In order to investigate the frost-heaving characteristics of wintering foundation pits in the seasonal frozen ground area, an outdoor in-situ test of wintering foundation pits was carried out to study the changing rules of horizontal frost heave forces, vertical frost heave forces, vertical displacement, and horizontal displacement of the tops of the supporting piles under the effect of groundwater and natural winterization. Based on the monitoring condition data of the in-situ test and the data, a coupled numerical model integrating hydrothermal and mechanical interactions of the foundation pit, considering the groundwater level and phase change, was established and verified by numerical simulation. The research results show that in the silty clay-sandy soil strata with water replenishment conditions and the all-silty clay strata without water replenishment conditions, the horizontal frost heave force presents a distribution feature of being larger in the middle and smaller on both sides in the early stage of overwintering. With the extension of freezing time, the horizontal frost heave force distribution of silty clay-sand strata gradually changes from the initial form to the Z shape, while the all-silty clay strata maintain the original distribution characteristics unchanged. Meanwhile, the peak point of the horizontal frost heave force in the all-silty clay stratum will gradually shift downward during the overwintering process. This phenomenon corresponds to the stage when the horizontal displacement of the pile top enters a stable and fluctuating phase. Based on the monitoring conditions of the in-situ test, a numerical model of the hydro-thermo-mechanical coupling in the overwintering foundation pit was established, considering the effects of the groundwater level and ice-water phase change. The accuracy and reliability of the model were verified by comparison with the monitoring data of the in-situ test using FLAC3D finite element analysis software. The evolution of the horizontal frost heaving force of the overwintering foundation pit and the change rule of its distribution pattern under different groundwater level conditions are revealed. This research can provide a reference for the prevention of frost heave damage and safety design of foundation pit engineering in seasonal frozen soil areas.

Snow cover is a critical factor controlling plant performance, such as survival, growth, and biomass, and vegetation cover in regions with seasonal snow (e.g., high-latitude and high-elevation regions), due to its influence on the timing and length of the growing season, insulation effect during winter, and biotic and abiotic environmental factors. Therefore, changes in snow cover driven by rising temperatures and shifting precipitation patterns are expected to alter plant performance and vegetation cover. Despite the rapid increase in research on this topic in recent decades, there is still a lack of studies that quantitatively elucidate how plant performance and vegetation cover respond to shifting snow cover across snowy regions. Additionally, no comprehensive study has yet quantitatively examined these responses across regions, ecosystems, and plant functional types. Here, we conducted a meta-analysis synthesizing data from 54 snow cover manipulation studies conducted in both the field and laboratory across snowy regions to detect how plants performance and vegetation cover respond to decreased or increased snow cover. Our results demonstrate that plant survival, aboveground biomass, and belowground biomass exhibited significant decreases in response to decreased snow cover, with rates of survival having the greatest decrease. In response to increased snow cover, plant survival, growth, biomass and vegetation cover tended to increase, except for plant belowground length growth and biomass, which showed significant decreases. Additionally, our quantitative analysis of plant responses to changes in snow cover across regions, ecosystems, and plant functional types revealed that cold regions with thin snow cover, tundra and forest ecosystems, and woody species are particularly vulnerable to snow cover reduction. Overall, this study demonstrates the strong controls that snow cover exerts on plant performance, providing insights into the dynamics of snow-covered ecosystems under changing winter climatic conditions.

