Soil Moisture Research Articles

Disentangling the Complex Effects of Seasonal Drought, Floor Mass, and Roots on Soil Microbial Biomass in a Subtropical Moist Forest

Severe seasonal droughts driven by global climate change significantly alter the cycling of carbon and nutrients in forest ecosystems, while the investigation into the impacts of floor mass and plant roots on soil microbial biomass within the context of recurrent seasonal droughts is still rare. To investigate the environmental determinants governing soil microbial biomass with the escalating severity of seasonal droughts, we conducted a study in a montane subtropical moist evergreen broad-leaved forest in southwestern China from June 2019 to May 2023. The study results revealed that soil microbial biomass, as well as soil moisture, floor mass, and plant roots, showed an apparent single-hump modal within one year. In the comparative analysis of the soil microbial biomass fluctuation amplitudes across control and watered plots, a discernible disparity was observed, indicating significant differences in microbial biomass dynamics between the respective experimental conditions. The pooled data revealed a statistically significant influence of seasonal drought, floor mass, plant roots, and their reciprocal interactions on the soil microbial biomass, highlighting these factors as pivotal determinants of microbial community dynamics. This study elucidates the interactive regulatory mechanisms by which seasonal drought, floor mass, and plant roots collectively modulate soil microbial biomass within tropical and subtropical forests, offering insights into the complex ecological processes governing microbial community dynamics. This interactive regulation might influence the trajectory of plant species and soil microbial communities, facilitating their adaptive development and evolutionary responses.

An automated AI-powered IoT algorithm with data processing and noise elimination for plant monitoring and actuating

This article aims to develop a novel Artificial Intelligence-powered Internet of Things (AI-powered IoT) system that can automatically monitor the conditions of the plant (crop) and apply the necessary action without human interaction. The system can remotely send a report on the plant conditions to the farmers through IoT, enabling them for tracking the healthiness of plants. Chili plant has been selected to test the proposed AI-powered IoT monitoring and actuating system as it is so sensitive to the soil moisture, weather changes and can be attacked by several types of diseases. The structure of the proposed system is passed through five main stages, namely, AI-powered IoT system design, prototype fabrication, signal and image processing, noise elimination and proposed system testing. The prototype for monitoring is equipped with multiple sensors, namely, soil moisture, carbon dioxide (CO2) detector, temperature, and camera sensors, which are utilized to continuously monitor the conditions of the plant. Several signal and image processing operations have been applied on the acquired sensors data to prepare them for further post-processing stage. In the post processing step, a new AI based noise elimination algorithm has been introduced to eliminate the noise in the images and take the right actions which are performed using actuators such as pumps, fans to make the necessary actions. The experimental results show that the prototype is functioning well with the proposed AI-powered IoT algorithm, where the water pump, exhausted fan and pesticide pump are actuated when the sensors detect a low moisture level, high CO2 concentration level, and video processing-based pests’ detection, respectively. The results also show that the algorithm is capable to detect the pests on the leaves with 75% successful rate.

Effects of Single‐Axis and Fixed‐Tilt Photovoltaic Array Construction on the Soil Seed Bank Characteristics in Semi‐Arid Grasslands

ABSTRACTWith the rapid global development of photovoltaic power generation, research on its impact on land and ecosystems has become increasingly significant. However, its impact on soil seed bank characteristics has yet to be better assessed. In this study, monitoring plots were established in a semi‐arid grassland undergoing solar energy development. This setup allowed us, for the first time, to investigate how soil seed bank characteristics respond to the construction of two typical photovoltaic array systems: single‐axis and fixed‐tilt systems. This study demonstrated that in both single‐axis and fixed‐tilt systems, the establishment of photovoltaic arrays resulted in a significant increase in soil seed density, with seed counts rising by approximately 47.5% compared with control sites without arrays. The aggregation effect of soil seed density under the photovoltaic array primarily occurred in the 0–10 cm soil layer. The soil seed density under the single‐axis arrays was higher than that under the fixed‐tilt arrays. The construction of photovoltaic arrays resets local soil and directly changes the micro‐environment—including reductions in solar radiation, decreases in average temperature by 0.1°C, and wind speed decreases by 1.5 m/s—which negatively affected the richness and diversity of the soil seed bank, resulting in a 21.1% decrease in species richness and a 10.1% reduction in seed diversity. Furthermore, this study highlights that seed germination in semi‐arid grasslands is under pressure due to environmental changes associated with photovoltaic construction areas. Specifically, soil moisture and organic matter were the key factors affecting the vegetation restoration potential of the entire construction area. We recommend selecting the single‐axis system of photovoltaic components. This selection is crucial, which considers both energy production efficiency and supports the facilitation of future vegetation ecosystem succession. Altogether, this study provides information for future land‐use planning in photovoltaic construction areas and sustainable development of photovoltaic power generation.

