This study examines permeability response to pore structure evolution under effective stress in six sandstone reservoirs of varying physical properties, using CTS, SEM, QEMSCAN, DP-NMR, fractal theory, and pore compression theory. The sandstones comprise quartz (82.56%), rock fragments (7.60%), clay minerals (5.66%), siliceous (0.1%–4.3%, avg. 2.21%), and carbonate (0.05%–6.95%, avg. 1.8%) cements, with intergranular pores, micropores, and minor intragranular dissolution pores. Stress sensitivity increases from Type I to III: large pores exhibit the highest sensitivity in Types I and III (compressibility: 0.011 and 0.005 MPa⁻¹), while small pores dominate in Type II (0.008 MPa⁻¹). Fractal dimensions of small/total pores (DS, DT) decrease in Types I/III, whereas in Type II, DT increases and DS first decreases then increases due to clay minerals; large pore fractal dimension (DL) increases modestly across all types. Permeability stress sensitivity is controlled by large pore volume changes, which correlate exponentially with effective stress. A predictive permeability model based on pore volume stress-strain theory provides theoretical guidance for hydrocarbon production.

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.

Agriculture is a major source of greenhouse gas (GHG) emissions. To clarify long-term trends and drivers, we estimate provincial agricultural GHG emissions in China from 2000–2020 using a life-cycle assessment (LCA) framework and apply Logarithmic Mean Divisia Index (LMDI) decomposition. Total emissions rose 10.71% (1250.96→1384.99 Mt), peaking in 2017 before declining. Across sources, agri-materials and manure management were the largest contributors in cropping and livestock systems, respectively. LMDI attributes emissions growth primarily to agricultural development, while improvements in emissions intensity offset part of this increase; urbanization generally exerted a smaller positive effect, and labor reductions dampened emissions. Regional heterogeneity is pronounced: northern (Tianjin, Hebei, Shanxi) and central (Henan, Hubei) provinces show fluctuating increases; the north-east exhibits steady growth; and the south-west (Chongqing, Sichuan, Guizhou) shows a fluctuating decline. These results highlight the need for region-specific mitigation strategies, emphasizing input efficiency, manure management, and structural adjustments to advance low-carbon agricultural development.

Although research on nutrient uptake in small streams has been extensive over the past 20 years, much less is known about coupled nutrient uptake, especially in a moderately nutrient-polluted stream. So, we selected a moderately nutrient-polluted stream (Zhangwa Creek) in Chaohu Lake basin, China, to examine the dynamics of coupled dissolved inorganic nitrogen (DIN, herein, ammonium nitrogen (NH₄-N) plus nitrate nitrogen (NO₃-N)) and soluble reactive phosphorus (SRP) uptake by performing a range of single- and dual-nutrient instantaneous additions experiments. The whole-reach uptake rate and saturation kinetics of nitrogen (N) and phosphorus (P) individually were estimated following the tracer additions for spiraling curve characterization (TASCC) model, and the dynamics of coupled DIN and SRP uptake in dual-nutrient additions were characterized through the response surface model. Comparisons of the ambient uptake length in single additions between DIN and SRP consistently indicated a slight P limitation. In contrast, results from response surfaces showed that the stream was likely limited by N or P or co-limited by them. Our results revealed that DIN and SRP uptake coupling effects could occur in a moderately nutrient-polluted stream. Moreover, we found that water temperature and pH might be central environmental variables influencing DIN and SRP uptake in the stream.

The Late Mesozoic coal-bearing strata on the eastern margin of the Shuangyashan Basin are significant potential sources of coal-derived gas resources; however, their complex structural evolution’s influence on the gas accumulation process is not yet fully understood. This study aims to investigate both the generation and evolution processes of coal-derived gas in the region and the key factors that control them by employing a combination of organic geochemical analysis, PetroMod basin modeling, and methane isothermal adsorption experiments, allowing us to systematically examine the factors that influence coal-derived gas generation and evolution. Geochemical data indicate that the coal-bearing strata are characterized by a high abundance of organic matter, are mature to highly mature, and are primarily composed of gas-prone Type III kerogen, providing a high-quality source for efficient coal-derived gas generation. Basin modeling clearly illustrates two critical structural–thermal events: Early Cretaceous rapid subsidence caused the coal-bearing strata to reach the hydrocarbon generation threshold and attain moderate maturity, while intense magmatic thermal events during the Late Cretaceous Yanshan period (with the peak geothermal heat flow reaching 91.5 mW/m²) resulted in a rapid increase in organic matter maturity, quickly approaching peak gas generation—with both of these events serving as the core triggers for efficient coal-derived gas generation. Methane isothermal adsorption experiments further demonstrate that the coal seams in the study area exhibit both high methane adsorption capacity and low critical desorption pressure, indicating good extractability. This study quantitatively reconstructs the key role of structural–thermal evolution in coal-derived gas generation, emphasizing the dominant influence of Late Cretaceous magmatic thermal events in enhancing resource potential and exploration prospects, as well as providing a scientific foundation for selecting and developing coal-derived gas exploration target areas in the eastern Shuangyashan Basin.