Freeze-thaw-induced N2O pulses could account for nearly half of annual N2O fluxes in cold climates, but their episodic nature, sensitivity to snow cover dynamics, and the challenges of cold-season monitoring complicate their accurate estimation and representation in global models. To address these challenges, we combined in situ automated high-frequency flux measurements with cross-ecoregion soil core incubations to investigate the mechanisms driving freeze-thaw-induced N2O emissions. We found that deepened snow significantly amplified freeze-thaw N2O pulses, with these similar to 50-day episodes contributing over 50% of annual fluxes. Additionally, freeze-thaw-induced N2O pulses exhibited significant spatial heterogeneity, ranging from 3.4 to 1184.1 mu g N m(-2) h(-1) depending on site conditions. Despite significant spatiotemporal variation, our results indicated that 68%-86% of this variation can be explained by shifts in controlling factors: from water-filled pore space (WFPS), which drove anaerobic conditions, to microbial constraints as snow depth increases. Below 43% WFPS, soil moisture was the overwhelmingly dominant driver of emissions; between 43% and 66% WFPS, moisture and microbial attributes (including denitrifying gene abundance, nitrogen enzyme kinetics, and microbial biomass) jointly triggered N2O emissions pulses; above 66% WFPS, microbial attributes, particularly nitrogen enzyme kinetics, prevailed. These findings suggested that maintaining higher soil moisture served as a trigger for activating microbial activity, particularly enhancing nitrogen cycling. Furthermore, we showed that hotspots of freeze-thaw-induced N2O emissions were linked to high root production and microbial activity in cold and humid grasslands. Overall, our study highlighted the hierarchical control of WFPS and microbial processes in driving freeze-thaw-induced N2O emission pulses. The easily measurable WFPS and microbial attributes predictable from plant and soil properties could forecast the magnitude and spatial distribution of N2O emission hot moments under changing climate. Integrating these hot moments, particularly the dynamics of WFPS, into process-based models could refine N2O emission modeling and enhance the accuracy of global N2O budget prediction.

Grapevines in cold regions are prone to frost damage in winter. Due to its adverse effects on soil structure, plant damage, high operational costs, and limited mechanization feasibility, buried soil overwintering has been gradually replaced by no-burial overwintering techniques, which are now the primary focus for mitigating frost damage in wine grapes. While current research focuses on the selection of thermal insulation materials, less attention has been paid to the insulation mechanism of covering materials and covering methods. In this study, we investigated the insulation performance of two covering materials (tarpaulin and insulation blanket) combined with six height treatments (5-30 cm) to analyze the effect of insulation space volume on no-buried-soil overwintering. The results show that the thermal insulation performance of the insulation blanket is significantly better than that of the tarpaulin. The 5 cm height treatment under the tarpaulin cover and the 25 cm height treatment under the insulation blanket cover exhibited the best thermal insulation performance. Using a neural network machine learning approach, we constructed a model related to the height of the insulation material and facilitate the model's accurate predictions, in which tarpaulin R2branches = 0.92, R220 cm = 0.99, and R240 cm = 0.99 and insulation blanket R2branches = 0.89, R220 cm = 0.98, and R240 cm = 0.99. The model predicted optimal insulation heights of 6 cm for the tarpaulin and 22 cm for the insulation blanket. Factors like solar radiation within the insulation space, ground radiation, airflow, and material thermal conductivity affect the optimal insulation height for different materials. This study used a neural network model to predict the optimal insulation heights for different materials, providing systematic theoretical guidance for the overwintering cultivation of wine grapes and aiding the safe development of the wine grape industry in cold regions.

Winter extreme low temperature events have been occurring frequently both before and after the winter season. The freezing resistance temperature of wheat is far lower than the intensity of low temperatures during the mid-winter period. Therefore, it is necessary to further quantify and evaluate the impact of low-temperature periods and durations during the early winter and the green-up period on the freezing resistance of wheat, based on different evaluation indicators. Through conducting experiments in an artificial low-temperature control chamber, this study investigates the critical temperature thresholds for the impact of different low-temperature periods and durations on the tiller and yield of winter wheat, as well as the critical temperature thresholds for soil effective negative accumulated temperature. The results demonstrate that (1) the tiller mortality rate (RT) and yield reduction rate (RY) of winter wheat during the winter increase with the severity and duration of low temperatures, showing an S-shaped curve. The winter wheat mortality rate during the early winter is related to the soil effective negative accumulated temperature in an exponential function, while the mid-winter and green-up stages have a linear relationship. (2) The freezing threshold temperatures for the RT, RY and soil negative accumulated temperature (SENAT) in different low-temperature periods (early winter, mid-winter, and green-up periods) range from - 11.7 to -17.9 degrees C, -9.4 to -15.6 degrees C, and 15.9 to 131.7 degrees Ch (2.2 to 16.8 degrees Cd), respectively. (3) The freezing threshold temperatures for the RT and RY in different low-temperature durations (1 day, 2 days, and 3 days) range from - 2.8 to -17.9 degrees C and - 9.4 to -15.6 degrees C, respectively. The findings of this study provide technical support and scientific guidance for the global cultivation structure and variety layout of winter wheat under the background of climate warming, as well as for the prevention and reduction of freezing damage and yield losses.

