Hydrothermal geothermal resources are abundant and widely distributed across China, with karst thermal water emerging as a key geothermal medium. The Luxi area, situated within the North China Craton karst belt, is a critical area for hydrothermal resource. However, its geothermal regime and controlling factors remain poorly understood. This study analyzed data from 26 newly drilled geothermal wells and thermal conductivity measurements from 46 Ordovician carbonate rocks to characterize the geothermal regime within different structural units and guide future exploration. Findings revealed significant spatial variations in geothermal characteristics: the QH field, near recharge zones, is dominated by thermal convection, with a mean temperature of 47.4°C, geothermal gradient of 23.55°C·km−1, and heat flow of 60.3 mW·m−2. Conversely, the FX field, influenced by deep faults, exhibits higher values (mean temperature: 62.8°C, gradient: 25.20°C·km−1, heat flow: 66.9 mW·m−2). The JC field, located in the central Heze uplift, presents intermediate values. Fault zones play a pivotal role in heat accumulation, particularly in shallow layers (< 3000 m), while deeper layers show exponential temperature variations along fault margins. Groundwater facilitates vertical heat convection, strongly correlating with the thickness of overlying clastic rock layers (R2 = 0.772, P = < 0.0001). Tight clastic rocks, such as Upper Paleozoic mudstone, act as effective seals for Ordovician karst reservoirs. This study enhances understanding of geothermal characteristics in Luxi area, providing theoretical guidance for resource exploration.
This study employs the Soil and Water Assessment Tool (SWAT) to investigate the dynamics of runoff and sediment in the Fuhe River Basin of Poyang Lake. After calibration and validation, the model’s coefficient of determination (R2) for both runoff and sediment yield exceeded 0.9, indicating high model accuracy. During the study period from 2001 to 2010, the cropland area in the Fuhe River Basin decreased by 195.9 km2, while forest and urban land areas increased by 105.4 km2 and 86.1 km2, respectively. By inputting the multi-year LULC data from 2001 to 2010 into the SWAT model, we assessed the impact of LULC changes on the multi-year water and sediment yields of the Fuhe River Basin. The results showed annual differences in water and sediment yields, both below 10 mm and 2 t/ha2, respectively. At the sub-basin scale, LULC changes had a significant impact on water and sediment yields. Under the 2010 baseline landuse scenario, the simulated runoff and sediment yields were 837.11 m3/s and 4.32 × 106 t, respectively. Compared to the baseline scenario, the reforestation scenario resulted in reductions in water and sediment yields by −1.0
The comprehensive investigation of sediment transport during flood events offers valuable insights into the hydrological and erosion processes of watersheds. It also plays a crucial role in flood disaster prevention and control. In this study, we focused on the northern earth-rocky mountainous areas of China and employed K-medoids clustering to classify a total of 261 flood events spanning from 1959 to 2021 into four distinct types. By comparing the sediment transport characteristics of different flood types and periods, as well as analyzing sediment source distribution using SSC-Q hysteresis loops, we obtained the following results. 1) The study period witnessed a notable decrease in the annual number of flood events and sediment yield, with reductions of 48.09
Under the dual pressures of high-density urbanization and rigid land resource constraints, new town development is shifting from incremental expansion to stock optimization, necessitating a clearer understanding of spatial reconstruction types and their ecological effects. Using Shanghai’s Five New Towns as the study areas, this study developed a “Production-Living-Ecological Space” (PLES) classification system based on 0.8-meter high-resolution remote sensing imagery from 2019 and 2024. Land-use transfer matrices, the Expansion Speed Index (ESI), the Standard Deviational Ellipse (SDE), and multi-scale landscape pattern indices were integrated to characterize reconstruction types, coupling patterns, and ecological structural responses. The results reveal pronounced non-equilibrium evolution among the five new towns, with a 7.57-fold difference in ESI. Land supply structure strongly shaped ecological outcomes: stock-replacement towns achieved concurrent gains in living and ecological spaces, with net ecological increases of 2.30 and 1.01 km2, whereas increment-dependent and stock-squeezed towns incurred net ecological losses ranging from −0.56 to −1.28 km2. Ecological performance was further mediated by the spatial coupling of expansion and restoration: co-located coupling enhanced connectivity, whereas partitioned or axial-conflict coupling led to macro-ecological fragmentation and, in some cases, a scale-differentiated pattern of micro-improvement but macro-decline. These findings indicate that ecological performance in high-density new towns is determined less by expansion scale alone than by land supply structure and the spatial organization of expansion-restoration coupling, thereby providing a basis for differentiated spatial governance in high-density urbanized regions.
