Search Results (824)

This study established a refined, distributed SWAT modeling framework that integrates elevation-band and snowmelt modules to reconstruct the alpine hydrological and sediment cycles of the Koshi River Basin (KRB) over the period 1990–2024, with climate scenarios constructed using the delta change approach. The KRB, a major transboundary watershed traversing China, Nepal, and India, was selected owing to its critical hydro-climatic role under the destabilizing “Asian Water Tower”; it generates substantial sediment yield, hosts the densest concentration of hydropower potential within the Ganges system, and spans an extreme vertical gradient from Mount Everest to the southern alluvial plains. Results reveal accelerated warming at a rate of 0.21 °C per decade and an overall warming–wetting trend, punctuated by an abrupt interdecadal shift around 2015. Precipitation dominated interannual streamflow variability, with enhanced rainfall triggering basin-wide sediment surges that overwhelmed the natural buffering capacity of the land surface. Conversely, rising temperatures intensified actual evapotranspiration, markedly depleting soil water and reducing total water yield and monsoon runoff, although sustained snow and glacier melt effectively elevated the dry-season low-flow baseline. The integrated climate forcing reshaped the disparity between hydrological extremes, imposing severe seasonal eco-hydrological stress that manifested as a pre-monsoon deficit in terrestrial green water and acute summer sediment outbursts for aquatic habitats. Furthermore, the flood regime exhibited an altered distribution, with mid-to-high frequency floods enhanced while low-frequency extreme flood peaks declined. The hydro-sedimentological regime consequently exhibits pronounced nonlinear responses to climate change, providing a critical, threshold-based scientific foundation for adaptive transboundary water resource management. Full article

Flood hazards are intensifying across Africa due to rapid urban expansion and hydro-climatic variability. This study develops a multi-metric geospatial framework combining extreme value analysis, hydrograph-based metrics, and dependence modelling to quantify flood magnitude, frequency, timing, and joint risk dynamics. Daily precipitation and streamflow reanalysis data (1985–2025) were analyzed for two contrasting Tanzanian catchments: the large Rufiji basin (RU) and the smaller Mirongo catchment (MW). Annual maxima were modelled using the Generalized Extreme Value (GEV) distribution, complemented by flow duration curves, peak-over-threshold detection, and regression-copula dependence analysis. Results reveal strong hydrological contrasts. RU exhibits amplified rare-event growth (design floods from ~2850 to 11,770 m³/s), extended recession persistence (>100 days), low flashiness, and long rainfall-runoff lags (~15 days), indicating storage-dominated behavior. MW shows smaller design floods (~80 to 370 m³/s), higher flashiness, and short lags (~4 days), reflecting rapid, rainfall-driven response. Gaussian copula parameters indicate moderate dependence in both basins (0.32 and 0.34), suggesting that joint dependence alone does not distinguish flood mechanisms without complementary metrics. The proposed framework improves basin-specific flood risk profiling and supports geospatial early-warning system design in data-scarce Sub-Saharan environments. Full article

Marine diesel engines generate high concentrations of sub-micron particulate matter (PM) that requires effective exhaust aftertreatment. While conventional wire-in-tube electrostatic precipitators (ESP) offer a low-drag solution, their practical efficiency is limited by particle re-entrainment at elevated flow velocities. This study investigates a novel application of corrugated cylindrical ducts—standard vibration-compensating couplings—as electrostatic collectors. A fully coupled two-dimensional axisymmetric COMSOL Multiphysics 6.4 model was developed, integrating turbulent flow (k–ε), electrostatics, ion charge transport, and particle tracing. Numerical results demonstrate that while smooth and corrugated geometries yield identical theoretical Deutsch–Anderson efficiency (61.1% at Uin = 0.5 m/s, the corrugated profile significantly suppresses re-entrainment. The corrugations reduce wall shear stress by a factor of 7.7 to 13.5 at flow velocities of 0.3–0.8 m/s, maintaining aerodynamic conditions below critical particle detachment thresholds. With a pressure drop penalty representing less than 6% of the localized corona power, these findings show that existing marine exhaust infrastructure can be repurposed as high-efficiency, low-re-entrainment particle collectors through the integration of cold plasma electrodes. Full article

