Search Results (1,681)

The present work examines a severe, long-lived bow echo in South China during 12–13 April 2016 and investigates the favorable factors for its strength and longevity using a series of 20 cloud-resolving ensemble experiments. Analysis of observational data indicated that this system developed near a surface front under unstable and favorable conditions with dynamic uplifting by approaching troughs at 500–700 hPa. After formation, it propagated rapidly toward the east–southeast across South China and made landfall in Southern Taiwan. The ensemble used four different datasets as initial and boundary conditions and started at five different initial times, whereby comparing the better-performing members with worse ones, four key factors promoting its strength and longevity were identified: (1) A stronger and moister low-level southwesterly flow to the south of the front to enhance convergence and moisture flux at the leading edge—where a stronger inflow with higher equivalent potential temperature (θ_e) values could feed into the bow echo—leading to a stronger and taller updraft and overall more abundant hydrometeors and rainfall; (2) stronger northwesterly to westerly winds near 700 hPa and thus stronger low-level vertical wind shear, resulting in a stronger rear inflow jet (RIJ), bookend vortices behind the bow apex, and, eventually, a faster propagation speed; (3) a deeper low to the northeast of the bow echo near 850 hPa, where its circulation also helped to bring in low-θ_e air from farther away and enhance the RIJ and cold pool; and (4) a convective initiation location farther to the east in a more favorable environment, with higher θ_e and a faster speed to remain in such a better environment. Helped by the above factors, the bow echo in the present case could reach the observed severity and long duration (~15 h) through interactions and reinforcement among its structural components, including the tilted updraft/downdraft, the low-level inflow and stratiform region, the RIJ and bookend vortices, and the cold pool and gust front. Full article

Rising human-caused nitrogen (N) deposition and increased rainfall variability threaten the capacity of temperate forests to sequester carbon. However, the combined effects of N enrichment and moisture changes on total soil respiration (Rs), including its autotrophic (Ra) and heterotrophic (Rh) components, remain poorly understood, especially in northern China’s warm-temperate forests. To explore this, a factorial field experiment was conducted at the Beijing Yanshan Earth Critical Zone National Research Station in Huairou District, Beijing. The experiment involved N addition (50 kg N ha⁻¹ yr⁻¹ as urea [CO(NH₂)₂]) and precipitation manipulation (±50% of ambient throughfall) during the 2024 growing season. Six treatments were implemented: control (CK), nitrogen addition (NA), 50% increased precipitation (W+50%), 50% decreased precipitation (W−50%), nitrogen addition with increased precipitation (NW+50%), and nitrogen addition with decreased precipitation (NW−50%). Under natural rainfall conditions, N addition increased Rs (+11.8%; p < 0.05). However, the effects of N largely depended on water availability: with increased rainfall, N addition significantly boosted Rs, Rh, and Ra by promoting fine root biomass and accelerating litter decomposition; under reduced rainfall, N addition still increased Rs, Rh, and Ra compared to drought alone (NW−50% vs. W−50%), though the extent of stimulation was considerably lower than under elevated precipitation, indicating that water availability influences the strength of N effects on forest soil respiration. Structural equation modelling (SEM; χ²/df = 1.8, RMSEA = 0.040, CFI = 0.97) revealed that water availability was a key mediator of the interaction between N addition and precipitation. These findings enhance understanding of how nitrogen supply and water availability interact in temperate forest soils, though further validation across other forest types and over longer periods remains necessary. Full article