The increasing impacts of tropical cyclones (TCs) under climate change necessitate a deeper understanding of boundary layer turbulence dynamics, particularly in urban landscapes dominated by high-rise building clusters in the coastal megacities. This study investigates the turbulent characteristics within the TC boundary layer (within 400 m, TCBL) of Typhoon Muifa (2022) during its landfall in Shanghai, China, using synchronized Doppler wind lidar observations from two sites 18 km apart: one site in Lujiazui, the dense downtown with dozens of skyscrapers, and the other in Baoshan, a rural countryside area. Key findings of this research reveal distinct turbulence regimes in the two environments. Maximum wind speeds were similar in the downtown (about 35 m s−1) and the countryside (about 37 m s−1), with downdraft magnitudes exceeding 5 m s−1 (Downtown) and 6.8 m s−1 (Countryside), respectively. The turbulence kinetic energy (TKE) profiles diverged significantly: urban TKE exhibited a non-monotonic vertical structure, peaking at 250 m before declining, whereas rural TKE decreased uniformly with height. Analysis of the estimable TKE budget terms identified shear production as the largest measurable source term of enhanced turbulence in the urban environment. This finding is consistent with the wind speed disparities and complex airflow interactions induced by building clusters. Meanwhile, the vertical advection term also was identified as a significant contributor in sustaining and developing turbulence. These results highlight the critical influence of urban morphology on TCBL dynamics, with implications for refining numerical simulations of wind fields, improving disaster resilience strategies for coastal megacities, and informing wind-resistant design standards for high-rise structures in typhoon-prone regions.
This study analyzed the present-day in situ stress field characteristics in the Longmaxi Formation of Luzhou area using methods of comprehensive logging calculations, numerical simulations, etc. Further, this paper analyzed and quantitatively evaluated the mechanical effectiveness of natural fractures in the target layer based on the relationship between in situ stresses and natural fracture development. The results reveal that the dominant orientation of present-day in situ stress in the Layer S1l1-1 is WNW-ESE-trending in the Luzhou area, affecting by local structures. The horizontal maximum principal stress in the Layer S1l1-1 ranges from 70 MPa to 125 MPa, with the majority between 85 MPa and 110 MPa. The horizontal minimum principal stress varies from 50 MPa to 100 MPa, predominantly falling between 75 MPa and 90 MPa. The stress distribution exhibits strong heterogeneous characteristics, significantly influenced by tectonic activity. Under the present-day in situ stress state, mechanically effective natural fractures constitute a small portion of the total fractures. The effectiveness is mainly influenced by factors such as fracture inclination, fault zone development, and the orientation of stress relative to fracture strike. The results are expected to support shale gas exploration and development in the Longmaxi Formation of Luzhou area, Sichuan Basin.
To enhance the monitoring of compound drought and heat events (CDHEs), in this study, we take Shaanxi Province as the study area and use the relative thresholds of daily maximum temperature and meteorological drought composite index to define and identify the CDHEs with different risk levels in this area from 1961 to 2023. This research focuses on the spatio-temporal distributions, duration, affected area and variation trend of the CDHEs. The results indicate that for different risk levels, the north of northern Shaanxi and the central and eastern parts of central Shaanxi are the high-frequency areas of the CDHEs, and high temperature has a great impact on the frequency of the CDHEs in these regions. The intensification of drought shows a larger influence on the frequency of the CDHEs in Hanzhong, southern Shaanxi Province. Before the 1990s, the frequency contribution of the non-persistent CDHEs varied larger than normal, and since the late 1990s, it has increased. In the 21st century, the frequency of the persistent CDHEs at different risk levels increased, especially those lasting more than 5 days. High temperature is the direct factor that leads to the growth in the frequency of the persistent CDHEs. For the same frequency, the persistent CDHEs tend to affect a larger area. In 1961–2023, the frequency of the CDHEs in Shaanxi Province showed an overall increasing trend, and the trend rates decreased from the east to the west. From the 1960s to the 1980s, the CDHE frequency in Shaanxi Province showed a clear decreasing trend. After the 1990s, it displayed a rising trend, and this increasing trend became especially noticeable since 2020. The CDHE frequency at four kinds of threshold combinations varied dramatically in 1991 or 1992, with larger increases along the Yellow River in northern Shaanxi, and in the west of central Shaanxi and the west of southern Shaanxi.
