Ecological risk is a dynamic reflection of ecosystem stability and harmonious social development. The role played by risk identification and evolutionary trend prediction as mediators between ecological risk management and prevention is complex. However, current studies have difficulty identifying where, when, and how ecological risk evolves. Here, we constructed a double evaluation index system of ecological risk source hazard and ecological risk receptor loss degree to quantitatively evaluate and simulate ecological risk in the upper Chang Jiang (Yangtze R.) (UYR). Then, we adopted the normal cloud model to identify the ecological risk level at different scales in the UYR. Finally, we leveraged set pair analysis to reveal the future evolution trend of ecological risk in the UYR. The following conclusions were drawn. 1) From 2015 to 2018, the ecological risk in the UYR exhibited significant spatial aggregation characteristics, with a spatial distribution pattern of “high in the west, low in the east”. The risk value increased from [0, 0.28] to [0, 0.32], an increase of 12.49%. 2) The ecological risk level of the UYR in 2015 and 2018 was in a high-alert state, but the risk value showed a downward annual trend. The comprehensive ecological risk value decreased from 0.5295 to 0.5135. 3) The ecological risk of 67% of the cities in the UYR will decrease in the future, and will increase in 33% of the cities. 4) The probability of geological disasters was the most significant ecological risk source in the UYR. Ecosystem service value significantly impacted ecological risk receptors loss degree in the UYR.

Paleoenvironmental reconstruction plays a pivotal role in providing insights into the uplift history of the Xizang Plateau during the Cenozoic. The Nima Basin, situated in the central Xizang Plateau, is crucial for studying the tectonic and geomorphic evolution of this region. The clastic composition and geochemical characteristics of the Niubao Formation hold considerable potential for unravelling the geological history and reconstructing depositional environments of central Xizang in the early Cenozoic. In this study, we present detailed geochemical characteristics to determine their provenance, paleoenvironmental conditions, and tectonic origins. The index of compositional variability (ICV > 1) of mudstones indicates that low compositional maturity sediments underwent weak sedimentary recycling. The chemical index of alteration (CIA: 59.8−72.9) reveals that parental rocks experienced a moderate chemical weathering degree. The paleoclimate indicators of the mudstones suggest an oxidizing and arid depositional environment, with a mean annual temperature (MAT) of 11.64°C ± 4.19°C. The geochemical evidence also demonstrates that the mudstones were derived from mixed felsic and intermediate igneous rocks that formed in a dominantly continental island arc tectonic setting. Similarities in the geochemical characteristics among the Niubao Formation and surrounding igneous rocks indicate that a continental-scale drainage system once drained westward in central Xizang. It is concluded that the central plateau experienced a cooler and drier climate coinciding with the presence of a large-scale drainage system during the late Eocene.

The hydrogen isotope composition of leaf wax n-alkanes (δ²H_alk) has been used to reconstruct hydroclimate conditions, yet the factors that influence it are not fully understood, particularly the control of soil pore water δ²H. This study monitored the temporal and vertical variations of peat pore water δ²H (δ²H_pw) from 2015 to 2019 in the Dajiuhu peatland, central China. Results showed that δ²H_pw was highly variable in the surface layers (0−10 cm; avg. −47‰, 1σ = 11‰) and remained almost constant in deeper depths (below 50 cm; avg. −56‰, 1σ = 2‰). The δ²H_pw of the 0−10 cm layer was strongly correlated with the preceding month’s precipitation δ²H (δ²H_p) in the adjacent area (r = 0.7, p < 0.01), indicating that δ²H_p is the main factor affecting the temporal variations of δ²H_pw in the upper layers. Moreover, the surface (0−10 cm) peat pore water slightly deviated from the local meteoric water line, suggesting that evaporation may also have an effect on the δ²H_pw. These findings emphasize the importance of precipitation isotope composition in interpreting the δ²H_alk in peat deposits under subtropical climates.

Nitrogen (N), one of the essential mineral elements, is involved in many biochemical processes and ultimately closely relates to agronomic yield. Our ability to monitor N concentrations in plants through direct tissue sampling or remote sensing has rapidly evolved as technology has advanced. However, functional relationships between morphological and physiological processes and tissue N have yet to be widely published and are needed to advance precision and predictive agricultural technologies further. Therefore, an experiment was conducted to determine the relationships between tissue N concentration and corn (Zea mays L.) morphological and physiologic characteristics. Plants were grown in pots under optimal conditions in sunlit controlled-environment chambers but with varying N supplies. Plant growth, developmental, and physiologic properties were monitored weekly. Shoot N content differed among treatments and declined over time for all treatment levels. Photosynthesis declined as N content decreased, but these decreases were largely non-stomatal limiting. Reductions in N content were due to declining chlorophyll and N balance index values and increasing flavonoids and anthocyanins. Stem elongation and leaf expansion were highly sensitive to declining N content. Below the soil surface, root growth and development rates fell and held a quadratic relationship with N content. Roots were less sensitive at low N stress levels than plant growth above the soil surface. The functional relationships produced from this study could help update crop simulation models and apply them to emerging precision agriculture technologies.