Physicochemical perturbation increases nitrous oxide production from denitrification in soils and sediments

Abstract. Atmospheric concentrations of nitrous oxide (N2O), a potent greenhouse gas that is also responsible for significant stratospheric ozone depletion, have increased in response to the intensified use of agricultural fertilizers and other human activities that have accelerated nitrogen cycling processes. Microbial denitrification in soils and sediments is a major source of N2O, produced as an intermediate during the reduction of oxidized forms of nitrogen to dinitrogen gas (N2). Substrate availability (nitrate and organic matter) and environmental factors such as oxygen levels, temperature, moisture, and pH influence rates of denitrification and N2O production. Here we describe the role of physicochemical perturbation (defined here as a change from the ambient environmental conditions) in influencing rates of denitrification and N2O production. Changes in salinity, temperature, moisture, pH, and zinc in agricultural soils induced a short-term perturbation response characterized by lower rates of total denitrification and higher rates of net N2O production. The ratio of N2O to total denitrification (N2O : DNF) increased strongly with physicochemical perturbation. A salinity press experiment on tidal freshwater marsh soils revealed that increased N2O production was likely driven by transcriptional inhibition of the nitrous oxide reductase (nos) gene and that the microbial community adapted to altered salinity over a relatively short time frame (within 1 month). Perturbation appeared to confer resilience to subsequent disturbance, and denitrifiers from an environment without salinity fluctuations (tidal freshwater estuarine sediments) demonstrated a stronger N2O perturbation response than denitrifiers from environments with more variable salinity (oligohaline and mesohaline estuarine sediments), suggesting that the denitrifying community from physicochemically stable environments may have a stronger perturbation response. These findings provide a framework for improving our understanding of the dynamic nature of N2O production in soils and sediments, in which changes in physical and/or chemical conditions initiate a short-term perturbation response that promotes N2O production that moderates over time and with subsequent physicochemical perturbation.

Chemical and Physical Aspects of Soil Health Resulting from Long-Term No-Till Management

The aim of this study was to compare the long-term effects of conventional tillage (CT) and no-till (NT) systems on the main soil properties that determine soil health. The research was conducted in a field experiment established in 1975 in Chylice, central Poland, at the WULS-SGGW Experimental Station Skierniewice. Soil samples collected from 0–10 and 10–20 cm of the mollic horizon of the Phaeozem were analysed for total organic carbon (TOC) content, fractional composition of SOM and spectroscopic properties of humin, soil structural stability, soil water retention characteristics and soil water repellency (SWR). The results showed that NT practice almost doubled the TOC in the 0–10 cm layer. However, optical parameters of humin indicated that NT management promoted the formation of humin with a lower molecular weight and lower degree of condensation of aromatic structures. In the NT 0–10 cm layer, a significant increase in the number of water-resistant macroaggregates was found. In the 0–10 cm layer, the water capacity increased by 9%, 18%, 22% and 26% compared to CT at (certain soil suction) pF values of 0.0, 2.0, 3.0 and 4.2, respectively. SWR occurs regardless of the cultivation method at a soil moisture equivalent to pF 4.2, and the greatest range of SWR was found in the NT 0–10 cm layer.

