Moisture pulse-reserve in the soil-plant continuum observed across biomes

Soil moisture evidence of self-restoration in coal mining subsidence area of Shendong Mining area: Cognition based on stable isotopes

The Shendong mining area is the largest coal production base in China. However, the area is dry and water scarce, and the ecological environment is fragile. Large scale mining of shallow and thick coal seam in mining area may affect surface moisture and threaten surface ecological security. In order to understand the change of soil moisture in subsidence area. The evaporation loss rate of undisturbed and subsidence soil in mining area was estimated by using water stable isotope technique. The soil particle size and moisture of undisturbed and subsidence soil were compared. The results showed that the soil particle size did not change significantly in the subsidence area, but the continuity of soil structure changed. The evaporation loss rate of soil in subsidence area is about 15 % lower than that of undisturbed soil, and the soil moisture of soil in subsidence area is about 10 % higher than that of undisturbed soil. Further, the Craig-Gordon model is more accurate than the Rayleigh model in estimating the evaporation loss of soil moisture. Our work showed that the soil structure of coal mining subsidence area becomes much looser and the loose cover formed by the surface soil is the main reason for the reduction of soil moisture evaporation. The significant increase of soil moisture will be beneficial to plant recovery and growth, and lay a water foundation for ecological self-restoration in subsidence area. This study is helpful to understand the influence of coal mining subsidence on soil and surface hydrology in arid and semi-arid areas, and has important significance for optimizing and improving the ecological reclamation model of mining areas in western China.

Dynamic regulation of water potential in Juniperus osteosperma mediates ecosystem carbon fluxes

Some plants exhibit dynamic hydraulic regulation, in which the strictness of hydraulic regulation (i.e. iso/anisohydry) changes in response to environmental conditions. However, the environmental controls over iso/anisohydry and the implications of flexible hydraulic regulation for plant productivity remain unknown. In Juniperus osteosperma, a drought-resistant dryland conifer, we collected a 5-month growing season time series of in situ, high temporal-resolution plant water potential ( Ψ ) and stand gross primary productivity (GPP). We quantified the stringency of hydraulic regulation associated with environmental covariates and evaluated how predawn water potential contributes to empirically predicting carbon uptake. Juniperus osteosperma showed less stringent hydraulic regulation (more anisohydric) after monsoon precipitation pulses, when soil moisture and atmospheric demand were high, and corresponded with GPP pulses. Predawn water potential matched the timing of GPP fluxes and improved estimates of GPP more strongly than soil and/or atmospheric moisture, notably resolving GPP underestimation before vegetation green-up. Flexible hydraulic regulation appears to allow J. osteosperma to prolong soil water extraction and, therefore, the period of high carbon uptake following monsoon precipitation pulses. Water potential and its dynamic regulation may account for why process-based and empirical models commonly underestimate the magnitude and temporal variability of dryland GPP.

Global critical soil moisture thresholds of plant water stress

During extensive periods without rain, known as dry-downs, decreasing soil moisture (SM) induces plant water stress at the point when it limits evapotranspiration, defining a critical SM threshold (θcrit). Better quantification of θcrit is needed for improving future projections of climate and water resources, food production, and ecosystem vulnerability. Here, we combine systematic satellite observations of the diurnal amplitude of land surface temperature (dLST) and SM during dry-downs, corroborated by in-situ data from flux towers, to generate the observation-based global map of θcrit. We find an average global θcrit of 0.19 m3/m3, varying from 0.12 m3/m3 in arid ecosystems to 0.26 m3/m3 in humid ecosystems. θcrit simulated by Earth System Models is overestimated in dry areas and underestimated in wet areas. The global observed pattern of θcrit reflects plant adaptation to soil available water and atmospheric demand. Using explainable machine learning, we show that aridity index, leaf area and soil texture are the most influential drivers. Moreover, we show that the annual fraction of days with water stress, when SM stays below θcrit, has increased in the past four decades. Our results have important implications for understanding the inception of water stress in models and identifying SM tipping points.

Drought-resistant trait of different crop genotypes determines assembly patterns of soil and phyllosphere microbial communities

Crop microbiomes are widely recognized to play a role in crop stress resistance, but the ecological processes that shape crop microbiomes under water stress are unclear. Therefore, we investigated the bacterial communities of two oat (Avena sativa) and two wheat (Triticum aestivum) genotypes under different water stress conditions. Our results show that the microbial assemblage was determined by the crop compartment niche. Host selection pressure on the bacterial community increased progressively from soil to epiphyte to endophyte pathways, leading to a decrease in bacterial community diversity and network complexity. Source tracing shows that soil is the primary source of crop microbial communities and that bulk soil is the main potential source of crop microbiota. It filters gradually through the different compartment niches of the crop. We found that the phyla Actinobacteria, Proteobacteria, Gemmatimonadota, and Myxococcota were significantly enriched in bacterial communities associated with crop-resistance enzyme activity. Crop genotype influenced the composition of the rhizosphere soil microbial community, and the composition of the phylloplane microbial community was affected by water stress. IMPORTANCE In this paper, we investigated the assembly of the plant microbiome in response to water stress. We found that the determinant of microbiome assembly under water stress was the host type and that microbial communities were progressively filtered and enriched as they moved from soil to epiphyte to endophyte communities, with the main potential source being bulk soil. We also screened for bacterial communities that were significantly associated with crop enzyme activity. Our research provides insights into the manipulation of microbes in response to crop resistance to water stress.