In arid and semi-arid regions, groundwater plays an important ecohydrological role in sustaining ecosystem stability under climate-warming-induced surface-water uncertainty. Disentangling precipitation and groundwater recharge effects on vegetation growth remains challenging, limiting robust identification of groundwater-dependent vegetation ecosystems (GDVEs) and quantitative ecological groundwater level estimation. Taking the Daihai Basin, a typical inland closed-lake basin, as a case study, we integrated multi-source remote-sensing data (2005–2025) with in situ groundwater monitoring to develop a comprehensive framework for ecohydrological response analysis and management quantification. Using an improved Mann–Kendall test together with spatiotemporal correlation analyses, we analyzed the spatial relationships between vegetation dynamics and groundwater depth. Results show: (1) basin-wide vegetation exhibits a greening trend (Sen’s slope = 0.00014) with spatial heterogeneity; (2) vegetation dependence on groundwater displays a clear threshold behavior, with low-cover areas (fractional vegetation cover, FVC < 0.3) showing relatively strong groundwater dependency (r = 0.698) whereas high-cover areas exhibit a weaker relationship; and (3) approximate ecological groundwater reference thresholds are estimated as 1.0 m (90% assurance) for forest land and 0.6 m for grass land (80% assurance). The proposed GDVE identification scheme provides a scientific reference for adaptive groundwater management and ecological assessment. Full article

Investigating the spatiotemporal dynamics of urban heat islands and their responses to urban forest (UF) landscape patterns is crucial for mitigating urban thermal stress. However, the nonlinear influence and conditional constraints of UF landscape composition and configuration on the warming effects across varying urbanization gradients remain inadequately understood. By integrating land use/cover data, MODIS-derived land surface temperature (LST), and meteorological datasets, this study employed the XGBoost-SHAP model to quantify the nonlinear and interaction effects of UF landscape patterns on developed and developing urban regions of the Pearl River Delta. The results indicate that (1) spatial clustering patterns of warming varied significantly between the two regions, with substantial seasonal heterogeneities (p < 0.05). Summer exhibited the most intense warming, characterized by more rapid temperature increase in developed areas than in developing regions. (2) Relative to UF landscape metrics, the proportion of impervious surfaces, precipitation, and temperature exerted greater influence on regional warming. Coverage area, fragmentation, and connectivity of UFs emerged as the primary landscape drivers modulating warming. In developed areas, spatial configuration metrics exerted greater influence on LST than compositional metrics. (3) The responses of LST to diverse UF landscape patterns are characterized by nonlinearity and pronounced threshold effects. These landscape thresholds vary by season, revealing critical tipping points for warming suppression; however, this regulatory effect is highly context-dependent. Specifically, under high percentages of impervious surface, the warming-suppression capacity of UFs intensifies with increasing percentage of UF area or core. Our findings highlight the necessity of strategic UF planning and forest fragmentation mitigation for developing effective climate resilience strategies. These results provide a foundation for adaptive planning tailored to specific urbanization stages and the implementation of targeted UF cooling strategies. Full article

Quantifying the nonlinear response of crop systems to meteorological driving factors remains a core challenge in agrometeorology. Although Explainable Artificial Intelligence (XAI) offers new approaches, existing SHAP-based threshold identification methods are largely confined to shifts in effect direction. Furthermore, a unified quantitative grading scale for interaction effects among factors is lacking. To explore the meteorological factor thresholds and interaction effect intensities affecting rice yield, rice unit yield and meteorological data from nine districts and counties in Ningbo City from 1995 to 2024 were utilized. Rice yield prediction models were constructed based on LASSO and six machine learning algorithms. Recursive Feature Elimination (RFE) based on the SHAP algorithm was conducted to screen out 11 core meteorological factors. Building upon this, two innovative methodological indicators were proposed. First, the Derivative Extrema Threshold (DET) was introduced as a supplement to the Zero-Crossing Threshold (ZCT). By locating the extremum points of the first derivative of the smoothed SHAP dependence plot curves, the critical positions where the effect intensity undergoes a qualitative change without a directional reversal were identified. Second, the Interaction Dominance Ratio (IDR) was proposed. This metric normalizes the interaction variability within a total effect framework and establishes a three-tier grading standard for strong, moderate, and weak interactions. It was observed that optimal performance was achieved by the LightGBM model after feature optimization (R² = 0.833). Direction reversal points with extremely narrow confidence intervals, such as an August cumulative precipitation of 210.6 mm and a June average temperature of 24.5 °C, were identified by the ZCT. Intensity mutation characteristics, such as the “weakening of the yield reduction effect” at a May cumulative precipitation of 64.9 mm, were further revealed by the DET. An Interaction Dominance Triangular Network, composed of the August–September average temperature, the June minimum temperature, and the August cumulative precipitation, was accurately characterized by the IDR analysis. This overcomes the constraints of traditional single-factor early warning systems. The “ZCT-DET-IDR” framework constructed in this study facilitates a methodological advancement from directional discrimination and intensity early warning to multi-factor synergistic analysis. This framework provides a quantifiable novel perspective for the refined early warning of regional agrometeorological disasters. Full article