Ionically crosslinked Sodium alginate – Galactoxyloglucan hydrogel beads as a controlled release system for sustainable urea delivery

The prolonged usage of chemical fertilizers often causes deleterious effects on the environment, and it remains an alarming issue in agricultural practices. Smart delivery of fertilizers achieved by hydrogels proved their efficiency in reducing risk in action. In this study, we investigated the optimization of urea-encapsulated hydrogel formulations and evaluated their physical and chemical attributes for agricultural applications. The proposed hydrogel formulation consists of ionically crosslinked two promising plant biopolymers (galactoxyloglucan and sodium alginate) which are specifically developed for the controlled release of urea fertilizer. The FT-IR analysis of the hydrogel beads confirmed the presence of biopolymer functional groups and urea. XRD analysis indicated the amorphous nature of the biopolymers. TGA revealed a thermal stability of up to 350°C. SEM images showed the porous structure of the hydrogel beads. To investigate the controlled release of urea, two kinetic models were employed: Korsmeyer-Peppas and Peppas-Sahlin. The results indicated a controlled release mechanism. The hydrogel beads demonstrated excellent water retention properties, maintaining soil moisture for up to 16 days. Additionally, degradability results demonstrated that in the soil environment, the hydrogel beads were degraded completely in 21 days. Therefore, we are confident that the formulated hydrogel bead exhibits eco-friendly and bio-compatible characteristics, which potentially mitigate the risks associated with urea fertilizers.

An improved optical trapezoid model to identify wetlands and irrigation water ponds in the arid irrigation district using Google Earth Engine

ABSTRACT In arid regions, wetlands support ecosystem with high biodiversity and productivity. Meanwhile, irrigation during the fallow period often results in the temporary formation of water ponds in the cropland, which plays a critical role in agricultural productivity. The prompt identification of wetlands and irrigation water ponds in irrigation districts is important for the protection of local wetland ecosystem and the effective management of irrigation water. Utilizing the OPtical TRApezoid Model (OPTRAM) for soil moisture estimation, we have proposed an improved version of OPTRAM aimed to identify wetlands and irrigation water ponds in irrigation district using Sentinel-2 data through Google Earth Engine platform. Different criteria were used for the identification of wetland and irrigation water pond, given the greater diversity associated with wetlands. Parameters for the wet edge determined from OPTRAM were used to identify wetlands, while an additional threshold was added to the model and calibrated in accordance with the irrigated area to identify irrigation water ponds. The improved OPTRAM was applied in Hetao Irrigation District (HID) of Northwest China from 2016 to 2023, where autumn irrigation applied in late autumn after crop harvesting was considered. The identified distributions of wetland and autumn irrigation were validated with observations from field survey and statistical data, and were also compared with other remote sensing products. Results show that the proposed model is effective in identifying both wetlands and irrigation ponds. Considering the wetlands, the distances from identified boundaries to the actual boundaries obtained from field surveys were calculated, revealing the mean absolute error of 6.99 m, alongside a validation accuracy ratio of 0.94 for points located within wetlands. For autumn irrigation extent, the overall accuracy is 0.90 based on the observations from field survey, with mean absolute relative errors for irrigated areas across four sub-irrigation districts recorded as 20.55%, 8.10%, 12.83%, and 11.38%, respectively, when compared with statistical data. The majority of wetlands within HID are small in size and scattered, with different trends observed in the wetland areas of sub-irrigation districts and Wuliangsuhai Lake. Autumn irrigated croplands are mainly concentrated in Jiefangzha sub-irrigation district while being scattered across other sub-irrigation districts, depicting an overall decreasing trend in the autumn irrigated area. In summary, the proposed model performed well in identifying both wetlands and irrigation ponds, and can offer valuable support for the conservation of wetland ecosystem and irrigation management.

Assessment of Soil Moisture in Vegetation Regions of Mu Us Sandy Land Using Several Aridity Indicators

Drought, a significant calamity in the natural domain, has extensive worldwide repercussions. Drought, primarily characterized by reduced soil moisture (SM), presents a significant risk to both the world environment and human existence. Various drought indicators have been suggested to accurately represent the changing pattern of SM. The study examines various indices related to the Drought Severity Index (DSI), Evaporation Stress Index(ESI), Vegetation Supply Water Index(VSWI), Temperature-Vegetation Dryness Index(TVDI), Temperature Vegetation Precipitation Dryness Index(TVPDI), Vegetation Health Index(VHI), and Temperature Condition Index (TCI). An evaluation was conducted to assess the effectiveness of seven drought indicators, such as DSI, ESI, TVPDI, VSWI, etc., in capturing the changes in SM in Mu Us Sandy Land. The research results indicated that DSI and ESI had the highest accuracy, while TVDI and VSWI showed relatively lower accuracy. However, their smaller fluctuations in the time series demonstrated stronger adaptability to different regions. Additionally, the delayed impact of aridity indices on soil moisture, variable attributes, temperature, and vegetation coverage in sandy land and grassland areas with low, medium, and high coverage all contributed to the effectiveness of the four aridity indices (DSI, ESI, VSWI, and TVPDI) in capturing the dynamics of soil moisture. The primary element that affects the effectiveness of TVDI is the divergence of the relationship curve between Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI), which is a kind of deterioration. This paper presents a very efficient approach for monitoring soil moisture dynamics in dry and semi-arid regions. It also analyzes the patterns of soil moisture changes, offering valuable scientific insights for environmental monitoring and ecological enhancement.

