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There is growing interest in using canopy temperature (Tc), including crop water Stress index (CWSI), for irrigation management. However, Tc varies greatly in one day, while soil water content (SWC) varies little, which may lead to different conclusions on whether irrigation is needed based on CWSI at different times. For this end, Tc of winter wheat was continuously monitored, and the data of such environmental factors as atmospheric temperature and soil water content (SWC) were simultaneously collected. CWSI was calculated based on empirical formulation and Tc and CWSI were generalized based on the normalization formulation. The correlation SWC between Tc and CWSI before and after generalization was compared and error analysis was based on SWC theoretical formula. The results showed: (1) the accuracy of SWC retrieval by Tc and CWSI increased firstly and then decreased with time during the day. The optimal time for Tc monitoring SWC was between 10:00 ~ 16:00 (R2 > 0.72) and the optimal time for CWSI monitoring SWC was between 9:00 ~ 18:00 (R2 > 0.69). (2) CWSI and Tc were mapped based on the relationship between crop water stress and soil water deficit and normalized canopy temperature expressions characterized the relationship between crop water stress and soil water deficit. (3) The accuracy of inversion of SWC by mapping Tc from 18:00 ~ 8:00 is increased from 0.5 ~ 0.6 to 0.7 ~ 0.8; the accuracy of soil water content inversion by mapping CWSI from 18:00 ~ 8:00 was improved from 0.2 ~ 0.4 to 0.4 ~ 0.6. (4) The theoretical expression of SWC deduced based on CWSI also proves that considering the relationship between crop water stress and soil water deficit change can effectively reduce the relative error from 30 to 5% in the morning and evening. This study contributes to the understanding of the reason why the correlation between Tc and SWC varies greatly during the day and solves the time-limited problem of thermal infrared remote sensing monitoring of crop water stress. [ABSTRACT FROM AUTHOR]

In mid-June to July 2023, North China witnessed extreme heatwaves, marked by intense near-surface warming with an advanced seasonal cycle of local air temperature. An unconventional upper-tropospheric cold vortex in early June, deviating from conventional "heat dome" patterns, preceded the heatwave extremes. The zonal SSTA gradient in Indo-Pacific warm pool initially suppressed Indian summer monsoon convection, which stimulated the cold vortex around North China via a tropical-extratropical teleconnection. This anomaly intensified the air-land thermal contrast, leading to increased sensible heating and reduced soil moisture in situ. The drier soil conditions maintained and further augmented sensible heating, escalating surface air temperature, and culminating in extraordinary heatwaves. The air column was then destabilized to mitigate the upper-level cold vortex. Historical records corroborate the extremity of the air-sea interactions in 2023. The ECMWF real-time subseasonal-to-seasonal (S2S) forecasts successfully capture the air-land feedback in both cold vortex and heatwave stages, albeit with an underestimation of heatwave intensity due to biases in soil moisture anomalies. Consequently, the initial cold vortex condition and air-land-sea interactions yield S2S predictability to the historic 2023 heatwaves in North China. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of the relationships between soil moisture, cloud cover, solar radiation, air temperature and humidity, and precipitation during the warm half of the year in East‐Central Europe over the years 1971–2020. The temporal and spatial variability of these meteorological elements is presented in association with the occurrence of anticyclonic blocking events over the study area. It demonstrates that changes in soil moisture in East‐Central Europe point to the combined influence of many meteorological factors resulting from the atmospheric circulation, and are an indicator of the comprehensive relationships among those factors. The main factors affecting soil water content are precipitation and evapotranspiration, which in turn depend on air humidity, cloudiness, intensity of solar radiation and air temperature. The increase in the frequency and duration of sequences of days with blocking events in East‐Central Europe has contributed to an increased probability of longer periods with soil moisture negative anomalies. [ABSTRACT FROM AUTHOR]

Selin Co basin, a representative lake basin situated in the central region of the Tibetan Plateau (TP), is characterized by extensive areas of frozen ground and has undergone a remarkable expansion in its lake area. Evapotranspiration, as an important variable within the lake water cycle, as well as its influencing factors are still under debate at this basin. Actual evapotranspiration (ETa) plays a vital role in influencing land-atmosphere interaction processes and the hydrological cycle. By quantifying the amount of water that is actually transpired by vegetation and evaporated from soil surfaces, ETa provides important information about the mechanisms controlling evapotranspiration. In this study, the advection-aridity (AA) model was employed to estimate ETa in Selin Co basin. The estimated ETa and its influencing factors were investigated between 2001-2018, with an emphasis on examining both spatial and temporal trends. The results demonstrated that the multiyear mean ETa in the basin was 430.3 mm (excluding lakes) with an overall significantly decreasing trend (-5.8 mm/yr). In terms of its seasonal variation, ETa exhibited highest value during summer, while experiencing comparatively lower rates in autumn and spring. Furthermore, the spatial distribution of both the annual and seasonal ETa exhibited a similar decreasing trend from the southeast to the northwest of the basin. The sensitivity of several meteorological variables to modifications in ETa was also examined. Results indicated that specific humidity was the predominant variable, followed by net radiation, air temperature, precipitation, atmospheric pressure, and wind speed. In addition, the area covered by permafrost and seasonally frozen ground was approximately about 1.58 × 104 km2 and 4.22 × 104 km2 in the basin, and permafrost degradation in the northern region of the Selin Co basin resulted in increased average soil moisture content and underground ice melting, leading to a subsequent increase in ETa. [ABSTRACT FROM AUTHOR]

Irrigation is becoming increasingly common in agriculture and is essential to meet the growing demand for food. Studies of the impact of irrigated areas on local meteorology reveal a strong influence on near‐surface conditions, although the extent of this influence varies considerably between locations. In addition, though theoretical evidence suggests that irrigation can create breeze‐like atmospheric boundary‐layer circulations, observational evidence is still lacking. This study investigates the effects of irrigation on the surface and atmospheric boundary layer in the Ebro basin, an intensively irrigated area with a semi‐arid climate in northeastern Spain. Observational data from the international field campaign Land Surface Interactions with the Atmosphere over the Iberian Semi‐arid Environment are analysed together with coupled surface–atmosphere model output to better understand and quantify the impact of irrigation on the lower atmosphere. A simple parametrization of irrigation is shown to improve the accuracy of the model. Results demonstrate that irrigation increases the average latent heat flux by over 200 W·$$ \cdotp $$m −2$$ {}^{-2} $$, reduces air temperature by 4.7°C, and increases specific humidity by 50% at 2 m during the day over the irrigated region of the domain. Moreover, irrigation limits convection and strongly stabilizes the atmospheric boundary layer. Notably, the study provides evidence for an irrigation‐induced breeze from the irrigated area to the semi‐arid area. These findings highlight the importance of considering irrigation in numerical models for weather forecasting, climate modelling and sustainable agricultural planning. [ABSTRACT FROM AUTHOR]