As a significant coal resource base in China, the Turpan-Hami Basin hosts substantial coal reserves with notable potential for trace element enrichment and mineralization. This study investigates the restrictions imposed by the sedimentary environment on trace element distribution through geochemical analysis of coal and depositional settings in the No. 3-2 coal seam of the XX Coal Mine. The results indicated that the coal in the seam is rich in vitrinite and is classified as a low-rank coal with ultralow ash content, high volatility, and moderate fixed carbon. The average contents of vitrinite, moisture, ash, volatile matter and fixed carbon are 83.54
Salinity is a complex and major abiotic stress that hinders plant growth and development, ultimately reducing crop yield. As the global salinity-affected area continues to increase, the development of salt-tolerant genotypes is one of the effective contemporary strategies to sustain crop production. Blackgram is an important pulse crop with high salinity susceptibility. However, few studies have evaluated its salt tolerance. In this study, we have optimized the critical salinity level to screen at the germination stage. The membership function value (MFV) method was employed to integrate multiple morphological and physiologic traits into a single comprehensive metric for evaluating salt tolerance in 80 blackgram genotypes. Genotypes were classified based on MFV thresholds into categories ranging from highly salt susceptible to highly salt tolerant, enabling robust differentiation among the 80 blackgram genotypes studied. This novel evaluation approach enhances the accuracy and practical applicability of salt tolerance screening in germplasm. Out of 80 genotypes, four were classified as highly salt tolerant (HST), eight as salt tolerant (ST), 57 as moderately salt tolerant (MST), two as salt susceptible (SS), and nine as highly salt susceptible (HSS). Based on the MFV analysis, the genotypes G20, G42, G79, and G80 were classified as highly salt tolerant. Similarly, genotypes G10, G13, G28, G37, G58, G63, G70, G72, and G74 were classified as highly salt susceptible. Among the measured traits, germination percentage (GP), germination index (GI) and germination vigor index (GVI) proved to be reliable indicators for assessing salt tolerance. These findings offer valuable insights for the evaluation and breeding of salt-tolerant blackgram genotypes at the germination stage.
El Niño-Southern Oscillation (ENSO) is the dominant climate mode on an interannual timescale and is teleconnected to synoptic extremes. In this study, we investigated changes in precipitation anomalies in key regions around the globe during strong Eastern Pacific (EP) El Niño events under two high-emission scenarios (RCP8.5 and SSP5-8.5) from the Coupled Model Intercomparison Project phase 5 and phase 6 (CMIP5 and CMIP6). The model projections revealed a discrepancy in the pattern of the precipitation anomalies between CMIP5 and CMIP6 when compared with observations. The model consensus on the features of these precipitation anomalies, however, indicated that for the majority of key regions, an increase in the frequency and standard deviation of severe events reflected the intensified atmospheric instability induced by strong EP events under global warming. The precipitation anomalies were enhanced over most of the low-latitude areas compared with the historical results, whereas anomalies in the midlatitude areas were more complex and not completely consistent with the signs of historical anomalies. The moisture budget indicated that the impact of moisture transport played an important role in triggering changes in precipitation anomalies for most of the low-latitude regions. We inferred that the low-latitude regions were more vulnerable to the influence of water vapor, suggesting a thermodynamic effect response to global warming during EP events. The midlatitude regions tended to be influenced by the effect of circulation anomalies or dynamic processes. Overall, the strong EP events under the high-emission scenario likely generated more complex and stronger teleconnections at a global scale.
Terrestrial ecosystems are experiencing rapid and unprecedented changes driven by global warming, precipitation changes, increasing atmospheric CO2, nitrogen enrichment, and land-use change. Understanding and predicting ecosystem responses to these interacting factors requires integrative approaches that combine empirical observations with advanced analytical and modeling frameworks. Over the past several decades, Luo Ecolab and its collaborators have pioneered a suite of innovative research methodologies, including long-term manipulative field experiments, global-scale meta-analyses, data assimilation, matrix-based modeling, and artificial intelligence (AI)-enabled data–model integration. In this review, we synthesize key advances across six thematic areas and demonstrate how these complementary approaches enhance mechanistic understanding and predictive capacity in ecosystem ecology. Specifically, experimental warming studies reveal that ecosystem responses are dynamic and frequently constrained by water and nutrient availability, and subject to acclimation over time. Meta-analyses provide robust quantitative syntheses across ecosystems, identifying consistent yet context-dependent effects of global change on ecosystem productivity, soil carbon cycling, and greenhouse gas emissions. Data assimilation bridges observations and process-based models, reducing uncertainty and improving predictions at site, regional, and global scales. The matrix modeling framework offers a unifying mathematical structure for carbon cycle models, enabling efficient computation, traceability analysis, and systematic diagnosis of model uncertainty. Emerging AI approaches, particularly knowledge-guided machine learning, further advance the integration of big data with ecological theory, unlocking new pathways for scientific discovery. Collectively, these advances demonstrate that ecosystem responses to global change are governed by complex interactions among climate, nutrient availability, and microbial processes. By integrating empirical data, theoretical frameworks, and computational innovations, this body of work provides a robust foundation for next-generation ecosystem modeling and ecological forecasting in an era of accelerating environmental change.