Unraveling the Relationships Between Trend of Dam Inflows, Hydrometeorological Variables and Vegetation in West and Southwest United States

Abstract This paper explored temporal changes in magnitude and seasonality of low, median, and high inflows of 51 dams across the West and Southwest United States over the 1993-2022 period. Changes in precipitation, air temperature (an indicator of snowpack and evaporation), soil moisture, and vegetation were also examined to identify potential reasons for the temporal trends in dam inflows. Using monotonic and non-monotonic tests, we found a general downward trend in dam inflows, particularly across the Upper Colorado and California regions. More than 30% of the dams showed a downward trend in their annual median inflows, high inflows during spring, and median inflows during fall. The downward trend of dam inflows was associated with decreasing precipitation and soil moisture, and rising temperatures. While vegetation exhibited positive associations with inflows, it did not seem to be a primary factor for explaining the inflow trends. We also observed shifts in the seasonality of low and high inflows; there was an increase in the proportion of inflows occurring during summer and fall, and a decrease in winter proportions for low inflows. Similarly, high inflows exhibited an increase in spring proportions and a decrease in fall proportions. Our changepoint analyses detected non-monotonic trends between 2002 and 2012 in ~13% of the dams; the majority were located in the Upper Colorado and California regions. More than half of these changepoints were in 2011, likely due to widespread droughts then. Our study has implications for reservoir managers to identify changes that dams experience over time and assist them in proposing actions that maintain the dams’ functionality.

Automation in Greenhouse using IoT

This work provides an IoT-based plant monitoring system for avoiding over-exploitation of resources, ensuring compact design with easy installation and the best environment for the plants to grow. This type of agricultural monitoring system provides environmental monitoring services that help the crop grow in good shape. The system offers the service of storing a database about the environment and soil information acquired from a wireless sensor network deployed in the planted area. Furthermore, it enables users to track environmental data regarding planted crops in real-time using any Internet-enabled device. Our project introduces automatic sunlight prevention and soil moisture control based on data received from the moisture and temperature sensors. The proposed work is implemented using a microcontroller, programmed accordingly to measure the temperature and moisture using respective sensors. It is well capable of controlling temperature by operating a fan, controlling a shade for blocking excess sunlight and controlling moisture by operating a watering device through a motor. The potential outcome of the project work can be used for greenhouse

How Severe Was the 2022 Flash Drought in the Yangtze River Basin?

Flash droughts, characterized by their rapid onset and severe impacts, have critical implications for the ecological environment and water resource security. However, inconsistent definitions of flash droughts have hindered scientific assessments of drought severity, limiting efforts in disaster prevention and mitigation. In this study, we propose a new method for explicitly characterizing flash drought events, with particular emphasis on the process of soil moisture recovery. The temporal and spatial evolution of flash droughts over the Yangtze River Basin was analyzed, and the severity of the extreme flash drought in 2022 was assessed by comparing its characteristics and impacts with those of three typical dry years. Additionally, the driving factors of the 2022 flash drought were evaluated from multiple perspectives. Results indicate that the new identification method for flash droughts is reasonable and reliable. In recent years, the frequency and duration of flash droughts have significantly increased, with the Dongting Lake and Poyang Lake basins being particularly affected. Spring and summer were identified as peak seasons for flash droughts, with the middle reaches most affected in spring, while summer droughts tend to impact the entire basin. Compared to 2006, 2011, and 2013, the flash drought in 2022 affected the largest area, with the highest number of grids experiencing two flash drought events and a development rate exceeding 15%. Moreover, the summer heat in 2022 was more extreme than in the other three years, extending from spring to fall, especially during July–August. Its evolution was driven by the Western Pacific Subtropical High, which suppressed precipitation and elevated temperatures. The divergence of water vapor flux intensified water shortages, while anomalies in latent and sensible heat fluxes increased surface evaporation and heat transfer, further disturbing the regional water cycle. This study provides valuable insights for flash drought monitoring and early warning in the context of a changing climate.