Soil organisms play a key role in carbon and nutrient cycling in forest ecosystems. While soil organisms are strongly influenced by litter chemistry and are highly sensitive to abiotic conditions, little is known about the interactive effects of these two factors. To address this gap in knowledge, we conducted a 10 week microcosm experiment in which we simulated the effects of climate change on soil ecology. More specifically, we studied relationships among litter nutrient concentration, microbial biomass, Collembola demographic parameters, and litter decomposition, exploring the potential impacts of increasing air temperature and decreasing soil moisture. To develop a gradient of nutrient concentrations, we created six tree litter mixtures with materials gathered from Quercus pubescens and its companion species. In contrast to microbes, we observed that Collembola abundance and litter decomposition were interactively affected by soil moisture and air temperature: the negative effect of increasing air temperature on Collembola abundance was amplified by reduced soil moisture, whereas the positive effect of increasing air temperature on litter decomposition disappeared under reduced soil moisture conditions. In contrast to fungi, the response of bacterial biomass and Collembola abundance to litter nutrient concentration was dependent on abiotic conditions. More specifically, the relationships between nutrients, especially calcium and magnesium, and bacterial biomass and Collembola abundance were less robust or disappeared under drier or warmer conditions. In conclusion, our findings underscore that ongoing climate change could affect soil organisms directly as well as indirectly, by altering their responses to litter nutrient concentrations. In addition, we found that nutrient‐rich habitats might be more affected than nutrient‐poor habitats by altered climatic conditions. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) partitioning distinguishes the soil evaporation (E) and plant transpiration (T) components and is crucial for understanding the land‐atmosphere interactions and ecosystem water budget. However, the mechanism and controls of ET partitioning for subtropical forests in heterogeneous environments remain poorly understood. Here, we present δ18O and δ2H of about 1,527 isotope samples including atmospheric water, soil and plant water during different seasons in 2 years of 2020–2021 from a coniferous forest across Southeast China. We used the isotopic mass balance of ecosystem water pools, the Craig‐Gordon model and the Keeling‐Plot method to partition T from ET (T/ET) and quantify the controls on T/ET. Results indicated that the uncertainty in the T/ET was principally from the soil water evaporation (δE) value, about 20–30 cm was found to be a reasonable evaporating front depth for estimating δE in this coniferous forest. T/ET presented a "U" shape diurnal pattern and varied from 66.7% to 89.9%. Isotope‐based T/ET in autumn with high temperatures and little rain was higher than those in the summer and winter seasons. Relative humidity (or vapour pressure deficit) dominated the diurnal T/ET variations (relative contributions of > 40%) in summer and autumn, while air temperature and soil water content were the main controls in winter. Our study also showed that δ18O‐derived T/ET was consistent with that of δ2H, although δ2H was found to be more stable in ET partitioning, the dual stable isotope approach should be employed in future studies for the uncertainties brought by samplings or measurements. The agreement between the isotope‐based T/ET and ET partitioning approach that uses eddy covariance and sap flux data was stronger at midday. These isotope‐inferred ET partitioning can inform land surface models and provide more insights into water management in subtropical forests. [ABSTRACT FROM AUTHOR]

The difference (DIF) between land surface temperature (Ts) and near surface air temperature (Ta) is the key indicator of the energy budget of the land surface, which has a more complex process than the individual Ts or Ta. However, the spatiotemporal variations and influencing factors of DIF remain incomplete. The contribution of vegetation and soil moisture (SM) as key driving factors to DIF is not yet clear. Here, we analyzed the spatiotemporal variation patterns of DIF in China from 2011 to 2023 using in situ Ts and Ta data. A convergent cross-mapping method was employed to explore the causal relationship between SM, NDVI and DIF, and subsequently calculated the contribution of NDVI and SM variations to DIF under different climatic backgrounds. The results indicate that during the study period, DIF values were all above 0 °C and showed a significant increasing trend with a national mean slope of 0.02 °C/a. In general, vegetation and SM have a driving effect on DIF, with vegetation contributing more to DIF (0.11) than SM (0.08) under different surface properties. The background values of SM and temperature have a significant effect on the spatial and temporal distribution of DIF, as well as the correlation of vegetation and soil moisture to DIF. The study outcomes contribute to a better understanding of the coupling relationship between the land surface and atmosphere, which are also crucial for addressing climate change and ecological environmental management. [ABSTRACT FROM AUTHOR]

Snow, especially in mountainous regions, plays a major role acting as a quasi-reservoir, as it gradually releases fresh water during the melting season and thereby fills rivers, lakes, and groundwater aquifers. For vegetation and irrigation, the timing of the snowmelt is crucial. Therefore, it is necessary to understand how snowmelt varies under different local conditions. While differences in slope aspect and vegetation (individually) were linked to differences in snow accumulation and ablation, this study connects the two and describes their influence on the soil moisture response to snowmelt. This research focuses on the catchment of the "Brunnenkopfhütte" (BKH) in Bavaria, southern Germany, where an automatic weather station (AWS) has operated since 2016. In addition, soil temperature and moisture monitoring systems in the surrounding area on a south aspect slope on an open field (SO), on a south aspect slope in the forest (SF), and a north aspect slope in the forest (NF) have operated since 2020. On snow-free days in winter, the soil temperature at the SF site was on average 1 °C lower than on the open site. At the NF site, this soil temperature difference increased to 2.3 °C. At the same time, for a 1 °C increase in the air temperature, the soil temperature increases by 0.35 °C at the NF site. In addition, at this site, snow cover disappeared approximately one week later than on the south aspect slopes. Snow cover at the SF site disappeared even earlier than at the SO site. Finally, a significant difference in the soil moisture response was found between the sites. While the vegetation cover dampens the magnitude of the soil moisture increases, at the NF site, no sharp increases in soil moisture were observed. [ABSTRACT FROM AUTHOR]

Aims: Quantitative characterization of the time-lag effect between canopy temperature and atmospheric temperature and its controlling factors in the agricultural ecosystem may contribute to a higher inversion accuracy of soil water content using canopy-air temperature information. Methods: Tc of winter wheat were continuously monitored, and the data of such environmental factors as solar radiation (Rs), atmospheric temperature (Ta), relative humidity (RH) and soil water content (SWC) were simultaneously collected. Results: Hysteresis existed between Tc and Ta over the diel cycles, and different weather and irrigation levels did not change the direction of the time lag loop. the key driver regulating the diel hysteresis pattern between Tc and Ta varied under different weather: on rainy days, key driver was Rs while on cloudy and sunny days, the key driver was RH. the multiple regression model indicated that together Rs, Ta, RH, and SWC explained 58 ± 10% of the variation of time-lag effect. Path analysis showed on rainy days the key driver (Rs and RH) could enhance the time-lag effect through other indirect factors (Ta and SWC); on cloud days the key driver (RH and SWC) could inhibit the time-lag effect through other indirect factors (Ta); On sunny days this mutual inhibition was further significant. Conclusions: These findings indicated a dynamic process of time-lag effect between Tc and Ta with different weather and different irrigation levels. This study contributes to the understanding of the time-lag effect and its driving factors and this analysis provides the basis for further improvement in monitoring crop water deficit. [ABSTRACT FROM AUTHOR]