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% and 31.55%, respectively); 2) the western area has more diverse patch types and a lower proportion of the largest patch type, whereas the eastern area has more concentrated dominant landscapes; 3) landscape diversity and complexity positively correlate with CESs supply and demand, with Shannon’s diversity index (SHDI) exhibiting the greatest impact on aesthetic value demand (correlation coefficient = 0.64). In contrast, landscape aggregation and dominance are negatively correlated, with the aggregation index (AI) most strongly affecting educational value supply (correlation coefficient = −0.81). These findings offer insights into enhancing the benefits of CESs, addressing service gaps, and optimizing future urban park layouts.

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% and 17.7%, respectively. The simulated hydrological processes aligned well with the actual trends. The results showed that the PSO algorithm could effectively optimize the parameters of the LXH model. After parameter calibration, the simulations of the LXH model met the requirements for basin flood forecasting and disaster reduction. This method could be applied to automate the parameter optimization process for distributed hydrological models, and the results of this study could serve as a reference for model calibration in other watersheds.

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.

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% and −11.6%, respectively. In contrast, the agricultural development scenario exacerbated soil erosion, leading to increases in water and sediment yields by 5.0% and 37.2%, respectively. On a seasonal timescale, results indicated that compared to the baseline scenario, the five extreme landuse scenarios led to increases in runoff and sediment yields in spring and winter by 11.3% and 162.6%, respectively, which were higher than the increases in summer and autumn of 3.0% and 150.0%, respectively, indicating a more significant impact of LULC changes in spring and winter.

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% and 34.01%, respectively. The suspended sediment concentration during flood events exhibited a substantial decline of 76.54% compared to the baseline period. 2) In the Mihe River Basin, the majority of sediment yield could be attributed to flood events classified as Types A and B. These flood types were characterized by short duration, high peak flow, and substantial runoff depth. Among them, Types A were significantly greater than Types B in terms of runoff and sediment transport. 3) The hysteresis loops observed in the Mihe River Basin predominantly displayed a figure-eight and clockwise pattern, indicating potential sediment sources within the river channel and banks. Addressing sediment challenges and ensuring sustainable watershed management practices require connecting these loop characteristics to future management initiatives, given abundant sediment sources near the Huangshan hydrological station.

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.

Reliable groundwater potential (GWP) mapping is essential for water resource planning, yet most studies emphasize algorithmic comparisons while neglecting how data constraints—specifically, limited sample sizes and high feature dimensionality—govern model reliability. This study examined these effects using Qinghai Province, China as a case study. A data set of 682721 grid cells and 20 environmental factors was established. To assess whether sample-size sensitivity generalizes across algorithms, a benchmark test was conducted with five machine learning methods: Random Forest (RF), Support Vector Machine, Multilayer Perceptron, Logistic Regression, and Gradient Boosting Decision Tree. Results show that sample size is the dominant factor across all tested architectures; models trained on fewer than 100 samples were universally unstable, yielding accuracies below 0.60. With RF as the representative model, performance rose sharply and converged near an accuracy of 0.82 and a macro F1 score of 0.78 once the training set reached 1000–2000 samples. Further analysis revealed that feature dimensionality played a secondary but notable role: retaining 8–10 key variables related to surface water connectivity, topography, and human activity improved accuracy and spatial coherence, whereas redundant features produced spatially fragmented predictions. Model interpretability was also data-dependent; SHAP values were unstable under small sample sizes but stabilized with sufficient data. Overall, this study demonstrates that sampling sufficiency and parsimonious feature selection are critical determinants of robust and interpretable GWP mapping, offering methodological guidance on data requirements that extends beyond algorithm choice.