Overwintering frost damage is a major challenge for the wine grape industry in northern China. This study investigates overwintering treatments to improve survival rates and mitigate frost damage in the wine grape production area of the northern foothills of the Tianshan Mountains. Seven overwintering treatments were tested: soil-covered striped cloth, striped cloth, sandwiched striped cloth, thickened striped cloth, double-layered striped cloth, heat-insulating striped cloth, and heat-insulating sandwich striped cloth. Temperature and humidity were continuously monitored during the overwintering period, both aboveground and at depths of 20 and 40 cm underground. By analyzing temperature trends, the duration of low temperatures, and temperature fluctuations, comprehensive overwintering indices were derived through principal component analysis to assess heat retention, moisture preservation, and the impact on grapevine survival. The results showed that the sandwiched striped cloth treatment provided the best insulation, with a 4.4 degrees C higher minimum daily temperature and a 356% increase in overwintering indices compared to striped cloth alone. The double-layer striped cloth treatment also improved safety, with a 130% increase in overwintering indices. Other treatments, including the soil-covered and the heat-insulating striped cloth, showed reduced performance. The sandwiched striped cloth and double-layer striped cloth treatments are recommended for northern China's wine grape regions, with further research needed to evaluate their economic viability.

Agricultural drought significantly affects crop growth and food production, making accurate drought thresholds essential for effective monitoring and discrimination. This study aims to monitor the threshold ranges for different drought levels of winter wheat during three growth periods using a multispectral Unmanned Aerial Vehicle (UAV). Firstly, based on controlled field experiments, six vegetation indices were used to develop UAV optimal inversion models for the Leaf Area Index (LAI) and Soil-Plant Analysis Development (SPAD) during the jointing-heading period, heading-filling period, and filling-maturity period of winter wheat. The results show that during the three growth periods, the DVI-LAI, NDVI-LAI, and RVI-LAI models, along with the DVI-SPAD, RVI-SPAD, and TCARI-SPAD models, achieved the highest inversion accuracy. Based on the UAV-inversed LAI and SPAD indices, threshold ranges for different drought levels were determined for each period. The accuracy of LAI threshold monitoring during three periods was 92.8%, 93.6%, and 90.5%, respectively, with an overall accuracy of 92.4%. For the SPAD index, the threshold monitoring accuracy during three periods was 93.1%, 93.0%, and 92%, respectively, with an overall accuracy of 92.7%. Finally, combined with yield data, this study explores UAV-based drought disaster monitoring for winter wheat. This research enriches and expands the crop drought monitoring system using a multispectral UAV. The proposed drought threshold ranges can enhance the scientific and precise monitoring of crop drought, which is highly significant for agricultural management.

