Abstract. The combined use of independent soil moisture data from radar and radiometer measurements in data assimilation (DA) systems is expected to yield synergistic performance gains due to their complementary strengths. This study evaluates the impact of simultaneously assimilating soil moisture retrievals from ASCAT (Advanced SCATterometer) and SMAP (Soil Moisture Active Passive) into the Korean Integrated Model (KIM) using a weakly coupled DA framework based on the National Aeronautics and Space Administration's Land Information System (LIS). The Noah land surface model (LSM) within LIS, which is the same as that used in KIM, is used to simulate land surface states and assimilate soil moisture retrievals. The impact of soil moisture DA is evaluated using independent reference datasets, assessing its influence on soil moisture analysis and numerical weather prediction performance. Overall, assimilating single-sensor soil moisture data, ASCAT or SMAP, into the LSM improves global soil moisture analysis accuracy by 4.0 % and 10.5 %, respectively, compared to the control case without soil moisture DA, achieving the most significant enhancements in croplands. Relative to single-sensor soil moisture DA, multi-sensor soil moisture DA yields more balanced skill enhancements for both specific humidity and air temperature analyses and forecasts. The most pronounced synergistic improvements by simultaneously assimilating both soil moisture products are observed in the 2 m air temperature analysis and forecast, especially when both soil moisture products have a positive impact. Precipitation forecast skill also improves with multi-sensor soil moisture DA, although the improvements are not consistent across regions and events. This paper discusses remaining issues for future studies to further improve the weather prediction performance of the KIM-LIS multi-sensor soil moisture DA system.

Abstract The frequency and severity of summertime synchronous extreme heatwaves across the Northern Hemisphere are increasing with global warming, threatening ecosystems, economies, and human health. Understanding the spatiotemporal characteristics of these heatwaves is therefore crucial. This study employs the event synchronization climate network method to objectively identify hotspot regions of synchronous extreme heatwaves and their dominant synchronization patterns. It further explores the associated large-scale atmospheric circulation patterns and soil moisture feedback processes. Results show that regions including most of Europe, the western Arabian Peninsula, East Asia, Southeast Asia, and the western and southern parts of North America, as well as Greenland, are susceptible to synchronous heatwaves. Notably, Southeast Asia and western North America show strong synchronization with the Caspian Sea, while East Asia and southern North America primarily synchronize with northern-central Europe. Southeast Asia–Caspian Sea and East Asia–northern-central Europe synchronization patterns are linked to wave-like anomalies suggestive of northwest-southeastward propagating Rossby waves, whereas western North America–Caspian Sea and southern North America–central Europe synchronization patterns correspond to zonal wave trains. These circulation patterns feature concurrent anticyclonic anomalies over synchronized heatwave regions, favoring warming through adiabatic subsidence and increased solar radiation. Moreover, concurrent local soil moisture drying increases the likelihood of their co-occurrences by positive land-atmosphere feedback, which likely acts to intensify and prolong heatwaves. These findings systematically map synchronous heatwave hotspots and synchronization patterns across the Northern Hemisphere, highlighting novel cross-latitudinal connections and establishing a foundation for future work to disentangle dynamic and thermodynamic influences.

The physical action of subsoiling increases water infiltration, allowing the infiltrated water layer to reach the root zone of crops, including soybean, which was the focus of this research. The study was conducted in the Soconusco region, Chiapas, located in the southern Pacific region of Mexico, where approximately 12,000 hectares of soybean are cultivated under rainfed conditions, supported by a six-month rainy season (May–October) suitable for crop growth and production. However, since 1975, soil preparation for planting has been carried out under variable moisture conditions which, combined with repeated machinery traffic during plowing and harrowing operations, has caused soil compaction, currently presenting varying levels of compaction. Therefore, research was conducted during 2019, 2022, and 2023 using an experimental design consisting of three treatments: subsoiling and two control treatments, conventional plowing and soil harrowing. Treatments were established in paired plots of 0.5 ha (50 m × 100 m), with subsoiling mainly performed during the dry season (April 2019) at a depth of 60 cm. In each treatment, infiltration was measured to estimate its parameters, including infiltration rate, cumulative infiltrated depth, and saturated hydraulic conductivity. Under the hypothesis that subsoiling increases infiltration and in situ soil moisture, the objective was to evaluate soil moisture behavior within the crop root profile as a function of subsoiling and infiltration and its effect on soybean yield under rainfed conditions.