Critical soil moisture thresholds of plant water stress in terrestrial ecosystems

Plant water stress occurs at the point when soil moisture (SM) limits transpiration, defining a critical SM threshold (θ_crit). Knowledge of the spatial distribution of θ_crit is crucial for future projections of climate and water resources. Here, we use global eddy covariance observations to quantify θ_crit and evaporative fraction (EF) regimes. Three canonical variables describe how EF is controlled by SM: the maximum EF (EF_max), θ_crit, and slope (S) between EF and SM. We find systematic differences of these three variables across biomes. Variation in θ_crit, S, and EF_max is mostly explained by soil texture, vapor pressure deficit, and precipitation, respectively, as well as vegetation structure. Dryland ecosystems tend to operate at low θ_crit and show adaptation to water deficits. The negative relationship between θ_crit and S indicates that dryland ecosystems minimize θ_crit through mechanisms of sustained SM extraction and transport by xylem. Our results further suggest an optimal adaptation of local EF-SM response that maximizes growing-season evapotranspiration and photosynthesis.

Prevalence and drivers of abrupt vegetation shifts in global drylands

The constant provision of plant productivity is integral to supporting the liability of ecosystems and human wellbeing in global drylands. Drylands are paradigmatic examples of systems prone to experiencing abrupt changes in their functioning. Indeed, space-for-time substitution approaches suggest that abrupt changes in plant productivity are widespread, but this evidence is less clear using observational time series or experimental data at a large scale. Studying the prevalence and, most importantly, the unknown drivers of abrupt (rather than gradual) dynamical patterns in drylands may help to unveil hotspots of current and future dynamical instabilities in drylands. Using a 20-y global satellite-derived temporal assessment of dryland Normalized Difference Vegetation Index (NDVI), we show that 50% of all dryland ecosystems exhibiting gains or losses of NDVI are characterized by abrupt positive/negative temporal dynamics. We further show that abrupt changes are more common among negative than positive NDVI trends and can be found in global regions suffering recent droughts, particularly around critical aridity thresholds. Positive abrupt dynamics are found most in ecosystems with low seasonal variability or high aridity. Our work unveils the high importance of climate variability on triggering abrupt shifts in vegetation and it provides missing evidence of increasing abruptness in systems intensively managed by humans, with low soil organic carbon contents, or around specific aridity thresholds. These results highlight that abrupt changes in dryland dynamics are very common, especially for productivity losses, pinpoint global hotspots of dryland vulnerability, and identify drivers that could be targeted for effective dryland management.

Tunneling-induced groundwater depletion limits long-term growth dynamics of forest trees

Human interventions such as tunnel construction have caused groundwater depletion, which substantially affected the functions of forest tree species and their communities. However, the extent to which tunneling-induced groundwater depletion (TIGD) degrades their function levels at various spatial-temporal scales under varying climate conditions remains still unclear. Researchers used stand-scale dendrological records to track and extract the effects of TIGD associated with a single or series of tunneling events (three tunneling events during 1999-2001, 2006-2008, and 2010-2013) on short- and long-term growth levels of two dominant drought-tolerant tree species across (karst and non-karst) landscapes affected by tunnel construction and landscapes not subjected to tunnel construction in a mountainous forest ecosystem located in the southwest of China. The results showed that growth responses of both trees stand to TIGD, and the TIGD-linked water losses of other available water sources were negative and widespread across tunnel-affected landscapes, particularly in the karst landscapes known as delicate landscapes. Tree stands with faster (more vigorous) growth rates showed more significant adverse growth levels in response to either tunneling-induced or drought-induced water stresses. Also, they showed the highest recovered growth levels in response to favorable climatic conditions. Moreover, the growth level in the tunnel-affected forest never fully recovered during six years of very wet weather (2012-2018) after the construction of the final (third) tunnel in 2010-2013. Current research shows that tunnel construction has a cumulatively detrimental impact on the long-term survival of the forest. Even with the mediation of long-term very wet circumstances, it can substantially restrict the development dynamics of the forest compared to drought.