Baiyangdian Lake, the largest freshwater lake in North China, plays a critical role in the ecological security of the Beijing–Tianjin–Hebei urban agglomeration. This study conducted systematic monitoring of Baiyangdian Lake from April 2023 to November 2024. Utilizing the Trophic State Index (TSI) and principal component analysis (PCA), we elucidated the impact mechanisms of extreme precipitation events on the water quality of shallow lakes. The results indicate that: (1) During the study period, Baiyangdian Lake exhibited moderate to severe eutrophication. The average total nitrogen (TN) concentration was 2.13 mg/L, exceeding the Class V threshold of the national surface water quality standard. The average total phosphorus (TP) concentration was 0.05 mg/L, far surpassing the recognized eutrophication threshold for freshwater lakes. (2) The average TSI was 49.6 ± 4.0, indicating the lake is in a transitional state from mesotrophy to eutrophy, with 64% of sampling sites classified as eutrophic. Nitrogen was identified as the primary limiting nutrient. (3) The 2023 extreme precipitation event exerted a significant three-phase impact on water quality: “dilution–legacy–restoration”. A clear dilution effect was observed from the pre-flood to the flood period (TN decreased from 1.52 to 1.04 mg/L). A pronounced legacy effect emerged post-flood, with the TN concentration sharply increasing to 4.22 mg/L in September 2023, the highest value recorded during the study. (4) PCA identified two major pollution sources: agricultural non-point source pollution (PC2, contribution: 25.4%) and domestic sewage/livestock farming (PC1, contribution: 27.6%). Correlation analysis further revealed that the flood event significantly altered the intrinsic relationships among parameters like nitrogen and phosphorus, reinforcing the dominance of agricultural non-point source pollution. (5) Source analysis suggests that external inputs are the primary contributors, while the internal loading from sediments is relatively limited. This study enhances the understanding of how shallow lakes respond to extreme climatic events and provides a scientific basis for lake management in the Beijing–Tianjin–Hebei region. Full article