Abstract Drought is a complex hazard with far-reaching socio-economic and ecological impacts that are often inadequately captured by conventional indices based solely on hydroclimatic anomalies. To bridge this gap, this study develops novel impact-based Combined Drought Indices (iCDIs) that directly relate hydroclimatic drivers to remotely sensed vegetation stress, here used as a proxy for observed agricultural impacts. The proposed framework employs a machine learning framework to optimize the selection of hydroclimatic predictors including precipitation, temperature, and soil moisture across distinct hydrological clusters of the European domain. This bottom-up ML-based approach outperforms traditional indices in reproducing observed vegetation responses across European river basins. Crucially, future projections of iCDIs under warming scenarios unveil a distinct northward shift in drought impacts, with Central Europe emerging as an increasingly vulnerable hotspot. Notably, this contrasts with patterns suggested by standardised indices, which typically emphasize Mediterranean regions as the primary areas of concern. This spatial reconfiguration of risk underscores the urgency of adopting impact-oriented drought diagnostics to support climate-resilient planning and adaptive risk governance in a rapidly changing climate.

Abstract. The legalization of medical cannabis in Morocco’s northern Rif region requires precision agriculture systems capable of supporting highly controlled, traceable and quality-driven cultivation. Medical cannabis is biologically sensitive to micro-variations in soil moisture, vapor pressure deficit (VPD), canopy temperature and nutrient levels, which makes it a demanding testbed for advanced decision-support methods. In this work, we propose and numerically evaluate an end-to-end hybrid quantum–classical framework that combines IoT sensor networks, Sentinel-2 and UAV imagery, GIS integration and quantum-enhanced analytics for regulated medical cannabis cultivation in the Al-Hoceïma region. The framework instantiates three quantum modules: (i) a variational quantum linear solver (VQLS) for Kriging-based spatial interpolation under sparse sensing, (ii) a variational quantum classifier (VQC) for early stress detection from multi-source features, and (iii) a Quantum Approximate Optimization Algorithm (QAOA) for constrained irrigation scheduling. All experiments are conducted on synthetic yet agro-ecologically calibrated data generated for a 4-hectare virtual plot; no real cannabis-field data or quantum hardware are used. In this controlled simulation setting, the quantum-inspired modules achieve moderate improvements over classical baselines (Kriging, Random Forest, neural networks, MILP), for example reducing interpolation RMSE by about 20% and improving early-stress F1-score by several percentage points. We explicitly do not claim hardware-level quantum advantage, nor do we provide a formal proof that VQLS or VQC must outper- form classical Kriging or machine learning in this regime. Instead, the contribution is a transparent formulation and simulation- based assessment of quantum-compatible workflows for precision agriculture in regulated contexts, together with a critical discus- sion of their current limitations and the conditions under which they might become competitive in practice.

Abstract Soil moisture drought (SMD) critically constrains terrestrial ecosystems and agricultural productivity, yet the long-term contribution of historical land use and land cover change (LULCC) to global drought trends remains insufficiently quantified. We quantify the impacts of historical LULCC on SMD characteristics (event number, duration, severity, intensity) over 1901-2014 using seven offline land surface simulations from the CMIP6 Land Use Model Intercomparison Project (LUMIP). LULCC intensifies SMD over more than half of the global land area, with the strongest increases in mid-latitude regions and particularly central North America. The drought-affected areas expanded throughout the twentieth century, with drought events becoming increasingly prolonged and severe. Diagnostic analyses of SMD during the boreal growing season, based on CESM2 experiments, indicate that the transition from natural vegetation to cropland and pasture slightly reduces surface net radiation while enhancing latent heat flux at the expense of sensible heat flux. This biophysical shift depletes soil water storage, subsequently amplifying SMD occurrence and intensity. Our findings demonstrate that historical LULCC is a critical amplifier of SMD, underscoring the need to account for land-use dynamics in drought attribution, future risk assessment and adaptation planning.