Observed data show an increase in the intensity and frequency of heavy precipitation events in various parts of the United States and other geographic regions worldwide, and projected climate data suggest these trends are likely to continue. Consequently, the conventional belief in the stationarity of precipitation time series and the traditional statistical frequency methodologies are now outdated. The nonstationary nature of heavy precipitation data necessitates the development of new approaches for frequency analysis to better represent the present day and projected precipitation frequency. This paper describes a framework to translate future climate scenarios into the nonstationary precipitation frequency estimates needed for hydrologists, developers, water managers, environmental planners, and other professionals. Two main approaches were designed to estimate timevarying precipitation frequency depths for multiple future time horizons up to 2075 based on the climate model runs from 1960 to 2100. One approach involves dividing the time period into sub-periods, treating each sub-period as stationary. This piecewise stationary method is referred to as the quasi-stationary (QS) approach. The second approach, referred to as the nonstationary (NS) approach, expresses precipitation frequency distribution statistics as a function of annual precipitation, time, or other covariates. These approaches were applied to the Northeastern United States for different data sources (LOCA vs. UWPD), types of data (AMS vs. PDS), and scenarios (RCP4.5 vs. RCP8.5). The study findings highlight the distinctive variations between different approaches and assumptions. A sensitivity analysis was conducted to identify critical decision points and assumptions in the modeling process. These results aim to assist researchers and practitioners in making informed choices for future nonstationary precipitation frequency studies.
Arbuscular mycorrhizal fungi (AMF) play a crucial role in ecosystem carbon storage and climate change mitigation. However, the relationship between AMF and soil organic carbon (SOC) dynamics under nitrogen (N) deposition remains poorly understood. In this global meta-analysis, we synthesized data from 438 observations across 45 studies. Results demonstrated a general decrease in AMF abundance under N addition treatments: both AMF biomass and root colonization rate declined by 11
Soil organic carbon (SOC) is a critical component of global carbon cycling and a key regulator of soil CO2 emission. However, the effects of agricultural activities, particularly tillage, on SOC sequestration are not fully understood. Here, we conducted a comprehensive mega-analysis of 24 individual meta-analyses to assess how conservation tillage practices, including no-till (NT), reduced tillage (RT), and mixed NT + RT, affect SOC sequestration. Overall, all conservation tillage types significantly increased SOC stocks, with RT showing the highest increase by 13.42
The urgency of addressing climate change has underscored the need for precise carbon management—an approach that precisely monitors, assesses, and manages carbon emissions and sequestration at fine spatial, temporal, and sectoral scales. This perspective paper examines the current state of precise carbon management, highlighting advancements in ground-based observations, remote sensing, process-based model, and machine learning. Despite these innovations, key challenges persist, including data fragmentation and interoperability, limited geographical and temporal monitoring coverage, difficulties in integrating multi-source data sets with varying resolutions, and insufficient public engagement and decision-support infrastructure. To address these barriers, we propose a roadmap that includes the development of standardized data frameworks, expansion of monitoring networks in underrepresented regions, creation of a foundational AI model for carbon data integration, and user-centric decision-support tools to bridge science-policy gaps. Together, these proposed strategies aim to enhance the accuracy, scalability, and transparency of carbon management strategies in support of global climate goals.
Soil microbial carbon use efficiency (CUE) plays a critical role in carbon (C) cycling and ecosystem functioning, yet its response to nitrogen (N) deposition remains poorly understood, particularly in planted forests. This study investigates how N addition affects microbial CUE and its underlying mechanisms in Populus deltoides plantations in coastal eastern China. Using a long-term field experiment with five levels of N addition (0–30 g N·m−2·yr−1), we measured microbial CUE, soil chemical properties, enzyme activities, and microbial community composition from 2018 to 2020. We found that N addition significantly reduced microbial CUE, primarily through N-induced stoichiometric imbalances and soil acidification. Excess N increased available N and decreased the DOC:AN ratio, driving microbial carbon limitation and reducing metabolic efficiency. Furthermore, N addition suppressed bacterial diversity and shifted microbial communities toward taxa with lower CUE. Model selection identified soil pH, available N, and DOC:AN as key predictors of microbial CUE. These findings highlight the dominant role of soil environmental factors—particularly nutrient stoichiometry and pH—in regulating microbial CUE. Our results suggest that excessive N deposition may compromise soil C sequestration in poplar plantations by altering microbial resource allocation and reducing microbial metabolic efficiency. Managing nutrient balance and maintaining microbial diversity are thus critical for sustaining soil health and carbon storage in forest ecosystems under increasing N deposition.