Climate change directly affects agricultural productivity, particularly in small island systems where ecosystems and livelihoods are highly exposed to climate variability. This study presents a comprehensive analysis of climate variability for the three districts North and Middle Andaman, South Andaman, and Nicobar, using a six-model CMIP6 ensemble under four SSP scenarios (SSP126, SSP245, SSP370, and SSP585), coupled with ensemble tree-based machine learning algorithms to project coconut yield responses. The historical data was analysed from 1981 to 2025 and the projection was from 2026 to 2100. Observed rainfall reveals a persistent north-to-south gradient, with South Andaman recording the highest mean annual rainfall (3408.40 mm) and Nicobar recording the lowest (2442.13 mm), alongside pronounced inter-annual variability and a discernible drying tendency post-2015. Nicobar consistently records the warmest mean Tmax (30.89 °C) and Tmin (24.11 °C), while North and Middle Andaman exhibit the greatest inter-annual temperature variability. Future projections indicate a robust and statistically significant warming across all districts and scenarios, with end-of-century Tmax increases reaching up to 4.05 °C (Nicobar, SSP585) and Tmin increases up to 3.73 °C (North and Middle Andaman, SSP585), accompanied by a progressive compression of the diurnal temperature range. Precipitation projections show modest wetting in the Andaman districts under most scenarios, while Nicobar exhibits a muted response, with SSP370 uniquely projecting a decline of approximately 69 mm below the observed baseline. Among the ten evaluated CMIP6 models, six (ACCESS-CM2, CMCC-ESM2, CNRM-ESM2-1, EC-Earth3-Veg-LR, GFDL-ESM4, and NorESM2-MM) were selected based on composite skill scores across rainfall, Tmax, and Tmin. Model selection was optimized independently for each district via Leave-One-Year-Out cross-validation with hyperparameter tuning, yielding district-specific best performers: GradientBoost for North and Middle Andaman (R² = 0.471), RandomForest for South Andaman (R² = 0.609), and ExtraTrees for Nicobar (R² = 0.289). K-Nearest Neighbours demonstrated competitive predictive skill in all three districts, confirming that instance-based learning can capture non-linear climate–yield relationships, though tree-based ensembles were preferred for their robustness and interpretability. Ensemble tree-based ML models and instance-based learning consistently outperformed all linear and kernel-based approaches, confirming the non-linear nature of climate–yield relationships in this setting. Coconut yield projections indicate above-baseline productivity gains of 3.4–21.5% in North and Middle Andaman and 24.6–36.8% in South Andaman, driven by favourable warming and precipitation trends, while Nicobar yields plateau at 7.7–13.7% above baseline, indicating thermal saturation of the climate yield response under already near-optimal thermal conditions. Notably, Nicobar exhibits a reversed yield–emission relationship wherein lower-emission pathways marginally outperform high-emission scenarios, likely reflecting avoidance of thermal stress thresholds. Inter-CMIP6-model uncertainty emerges as the dominant source of projection spread, exceeding scenario uncertainty across most districts, underscoring the critical importance of multi-model ensemble frameworks for robust agricultural climate impact assessments in data-sparse tropical island environments. Full article

Northeast China’s black soil region serves as a critical cornerstone of national food security, yet accelerating soil degradation, characterized by declining soil organic matter (SOM) and rising bulk density (BD), threatens the productive capacity of its farmland. Understanding how soil physicochemical properties regulate crop yields in this ecologically heterogeneous landscape is essential for sustainable agricultural development. Here, 2916 soil samples from 201 counties across six ecological zones were analyzed in conjunction with county-level rice and maize yield records. Our findings revealed that crop yield determinants are fundamentally governed by regional resource endowment characteristics rather than uniform factors. In areas characterized by sandy soil texture, low precipitation (<400 mm yr⁻¹), and inherently low fertility, elevated bulk density (BD, >1.34 g cm⁻³) and alkaline soil conditions (pH > 7.0) constitute the primary constraints to productivity through restricting root development. Conversely, in regions with fertile mollisols and high baseline soil organic matter (SOM > 40 g kg⁻¹), nutrient dynamics emerge as the dominant yield-regulating factors. For volcanic soil landscapes with strong phosphorus fixation capacity, available phosphorus deficiency represents the critical bottleneck for maize production. Path analysis further demonstrates that BD and pH operate predominantly through indirect mechanisms, modulating SOM accumulation and nutrient cycling rather than directly constraining yield. Threshold analysis identified that BD exceeding 1.34 g cm⁻³ and SOM below 26 g kg⁻¹ markedly reduce productivity, while SOM levels above 40 g kg⁻¹ yield diminishing marginal returns. These findings advance our mechanistic understanding and provide scientific foundations for spatially differentiated soil conservation and precision nutrient management strategies essential for sustaining grain production capacity in northeast China’s black soil region. Full article

Both variability in precipitation and rainfall extremes are key drivers of landslide activity, yet their combined influence with antecedent moisture conditions remains insufficiently quantified at regional or local scales. In this study, daily precipitation records over the past 25 years (2000–2024) were analyzed for five meteorological stations in Northern Croatia across multiple temporal scales. The aim was to investigate the impact of precipitation patterns and regime changes on landslide triggering in Hum na Sutli and the wider area. Statistical analyses (linear regression, Mann–Kendall trend assessment, and Pearson correlation) were applied, and antecedent wetness was quantified using the antecedent precipitation index (API). Results indicate weak, statistically insignificant positive trends in annual precipitation, accompanied by strong interannual variability and coherent regional behavior. Seasonal analysis reveals the dominance of warm-season precipitation with pronounced extremes, while short-duration and multi-day rainfall events exhibit high variability and clustering. The 2024 Hum na Sutli landslide coincided with elevated cumulative precipitation and sustained high API values, despite the absence of exceptionally extreme single-day rainfall events. These findings highlight the critical role of antecedent moisture accumulation combined with episodic high precipitation in slope failure. The study supports a conceptual model in which landslide triggering is governed by the interaction of preconditioning and short-term hydrometeorological factors, providing a basis for improved hazard and risk assessment. Additionally, preliminary rainfall threshold values are proposed as practical early-warning guidance for local communities in landslide-prone regions in Northern Croatia. Full article