Granite residual soil slopes are highly water-sensitive and prone to rapid collapse, strength degradation, and rainfall-induced erosion. This study investigates the improvement effects and underlying mechanisms of carboxymethyl cellulose (CMC) on the water stability, mechanical properties, and rainfall erosion resistance of granite residual soil from Fuzhou, Fujian Province, China. Laboratory tests, including unconfined compressive strength (UCS) tests, direct shear tests, disintegration tests, slope rainfall scouring model experiments, X-ray diffraction test (XRD) and scanning electron microscopy (SEM) observations, were conducted to evaluate the performance and microstructural behavior of CMC-stabilized soils. The results indicate that the addition of CMC significantly enhances soil resistance to disintegration: the 24 h disintegration ratio decreased to 0.5% at 0.5% CMC content. The incorporation of CMC can significantly enhance the unconfined compressive strength (UCS) of the soil and lead to an increase in cohesion, while its effect on the internal friction angle is limited. Under simulated rainfall conditions (30° slope, 120 mm·h⁻¹ rainfall intensity, 60 min duration), slopes stabilized with 0.5% CMC exhibited suppressed rill formation and a 47.5% reduction in sediment yield, accompanied by delayed moisture increase at different depths and reduced infiltration rates. Microstructural analyses reveal that CMC hydration forms gel-like films and filamentous bridges, promoting particle aggregation and pore filling, thereby constructing a denser particle network without generating new chemical compounds. This microstructure collectively enhances soil disintegration resistance, mechanical strength, and slope erosion resistance. Full article

Against the backdrop of multi-source water transfers and increasingly frequent extreme rainfall, short-term deterioration of reservoir inflow water quality has become a key risk to intake safety, treatment operations, and urban water-supply security. Traditional assessments based on static thresholds and annual or seasonal averages often fail to identify high-risk periods at the event scale. Using continuous online monitoring data from 2021 to 2024 for the inflow of Yuqiao Reservoir, Tianjin, China, this study developed a month-specific dynamic-threshold framework and green/yellow/red risk windows and integrated a reach-wise river–reservoir routing scheme; a two-box decay model; and a three-class risk trigger into a unified analytical framework for long-term background characterization, event propagation analysis, source-contribution interpretation, and early-warning evaluation. Results show that the permanganate index (COD_Mn) exhibits an overall stable-to-declining background with pronounced wet-season pulses, whereas total nitrogen (TN) and total phosphorus (TP) remain at moderate-to-high levels, with yellow/red risk windows clustering markedly in the wet season. In typical red and yellow events, nitrogen contributions from upstream control sections progressively accumulate toward the reservoir inlet along the river–reservoir cascade system, whereas in some events the residual contribution from unmonitored near-inlet inflows becomes dominant. The COD_Mn-based three-class trigger achieves an overall accuracy of approximately 71.5% and shows comparatively strong identification of yellow-level risk, while remaining conservative for red-level alarms. These findings indicate that coupling month-specific dynamic thresholds with event-scale routing-decay analysis and trigger-based classification can support inflow monitoring, intake-risk early warning, and coordinated operation of key upstream reaches and near-reservoir control zones in water-transfer–reservoir integrated systems. Full article

Lake Erhai is an important plateau freshwater lake in China. It serves not only as a crucial drinking water source for the local region but also as the core area of the Cangshan Erhai National Nature Reserve. Consequently, Lake Erhai plays an extremely significant role in the local economy, society, and ecology. Since 2000, the water quality of Lake Erhai has continuously deteriorated, showing a eutrophic trend. To identify the primary driving forces behind these water quality changes, this study employed stepwise regression analysis. Climate conditions, socio-economic development within the basin, and implementation of environmental protection measures (IEPMs) were considered influencing factors for a comprehensive and systematic analysis of Lake Erhai’s water quality. The results indicate that rising air temperature may increase total phosphorus (TP) concentration, while rainfall may elevate both TP and total nitrogen (TN) levels. In contrast, higher wind speed may reduce chemical oxygen demand (COD_Mn), TP, and TN concentrations. Socio-economic development, meanwhile, may contribute to increased COD_Mn concentration. Based on these findings, this paper proposes recommendations focusing on formulating more effective non-point source pollution control measures and strengthening water quality monitoring in Lake Erhai during summer. By identifying the key natural and anthropogenic drivers of water quality changes in Lake Erhai, this study provides a scientific basis for the development of targeted pollution control strategies and directly contributes to the protection of clean water sources. Moreover, its revelation of the coupled impacts of climate change and socio-economic activities enhances understanding of plateau lake ecosystem resilience. This insight is critical for ensuring regional ecological security and serves as a model for advancing sustainable development goals in similar lake systems worldwide. Full article

