Effects of soil water and heat relationship under various snow cover during freezing-thawing periods in Songnen Plain, China

Long-term snow alters the sensitivity of nonstructural carbohydrates of Syntrichia caninervis to snow cover: Based on a 7-year experiment

The dynamics of nonstructural carbohydrates (NSC) profoundly affect productivity and ecological adaptability to adversity in plants. Global warming induced the frequent occurrence of extreme precipitation events that altered the winter snow pattern in deserts. However, there is a lack of understanding of how desert mosses respond to long-term snow cover change at the NSC level. Therefore, in this study, long-term (7-years) winter snow removal (-S), ambient snow (CK), and double snow (+S) experiments were set in the field to investigate the content of NSC and its component in Syntrichia Caninervis. Our results showed that changes in snow depth, snow years, and their interaction significantly affected NSC and its component of Syntrichia caninervis. Compared to snow removal, NSC, soluble sugar, and starch significantly decreased with the increasing snow depth. The ratio of soluble sugar to starch significantly increased, while NSC and soluble sugar gradually returned to the normal level with an increase in snow years. It is worth mentioning that snow removal significantly reduced the soluble sugar to starch ratio compared to ambient snow depth, whereas the double snow experiment significantly increased the ratio of soluble sugar to starch during winter. This indicated an obvious trade-off between carbon utilization and carbon storage in Syntrichia caninervis. Snow removal stimulated Syntrichia caninervis to store sufficient carbon sources by starch accumulation for its future growth, while double snow promoted its current growth by soluble sugar accumulation. The variance in decomposition showed that soil physical and chemical properties, snow cover, and their interaction explained 83% of the variation in NSC and its components, with soil and plant water content, pH, and electrical conductivity (P-WC, S-WC, S-pH, and S-EC) as significant predictors. This highlights that snow indirectly affected NSC and its component contents by changing soil physical and chemical properties; however, long-term changes in snow cover could slow down its sensitivity to snow.

Root-zone soil moisture estimation based on remote sensing data and deep learning

Soil moisture in the root zone is the most important factor in eco-hydrological processes. Even though soil moisture can be obtained by remote sensing, limited to the top few centimeters (<5 cm). Researchers have attempted to estimate root-zone soil moisture using multiple regression, data assimilation, and data-driven methods. However, correlations between root-zone soil moisture and its related variables, including surface soil moisture, always appear nonlinear, which is difficult to extract and express using typical statistical methods. The artificial intelligence (AI) method, which is advantageous for nonlinear relationship analysis and extraction is applied for root-zone soil moisture estimation, but by only considering its separate temporal or spatial correlations. The convolutional long short-term memory (ConvLSTM) model, known to capture spatiotemporal patterns of large-scale sequential datasets with the advantage of dealing with spatiotemporal sequence-forecasting problem, was used in this study to estimate root-zone soil moisture based on remote sensing-based variables. Owing to limitation of regional soil moisture observation data, the physical model Hydrus-1D was used to generate large and spatiotemporal vertical soil moisture dataset for the ConvLSTM model training and verification. Then, normalized difference vegetation index (NDVI) etc. remote sensing-based factors were selected as predictive variables. Results of the ConvLSTM model showed that the fitting coefficients (R²) of the root-zone soil moisture estimation significantly increased compared to those achieved by Global Land Data Assimilation System products, especially for deep layers. For example, R² increased from 0.02 to 0.60 at depth of 40 cm. This study suggests that a combination of the physical model and AI is a flexible tool capable of predicting spatiotemporally continuous root-zone soil moisture with good accuracy on a large scale.

X-ray Stain Localization with Near-Field Ptychographic Computed Tomography

Although X-ray contrast agents offer specific characteristics in terms of targeting and attenuation, their accumulation in the tissue on a cellular level is usually not known and difficult to access, as it requires high resolution and sensitivity. Here, quantitative near-field ptychographic X-ray computed tomography is demonstrated to assess the location of X-ray stains at a resolution sufficient to identify intracellular structures by means of a basis material decomposition. On the example of two different X-ray stains, the nonspecific iodine potassium iodide, and eosin Y, which mostly interacts with proteins and peptides in the cell cytoplasm, the distribution of the stains within the cells in murine kidney samples is assessed and compared to unstained samples with similar structural features. Quantitative nanoscopic stain concentrations are in good agreement with dual-energy micro computed tomography measurements, the state-of-the-art modality for material-selective imaging. The presented approach can be applied to a variety of X-ray stains advancing the development of X-ray contrast agents.