Potential of conservation agriculture practice in climate change adaptation and mitigation in Ethiopia: a review

Land degradation is a major problem in Ethiopia, as it contributes to climate change by releasing greenhouse gases (GHG) and slowing carbon sequestration rates. The objective of this review was to assess the role of conservation agriculture (CA) in climate change adaptation and mitigation in Ethiopia. The critical review method processes for identifying and synthesizing peer-reviewed research and review articles, reports, proceedings, and book chapters were followed, with materials obtained from relevant search engines. The findings of the various reports revealed that minimum tillage assists in soil moisture conservation when compared to conventional tillage. Conservation tillage maintains crop residue, reduces soil temperature significantly, and increases nutrient buildup in the surface soil layer, all of which lead to higher crop growth and production thus help as adaptation to climate change. Furthermore, agriculture and other land uses significantly contribute to greenhouse gas emissions; nevertheless, conservation agricultural methods improve soil organic carbon (SOC), soil aggregation, and carbon in aggregate, as well as soil health that contribute to climate changing mitigation. Several studies found that soil health indicators such as soil aggregation, soil organic carbon storage, soil enzymes, and microbial biomass improved under conservation tillage practices, potentially improving the carbon-nitrogen cycle, soil stability, and overall crop productivity. In terms of climate adaptation and mitigation, CA is one of the non-substitutable choices for reducing greenhouse gas emissions. Crop diversity, increased nitrogen consumption efficiency, crop rotation, improved soil carbon sequestration methods; crop residue retention, minimum soil disturbance, manure incorporation, and integrated farming systems are all important factors in minimizing GHG emissions. Moreover, factors impeding CA adoption include a lack of appropriate equipment and machinery, weed control methods, the use of crop residue for fuel wood and animal feed, a lack of awareness about the benefits of CA on soil health and sustainability, and a lack of government technical and financial support for smallholder farmers. Adoption and scaling up of CA practices are critical for ensuring a sustainable development goal and resilient future. Thus, relevant stakeholders should consider the aforementioned considerations while promoting the technology on a large scale through integration with enhanced technology.

Improving subseasonal forecast skill in the Norwegian Climate Prediction Model using soil moisture data assimilation

AbstractThis study shows the importance of soil moisture (SM) in subseasonal-to-seasonal (S2S) predictions at mid-latitudes. We do this through introducing the Norwegian Climate Prediction Model Land (NorCPM-Land), a land reanalysis framework tailored for integration with the Norwegian Climate Prediction Model (NorCPM). NorCPM-Land assimilates blended SM data from the European Space Agency’s Climate Change Initiative into a 30-member offline simulation of the Community Land Model with fluxes from the coupled model. The assimilation of SM data reduces error in SM by 10.5 % when validated against independent SM observations. It also improves latent heat flux estimates, illustrating that the adjustment of underlying SM significantly augments the capacity to model land surface dynamics. We evaluate the added value of land initialisation for subseasonal predictions, by comparing the performance of hindcasts (retrospective prediction) using the standard NorCPM with a version where the land initial condition is taken from NorCPM-Land reanalysis. The hindcast covers the period 2000 to 2019 with four start dates per year. Land initialisation enhances SM predictions, reducing error by up to 2.5-month lead time. Likewise, the error for precipitation and temperature shows improvement up to a lead time of 1.5-month. The largest improvements are observed in regions with significant land-atmospheric coupling, such as the Central United States, the Sahel, and Central India. This method further enhances the prediction of extreme temperature variations, both high and low, with the most notable improvements seen in regions at mid and high latitudes, including parts of Europe, the United States, and Asia. Overall, our study provides further evidence for the significant role of SM content in enhancing the accuracy of subseasonal predictions. This study introduces a technique for improved land initialisation, utilising the same model employed in climate predictions.