SNOSWAB (Snow, Soil Water and Water Balance) is a unique online deterministic model built using tipping-bucket approaches that allows for the daily estimation of (i) snowpack processes; (ii) soil water content; and (iii) soil water budget. SNOSWAB is most suitable for modeling field-scale processes for vertically and horizontally homogeneous soils, and its applicability is not limited to specific climate zones or geographical areas. The model is freely available, and its streamlined online interface integrates powerful calibration, visualization and data export routines. In this study, SNOSWAB development and a conceptual model, as well as an example of its application using data collected during a 12-year (2008–2019) field study conducted at the Agriculture and Agri-Food Canada Harrington Experimental Farm (HEF) on Prince Edward Island (PEI), Canada, are presented. Input data consisting of daily air temperature, total precipitation, rainfall and evapotranspiration were used in conjunction with soil properties and daily soil water content, snowpack thickness, surface runoff and groundwater recharge to calibrate (2010–2014) and validate (2015–2019) the model. For both the calibration and validation simulations, the statistical indicators used for evaluating model performance indicated, in most cases, high model fitness (i.e., R2 > 0.5, NRMSE < 50% and −25% < PBIAS < 25%) for the various time intervals and parameters analyzed. SNOSWAB fills an existing gap in the online environment and, due to its ease of use, robustness and flexibility, shows promise to be adopted as an alternative for more complex, standalone models that might require extensive resources and expertise. [ABSTRACT FROM AUTHOR]

Soil water and soil temperature are important ecological factors and driving forces for ecosystem restoration and sustainable development, possessing great significance for climate modeling and prediction. The Hengduan Mountains in southeastern Tibet, China, are located in a climate-change-sensitive area, and the study of soil hydrothermal dynamics in this area is of great significance for local and global climatic change and water resource utilization. This study, based on the soil hydrothermal and meteorological data of the Hengduan Mountain area in Southeast Tibet, analyzes the dynamic change patterns of soil hydrothermal and meteorological factors and explores their influencing relationships. It was found that the dynamic change in soil water content affected by precipitation was "bimodal" type. Among the meteorological factors, soil water content has the strongest correlation with relative humidity. The intra-annual variation curve of soil temperature is similar to that of the atmospheric temperature, showing a "unimodal" type, and has the highest correlation with atmospheric temperature. Specifically, it takes 70 mm and 170 mm of precipitation to change the soil water content and soil temperature at the 150 cm depth. For every 20 °C change in atmospheric temperature, soil temperature above 150 cm changes by an average of 7.2 °C. [ABSTRACT FROM AUTHOR]

The current representation of key processes in land surface models (LSMs) for estimating water and energy balances still relies heavily on empirical equations that require calibration oriented to site-specific characteristics. When multiple parameters are used, different combinations of parameter values can produce equally acceptable results, leading to a risk of obtaining "the right answers for the wrong reasons", compromising the reproducibility of the simulations and limiting the ecological interpretability of the results. To address this problem and reduce the need for free parameters, here we present novel formulations based on first principles to calculate key components of water and energy balances, extending the already parsimonious SPLASH model v.1.0 (, GMD). We found analytical solutions for many processes, enabling us to increase spatial resolution and include the terrain effects directly in the calculations without unreasonably inflating computational demands. This calibration-free model estimates quantities such as net radiation, evapotranspiration, condensation, soil water content, surface runoff, subsurface lateral flow, and snow-water equivalent. These quantities are derived from readily available meteorological data such as near-surface air temperature, precipitation, and solar radiation, as well as soil physical properties. Whenever empirical formulations were required, e.g., pedotransfer functions and albedo–snow cover relationships, we selected and optimized the best-performing equations through a combination of remote sensing and globally distributed terrestrial observational datasets. Simulations at global scales at different resolutions were run to evaluate spatial patterns, while simulations with point-based observations were run to evaluate seasonal patterns using data from hundreds of stations and comparisons with the VIC-3L model, demonstrating improved performance based on statistical tests and observational comparisons. In summary, our model offers a more robust, reproducible, and ecologically interpretable solution compared to more complex LSMs. [ABSTRACT FROM AUTHOR]

The DNDC (DeNitrification-DeComposition) model (version 9.5) was applied to predict the differences in transpiration and photosynthesis rates of perennial grasses (red clover and timothy), and autotrophic respiration of a sandy Spodosol. The input parameters for two growing seasons (from 1st of May to 31st of August in 2010 and 2015) contrasting in meteorological conditions were used in the modeling experiment. In 2010, the mean air temperature of the period was 14.1 ±3.3 °C and the total precipitation – 0.1796 m, while in 2015 the mean air temperature was 16.8 ±5.5 °C and the total precipitation – 0.538 m. These meteorological parameters were unfavorable for plants in 2010 and favorable in 2015. The results have shown that the DNDC model adequately predicted the weather-induced differences in total and mean transpiration rates of perennial grasses: 0.12204 m. and 0.00099 ±0.00040 m.day−1, respectively, under favorable meteorological conditions of 2015 and 0.05969 m. and 0.00049 ±0.00035 m.day−1, respectively, under unfavorable meteorological conditions of 2010. Dynamics of daily transpiration rates of plants was significantly (r = 0.34 p <0.001) correlated with soil water content only under unfavorable meteorological conditions. Mean values of simulated photosynthesis rates were equal to 84.4 ±27.9 kg.C.ha−1.day−1 in 2015 and 52.3 ±23.4 kg.C.ha-1.day−1 in 2010. There were significant differences (p <0.001) in the mean values of photosynthesis rates between the two weather scenarios. The results of one-way analysis of variance (ANOVA) have shown that the rates of autotrophic respiration were significantly (p <0.001) higher under favorable (8.14 ±2.25 kg.C.ha−1.day−1) than under unfavorable (5.17 ±2.19 kg.C.ha−1.day−1) meteorological conditions. [ABSTRACT FROM AUTHOR]

Ongoing climate change is likely to put increased selection pressures on the phenology of plants, yet for many species their abilities to respond to environmental cues are unknown. The present research focuses on using herbarium specimens to examine how 14 native plant species in Alberta have adjusted or adapted to changes in temperature and precipitation over the past century. We specifically investigate the impact of flowering-time responses and determine (1) whether herbaria collections contain sufficient evidence of these phenological responses to climate in plant species in Alberta, and (2) whether the responses are dependent on the typical moisture regime of their habitat. We compared plants from mesic and xeric habitats in terms of their phenological responses to air temperature and precipitation. In this study, the taxonomic relationships between the species were considered by selecting 14 species representing seven different angiosperm orders (one pair of species for each order). By collating data on the peak flowering date over the past century using preserved specimens, we found that on average, species from xeric habitats are more responsive to temperature, but not precipitation. This tendency might be explained by the thermal properties of mesic habitats, a finding that may lead to ways to predict the degree to which environmental cues will govern flowering. [ABSTRACT FROM AUTHOR]