Three-Dimensional Model of Soil Water and Heat Transfer in Orchard Root Zone under Water Storage Pit Irrigation

To reveal the water and heat transfer characteristics of the orchard soil under water storage pit irrigation and to regulate the distribution of soil water and heat for improving apple quality and increasing yield, a 3D soil water and heat transfer model of orchards under water storage pit irrigation was established. The model not only considered the influences of root water uptake, precipitation, evaporation, and irrigation, but also simulated the infiltration process of the variable water head in the pit according to the principle of mass conservation and introduced the pit coefficient to simulate the difference in radiation in the pit to describe the influence of the pit on the model. Verify and analyze the simulation results. Results showed that the variation trend of simulated soil moisture and heat was consistent with that of measured data. The mean absolute percentage error, root mean square error, and mean absolute deviation were 3.23%, 0.9460, and 0.6984 for soil temperature and 10.05%, 0.0269, and 0.0214 for water content after irrigation, respectively. The simulation results have high accuracy and show that the soil moisture content centers on the pit with an ellipsoid distribution and tends to be uniform over time. The soil temperature was higher in the 4–5 cm area near the soil surface and the wall of pit, and it remarkably changed with time. The intraday variation of soil temperature was mainly affected by atmospheric temperature, but a certain lag was observed compared with the change of atmospheric temperature. With the increase of the irrigation amount, the distribution range of soil moisture content and water high value area increased, while the average and maximum soil temperature decreased. With the increase of irrigation water temperature to 18–24 h after irrigation, the soil temperature in the ellipsoidal area around the pit remarkably increased. The model established in this paper can be used to simulate the hydrothermal status of the soil in the field under water storage pit irrigation. The results prove that the water storage pit irrigation can effectively improve the hydrothermal status of the middle-deep soil and promote the root system of fruit trees to absorb water.

The effects of soil freeze-thaw processes on water and salt migrations in the western Songnen Plain, China

Seasonally freeze-thaw (FT) processes affect soil salinisation in cold and arid regions. Therefore, understanding the mechanisms behind soil salinisation during winter and spring is crucial for management strategies effectively alleviating this. This study aimed to explore the soil FT characteristics and their influences on soil water and salt migrations to clarify the underlying mechanism of the springtime soil salinisation in the western Songnen Plain, China. The spatiotemporal distributions of soil water and salt, frozen depths and soil temperatures were examined at depths of 0–200 cm in three typical landscapes (farmland, Leymus chinensis (Trin.) Tzvel (LT) grassland and alkali-spot (AS) land) from October 2015 to June 2016. Results indicated that the strongest freezing process occurred in AS land, which was characterised by the deepest frost depth (165 cm) and highest freezing rate (3.58 cm/d), followed by LT grassland, and then farmland. The freeze-induced upward redistribution and enrichment of soil water and salt caused the rise and expansion of the soil salification layer, which was the main source of explosive accumulations of surface salt in springtime. Therefore, the FT processes contributed to the surface soil salinisation and alkalisation. Landscapes also affected soil water and salt migrations during FT processes, with the trend being AS land > LT grassland > farmland.

Spatiotemporal characteristics of hydrothermal processes of the active layer on the central and northern Qinghai-Tibet plateau

The spatial and temporal variations of the seasonal freeze-thaw cycles are important in understanding the ecological and hydrological processes and biogeochemical cycle associated with permafrost degradation caused by climate change, although observational data on the soil hydrothermal dynamics within the active layer of the permafrost region at the central and northern Qinghai-Tibet Plateau (QTP) are extremely scarce. In this study, soil temperature and moisture date from 11 observational sites along the Qinghai-Tibet Highway from 2010 to 2014 were used to analyze the freeze-thaw cycles of the active layer. The results revealed that mean annual ground surface temperature (MAGST) and mean annual temperature at the top of permafrost (TTOP) were the most closely related to the onset dates of soil freezing and thawing. The onset dates of soil freezing from bottom to top did not occur earlier than those from top to bottom. The differences between the onset dates of the two freezing directions and the proportion of bottom-up freezing depth increased with decreasing TTOP. The unfrozen water content of the cooling process was always higher than that of the warming process during the freezing stage. The hysteresis effect of the unfrozen water content could also be observed in the field experiment, and the maximum hysteresis levels occurred at their corresponding soil freezing points. Soil organic matter and soil moisture associated with vegetation cover are essential for water-heat exchanges between atmosphere and permafrost beneath active layer. We suggest that a better protected plant ecosystem, helps preserving the underlying permafrost on the Qinghai-Tibet Plateau.