Mapping Soil Surface Moisture of an Agrophytocenosis via a Neural Network Based on Synchronized Radar and Multispectral Optoelectronic Data of SENTINEL-1,2—Case Study on Test Sites in the Lower Volga Region

In this article, the authors developed a novel method for the moisture mapping of the soil surface of agrophytocenosis using a neural network based on synchronized radar and multispectral optoelectronic data from Sentinel-1,2. The significance of this research lies in its potential to enhance precision farming practices, which are increasingly vital in addressing global agricultural challenges such as water scarcity and the need for sustainable resource management. To verify the developed method, data from two experimental plots were utilized. These plots were located on irrigated soybean crops, with the first plot situated on the right bank (plot No. 1) and the second on the left bank (plot No. 2) of the lower Volga River. Two experimental soil moisture geodatasets were created through measurements and geo-referencing points using the gravimetric method (for plot No. 1) and the proximal sensing method (for plot No. 2) employing the Soil Moisture Sensor ML3-KIT (THETAKIT, Delta). The soil moisture retrieval algorithm was based on the use of a neural network to predict the reflection coefficient of an electro-magnetic wave from the soil surface, followed by inversion into soil moisture using a dielectric model that takes into account the soil texture. The input parameter of the neural network was the ratio of the microwave radar vegetation index (calculated based on Sentinel-1 data) to the index (calculated based on the data of multispectral optoelectronic channels 8 and 11 of Sentinel-2). The retrieved soil moisture values were compared with in situ measurements, showing a determination coefficient of 0.44–0.65 and a standard deviation of 2.4–4.2% for plot No. 1 and similar metrics for plot No. 2. The conducted research laid the groundwork for developing a new technology for remote sensing of soil moisture content in agrophytocenosis, serving as a crucial component of precision farming systems and agroecology. The integration of this technology promotes sustainable agricultural practices by minimizing water consumption while maximizing crop productivity. This aligns with broader environmental goals of conserving natural resources and reducing agricultural runoff. On a larger scale, data derived from such studies can inform policy decisions related to water resource management, guiding regulations that promote efficient water use in agriculture.

Study of Machine Learning Techniques for the Estimation of Soil Moisture in Agriculture

Soil moisture is crucial in various fields and monitoring it to guide irrigation is challenging. Machine learning has emerged as a promising tool to predict soil moisture levels accurately. This study evaluates machine learning techniques for this task, training models with meteorological variables and direct soil moisture measurements. Four machine learning algorithms were implemented, highlighting the Gradient Boosting Regressor as the most effective. In addition, a processed data set that combines meteorological and soil moisture measurements is presented, hoping it will be helpful for future research. This approach seeks to improve the compression and predictability of soil moisture, which is crucial for agricultural planning and water management in agriculture

Differences in the Effects of Three Water Elements on Vegetation in Different Climatic Regions: Insights From the Yellow River Basin, China

ABSTRACTThe effects of water on vegetation have always been a concern. It is an important support as well as a major limiting factor with respect to vegetation growth. By analysing the spatiotemporal changes and correlations between precipitation (PRE), soil moisture (SM), vapour pressure deficit (VPD) and normalized difference vegetation index (NDVI) in the Yellow River Basin, we explored the different effects of three water elements on vegetation in different climatic regions. Our findings reveal the following: (1) NDVI and the three water elements report an increasing trend in the Yellow River Basin, with NDVI increasing most significantly. 92.1% of the Yellow River Basin showed an increase in NDVI. (2) Vegetation in the Yellow River Basin was most positively affected by PRE, followed by SM and VPD. PRE mainly affected the natural vegetation on both sides of the boundary between the arid and semi‐arid regions and the semi‐humid regions. SM mainly affected the natural vegetation in the arid and semi‐arid regions, whereas VPD mainly affected the crops in the irrigation areas, and the irrigation areas in arid regions were affected the most. These findings contribute to a deeper understanding of the relationship between water elements and vegetation.

Smart technology-based microclimate control system for greenhouse

This study proposes an agricultural management system designed to enhance productivity by leveraging artificial intelligence and Internet of Things (IoT) technologies. It addresses the complex microclimate conditions within greenhouses by integrating microcontrollers and sensors to monitor parameters such as temperature, humidity, and soil moisture, enabling precise environmental management. Web and mobile applications provide interactive interfaces, allowing users to monitor real-time factors influencing the greenhouse environment. The system can execute actions like activating fans or irrigation pumps based on user inputs or predefined settings. In addition to improving yields, the system aims to create a dataset that can be used for assessing the effectiveness of future machine learning models. Important considerations include data privacy, training farmers to effectively use the technology, and integrating the system with traditional farming practices, thereby enhancing sustainability. Overall, this study aspires to continuously improve agricultural practices by combining modern technology with traditional knowledge, ultimately aiming for a more efficient and sustainable agricultural landscape.