This study examines the multi-scale relationships between extreme climate indices and maize yield from a hydrothermal perspective, across both temporal (long-term trends, interannual anomalies, and abrupt changes) and spatial (regional and grid) scales in the Chengdu–Chongqing region, using long-term meteorological (1985–2025) and crop yield (1982–2015) datasets. Results reveal pronounced warming and drying trends, characterized by increasing warm-related temperature extremes and consecutive dry days, along with a decline in cold extremes. A shift toward drier conditions occurred around 2005, while temperature extremes have exhibited stepwise changes since the late 1990s. Maize yield shows a significant upward trend with an abrupt increase around 1997, closely linked to reduced cold stress. Scale-dependent analyses reveal that climate-yield relationships are primarily expressed through long-term hydrothermal changes rather than short-term variability, with maize yield showing positive responses to warm conditions and prolonged dry spell duration, and negative responses to cold extremes and excessive precipitation. In contrast, relationships based on interannual anomalies are weak and spatially inconsistent, suggesting limited sensitivity of yield to short-term climate variability due to system buffering and agricultural adaptation. Spatially, climate–yield relationships exhibit marked heterogeneity, with temperature constraints dominating in the western region and moisture-related effects being more pronounced in the central–eastern basin. Mechanistically, abrupt change analysis indicates two distinct controls: cold extremes act as threshold constraints associated with rapid yield shifts, whereas warming and drying exert gradual cumulative effects on productivity. Overall, maize yield dynamics are more strongly associated with long-term hydrothermal evolution than interannual variability, highlighting the importance of distinguishing temporal scales, hydrothermal regimes and long-term agricultural system evolution in climate–crop assessments under ongoing climate change. Full article

The Eastern Himalayan Syntaxis in the southeastern margin of the Tibetan Plateau is a tectonically active, deeply incised, high-relief region with frequent landslides. However, the long-term evolution of landslide susceptibility and the temporal behavior of its dominant conditioning factors remain insufficiently understood. This study compiled a 30-year inventory of 1350 landslides from multi-source remote-sensing data and divided it into three periods: P1 (1991–2000), P2 (2001–2010), and P3 (2011–2020). Period-specific random forest models were developed for susceptibility mapping, and Tree-SHAP was used to interpret temporal changes in dominant factors and their nonlinear responses. The models showed reliable performance, with AUC values of 0.887, 0.848, and 0.900, respectively. Susceptibility patterns showed broad temporal stability with localized reorganization, with unchanged areas accounting for 55.62%, 51.62%, and 58.51% of the P1–P2, P2–P3, and P1–P3 transitions, respectively. High and very high susceptibility zones were persistently concentrated along the Yarlung Tsangpo–Parlung Tsangpo–Yigong Tsangpo river system and major tributary junctions. SHAP results identified elevation, slope gradient, terrain curvature, NDVI, and annual precipitation as the persistent core factor group, whereas drainage proximity, the seismic disturbance proxy, and road proximity showed stronger period-dependent effects. Nonlinear SHAP responses revealed threshold-saturation, overall decreasing or distance-decay, threshold-transition, and inverted U-shaped patterns. These findings indicate that susceptibility evolution reflects the coupling between persistent geomorphic predisposition and stage-dependent environmental and disturbance-related modifiers, providing a basis for identifying persistent and stage-specific high-susceptibility zones in high-relief valley regions. Full article