This paper documents the preliminary observations and applications of a Ka-band cloud radar newly installed at the Hong Kong International Airport. A special scanning strategy of the cloud radar was developed and is described in detail. The radar provides reasonable cloud base height data as compared with a co-located laser ceilometer, by identifying the lowest vertical layer with reflectivity > −30 dBZ and at least 150 m thick, filtering measurements influenced by rainfall, and removing noise with differential reflectivity thresholds. As demonstrated in a heavy rain case study, the radar provides good estimates of the cloud top height as well, consistent with the cloud liquid water content profiles from a microwave radiometer. The various applications of the cloud radar are then explored, including (1) observations of supercooled liquid water in clouds associated with a late-season tropical cyclone in the South China Sea, (2) monitoring of low visibility in light rain or mist at the airport region using reflectivity as well as Doppler velocity data, and (3) monitoring severe weather such as windshear and turbulence to be encountered by departing aircraft due to low-level jets and initiation of heavy rain, using the Doppler velocity and spectrum data. These observations demonstrated the robustness in the cloud radar in the observation of high clouds and the applicability of the radar’s Doppler velocity in plan position indicator scans under light rain situations. Potential research with the radar, such as visibility maps, turbulence intensity maps, and automatic cloud observations, is also discussed. Full article

Groundwater drainage-induced karst collapse is a major geohazard in coal-mining regions of central Hunan, threatening residential safety and infrastructure. This study focuses on the Tuoshan minefield in the Jinzhushan mining area by integrating multi-source field data, including surveys of 170 collapse points, long-term groundwater monitoring at six boreholes, and high-density electrical geophysics. A topographically corrected MODFLOW seepage-field model is developed and calibrated for 2014 (RMSE = 0.32 m; NSE = 0.85) and validated for 2015–2016 (RMSE = 0.41 m; NSE = 0.81). To address the large groundwater-level simulation errors commonly encountered in subtropical hilly karst mining settings, the model incorporates a topographic correction, improving simulation accuracy by 12% relative to an uncorrected model. The simulations capture rapid “steep rise–slow fall” groundwater dynamics: Heavy rainfall (>100 mm/day) raises groundwater levels by 2.8–3.1 m within 2–3 days, whereas pumping (200 m³/h) causes a 1.9–2.2 m decline within one week. A 1.2 km drawdown funnel forms and overlaps with 89% of collapse points, indicating that seepage-field evolution and groundwater-level decline control collapse clustering, with soil suffusion and soil–water–rock interaction acting as key amplifying processes. Based on Terzaghi’s effective stress principle and the Theis solution, a collapse prediction formula is derived and validated using measured events (accuracy = 87.5%), and a region-specific critical hydraulic gradient (i_n = 0.85) is determined, lower than values reported for North China. The proposed workflow provides quantitative thresholds and model-based guidance for karst collapse prevention in subtropical mining areas. Full article

As a major ecological safeguard in northwestern China and an important corridor for the Belt and Road Initiative, the Hexi Corridor holds strategic significance for improving landscape structure and enhancing regional ecological security. Focusing on the Hexi Corridor, this study develops a landscape ecological risk (LER) index based on land use (LU) data from 2000, 2010, and 2020. The study employs ArcGIS spatial analysis and XGBoost-SHAP, an interpretable machine learning method, to analyze the spatiotemporal evolution of LU and LERs, as well as their driving factors. Furthermore, the Markov-FLUS model is utilized to simulate and predict LU and LER spatial patterns under multiple scenarios for 2030. The results show that: (1) The dominant land type in the Hexi Corridor is unused land, accounting for 67.33%. During the research period, the extents of unused land, grassland, and forestland showed a steady decline, while built-up land and cropland increased. (2) LERs are categorized into five types, with high risk being the most prevalent, accounting for 52.02%. Between 2000 and 2020, the total area of higher and high risks decreased by 4312 km², indicating an overall decrease in LER across the region. (3) LER is primarily influenced by annual rainfall, population density, distance to main roads, and distance to rivers. (4) Marked variations in LU patterns and LER are observed across different development scenarios projected for 2030. Full article

