart farming, research on IoT and artificial intelligence (AI) is still uncommon since most IoT cannot make predictions like AI. Because basically, some IoT can't make predictions as AI does. In this Study, predictions were made by looking at the regression results in the form of root mean square error (RMSE) and absolute error. The results show a strong and weak correlation between features (positive or negative). The best prediction results are obtained by XGBoost when predicting temperature (RMSE 6.656 and absolute error 3.948) and (soil moisture 17.151 and absolute error 11.269). However, using different parameters (RMSE RF and absolute error DT) on RF and DT resulted in good and distinct results. Linear regression, on the other hand, produced unsatisfactory and poor result.&lt;/span&gt;

Tree crowns broken off by windstorms are an unstable life raft for Collembola

There are a number of ways to clear the aftermath of a windthrow disturbance of forest stands, the most common practice being to remove all broken trees and broken-off crowns lying on the ground. This practice leads to complete exposure of the soil, which deprives soil invertebrates (including Collembola) of the protection of trees that affords them a chance of surviving. Accordingly, following a windthrow disturbance of pine stands in 2017, a three-year study of collembolan assemblages was undertaken in stands spared from salvage logging. We aimed to test the effect of three different levels of disturbance (severely, moderately and least disturbed stands with a canopy cover of 0–20%, 20–60% and 60–90%, respectively) on the survival of Collembola assemblages and to determine its association with changes in the soil environment and in the LAI index. Additionally, in the severely and moderately disturbed stands, Collembola were sampled between crowns of fallen trees and under the crowns. There were no significant differences in density, species richness and proportions of individuals of belowground “soil” and aboveground “epedaphic” species between the Collembolan assemblages that were associated with the degree of windthrow disturbance and time since disturbance. The study confirmed the presence of a significantly higher number of species and proportion of “epedaphic” species, and a lower proportion of “soil” species in the assemblages sampled under fallen tree crowns than between crowns. Analysis of principal response curves (PRC) yielded unexpected results as it indicated that these differences were significant only in the first year post-disturbance, thus suggesting a very short-lasting protective effect of tree crowns on Collembola, RDA analysis with preselected factors from environmental variables of interest (LAI of standing and fallen tree crowns, soil respiration, soil temperature and humidity, soil pH and soil nitrogen and carbon content) indicated the LAI index as significant for the Collembolan assemblages in the first yearpost-disturbance, soil moisture in the second year, and soil temperature in the third year. This sequence of significant indices over a three year period is compatible with the fallen crowns becoming more and more thinned as a result of needles falling off (from shade to full exposure to sunlight). We nevertheless postulate that at least some trees or their crowns lying on the ground should be left in place during clearance of windthrow-affected tree stands to facilitate restoration of the soil biota.

Identifying Subsurface Connectivity From Observations: Experimentation With Equifinality Defines Both Challenges and Pathways to Progress

ABSTRACTLinkages between landscapes and streams are increasingly described in terms of hydrological connectivity. The ability to effectively distinguish different patterns of water movement through catchments makes connectivity particularly interesting to both scientists and practical water managers. Hydrometric data (groundwater levels, soil moisture and streamflow) are often employed to infer the connection between the landscape and its drainage network. Such observational data, however, are insufficient to infer subsurface connectivity in humid settings with perennial stream flow, due to the risk of equifinality. To quantify how much subsurface flow patterns can differ and still be consistent (equifinal) with comprehensive observations of hillslope groundwater levels and stream runoff (the hydrometric data), this study used a modelling experiment based on a well‐characterised field site. Particle‐tracking simulations at different flow rates defined the water flow paths and transit times of two virtual hillslopes that differed profoundly in the vertical distribution of the saturated hydraulic conductivity. Even though the simulated weekly stream flows and groundwater levels were similar (i.e., the hillslopes were hydrometrically equifinal) particle velocities and water ages at specific locations along these hillslopes differed by orders of magnitude. Flow path lengths and catchment transit times varied up to several 100%. The hillslope‐ and stream‐based metrics used to describe connectivity also varied with stream flow rates. These results underline the need to recognise the risks for equifinality when inferring subsurface connectivity from hydrometric observations alone, even when those observations are comprehensive. The results also highlight the value of model simulations for quantifying the uncertainty in the inferred connectivity, targeting the best sampling locations/times to reduce this uncertainty with tracer data and better understanding the way connectivity influences stream chemistry.

Root physiological and morphology processes co-regulate the growth of Chinese-fir saplings in response to warming and precipitation reduction in the sub-tropical regions

Subtropical China is projected to experience elevated temperature greater than the mean global temperature increase and is accompanied by reduced precipitation. The plasticity of roots to changing environment strongly influences ecosystem feedbacks to climate change. However, knowledge gaps on the individual and combined effects of warming and precipitation reduction on root systems hinder our ability to accurately predict the growth and adaptability of forests under future climate change. To examine the effects of warming (W) and precipitation reduction (P) on roots physiology and morphology of Chinese-fir saplings, we used a randomized complete block design with factorial soil warming (ambient, ambient + 5℃) and precipitation reduction (ambient, ambient-50%) treatments. A full excavation method was adopted to obtain roots, then we measured the root physiology (osmoregulatory substances, oxidant substances, protective enzymes, endogenous hormones), morphology (specific root length, SRL; surface root area, SRA; root tissue density, RTD). The content of carbon and nitrogen, isotopes (δ13C and δ15N); soil temperature, soil moisture and sapling growth were also measured. We found that compared with the control, W decreased the abscisic acid (IAA) content; P increased the contents of hydrogen peroxide (H2O2) and proline (Pro), and decreased the contents of IAA and cytokinin (CTK); warming plus precipitation reduction (WP) increased the Pro content, and decreased the contents of IAA and CTK. In addition, the effects of W and P on root morphology varied with soil depth and root diameter class. W, P, and WP all increased fine root SRL and SRA in deep soil. Warming and precipitation reduction could affect physiological traits (e.g. non-enzymatic substances and antioxidant enzymes) and subsequently morphological traits via influencing soil environment and root tissue chemistry. Collectively, the results indicated that Chinese-fir saplings responded to warming and precipitation reduction by comprehensive regulation of the non-enzymatic substances (e.g., osmotic substances and endogenous hormones) of fine roots and changing root morphological characteristics in deep soil.