Woody Encroachment Modifies Subsurface Structure and Hydrological Function

ABSTRACTWoody encroachment—the expansion of woody shrubs into grasslands—is a widely documented phenomenon with global significance for the water cycle. However, its effects on watershed hydrology, including streamflow and groundwater recharge, remain poorly understood. A key challenge is the limited understanding of how changes to root abundance, size and distribution across soil depths influence infiltration and preferential flow. We hypothesised that woody shrubs would increase and deepen coarse‐root abundance and effective soil porosity, thus promoting deeper soil water infiltration and increasing soil water flow velocities. To test this hypothesis, we conducted a study at the Konza Prairie Biological Station in Kansas, where roughleaf dogwood (Cornus drummondii) is the predominant woody shrub encroaching into native tallgrass prairie. We quantified the distribution of coarse and fine roots and leveraged soil moisture time series and electrical resistivity imaging to analyse soil water flow beneath shrubs and grasses. We observed a greater fraction of coarse roots beneath shrubs compared to grasses, which was concurrent with greater saturated hydraulic conductivity and effective porosity. Half‐hourly rainfall and soil moisture data show that the average soil water flow through macropores was 135% greater beneath shrubs than grasses at the deepest B horizon, consistent with greater saturated hydraulic conductivity. Soil‐moisture time series and electrical resistivity imaging also indicated that large rainfall events and greater antecedent wetness promoted more flow in the deeper layers beneath shrubs than beneath grasses. These findings suggest that woody encroachment alters soil hydrologic processes with cascading consequences for ecohydrological processes, including increased vertical connectivity and potential groundwater recharge.

Global Prediction of Flash Drought Using Machine Learning

AbstractFlash droughts are rapidly developing extreme weather events with sudden onset and quick intensification. Global prediction of flash droughts at sub‐seasonal time scales remains a great challenge. Current state‐of‐the‐art dynamic models subject to large errors and demonstrate low skills in global flash drought prediction. Here, we develop a machine learning‐based framework that uses meteorological forecasts as inputs to predict global root‐zone soil moisture and flash droughts from 1 day to 2 week lead times. The results indicate that 33% and 24% global flash drought onset and termination events can be correctly predicted by machine learning at 7 day lead time, versus 19% and 11% fractions by state‐of‐the‐art dynamic model. The developed machine learning model demonstrates substantial improvements over dynamic model in global soil moisture prediction, and thus enhances global flash drought forecasting skills in space and time. The presented framework may benefit global flash drought prediction and early warning at sub‐seasonal scales.

Greater moisture impacts on radial growth of Larix sibirica in the eastern Altay Mountains since the 1990s.

Against the background of climate warming and humidification, the so-called 'divergence problem' reduces the stability of tree rings in response to climate, and affects the reliability of tree-ring reconstruction. Investigation of the divergence problem is crucial to improve our understanding of the response patterns of trees to climate warming, and provide a scientific basis for accurate climate reconstruction. Based on tree-ring width data for Siberian larch (Larix sibirica Ledeb.) growing at low elevations in the eastern Altay Mountains, we analyzed the relationship between radial growth of trees and climatic factors in the context of abrupt climate change in this region. We calculated the proportional contribution of five climatic factors to the radial growth of trees, and discussed the response mechanism of radial growth of L. sibirica in combination with large-scale atmospheric circulation patterns. The radial growth of L. sibirica was mainly constrained by water availability. Before climate warming (1961-1990), the radial growth of L. sibirica was mainly limited by temperature in the previous June. After abrupt climate warming (1991-2020), there was a significant positive correlation between growth and soil moisture in the previous winter, suggesting that high temperatures in the following spring would limit tree radial growth if water availability was low. The attribution analysis results revealed that, before 1990, the proportional of relative contribution of temperature to radial growth of trees exceeded 60%. Since 1990, the proportional of relative contribution of water (precipitation and volumetric soil water) to growth of L. sibirica increased. This might reflect the combined effects of local climatic conditions and changes in large-scale atmospheric circulation.