Karst aquifers present unique challenges for groundwater level prediction due to their dual-porosity structures and highly nonlinear hydrological responses. This study systematically evaluates nine machine learning and deep learning models (RF, XGBoost, LSTM, CNN, Transformer, N-BEATS, CNN-LSTM, Seq2Seq-LSTM, and Attention-Seq2Seq-LSTM) for rainfall-driven groundwater level forecasting in the Maocun subterranean river catchment, Guilin, Guangxi, China. Two years of hourly high-frequency data from three monitoring sites representing distinct hydrogeological zones (recharge, flow, and discharge) were employed within a multidimensional evaluation framework integrating single-step accuracy, multi-step stability, and computational efficiency. Results indicate that the Transformer achieved the highest single-step prediction accuracy, attaining the lowest RMSE (0.130–0.606 m) and highest R² (0.813–0.965) across all three sites. CNN-LSTM offered the best balance between predictive performance and computational cost, requiring an average training time of only 27.97 s and 28.0 convergence epochs. N-BEATS demonstrated superior long-term stability in 12-steps-ahead forecasting, achieving R² = 0.914 at ZK1, outperforming all other architectures. More fundamentally, hydrogeological complexity exerted a dominant control on predictive skill that systematically outweighed differences arising from model architecture. All models yielded R² below 0.813 at the geologically complex ZK2 site, whereas R² exceeded 0.950 across all models at ZK1, indicating that aquifer complexity, rather than algorithm selection, constitutes the primary constraint on prediction feasibility. This study presents the first application of N-BEATS to karst groundwater level forecasting and proposes a replicable multidimensional evaluation framework, providing a scientific reference for intelligent modelling of complex karst systems. Full article

Precipitation is a fundamental element in terrestrial water circulation and ecosystem hydrological balance. The occurrence of concentrated precipitation is closely linked to vegetation growth and soil fertility rather than accumulated or averaged precipitation. Despite its importance, the characteristics of continuous precipitation and its specific effects on vegetation cover remain uncertain. In this study, we formulated a new continuous precipitation index system, including $C{P}_d$ (continuous precipitation days); $AC{P}_t$ (annual continuous precipitation times); $C{P}_a$ (continuous precipitation amount); and $FCP$ (frequency in different ranges of $AC{P}_a$). We utilized daily precipitation data from 467 meteorological stations across China, which were divided into eight vegetation type regions. We observed that the spatial distribution of continuous precipitation differed to varying degrees from accumulated precipitation. The national average of $MAC{P}_a$ for a single event was 16.7 mm, ranging from 3.8 mm in the temperate desert region to 37.1 mm in the tropical monsoon forest and rainforest region. Similarly, the national average of $MC{P}_d$ ($MMC{P}_d$) for a single event was approximately 2.3 or 9 days. At the regional level, the tropical monsoon forest and rainforest region experienced the longest $MMC{P}_d$. Furthermore, the national average of $MAC{P}_t$ occurrences for 1 year was 57.7 times, varying from 29.8 times in the temperate desert region to 77.9 times in the tropical monsoon forest and rainforest region. Vegetation responses to precipitation regimes exhibit significant regional heterogeneity across China. Our analysis reveals that $MAC{P}_t$ and $M{P}_a$ show markedly positive correlations with vegetation growth. In subtropical monsoon climate zones, particularly the Yunnan–Guizhou Plateau and Qinling Mountains, $MAC{P}_t$ demonstrates strong positive correlations (r = 0.6–1.0) with NDVI, where sustained rainfall provides stable moisture availability for vegetation. While a positive correlation between vegetation (NDVI) and mean annual consecutive precipitation is observed in some arid northern regions, in ecosystems such as the Loess Plateau (TG/TM), vegetation growth shows greater dependence on $M{P}_a$, highlighting the crucial role of total precipitation amount in water-limited ecosystems. Notably, extreme precipitation events display dual effects on vegetation dynamics. Prolonged heavy rainfall ($MMC{P}_d$/$MMC{P}_a$) exhibits significant negative impacts on NDVI (r = −1.0 to −0.6) in topographically complex regions, including the Hengduan Mountains and Yangtze River Basin (SE), likely due to induced soil erosion and waterlogging stress. Our findings underscore the importance of incorporating continuous precipitation indices to evaluate and forecast the influence of precipitation on ecosystem stability. This understanding is vital for developing informed conservation and management strategies to address current and future climate challenges. Full article