Estimation and Restoration of Dried Soil Layers in a Slope‐Gully Unit of the Chinese Loess Plateau

ABSTRACTDried soil layer (DSL) is a phenomenon of deep soil desiccation caused by soil water content (SWC) deficiency. Relevant studies in the fragmented terrain of Chinese Loess Plateau (CLP) remain limited. In a typical slope‐gully unit near the Liudaogou catchment, SWC was measured using neutron probes on 19 occasions at 15 observational locations. In order to reveal the temporal stability and elimination degree of DSLs, available soil moisture (ASM) and DSL were estimated by representative sites which were determined through the temporal stability method, and the reliability of simulating mean condition of the study area via representative locations was assessed. Results show that: (1) the dynamics of DSL was characterised by complexity and diversity. The ASM within the DSL (DSL‐ASM), ASM within the sandwiched DSL (SDSL‐ASM) and quantitative index (QI) varied within the range of 2.75%–3.11%, 2.98%–4.22% and 0.254–0.356, respectively. (2) The possibility of development and recovery for DSL and SDSL in deep layers were less than that in shallow layers. The maximum depth of DSL (DSLMD) was significantly and negatively related to the standard deviation of relative difference (SDRD) of DSL‐ASM, the maximum depth of SDSL (SDSLMD) was negatively related to the SDRD of SDSL‐ASM. (3) The prediction results of ASM above 300 cm depth were more accurate than other layers (R2 = 0.89). The DSL‐ASM had more accurate ability of prediction than SDSL‐ASM and QI. On the analysis of time stability characteristics of ASM and DSLs, the locations of A2 and C3 can better represent the mean conditions of ASM at three and four soil layers, respectively. C2, A1 and A1 can better represent the average levels of DSL‐ASM, SDSL‐ASM and QI, respectively (R2 = 0.43, 0.14 and 0.18). (4) The restoration degrees of DSLs mainly showed no elimination and slight elimination, the DSLs cannot be completely eliminated within a short time. We proposed that scientific regulation of SWC can alleviate the formation and development of DSLs at a certain extent, and provide the possibility for DSLs nonoccurrence.

Diffusion law and diffusion model for backfill grouting in loess shield tunnel at different soil moisture

Loess is a special type of soil whose properties are significantly affected by water. However, the grout diffusion law for backfill grouting in loess shield tunnels remains unknown. Based on a visual model experimental device, three experiments were conducted with 10%, 20%, and 30% loess moisture. A finite discrete element method was used to verify the grout diffusion mode, and parameters such as the tunnel buried depth, grout viscosity, and elastic modulus were considered to analyse the grout diffusion law. Experiments and numerical simulations show that the screening diffusion of grout occurs at low loess moisture, whereas splitting diffusion occurs at high loess moisture. The farthest splitting diffusion distance decreases as the tunnel buried depth, grout viscosity, and elastic modulus increase. In addition, based on capillary theory and geotechnical strength criteria, screening diffusion and splitting diffusion models were established. This study investigated the grout diffusion law and grout diffusion model, providing a reference for the design and construction of loess shield tunnels.

Rapid declines in species diversity and occurrence of common plant species are related to nutrient availability and soil moisture in open habitats

AbstractAimsEnvironmental changes in Europe influence plant community composition, but the literature quantifying these changes often shows inconsistent trends, due mostly to heterogeneous survey methods. Here, we investigated temporal changes in plants over 12 years at the species and community level at a regional scale, using a standardized, plot‐based monitoring scheme.LocationData originated from 1,389 permanent plots of a standardized monitoring scheme targeting plant communities. Plots were distributed in the Burgundy region (France), initially grouped into 175 (2 km × 2 km) grid cells containing eight 10‐m2 plots each, that were surveyed in at least 2 years between 2009 and 2020.MethodsWe characterized changes in vascular plants in 10‐m2 plots by examining the temporal changes in the probability of occurrence of common species, changes in species diversity using species richness, Shannon–Wiener and Pielou's indices and changes in abundance‐weighted mean community ecological preferences using Ellenberg indicator values.ResultsAcross 198 common species, probability of occurrence in the region has shown a decline since 2009. This decline is associated with a general decrease by 13% of both species richness and the Shannon index between 2009 and 2020. This trend was stronger in annual crops and grasslands, whereas forest diversity remained relatively constant over time. Pielou's index diminished on average, except in natural forests. Mean community Ellenberg indicator values suggested slight changes in plant community composition, with an increasing preference for nutrient‐poor soils and Atlantic conditions over time.ConclusionsThe observed biodiversity loss in the Burgundy region is consistent with a widespread shift in community composition in response to environmental change. Existing conservation measures do not seem to compensate for the average losses, indicating that these measures are still inadequate to protect plant communities. Our approach also emphasizes the speed at which plant communities are changing and thus the need for better monitoring of the European flora.