Context Soil degradation and water scarcity pose critical challenges for vineyard sustainability in semi-arid regions. Objectives This study evaluates the long-term effects of spontaneous cover cropping (CC) on soil health in a rainfed vineyard in central Spain, managed without irrigation or pesticides for over two decades. Methodology By comparing soils under CC and conventional tillage (TILL), we assessed changes in soil physical properties (porosity and water retention), nutrient content, and microbial function up to 30 cm depth. Stepwise regression analysis was used to explore management-driven relationships among CC, soil properties and nutrient dynamics. Results Soils under CC showed significantly higher organic matter content (1.74 ± 0.37% in the topsoil with CC vs. 0.83 ± 0.24% in TILL) and porosity (51.2 ± 2.5% vs. 44.0 ± 1.8% at 10–30 cm depth). Available phosphorus tended to be higher under CC (19.13 ± 0.60 mg/kg in CC vs. 14.13 ± 4.52 mg/kg in TILL), and this trend was further supported by stepwise analysis, which identified P availability as a variable influenced by management practices. Enzymatic activities were consistently elevated under CC, particularly in the topsoil; β-glucosidase (25 ± 9 mU/g) nearly doubled the value observed under tillage. Although soil water availability showed a non-significant trend in the topsoil (10 cm), it was higher in the subsoil (30 cm) under CC (29.0 ± 0.97% vs. 25.1 ± 0.32% in TILL). The stepwise regression analysis supported a management-driven model where CC influence soil organic matter (SOM) and nutrient availability. SOM and soil moisture strongly influenced extractable phosphorus (R² = 0.790, p =0.0047) and mineral nitrogen (R² =0.907, p =0.00018), with moisture at the time of sampling emerging as the dominant driver of nitrogen dynamics. Conclusions The analysis supports a management-driven pathway in which long-term vegetation inputs under CC enhance SOM accumulation. Soil water availability tended to increase in the subsoil under CC, while SOM together with short-term moisture conditions emerged as the main regulators of nutrient dynamics under semi-arid conditions. Implications By aligning ecological processes with practical agronomic outcomes such as nutrient retention and soil structure, these findings offer compelling evidence to support the broader adoption of sustainable ground cover practices in Mediterranean viticulture.

Abstract The Qinghai-Tibet Plateau (QTP) is highly vulnerable to climate change, with permafrost changes exerting substantial impacts on regional ecosystems, hydrological stability, and cryospheric water resources. This study combines Coupled Model Intercomparison Project Phase 6 (CMIP6) data with four machine learning algorithms, including Support Vector Regression (SVR), Categorical Boosting (CatBoost), Light Gradient Boosting Machine (LightGBM), and Deep Neural Network (DNN), to construct optimal models for predicting permafrost extent and maximum seasonal soil freeze depth (SFD) across the QTP. The relative importance and marginal effects of predictors were further evaluated using Random Forest (RF) analysis and Shapley Additive exPlanations (SHAP). Permafrost evolution from 2025 to 2100 was projected under four Shared Socioeconomic Pathway scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5). Results revealed that the DNN (R 2 = 0.961) outperforms other models in capturing frozen-ground dynamics. Key drivers of SFD were identified as summer-autumn precipitation, temperature, and non-frozen season soil moisture. Permafrost is projected to continuously degrade into seasonally frozen ground, with transition zones expanding from plateau margins toward the interior, leading to increased spatial fragmentation. SFD is expected to decline at rates strongly correlated with emission scenarios, with the most pronounced reductions along current permafrost margins and gradually extending inland. Notably, permafrost stability in high-altitude regions will face increasing challenges. The Northern Tibetan Plateau, the margins of the Qaidam Basin, and the foothills of the Gangdise Mountains were identified as high-risk zones for frozen-ground degradation under continued CO 2 emission trajectories.

Climate change is reshaping plant disease dynamics and pathogen distribution, creating substantial risks for agricultural sustainability and food security. Increasing temperatures, altered precipitation regimes, elevated atmospheric carbon dioxide concentrations, and more frequent extreme weather events are modifying interactions among host plants, pathogens, and their surrounding environment. Higher temperatures accelerate pathogen reproduction, shorten latent periods, and reduce overwintering mortality, enabling multiple infection cycles within a single season. Geographic expansion of pathogens toward higher latitudes and elevations is becoming more evident as thermal barriers decline. Variability in rainfall intensity and humidity directly influences spore germination, infection efficiency, and dispersal processes, thereby altering epidemic timing and severity. Periods of drought and heat stress weaken host defense mechanisms, disrupt physiological balance, and enhance vulnerability to necrotrophic and opportunistic pathogens. Elevated CO₂ modifies plant morphology, canopy density, and carbon-to-nitrogen ratios, which can influence pathogen aggressiveness and disease expression. Insect vectors are responding to warming trends through extended activity periods and range expansion, increasing the transmission potential of viral and bacterial diseases. Soil-borne pathogens are affected by changes in soil temperature, moisture fluctuations, and shifts in microbial community composition, influencing soil suppressiveness and long-term plant health. Technological progress in epidemiological modeling, climate-based forecasting, and remote sensing strengthens predictive capacity for disease outbreaks, though uncertainty persists due to complex, interacting variables. Adaptive strategies such as breeding for climate-resilient cultivars, integrated disease management, biological control approaches, and climate-responsive agronomic practices are critical for reducing future risks. Enhanced monitoring networks and interdisciplinary collaboration remain essential for managing emerging plant health challenges under continuing environmental change.