The implementation of real-time dynamic monitoring of disaster formation and severity is essential for the timely adoption of disaster prevention and mitigation measures, which in turn minimizes disaster-related losses and safeguards agricultural production safety. This study establishes a low-temperature disaster (LTD) monitoring system based on machine learning algorithms, which primarily consists of a module for identifying types of disasters and a module for simulating the evolution of LTDs. This study firstly employed the KNN model combined with a piecewise function to determine the daily dynamic minimum critical temperature for low-temperature stress (LTS) experienced by winter wheat in the Huang-Huai-Hai (HHH) region after regreening, with the fitting model's R2, RMSE, MAE, NRMSE, and MBE values being 0.95, 0.79, 0.53, 0.13, and 1.716 x 10-11, respectively. This model serves as the foundation for determining the process by which winter wheat is subjected to LTS. Subsequently, using the XGBoost algorithm to analyze the differences between spring frost and cold damage patterns, a model for identifying types of spring LTDs was developed. The validation accuracy of the model reached 86.67%. In the development of the module simulating the evolution of LTDs, the XGBoost algorithm was initially employed to construct the Low-Temperature Disaster Index (LTDI), facilitating the daily identification of LTD occurrences. Subsequently, the Low-Temperature Disaster Process Accumulation Index (LDPI) is utilized to quantify the severity of the disaster. Validation results indicate that 79.81% of the test set samples exhibit a severity level consistent with historical records. An analysis of the environmental stress-mitigation mechanisms of LTDs reveals that cooling induced by cold air passage and ground radiation are the primary stress mechanisms in the formation of LTDs. In contrast, the release of latent heat from water vapor upon cooling and the transfer of sensible heat from soil moisture serve as the principal mitigation mechanisms. In summary, the developed monitoring framework for LTDs, based on environmental patterns of LTD formation, demonstrates strong generalization capabilities in the HHH region, enabling daily dynamic assessments of the evolution and severity of LTDs.

From 2016 to 2019, 128 organic and conventional spring and winter pea fields in Germany were surveyed to determine the effects of cropping history and pedo-climatic conditions on pea root health, the diversity of Fusarium and Didymella communities and their collective effect on pea yield. Roots generally appeared healthy or showed minor disease symptoms despite the frequent occurrence of 4 Didymella and 14 Fusarium species. Soil pH interacted with the occurrence of the Fusarium oxysporum species complex (FOSC) and F. tricinctum that correlated with reduced or increased soil pH values, respectively. While legumes in the cropping history or reduced time between legumes correlated with occurrence of D. pinodella and to a lesser degree with the members of the F. solani species complex (FSSC), the reverse was true at least in organic spring peas for F. redolens. Only in conventional systems increased root infections with F. redolens and the FSSC were linked to root rot incidence whereas yields negatively correlated with the FOSC and positively with F. tricinctum isolation frequencies. Overall, this study shows that pea root rot pathobiome is rather stable and that the damage caused is mostly due to the interaction with environmental conditions.

Winter storms cause severe damage in German forests. Different modelling approaches have already been used to try and map endangered areas to minimize the risk of wind damage by stand adaption. Prevalent models for Germany include empirical-statistical and hybrid-mechanistic models, such as ForestGALES (FG). As of yet, FG is not extensively used in Germany as its parametrization requires extensive experimental efforts to derive regionally sensitive species-specific parameters. Here, we implement a statistical calibration approach for German forest conditions with observed damage from single tree data, soil types, topography (topex) and gust speed data. We use simulated annealing to generate new species-specific values for the tree species, Norway spruce, European beech, and Douglas fir from within the range of all coniferous (deciduous) species for Norway spruce and Douglas fir (European beech) and an additional 10 % buffer around the default species-specific values for each species. We compare two optimization approaches: First, we aim to maximize the Matthew's correlation coefficient (MCC), which is calculated from the confusion matrix, applying a fixed classification threshold of 0.5. In comparison to the optimization at a fixed threshold, we optimized the species-specific parameters by maximizing the area-under-curve (AUC) value directly generated from the receiver-operator characteristic (ROC) analysis. We compare our statistical parametrizations for the considered species to those currently implemented in FG and validate the resulting damage probabilities based on confusion matrices and related performance measures. We created separate parametrizations for a single-tree and stand-wide analysis of storm damage risk, which we validated with gust speed data for Germany. Our results show, that for the single-tree method, MCC improved for all species: By 0.26 (0.22) for the calibration (validation) subset for Douglas fir, by 0.22 (0.18) for Norway spruce and by 0.08 (0.05) for European beech. The optimization for the stand-method shows an increase in MCC as well, with results not being considered due to low numbers of observation data. We show that for German forests, FG's predictive capability can be improved by statistical optimization when no tree-pulling data is available, which could be valuable for creating further regionalizations of FG.