This study constructed the most extensive landslide relic inventory in Nyingchi to date based on high-resolution imagery and DEM, which includes 12461 landslide relics covering a total area of 5179 km2. High landslide densities are observed in four key areas: the south-west of Nang County, the north-east of Mainling County, the south of Medog County, and the east of Zayu County. In terms of land use types, shrublands contain the largest number of landslides, while croplands are associated with the largest-scale landslides. Regarding hydrological proximity, most landslides are distributed within 1 km of rivers, yet the largest total area of landslides is concentrated in the zone 1–2 km away from rivers. In terms of geological and tectonic factors, the largest proportion of landslide area occurs in regions 20–40 km from active faults, whereas the highest landslide point density is found 120–140 km from active faults. From a stratigraphic perspective, Triassic strata host both the highest number of landslides and the largest proportion of landslide area. Regarding hydroclimatic conditions, most landslides occur in regions with mean annual temperatures above 20°C, yet the largest landslide area is distributed in the 0°C–5°C range. Furthermore, areas with mean annual rainfall below 400 mm exhibit both the highest number of landslides and the greatest proportion of landslide area. This inventory fills data gaps in remote areas of Nyingchi and provides a robust foundation for regional landslide risk prevention, engineering construction safety, and territorial spatial planning.
Multi-scale mechanical research has become an effective approach for analyzing the behavior of complex materials, enabling the prediction of macroscopic properties from microscale characteristics. Pyrite, a key mineral affecting the mechanical properties of shale, has attracted considerable attention due to its brittleness and distinctive microstructure. However, the specific effects of pyrite’s microscopic features on shale mechanics have not been systematically investigated. In this study, Python-based machine learning techniques are employed to establish an automated framework for quantitatively analyzing pyrite’s microscopic characteristics. The influence of pyrite content, particle size, shape factor, and distribution on shale mechanical properties and fracture network formation is examined using a random forest regression algorithm and numerical simulations. The results show that pyrite content exerts the strongest influence on shale mechanics, followed by shape factor, while particle size and distribution mode have comparatively weaker effects. Compressive strength and fracture behavior are particularly sensitive to elevated pyrite content and shape factor. All four factors (pyrite content, particle size, shape factor, and particle distribution) affect mechanical properties through stress concentration, with shape factor additionally governing particle interlocking. Distribution mode further modulates the mechanical response by influencing the formation of force chain networks. Numerical simulations reveal that fracture network development is optimized when pyrite content is 3.00
Urban parks provide crucial cultural ecosystem services (CESs) that enhance the well-being of residents. Landscape composition and configuration determine the supply of CESs, which in turn affects the matching of the supply and demand of the CESs. However, there is limited research on how landscape-level processes influence this supply and demand. Therefore, this study focuses on Chongqing’s central urban area using a social value model that combines questionnaire surveys and environmental variables to assess the CESs supply, whereas social media data quantify the demand for CESs. Landscape indices are used to evaluate landscape-level processes. Pearson correlation and linear fitting analyses explore the relationships between landscape indices and CESs benefits, whereas multiscale geographically weighted regression (MGWR) further reveals the spatially differentiated impacts of landscape configuration on the relationship between the CESs supply and demand. The results indicate that 1) the supply and demand of CESs are concentrated in the western part of the city, with educational value having the lowest value index of 7, whereas entertainment and aesthetic values have higher demands (42.37
Calibrating parameters in distributed hydrological models is challenging because of the large number of parameters involved. In this study, a distributed physical hydrological model known as the Liuxihe (LXH) model was taken as a case study. We employed an automated algorithm-Particle Swarm Optimization (PSO) to calibrate the parameters of the LXH model. Following optimization, we assessed the model efficiency by simulating the flood process in the Beijiang Basin in Guangxi, China. The model outputs were compared with the measured values, and the results were satisfactory. The Nash coefficient and flood error were 83.9