Previous studies have emphasized the significant influence of the atmospheric heat source of the Tibetan Plateau (TPHS) on the weather and climate in East Asia, but the causes and mechanisms of the TPHS variations remain unclear. In this study, the physical linkage between the winter northern Eurasian snow depth and early summer (May and June) TPHS during 1950–2019 is investigated. Our results indicate that excessive winter northern Eurasian snow can decrease the early summer TPHS through a delayed hydrological effect. In winter, excessive northern Eurasian snow depths lead to more snowmelt and wetter local soil in early summer. The wetter soil not only reduces the surface temperatures in northern Eurasia but also cools the atmosphere in the middle and upper troposphere. This cooling enhances the meridional temperature gradient between northern and southern Eurasia, which reinforces the westerly jet stream at mid-latitudes. The strong westerly jet stream enhances the sensible heat flux over the Tibetan Plateau (TP) by increasing the near-surface wind speed. Moreover, the enhanced meridional temperature gradient generates an anticyclonic and high-pressure anomaly over the southwestern side of the TP, thus decreasing the latent heat release over the TP. Because early summer marks the beginning of the rainy season on the TP, the TPHS in early summer is dominated by latent heat released by precipitation, which means that the wetter soil caused by the excessive winter snow depth in northern Eurasia eventually weakens the TPHS in early summer. [ABSTRACT FROM AUTHOR]

Changes are projected for the boreal biome with complex and variable effects on forest vegetation including drought‐induced tree mortality and forest loss. With soil and atmospheric conditions governing drought intensity, specific drivers of trees water stress can be difficult to disentangle across temporal scales. We used wavelet analysis and causality detection to identify potential environmental controls (evapotranspiration, soil moisture, rainfall, vapor pressure deficit, air temperature and photosynthetically active radiation) on daily tree water deficit and on longer periods of tree dehydration in black spruce and tamarack. Daily tree water deficit was controlled by photosynthetically active radiation, vapor pressure deficit, and air temperature, causing greater stand evapotranspiration. Prolonged periods of tree water deficit (multi‐day) were regulated by photosynthetically active radiation and soil moisture. We provide empirical evidence that continued warming and drying will cause short‐term increases in black spruce and tamarack transpiration, but greater drought stress with reduced soil water availability. Plain Language Summary: This research explores how climate change could impact the water stress experienced by black spruce and tamarack trees in the western boreal forest of Canada. We focused on a key measure called "tree water deficit" to understand if the trees were under stress due to insufficient water. We examined how tree water deficit relates to environmental factors such as temperature, sunlight, and soil moisture. The findings revealed that, on a daily basis, factors like sunlight and temperature cause trees to release more water into the air. However, over longer periods (days to weeks), the amount of water in the soil becomes crucial, suggesting that trees might face water stress during dry spells. So, while trees could grow more on hotter, sunnier days, they could also experience water stress and reduced growth if the soil becomes too dry for an extended period. This study helps us grasp how various factors interact to influence tree water stress in the boreal forest, providing insights important for managing these ecosystems in a changing climate. Key Points: A novel approach to determine environmental controls of tree water deficit across time scales with wavelet analysis and Granger causalitySoil moisture emerges as a significant control of tree water deficit in boreal trees at longer scales (multi‐days)Daily productivity gains with warming will be mitigated by decreased soil water availability in longer periods of tree water deficit [ABSTRACT FROM AUTHOR]

Crop water stress index (CWSI) has been widely used in soil moisture monitoring. However, the influence of the time lag effect between canopy temperature and air temperature on the accuracy of soil moisture monitoring with different CWSI models has not been further investigated. Therefore, based on the continuous record of canopy temperature and air temperature, this study explored the influence of canopy-air temperature hysteresis on the diagnosis of soil moisture with three CWSI models (CWSIT—theoretical, CWSIE—empirical, CWSIH—hybrid). The results show (1) the peak time of canopy temperature was ahead of that of air temperature, and the lag time varied under different soil moisture conditions. When the soil moisture was seriously deficient, the lag time decreased. However, from jointing-heading period to filling-ripening period, the lag time became longer. (2) The values of CWSIT, CWSIE, and CWSIH decreased when the time lag effect was considered. In jointing-heading period, heading-filling period, and filling-ripening period, CWSIT had the highest accuracy in soil moisture monitoring without the consideration of the time lag effect. When the time lag effect was considered, the monitoring accuracy of CWSIE and CWSIH was greatly improved and higher than that of CWSIT, while that of CWSIT was reduced. The findings provided a basis for further improving the accuracy of soil moisture monitoring with CWSI models. [ABSTRACT FROM AUTHOR]

Relative soil moisture is of great significance to the growth and yield of sugarcane. In this study, we use the relative soil moisture from the China Meteorological Administration Land Data Assimilation System (CLDAS) to dynamically evaluate the water requirement of sugarcane and its growth adaptability at different growth stages. Based on the data of relative soil moisture, air temperature, precipitation, and soil temperature, a sugarcane yield model is established to analyze the projected change trends of sugarcane yield in China from 2020 to 2100 under three future scenarios. Analysis results show that sugarcane requires more water during the elongation stage but less water at the ripening stage. The relative soil moisture from the CLDAS can be used to calculate the proportion of the daily suitable area to the total planting area. The combining of relative soil moisture data and water requirement indicators can better characterize the water requirement during sugarcane growth. Suitable relative soil moisture during the tillering and elongation stages is the most critical factor that directly affects the sugarcane yield. From 2020 to 2100, sugarcane yield will increase first and then decrease sharply. The increase in emissions can lead to an apparent downward trend in sugarcane yield. Based on the CLDAS data and water requirement indicators, a new method for monitoring the sugarcane growth throughout the growth period is proposed in this study. In the SSP370 and SSP460 scenarios, the sugarcane yield showed a downward trend, and there were mutations in 2064 and 2052, respectively. After the mutation, the yield decline trend was more obvious. Under the SSP585 scenario model, the sugarcane production showed an upward trend from 2022 to 2033 and a downward trend after 2033, and a mutation occurred in 2051. After the mutation, the downward trend of sugarcane production was more obvious. [ABSTRACT FROM AUTHOR]

Seasonal snow cover is a key global climate and hydrological system component drawing considerable attention due to global warming conditions. However, the spatiotemporal snow cover patterns are challenging in western Jilin, China due to natural conditions and sparse observation. Hence, this study investigated the spatiotemporal patterns of snow cover using fine-resolution passive microwave (PMW) snow depth (SD) data from 1987 to 2018, and revealed the potential influence of climate factors on SD variations. The results indicated that the interannual range of SD was between 2.90 cm and 9.60 cm during the snowy winter seasons and the annual mean SD showed a slightly increasing trend (P > 0.05) at a rate of 0.009 cm/yr. In snowmelt periods, the snow cover contributed to an increase in volumetric soil water, and the change in SD was significantly affected by air temperature. The correlation between SD and air temperature was negative, while the correlation between SD and precipitation was positive during December and March. In March, the correlation coefficient exceeded 0.5 in Zhenlai, Da'an, Qianan, and Qianguo counties. However, the SD and precipitation were negatively correlated over western Jilin in October, and several subregions presented a negative correlation between SD and precipitation in November and April. [ABSTRACT FROM AUTHOR]