Reduced tillage and cover crop effects on soil moisture and infiltration

The interactive effects of temperature and soil moisture on carbon dioxide emissions in agricultural systems during seasonal extremes

A composite ecological drought index integrating multi-source water and heat stress with time-lag effects: Insights from the Yellow River Basin.

To explore the interactions among key environmental factors in solar greenhouses under sunny winter conditions, the Pearson correlation coefficient method and regression modeling were employed to analyze highly correlated environmental factors. Multiple regression models were subsequently established for factors with consistent variation trends, based on the fitting performance of optimal regression models. The results indicated that solar radiation intensity exhibited a strong negative correlation with air humidity, with a second-order polynomial fitting R 2 of 0.860. Temperature and humidity were negatively correlated, achieving a third-order polynomial fitting R 2 of 0.962. Temperature and CO 2 concentration also showed a strong negative correlation, with a third-order polynomial fitting R 2 of 0.965. Air humidity and CO 2 concentration displayed a high negative correlation, and the nonlinear Boltzmann model yielded an excellent fitting effect. Solar radiation intensity and CO 2 concentration were highly positively correlated, with a fifth-order polynomial fitting R 2 of 0.822. Soil temperature and soil moisture were moderately highly positively correlated, with a fifth-order polynomial fitting R 2 of 0.618. In summary, the established coupling models between greenhouse environmental factors are suitable for predicting environmental conditions in greenhouses, reducing test consumables, and are characterized by simplicity in calculation and operation.

Despite the widely recognized importance of grassland restoration for soil multifunctionality (SMF), its temporal dynamics along the restoration chronosequence and the relative contributions of bacterial and fungal diversity to SMF remain poorly understood, particularly in alpine grasslands. Here, we examined SMF along an alpine grassland restoration chronosequence (1, 5, 7, 13, and 20 years) on the Qinghai–Tibet Plateau. We found that SMF exhibited a pronounced non-linear trajectory, increasing by 39.13% from year 1 to year 7, subsequently declining by 50% and 46.88% at years 13 and 20, respectively, relative to the peak at year 7. Fungal richness varied markedly across the restoration chronosequence, peaking in year 5 with a 16.03% increase relative to year 1, and was positively associated with SMF, whereas bacterial richness showed no significant relationship. Structural equation modeling further confirmed that, along with soil moisture, fungal richness was significantly associated with SMF. Together, our findings highlight fungal diversity as a key driver of SMF during alpine grassland restoration and improve process-based predictions of alpine grassland functioning under ongoing climate change.

Flash droughts cause more rapid declines in gross primary productivity than slow droughts on the Qinghai-Tibetan Plateau (2000-2023).

ABSTRACT Accurate estimation of actual evapotranspiration (ETₐ) is essential for hydrological modelling and drought assessment. Using 270 489 daily observations from 115 FLUXNET2015 sites (2000–2014), this study benchmarks deep-learning and machine-learning model for daily ETₐ prediction. Predictors from Google Earth Engine (GEE) include radiation, temperature, humidity, precipitation, soil moisture, NDVI, and seasonal indicators. Data-driven architectures (ANN, XGBoost, ANN-ET, LSTM, TFT) are evaluated using a strict leaveone-site-out protocol to ensure full spatial independence. Temporal models markedly outperform static approaches: LSTM and TFT reach mean R2 ≈ 0.82–0.83 and RMSE ≈ 0.50–0.52 mm day—1, versus R2 ≈ 0.30–0.38 for ANN, ANN-ET, and XGBoost. Calibrated physical ET products (MOD16, GLDAS, ERA5-Land) are compared globally after linear adjustment and show lower skill. Monte Carlo perturbations yield narrow 95% prediction intervals and minimal RMSE drift for temporal models. Climate- and biome-stratified analyses show consistently stronger performance of temporal architectures.

Long-term biochar addition improves post-rice wheat production by ameliorating soil mechanical impedance and moisture condition as well as promoting root growth

Beyond elevation: Unravelling land cover–elevation interactions in governing soil moisture variability in the lower Himalayas

Modeling vegetation carbon stock and soil greenhouse gas emission dynamics in undrained degraded peat swamp forests of Indonesia and Peru.

High-resolution surface and rootzone soil moisture over US cropland: A novel framework assimilating multi-source remote sensing data, machine learning, and the Layered Green and Ampt Infiltration with Redistribution model