Uncovering the critical soil moisture thresholds of plant water stress for European ecosystems

Understanding the critical soil moisture (SM) threshold (θ_crit ) of plant water stress and land surface energy partitioning is a basis to evaluate drought impacts and improve models for predicting future ecosystem condition and climate. Quantifying the θ_crit across biomes and climates is challenging because observations of surface energy fluxes and SM remain sparse. Here, we used the latest database of eddy covariance measurements to estimate θ_crit across Europe by evaluating evaporative fraction (EF)-SM relationships and investigating the covariance between vapor pressure deficit (VPD) and gross primary production (GPP) during SM dry-down periods. We found that the θ_crit and soil matric potential threshold in Europe are 16.5% and -0.7 MPa, respectively. Surface energy partitioning characteristics varied among different vegetation types; EF in savannas had the highest sensitivities to SM in water-limited stage, and the lowest in forests. The sign of the covariance between daily VPD and GPP consistently changed from positive to negative during dry-down across all sites when EF shifted from relatively high to low values. This sign of the covariance changed after longer period of SM decline in forests than in grasslands and savannas. Estimated θ_crit from the VPD-GPP covariance method match well with the EF-SM method, showing this covariance method can be used to detect the θ_crit . We further found that soil texture dominates the spatial variability of θ_crit while shortwave radiation and VPD are the major drivers in determining the spatial pattern of EF sensitivities. Our results highlight for the first time that the sign change of the covariance between daily VPD and GPP can be used as an indicator of how ecosystems transition from energy to SM limitation. We also characterized the corresponding θ_crit and its drivers across diverse ecosystems in Europe, an essential variable to improve the representation of water stress in land surface models.

Error Propagation in Microwave Soil Moisture and Vegetation Optical Depth Retrievals

Satellite soil moisture and vegetation optical depth [(VOD); related to the total vegetation water mass per unit area] are increasingly being used to study water relations in the soil-plant continuum across the globe. However, soil moisture and VOD are typically jointly estimated, where errors in the optimization approach can cause compensation between both variables and confound such studies. It is thus critical to quantify how satellite microwave measurement errors propagate into soil moisture and VOD. Such a study is especially important for VOD given limited investigations of whether VOD reflects in situ plant physiology. Furthermore, despite new approaches that constrain (or regularize) VOD dynamics to reduce soil moisture errors, there is limited study of whether regularization reduces VOD errors without obscuring true vegetation temporal dynamics. Here, we find that, across the globe, VOD is less robust to measurement error (more difficult for optimization methods to find the true solution) than soil moisture in their joint estimation. However, a moderate degree of regularization (via time-constrained VOD) reduces errors in VOD to a greater degree than soil moisture and reduces spurious soil moisture-VOD coupling. Furthermore, despite constraining VOD time dynamics, regularized VOD variations on subweekly scales are both closer to simulated true VOD time series and have global VOD post-rainfall responses with reduced error signatures compared to VOD retrievals without regularization. Ultimately, we recommend moderately regularized VOD for use in large scale studies of soil-plant water relations because it suppresses noise and spurious soil moisture-VOD coupling without removing the physical signal.

Synergistic integration of optical and microwave satellite data for crop yield estimation

Developing accurate models of crop stress, phenology and productivity is of paramount importance, given the increasing need of food. Earth observation (EO) remote sensing data provides a unique source of information to monitor crops in a temporally resolved and spatially explicit way. In this study, we propose the combination of multisensor (optical and microwave) remote sensing data for crop yield estimation and forecasting using two novel approaches. We first propose the lag between Enhanced Vegetation Index (EVI) derived from MODIS and Vegetation Optical Depth (VOD) derived from SMAP as a new joint metric combining the information from the two satellite sensors in a unique feature or descriptor. Our second approach avoids summarizing statistics and uses machine learning to combine full time series of EVI and VOD. This study considers two statistical methods, a regularized linear regression and its nonlinear extension called kernel ridge regression to directly estimate the county-level surveyed total production, as well as individual yields of the major crops grown in the region: corn, soybean and wheat. The study area includes the US Corn Belt, and we use agricultural survey data from the National Agricultural Statistics Service (USDA-NASS) for year 2015 for quantitative assessment. Results show that (1) the proposed EVI-VOD lag metric correlates well with crop yield and outperforms common single-sensor metrics for crop yield estimation; (2) the statistical (machine learning) models working directly with the time series largely improve results compared to previously reported estimations; (3) the combined exploitation of information from the optical and microwave data leads to improved predictions over the use of single sensor approaches with coefficient of determination R≥ 2 0.76 ; (4) when models are used for within-season forecasting with limited time information, crop yield prediction is feasible up to four months before harvest (models reach a plateau in accuracy); and (5) the robustness of the approach is confirmed in a multi-year setting, reaching similar performances than when using single-year data. In conclusion, results confirm the value of using both EVI and VOD at the same time, and the advantage of using automatic machine learning models for crop yield/production estimation.