The shelterbelt is an indispensable barrier to the ecological and economic development of an oasis. Soil moisture, groundwater and irrigation greatly affect the shelterbelt water consumption and development. In this study, the transpiration rate of shelterbelt trees, soil moisture and meteorological data were collected to determine the effects of soil moisture and meteorological factors on the water consumption of different shelterbelt tree species via multivariate statistical methods. The results showed that the water consumption rate was positively correlated with solar radiation, air temperature and precipitation. Moreover, the leaf transpiration rate exhibited the trend of P. Russkii Jabl. P. alba P. simonii Carr.>P. nigracv, while the average daily water consumption decreased in the order of P. alba>P. Russkii Jabl.>P. simonii Carr.>P. nigracv. The average daily water consumption levels of P. alba, P. Russkii Jabl., P. simonii Carr. and P. nigracv were (9.15±0.92) kg/(tree·d), (6.95±1.41) kg/(tree·d), (4.43±1.32) kg/(tree·d), and (1.58±0.18) kg/(tree·d), respectively. Over the growing season, the soil water consumption levels of P. alba, P. Russkii Jabl., P. simonii Carr., and P. nigracv in each shelterbelt tree stand reached 674.8, 336.9, 358.1 and 161.7 kg, respectively. More than 96% of the soil moisture lost was provided by the upper 120-cm soil layer. Understanding the influence and contribution of soil water and meteorological factors to shelterbelt water consumption is beneficial for shelterbelt management and protection. [ABSTRACT FROM AUTHOR]

Introduction: The components of nighttime sap flux (En), which include transpiration (Qn) and stem water recharge (Rn), play important roles in water balance and drought adaptation in plant communities in water-limited regions. However, the quantitative and controlling factors of En components are unclear. Methods: This study used the heat balance method to measure sap flow density in Vitex negundo on the Loess Plateau for a normal precipitation year (2021) and a wetter year (2022). Results: The results showed that the mean values were 1.04 and 2.34 g h-1 cm-2 for Qn, 0.19 and 0.45 g h-1 cm-2 for Rn in 2021 and 2022, respectively, and both variables were greater in the wetter year. The mean contributions of Qn to En were 79.76% and 83.91% in 2021 and 2022, respectively, indicating that the En was mostly used for Qn. Although the vapor pressure deficit (VPD), air temperature (Ta) and soil water content (SWC) were significantly correlated with Qn and Rn on an hourly time scale, they explained a small fraction of the variance in Qn on a daily time scale. The main driving factor was SWC between 40-200 cm on a monthly time scale for the Qn and Rn variations. Rn was little affected by meteorological and SWC factors on a daily scale. During the diurnal course, Qn and Rn initially both declined after sundown because of decreasing VPD and Ta, and Qn was significantly greater than Rn, whereas the two variables increased when VPD was nearly zero and Ta decreased, and Rn was greater than Qn. Discussion: These results provided a new understanding of ecophysiological responses and adaptation of V. negundo plantations to increasing drought severity and duration under climate changes. [ABSTRACT FROM AUTHOR]

In recent decades, theoretical studies and numerical models of thermal tides have gained attention. It has been recognized that tides have a significant influence on the dynamics of the middle and upper atmosphere, as they grow in amplitude and propagate upwards, they transport energy and momentum from the lower to the upper atmosphere, contributing to the vertical coupling between atmospheric layers. The superposition of tides with other atmospheric waves leads to non-linear wave-wave interactions. However, direct measurements of thermal tides in the middle atmosphere are challenging and often are limited to satellite measurements at the tropics and low latitudes. Due to the orbit geometry such observations provide only a reduced insight into the short-term variability of atmospheric tides. In this manuscript, we present tidal analysis from 5 years of continuous observations of middle atmospheric temperatures. The measurements were performed with the ground-based temperature radiometer TEMPERA, which was developed at the University of Bern in 2013 and was located partially in Bern (46.95° N, 7.45° E) and Payerne (46.82° N, 6.94° E). TEMPERA achieves a temporal resolution of 1–3 h and covered the altitude range between 25–55 km. Using an adaptive spectral filter with a vertical regularization (ASF2D) for the tidal analysis, we found maximum amplitudes for the diurnal tide of approximately 2.4 K accompanied by seasonal variability. The maximum amplitude was reached on average at an altitude of 43 km, which also reflected some seasonal characteristics. We demonstrate ;that TEMPERA is suitable to provide continuous temperature soundings at the stratosphere and lower mesosphere with a sufficient cadence to infer tidal amplitudes and phases for the dominating tidal modes. Furthermore, our measurements exhibit a dominating diurnal tide and smaller amplitudes for the semidiurnal and terdiurnal tides at the stratosphere. [ABSTRACT FROM AUTHOR]

Primary production in the Sea of Okhotsk is largely supported by dissolved iron (dFe) transported by the Amur river, indicating the importance of dFe discharge from terrestrial environments. However, little is known about the mechanisms of dFe discharge into the Amur river, especially in terms of long-term change in dFe concentration. In the Amur river, extreme increase in dFe concentration was observed between 1995 and 1997, the cause of which remains unclear. As a cause of this iron anomaly, we considered the impact of permafrost degradation. To link the permafrost degradation to long-term variation in dFe concentration, we examined the changes in annual air temperature (Ta), accumulated temperature (AT), and net precipitation for three regions (northeast, south, and northwest) of the basin between 1960 and 2006. Ta and AT were relatively high in one out of every few years, and were especially high during 1988–1990 continuously. Net precipitation in late summer (July to September) has increased since 1977 and has stayed positive until 2006 throughout the basin. Most importantly, we found significant correlations between Ta and late summer dFe concentration with a 7-year lag (r = 0.54–0.69, p < 0.01), which indicate a close relationship between high Ta in year Y and increased late summer dFe concentration in year Y + 7. This correlation was the strongest in northeastern Amur basin where permafrost coverage is the highest. Similar 7-year lag correlation was also found between AT in the northeastern basin and late summer dFe concentration (r = 0.51, p < 0.01). Based on our findings, we propose the following hypothesis as a cause of iron anomaly. (1) Increased net precipitation since 1977 has increased soil moisture, which created suitable conditions for microbial dFe generation; (2) permafrost degradation during the warm years of 1988–1990 promoted iron bioavailability and led to the intensive dFe generation in the deeper part of the active layer; and (3) dFe took approximately 7 years to reach the rivers and extremely increased dFe concentration during 1995–1997. This is the first study to suggest the time-lagged impact of permafrost degradation on iron biogeochemistry in the Amur river basin. [ABSTRACT FROM AUTHOR]

Air temperature variability and prediction are important considering the potential implications on human health, water resource management, agriculture and land-atmosphere interaction. In this paper, we investigate the delayed influence of El Niño on spring Surface Air Temperature (SAT) variability over India using observations and assess its predictability in coupled climate models. Analysis suggests that El Niño, at its decaying phase, exerts a strong influence on the spring SAT over Southern Peninsular India (SPI). This warming is mainly induced by an anomalous anticyclone, which extends from the Western North Pacific (WNP) region to the SPI. The anomalous anticyclone-induced downdraft reduces the cloud cover and increases incoming shortwave radiation at surface, which in turn causes a dry atmospheric column and dry soil moisture over this region. As a result of this decreased soil moisture, latent heat flux into atmosphere reduces and enhances sensible heat transfer, further contributing to dry soil moisture. Both dry soil moisture and anomalous anticyclone contributes to enhanced SAT over SPI region. It is found that 6 years out of the top 10 warmest years over the SPI region correspond to the decaying phase of El Niño, suggesting the importance of the present study. It is also found that the intense SAT warming over SPI aids in increasing frequencies of Discomfort Index hours (exceeding 28 and 30 °C in a day) and the Universal Thermal Climate Index during El Niño decaying spring years. Further, we have examined the predictability of the spring SAT during decaying El Niño years based on the Asia-Pacific Economic Cooperation Climate Center (APCC) models. It is noted that most of the APCC models reasonably predicted the spatial distribution of warm SAT over India during the El Niño decaying spring season at 1 and 3 month lead. This study highlights the predictability of spring SAT a season in advance over India, which would help in planning better adaptation and mitigation strategies. [ABSTRACT FROM AUTHOR]