Heavy precipitation in Northeast China is frequently modulated by the Northeast China Cold Vortex (NCCV), although the microphysical processes within squall lines under such conditions remain insufficiently understood. This study presents a comprehensive analysis of an NCCV-influenced squall line in Liaoning Province, utilizing coordinated S-band polarimetric radar and surface disdrometer observations. The raindrop size distribution (DSD) characteristics and three-dimensional microphysical structure are systematically examined for both convective and stratiform regimes. A comparative analysis of DSD and warm-rain microphysical mechanisms is also conducted with a Mei-yu event. Results show that convective rain in the NCCV squall line exhibits a continental-type DSD, characterized by fewer but larger raindrops compared to other heavy rainfalls in China. In contrast, the Mei-yu frontal convection under NCCV influence exhibits a transitional DSD pattern between the maritime and continental types, with raindrops smaller and denser than those in the NCCV squall line. Vertical structure of the mature squall line shows prominent differential reflectivity (Z_DR) and specific differential phase (K_DP) columns above the melting level within the convective region, indicating vigorous riming growth of graupel and hail driven by strong updrafts. Meanwhile, the stratiform region is characterized by ice crystals and aggregates, formed primarily through deposition and aggregation processes. The subsequent melting of ice-phase particles followed by collision–coalescence and evaporation-driven size sorting shapes the large but sparse raindrops in the NCCV squall line. Comparison with Mei-yu convection demonstrates that surface DSD is shaped by environmental conditions and vertical microphysics. The drier, more unstable environment in the NCCV squall line favors deep convection with active ice-phase processes, while the relatively moist and stable environment of the Mei-yu convection supports shallower convection dominated by warm-rain processes. Future multi-case studies with integrated observations are needed to quantify how environmental and aerosol conditions modulate these heavy precipitation processes. Full article

This study evaluates the impact of assimilating the Crustal Movement Observation Network of China (CMONOC) global navigation satellite system (GNSS) tropospheric products on heavy-rainfall simulation over the complex terrain of the Sichuan Basin. Using the Weather Research and Forecasting model with the WRF Data Assimilation (WRF/WRFDA) three-dimensional variational (3DVar) system, we conducted a control (CTRL) experiment and a data-assimilation (DA) experiment for a primary heavy-rainfall event during 10–12 August 2020. The DA experiment applied 6 h cycling assimilation of station-based zenith total delay (ZTD) and precipitable water vapor (PWV). Compared with CTRL, DA improved the placement of the primary rainband and the depiction of peak rainfall. On 10 August, the observed rainfall core (~40 mm) over the northwestern basin was underestimated in CTRL (~15 mm) but was strengthened in DA (~25 mm). Hourly verification at a threshold of 2 mm h⁻¹ showed a higher maximum Threat Score (TS) in DA (0.292) than in CTRL (0.250), and the largest instantaneous gain reached 0.061. For 72 h accumulated precipitation, TS was higher in DA across multiple thresholds (≥10, ≥25, ≥50, and ≥100 mm), with the most pronounced improvement for heavier rainfall categories. Diagnostic analysis indicates that GNSS assimilation introduces dynamically consistent low-level moistening and strengthened convergence at 850 hPa, together with a better-aligned vertical ascent structure during the key stage of the event. An additional heavy-rainfall event during 21–23 August 2021 was further examined as a compact robustness test, and the results showed a generally consistent improvement in precipitation distribution and TS after GNSS assimilation. Overall, the present results suggest that cycling assimilation of CMONOC GNSS ZTD/PWV products can provide effective moisture constraints and improve heavy-rainfall simulation over the Sichuan Basin in the examined cases. Full article