This study examines the meteorological and gaseous pollutant drivers of PM_2.5 under mild, moderate, and severe pollution conditions in the Beijing–Tianjin–Hebei region, with the aim of supporting sustainable air quality management. Daily observations from approximately 65 monitoring stations from 1 November 2021 to 31 October 2024 were used, including PM_2.5, four gaseous pollutants (SO₂, NO₂, CO, and O₃), and five meteorological variables: temperature, pressure, relative humidity, precipitation, and wind speed. A CatBoost–SHAP framework was adopted, with CatBoost used for station-level spatial prediction of PM_2.5 and SHAP applied to interpret variable contributions. Based on predefined PM_2.5 thresholds, 425 pollution days were classified into those three pollution-level scenarios. These pollution days occurred mainly in winter and spring, with higher frequencies in Handan, Baoding, and Shijiazhuang, followed by Tianjin and Beijing. The model performed well across the three pollution-level scenarios. The severe-pollution scenario achieved the highest R², indicating a clearer spatial structure under high-PM_2.5 conditions. Although absolute RMSE and MAE increased with pollution severity, their normalized values changed little, suggesting that larger errors mainly reflected stronger spatial heterogeneity at higher PM_2.5 concentrations. SHAP results showed that CO, precipitation, wind speed, and temperature dominated the prediction structure. CO was the most stable and influential predictor, but its importance should be interpreted as an indicator of combustion-related pollution accumulation rather than direct causality. Precipitation represented event-dependent wet scavenging, wind speed reflected dispersion conditions, and temperature captured seasonal and thermal background effects. SHAP dependence analysis further indicated that CO had the clearest direct dependence, whereas wind speed and temperature were more background-dependent, and precipitation acted as an episodic nonlinear regulator. Full article

Arid and semi-arid shelterbelts must provide long-term ecological protection under chronic water scarcity, high evaporative demand, and rising salinization risk, yet management still lacks an integrated framework linking irrigation, root-zone salt dynamics, and woody plant performance. Here, we synthesize evidence on water–salt–root linkages in drip-irrigated shelterbelts and related dryland woody systems from a structured Web of Science Core Collection search (1 January 2000–1 January 2026). The evidence shows that shelterbelt performance is governed not by water or salinity alone, but by a coupled root-zone system: localized irrigation creates moisture–salt heterogeneity, salts accumulate near evaporative fronts and emitter margins, and roots redistribute depth, density, and uptake zones. In hyper-arid saline-drip systems, precipitation may be only ~24.6 to <50 mm yr⁻¹, evaporation > 3000–3639 mm yr⁻¹, groundwater salinity 2.8–29.7 g L⁻¹, active roots 20–80 cm, and salt mainly in the 0–20 cm surface layer. Irrigation thus acts as both the basis of establishment and a source of long-term vulnerability, particularly where saline groundwater or other non-conventional water sources are used. Management options can improve root-zone habitability, but shelterbelt-specific thresholds and integrated indicators remain limited. This review proposes a root-zone-centered framework supporting predictive regulation. Full article

Rainfall contributes to slope instability when infiltrating water reduces matric suction and elevates pore water pressure beyond critical thresholds. Empirical rainfall intensity–duration (I-D) thresholds define the minimum rainfall conditions necessary to initiate landslides and are widely adopted in regional early warning systems. This study derives I-D thresholds for shallow landslide initiation in South-East Queensland (SEQ), Australia, using quantile regression applied to 104 rainfall-induced shallow landslide events recorded between 1974 and 2018. Thresholds at the 2nd, 10th, 50th, and 90th percentiles were derived over a duration range of 0.3 to 383 h and intensity range of 0.15 to 13.7 mm h⁻¹. The 2nd percentile, adopted as the conservative regional early warning threshold, is expressed as I = 0.719 × D^−0.220, where I is rainfall intensity (mm h⁻¹) and D is event duration (h). To facilitate inter-regional comparability, normalised thresholds expressed in terms of mean annual precipitation (MAP) were also derived, yielding a 2nd percentile threshold of I_MAP = 6.070 × 10⁻⁴ × D^−0.207. Both I-D and I_MAP -D thresholds fall substantially below existing global benchmarks, reflecting the pronounced susceptibility of SEQ’s deeply weathered residual soils to infiltration-driven failure. Independent validation against real-time tilt sensor and volumetric water content monitoring data from five kinematic failure events recorded at Maleny, Queensland (2016–2020), confirmed that all events plotted above the 2nd percentile threshold, with zero false negatives. The results provide a quantitative, operationally validated framework for regional shallow landslide early warning in subtropical Australia. Full article