Canopy conductance (gc) is a crucial parameter in simulating evapotranspiration and modulating water exchange, but its variation mechanism has regional uncertainties and complex environmental co‐controls. In addition, the effect of extreme rainfall on gc cannot be ignored under the changing climate. Here, we investigated the variation and environmental controls on gc and the effect of extreme rainfall events in a Cunninghamia lanceolata forest across the subtropical area of Southern China. In July 2020, an extreme rainstorm hit the source area of the Xin'an River, with the cumulative rainfall on July 7 and 8 reaching 216.6 mm. The thermal diffusion probe method was used to measure the density of sap flow, and the environmental factors such as air temperature (Ta), net solar radiation (Rn) and soil water content (SWC) were monitored during the growing seasons of 2020 and 2021. Ultimately, gc obtained by the Penman‐Monteith equation was adopted since the result from the Köstner equation was overestimated. gc showed a unimodal curve on the diurnal scale, and this characteristic was more obvious after the extreme rainfall. Daily gc appeared a fluctuating pattern with a maximum in summer. gc was simultaneously affected by Ta, Rn, water vapour pressure difference (VPD), SWC, among which Ta was the most significant driving factor at both the diurnal and daily timescales. The regulation of Ta, VPD and SWC on gc had obvious thresholds, and the most definite response mode was VPD (2020: 1.25 kPa; 2021: 0.95 kPa). SWC and Ta were the dominant factors after the rainfall period, and the promotion effect of VPD on post‐rainy days turned to inhibition effect on typical sunny days. These findings will further reveal the water exchange mechanism between atmosphere and vegetation and impacts of environmental factors in subtropical coniferous forests, especially after the extreme rainfall events. [ABSTRACT FROM AUTHOR]

With global warming, forests are facing an increased exposure to compound soil and atmospheric drought (CSAD) events, characterized by low soil water content (SWC) and high vapor pressure deficit (VPD). Such CSAD events trigger responses in both ecosystem and forest floor CO2 fluxes, of which we know little about. In this study, we used multi-year daily and daytime above canopy (18 years; 2005–2022) and daily forest floor (five years; 2018–2022) eddy-covariance CO2 fluxes of a Swiss forest site (montane mixed deciduous forest; CH-Lae). The objectives were (1) to characterize CSAD events at CH-Lae; (2) to quantify the impact of CSAD events on ecosystem and forest floor daily CO2 fluxes; and (3) to identify the major drivers and their temporal contributions to changing ecosystem and forest floor CO2 fluxes during CSAD events and CSAD growing seasons. Our results showed that the growing seasons of 2015, 2018, and 2022, were the top three driest (referred as CSAD years) at CH-Lae since 2005, with similar intensity and duration of the respective CSAD events, but considerably different pre-drought conditions. The CSAD events reduced daily mean net ecosystem productivity (NEP) in all three CSAD years, with highest reduction during 2022 (30 % decrease). This reduction in daily mean NEP was largely due to decreased gross primary productivity (GPP; >15 % decrease) rather than increased ecosystem respiration (Reco) during CSAD events. Furthermore, forest floor respiration (Rff) decreased during the CSAD events in 2018 and 2022 (no measurements in 2015), with a larger reduction in 2022 (>40 %) than in 2018 (<25 %) compared to the long-term mean (2019–2021). Using data-driven machine learning methods, we identified the major drivers of NEP and Rff during CSAD events. While daytime mean NEP during 2015 and 2018 CSAD events was limited by VPD or SWC, respectively, daytime mean NEP during the 2022 CSAD event was strongly limited by both SWC and VPD. Air temperature always had always negative effects, net radiation positive effects on daytime mean NEP during all CSAD events. Daily mean Rff during the 2018 CSAD event was driven by soil temperature and SWC, but severely limited by SWC during the 2022 CSAD event. We found that a multi-layer analysis of CO2 fluxes in forests is necessary to better understand forest responses to CSAD events, particularly if the first signs we saw of acclimation to such CSAD events for our forest are found elsewhere as well. We conclude that such events have multiple drivers with different temporal contributions, making prediction of site-specific CSADs and forest long-term responses to such conditions more challenging. [ABSTRACT FROM AUTHOR]

The frequency and duration of observed concurrent hot and dry events (HDEs) over China during the growing season (April–September) exhibit significant decadal changes across the mid-1990s. These changes are characterized by increases in HDE frequency and duration over most of China, with relatively large increases over southeastern China (SEC), northern China (NC), and northeastern China (NEC). The frequency of HDEs averaged over China in the present day (PD, 1994–2011) is double that in the early period (EP, 1964–81); the duration of HDEs increases by 60%. Climate experiments with the Met Office Unified Model (MetUM-GOML2) are used to estimate the contributions of anthropogenic forcing to HDE decadal changes over China. Anthropogenic forcing changes can explain 60%–70% of the observed decadal changes, suggesting an important anthropogenic influence on HDE changes over China across the mid-1990s. Single-forcing experiments indicate that the increase in greenhouse gas (GHG) concentrations dominates the simulated decadal changes, increasing the frequency and duration of HDEs throughout China. The change in anthropogenic aerosol (AA) emissions significantly decreases the frequency and duration of HDEs over SEC and NC, but the magnitude of the decrease is much smaller than the increase induced by GHGs. The changes in HDEs in response to anthropogenic forcing are mainly due to the response of climatological mean surface air temperatures. The contributions from changes in variability and changes in climatological mean soil moisture and evapotranspiration are relatively small. The physical processes associated with the response of HDEs to GHG and AA changes are also revealed. [ABSTRACT FROM AUTHOR]

Shading is an effective agronomic technique to protect tea plants from intense sunlight. However, there are currently very few studies on more effective shading methods to improve the quality of summer tea. In this study, 'Longjing43' plants were grown under four different shading treatments for 14 days, with no shading as the control. Among the four shading treatments, double-layer-net shadings had the most positive impact on the tea quality, resulting in higher levels of amino acids but lower levels of tea polyphenols. Additionally, double-layer-net shadings provided more suitable microenvironments for tea plants. The tea leaves in T4 (double nets 50 cm above the plant canopy) contained 16.13 mg∙g−1 of umami and sweet amino acids, which was significantly higher than in other treatments. T4 had the lowest air temperature and the most suitable and stable soil water content. Interestingly, the ratio of red light to far-red light in T4 was only 1.65, much lower than other treatments, which warrants further study. In conclusion, the microenvironment induced by shading can greatly affect the tea quality, and double-layer-net shading is better for improving the quality of summer tea. [ABSTRACT FROM AUTHOR]