Characterizing surface runoff and the transport process of non-point source pollutants (NSPs) carried by this runoff is crucial for identifying key source areas, estimating pollution loads entering water bodies, and implementing pollution control, which is particularly important in regions dominated by smallholder farming in China. Currently, most of the commonly used NSP models originated from international countries and have shortcomings such as high data requirements, high generalization degrees, and requiring the calibration of numerous parameters in the application process. Therefore, a distributed non-point source pollution model based on the time-varying gain and stormwater runoff response was constructed, designed for application at the watershed scale. This study describes the construction of the model, introducing its principles and structure through three key modules: a rainfall–runoff module, a soil erosion module, and a pollutant migration and transformation module. The proposed model was used to simulate the rainfall–runoff, soil erosion, and nutrient migration and transformation processes at different spatiotemporal scales. Although it achieved the best performance at the monthly and annual scales, its simulation results at the daily and hourly scales still met the relevant requirements, with relative errors within 20% and Nash–Sutcliffe Efficiency (NSE) coefficients of approximately 0.7. The annual sediment delivery ratios for the Yangliu Small Watershed and the basin above the Ankang section in 2022 were determined to be 0.445 and 0.36, respectively. The pollutant processes corresponding to different runoff events in the Yangliu Small Watershed were simulated, and the average NSE for total nitrogen (TN), ammonia nitrogen (NH₃-N), nitrate nitrogen (NO₃-N), total phosphorus (TP), and soluble reactive phosphorus (SRP) were determined to be 0.69, 0.74, 0.79, 0.71, and 0.71, respectively. For the basin above the Ankang section, the NSE coefficients for the simulation of NH₃-N and TP pollutant processes were 0.78 and 0.83, respectively. The model demonstrated robust applicability across various spatial (ranging from small to large watersheds) and temporal (hourly−daily−monthly−annual) scales, and exhibited stability across different basins in a semi-humid region of China. The model is characterized by a parsimonious parameter set, ease of calibration, and strong spatiotemporal versatility, thus providing an efficient and reliable tool for non-point source pollution simulation. Full article

Against the backdrop of accelerating climate change, extreme weather events have increasingly caused yield losses in agricultural crops. Meanwhile, they undermine the stability of production systems, posing an increasingly severe threat to agriculture. This study draws on the “diversity–stability” hypothesis to construct a country-level measure of agricultural production resilience in China (ARES). Using output time series for multiple agricultural products, we capture the co-movements of shocks and system resilience through output stability and volatility. By combining ARES with climate exposure measures, we assemble a panel dataset covering 1343 counties over the period 2000–2023 and employ a dynamic panel threshold model to jointly account for persistence in ARES and state-dependent nonlinearities in climate impacts. The results reveal significant path dependence in ARES and pronounced threshold effects across climate dimensions. In the full sample, extreme high-temperature days become significantly detrimental after crossing the threshold, whereas extreme low-temperature days become significantly beneficial in the high-exposure regime. Extreme rainfall days and extreme drought days generally exhibit positive effects that weaken markedly beyond their respective thresholds, indicating diminishing marginal gains in ARES under severe exposure. The comprehensive climate physical risk index significantly suppresses ARES when it is below the threshold value; however, after surpassing the threshold, its marginal effect becomes significantly weaker. Heterogeneity analyses across hilly, plain, and mountainous areas, as well as nationally designated key counties for poverty alleviation and development, further show that threshold locations and regime-specific effects differ substantially by terrain and development conditions. These findings highlight the need for “threshold-based” climate adaptation governance, emphasizing targeted investments and risk-financing instruments to prevent ARES collapse under tail-risk regimes. Full article

Heilongjiang Province is China’s largest commercial grain-producing base, meaning that understanding the stability and climatic sensitivity of its major crops are essential for national food security. Using statistical and meteorological data from 2004 to 2023, this study systematically examines the impacts of climate change on cropping structure, yield dynamics, and production stability. The results show that over two decades the total grain crops-sown area and the yield per unit area increased by 79.4% and 38.4%, respectively. The cropping pattern shifted from a diversified structure to a maize-soybean-rice dominated pattern, while the wheat area declined by 92.2%. Additionally, mean and extreme yield fluctuations decreased by 52.3% and 42%, respectively. Rice exhibited the highest yield stability, whereas maize and soybeans experienced marked reductions in interannual variability. Spatial analysis identified Harbin and Daqing as hotspots for yield stability risk, characterized by higher yield standard deviations relative to other cities in the province. Climate elasticity analysis revealed that soybeans and rice were sensitive to warming, while wheat responded positively to increased rainfall. Overall, Heilongjiang’s grain production system has expanded and become more stable at the provincial scale, but it remains vulnerable to emerging climatic risks. Strengthening climate adaptation through crop-specific management, varietal improvement, and field water regulation is vital for enhancing system resilience and sustaining food production in cold-region agroecosystems. Full article