Drought is a natural disaster with severe global agricultural and economic impacts. Accurate drought indices are needed for improved assessment and monitoring; however, most existing drought indices poorly represent agricultural drought due to complex interactions among meteorological factors, crop and soil conditions. Here, we compute an integrated drought condition index (IDCI) based on the 3-month standardized precipitation evapotranspiration index (SPEI3), vegetation cover index (VCI) and soil moisture condition index (SMCI). We apply the IDCI to monitoring agricultural drought in Xinjiang, China. After regional evaluations with soil moisture, precipitation and air temperature observations, as well as with the scaled crop yields index, the IDCI was used to describe spatiotemporal changes in regional drought in Xinjiang during 2000–2018, revealing adverse impacts on crop yield (beet, wheat and vegetables). The IDCI is strongly correlated with observed soil moisture and performs better than SMCI, VCI or SPEI3, demonstrating that the IDCI is suitable for agricultural drought monitoring. The most severe drought occurred in the spring to autumn of 2008. Droughts before 2008 were more serious than those after 2008, in terms of both severity and frequency. Droughts in northern, southern and eastern Xinjiang, as well as in the Tianshan Mountains, were generally increasing before 2008 and then weakened after 2008. [ABSTRACT FROM AUTHOR]

The research presented in this paper aims at providing a statistical model that is capable of estimating soil water content based on weather data. The model was tested using a long-time series of field experimental data from continuous monitoring at a test site in Oltrepò Pavese (northern Italy). An innovative statistical function was developed in order to predict the evolution of soil–water content from precipitation and air temperature. The data were analysed in a framework of robust statistics by using a combination of robust parametric and non-parametric models. Specifically, a statistical model, which includes the typical seasonal trend of field data, has been set up. The proposed model showed that relevant features present in the field of experimental data can be obtained and correctly described for predictive purposes. [ABSTRACT FROM AUTHOR]

The ability of plants to adjust to the adverse effects of climate change is important for their survival and for their contribution to the global carbon cycle. This is particularly true in the Mediterranean region, which is among the regions that are most vulnerable to climate change. Here, we carried out a 2-year comparative ecophysiological study of ecosystem function in two similar Eastern Mediterranean forests of the same tree species (Pinus halepensis Mill.) under mild (Sani, Greece) and extreme (Yatir, Israel) climatic conditions. The partial effects of key environmental variables, including radiation, vapor pressure deficit, air temperature and soil moisture (R g, D , T and soil water content (SWC), respectively), on the ecosystems' CO2 and water vapor fluxes were estimated using generalized additive models (GAMs). The results showed a large adjustment between sites in the seasonal patterns of both carbon and water fluxes and in the time and duration of the optimal period (defined here as the time when fluxes were within 85% of the seasonal maximum). The GAM analysis indicated that the main factor influencing the seasonal patterns was SWC, while T and D had significant but milder effects. During the respective optimal periods, the two ecosystems showed strong similarities in the fluxes' responses to the measured environmental variables, indicating similarity in their underlying physiological characteristics. The results indicate that Aleppo pine forests have a strong phenotypic adjustment potential to cope with increasing environmental stresses. This, in turn, will help their survival and their continued contribution to the terrestrial carbon sink in the face of climate change in this region. [ABSTRACT FROM AUTHOR]

This study investigates the performance of the latter NCEP Unified Forecast System (UFS) Coupled Model prototype simulations (P5–P8) during boreal summer 2011–17 in regard to coupled land–atmosphere processes and their effect on model bias. Major land physics updates were implemented during the course of model development. Namely, the Noah land surface model was replaced with Noah-MP and the global vegetation dataset was updated starting with P7. These changes occurred along with many other UFS improvements. This study investigates UFS's ability to simulate observed surface conditions in 35-day predictions based on the fidelity of model land surface processes. Several land surface states and fluxes are evaluated against flux tower observations across the globe, and segmented coupling processes are also diagnosed using process-based multivariate metrics. Near-surface meteorological variables generally improve, especially surface air temperature, and the land–atmosphere coupling metrics better represent the observed covariance between surface soil moisture and surface fluxes of moisture and radiation. Moreover, this study finds that temperature biases over the contiguous United States are connected to the model's ability to simulate the different balances of coupled processes between water-limited and energy-limited regions. Sensitivity to land initial conditions is also implicated as a source of forecast error. Above all, this study presents a blueprint for the validation of coupled land–atmosphere behavior in forecast models, which is a crucial model development task to assure forecast fidelity from day one through subseasonal time scales. [ABSTRACT FROM AUTHOR]

The interplay between metal contamination and climate change may exacerbate the negative impact on the soil microbiome and, consequently, on soil health and ecosystem services. We assessed the response of the microbial community of a heavy metal-contaminated soil when exposed to short-term (48 h) variations in air temperature, soil humidity or ultraviolet (UV) radiation in the absence and presence of Enchytraeus crypticus (soil invertebrate). Each of the climate scenarios simulated significantly altered at least one of the microbial parameters measured. Irrespective of the presence or absence of invertebrates, the effects were particularly marked upon exposure to increased air temperature and alterations in soil moisture levels (drought and flood scenarios). The observed effects can be partly explained by significant alterations in soil properties such as pH, dissolved organic carbon, and water-extractable heavy metals, which were observed for all scenarios in comparison to standard conditions. The occurrence of invertebrates mitigated some of the impacts observed on the soil microbial community, particularly in bacterial abundance, richness, diversity, and metabolic activity. Our findings emphasize the importance of considering the interplay between climate change, anthropogenic pressures, and soil biotic components to assess the impact of climate change on terrestrial ecosystems and to develop and implement effective management strategies. [ABSTRACT FROM AUTHOR]

Aside from plant breeding and agricultural inputs, understanding and interpreting the Genotype × Environment (G × E) interaction has contributed significantly to the increase in wheat yield. In Central Macedonia, Greece, fifteen commercially important durum wheat cultivars and one landrace were tested in six cultivation environments classified into high- and low- productivity environments. This study aimed to identify the most productive and stable durum wheat genotypes across Mediterranean farming systems through a comparative examination of genotype plus genotype by environment (GGE) biplot alongside fifteen parametric and non-parametric stability models. In the organic (low productivity) environment, cultivar Zoi and the landrace Lemnos showed remarkable results, indicating a potential solution for biological agriculture. For the late-sowing (low productivity) environment, some widespread varieties such as Mexicali-81, Meridiano, and Maestrale had excellent performance, showing potential to overcome more adverse conditions during critical grain filling periods such as higher air temperature and deficient soil moisture, i.e., conditions that correlate with climate change. Evaluation of genotypes in all environments for a combination of high yield and stable production, showed that the best genotypes were G8 (Simeto), G2 (Canavaro), and G12 (Elpida). In the subgroup with the three high-productivity environments, G12 (Elpida), G8 (Simeto), and G6 (Mexicali-81) were the best genotypes, followed by G2 (Canavaro), while in the low-productivity subgroup, the G2 (Canavaro), G13 (Zoi) and G8 (Simeto) genotypes were the best. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) plays a crucial role in connecting various ecological processes in terrestrial ecosystems, as vegetation growth heavily relies on SM availability. Despite many studies evaluating the impacts of SM on vegetation activity, changes in vegetation sensitivity to SM are still poorly understood. In this study, we evaluated the spatiotemporal changes of vegetation sensitivity to SM across the Loess Plateau (LP) region from 1982 to 2010 using satellite‐based normalised difference vegetation index (NDVI) and reanalysis SM data. Our results showed a significant increase in NDVI (slope = 0.0015/year, p < 0.001) and a slight decrease in SM (slope = −0.0001 m3/m3/year, p = 0.7) over the past 29 years, indicating a greening trend in the LP. By establishing a multiple linear model, we found that the sensitivity of vegetation dynamics to SM has significantly increased during the same period (slope = 0.029, p < 0.05). Spatially, vegetation in over half (51.2%) of the total area showed enhanced sensitivity to SM. Further analysis using the Lindeman–Merenda–Gold method showed that atmospheric CO2 was the major contributor (27.20%) to the sensitivity changes, followed by nitrogen deposition (23.70%) and air temperature (20.11%). Ecosystem structure changes (represented by non‐tree land cover fractions) and precipitation shifts contributed 16.13% and 12.83%, respectively, to sensitivity changes. These findings provide a better understanding of the spatiotemporal pattern and underlying mechanisms of ecosystem sensitivity to SM in the greening and warming LP. [ABSTRACT FROM AUTHOR]

The correlation coefficient between soil moisture and weather variables such air temperature, precipitation, rainfall, and surface temperature is proposed by this study. The region of mid Asia was subjected to the model. The findings demonstrated a strong inverse relationship between soil moisture and surface temperature, a corresponding inverse relationship between soil moisture and air temperature, and a strong inverse relationship between soil moisture and precipitation. The soil moisture derived from NASA's LPRM_AMSR2 data was observed to have a correlation coefficient of 0.45 with gridded rainfall, and -0.59 with air temperature and ground temperature. The Aphrodite's Water Resource was employed to conduct this analysis, and the resulting meteorological factors taken into consideration were from there. Applying various weather parameters as the corresponding input to make a prediction about soil moisture and then measuring the accuracy with an LSTM model. [ABSTRACT FROM AUTHOR]

Air temperature holds significant importance in microclimate and environmental health studies, playing a crucial role in weather regulation. There is a need to develop a reliable model capable of accurately capturing air temperature variations. In this study, we focused on the Amazon-Cerrado transitional forest, constructing a robust predictive model for hourly temperature fluctuations. This forest, situated approximately 50 km northwest of Sinop, Mato Grosso, Brazil, is a transitional area, making it important to investigate its climatic behavior and ecosystems. We estimated air temperature using machine learning techniques such as Random Forest, Gradient Boosting, Multilayer Perceptron, and Support Vector Regressor, aiming to evaluate the most effective models based on relevant metrics. Performance assessments were conducted during both dry and rainy seasons to verify their adaptability. The top-performing Random Forest model demonstrated Willmott and Spearman indexes above 0.97. The air relative humidity, solar radiation, and volumetric soil water content were identified as the most important features, evaluated with Willmott and Spearman indexes above 0.95 in a model with such dimensionality reduction. These results underscore the efficacy of machine learning techniques in accurately estimating air temperature. [ABSTRACT FROM AUTHOR]

Farmland shelterbelts provide an ecological barrier to oases, but their growth is inhibited or even dies under water scarcity. Therefore, it is necessary to understand the response of farmland shelterbelts to environmental changes for their own survival in order to promote the long-term healthy development of agroforestry systems and improve water management in oases in the northwest arid region. In this study, we measured the sap flow and environmental factors to determine the transpiration dynamics and its influencing factors. The seasonal variations in water use strategy of Populus alba var. pyramidalis Bunge were identified by using stable isotopes of δ18O in xylem, soil water and groundwater coupled with the Iso-Source model. The results showed that the transpiration of the populus was significantly higher in the growing season (more than 80% of the total transpiration) than in the non-growing season, and the transpiration was closely related to air temperature, vapor pressure deficit, soil water content, and soil temperature. The water use strategy had seasonal variations. The populus obtained water predominantly from 0–40 cm soil water (approximately 75%) in the wet spring and then switched to deeper water sources (40–150 cm soil water and groundwater) as the season progressed. The contribution of deeper water sources increased to 84–92% in summer and autumn. This suggested that Populus alba var. pyramidalis Bunge has ecological plasticity, as it could explore deeper water sources as the water stress increased. The ability to switch rapidly among different water sources could put a plant at an advantage if competition for water occurs within the ecosystem. Our findings can provide insights into temporal transpiration dynamics as well as the strategy adopted by farmland shelterbelts to counteract the drought stress associated with the shallow soils in desert oasis ecosystems. [Display omitted] • The growing season's shelterbelt transpiration accounted for about 80% of total annual transpiration. • Transpiration in the desert oasis shelterbelt is more sensitive to deep soil water. • Shelterbelt water sources changed from 0–40 cm soil water to deeper sources as the season progressed. • The shelterbelt coped with variations in soil water availability through root plasticity. [ABSTRACT FROM AUTHOR]

• Wavelet denoising optimizes stabilization of temperature variation analysis. • Data fusion of humidity and temperature boosts prediction trend accuracy. • Multi-step, time series method is proposed for air and soil temperature forecasting. Greenhouses stand as key infrastructural components in contemporary agriculture, facilitating the perennial availability of vegetables. Harnessing accurate, real-time environmental data, particularly air and soil temperature, is pivotal to promoting the facility's efficiency in pest and disease alarming and vegetable production. Nevertheless, despite consistent long-term temperature trends, short-term fluctuations prove to be considerably nonlinear, time-lagged, and multivariate-coupled due to the unique microclimate of the greenhouse. Addressing this, we propose a novel short-term multi-step prediction model that assimilates a range of environmental data including air temperature, humidity, soil temperature, and soil moisture content at diverse greenhouse heights. This model utilizes an Attention-LSTM based time series approach to accurately predict multi-step short-term temperature variations within the greenhouse. By employing a modest amount of historical data (approximately 48 h), the model provides future temperature forecasts within a range of 30 to 480 min with high accuracy. When benchmarked against renowned models such as RNN, GRU, and LSTM networks, our proposed models display impressive performance. It achieved R2 values of 0.93, 0.94, 0.95, and 0.86 for 1, 4, 8, and 16-step predictions, respectively, in the domain of air temperature forecasting. The corresponding MSE values stood at 0.42, 0.32, 0.27, and 0.81. In parallel, for soil temperature forecasts, our model recorded R2 values of 0.96, 0.94, 0.92, and 0.89 for 1, 4, 8, and 16-step predictions, respectively, alongside MSE values of 0.36, 0.48, 0.73, and 0.99 for the equivalent steps. To conclude, these results reveal the exceptional predictive aptitude of our model for both air and soil temperature forecasts within greenhouse environments, validating its standing as an effective tool for the optimization of vegetable farming practices. Consequently, our model demonstrates its ability to yield precise predictions of environmental variables within greenhouses, offering crucial early warning mechanisms for the enhancement of vegetable cultivation processes. [ABSTRACT FROM AUTHOR]