Mangrove forests are significant ecosystems worldwide and play a crucial role in maintaining the biodiversity of intertidal zones in tropical and subtropical regions. However, most mangroves have experienced large-scale losses due to anthropogenic activities and natural stress from environmental factors. Here, the dynamic changes in mangroves in the Dandou Sea (DDS) of the Beibu Gulf between 1987 and 2021 were analyzed via multispectral satellite remote sensing data from the Google Earth Engine Platform. The results indicated that the area of mangroves in the DDS increased from 225.90 ha in 1987 to 451.76 ha in 2021. Throughout this period, the overall mangrove area in the DDS, as well as in its western and central parts, underwent a rapid growth phase from 1987 to 1996, followed by a slow growth phase from 1997 to 2011, and eventually entered a stagnation phase from 2013 to 2021. Moreover, due to the biological invasion caused by Spartina alterniflora, the mangrove forests in this area tended toward fragmentation. Moreover, S. alterniflora suppressed the spread of mangrove forests, accounting for up to 41.69% of the total loss. In a similar vein, the local high-intensity economic activities within the tidal flat accounted for 32.55% of the mangrove loss. Additionally, the expansion of aquaculture ponds and construction land directly accounted for 9.45% and 7.91% of the mangrove loss, respectively. Furthermore, the establishment of mangrove nature reserves played a positive role in the restoration and expansion of mangroves in the DDS. Our results also demonstrated that sea level rise had little impact on mangrove retreat.

Electricity constitutes a fundamental pillar of both the national economy and contemporary lifestyles. Monitoring electric power consumption (EPC) has important implications for energy planning, energy conservation and emission reduction, energy security, and smart city development. However, the current monitoring and evaluation of EPC is less accurate and does not allow for real-time monitoring and evaluation of EPC. This study established an EPC assessment model based on EPC data, nighttime light remote sensing technology, and GIScience methodology, aiming to analyze the spatiotemporal variation of EPC in three major urban agglomerations of China from 2012 to 2020 and estimate EPC in 2025. Furthermore, the spatial correlation of EPC was explored using Moran’ s I spatial analysis method. The results indicate that the established model has an average accuracy of 77.56% and can be used for accurate and real-time estimation of EPC. The EPC showed an increasing trend from 2012 to 2020, with the Yangtze River Delta urban agglomeration (YRD) exhibiting the highest growth rate, as high as 49.60%. The EPC in the Beijing-Tianjin-Hebei urban agglomeration (BTH) showed a negative spatial correlation. However, the YRD and the Guangdong-Hong Kong-Macao Greater Bay Area urban agglomeration (GBA) exhibited significant positive spatial correlation in EPC. The findings of this study serve a scientific basis and reference data for the development of energy policies and strategies. Furthermore, this study can help to achieve the “carbon peaking and carbon neutrality goals” proposed by the Chinese government.

Carbon and water fluxes of savannas and grasslands have large seasonal dynamics and inter-annual variation. In this study, we selected five savanna and grassland sites, each of them having 10+ years (11−21 years) of eddy covariance (EC) data, and a total of 85 site-years at these five sites which offers a unique opportunity for data analyses and model evaluation. We ran a long-term simulation (2000−2021) of the vegetation photosynthesis model (VPM, v3.0) and vegetation transpiration model (VTM, v2.0) to investigate the seasonal dynamics, interannual variation, and decadal trends of modeled gross primary production (GPP_VPM) and transpiration (T_VTM) at these sites. The seasonal dynamics of daily GPP_VPM and T_VTM track well with the seasonal dynamics of EC-based GPP (GPP_EC, R²: 0.76−0.93) and evapotranspiration (ET_EC, R²: 0.69−0.92). The inter-annual variation of annual GPP_VPM tracked well that of annual GPP_EC, with the linear regression slopes for GPP_EC versus GPP_VPM-EC ranging from 0.89 to 1.11. The simulation results of GPP_VPM and T_VTM using two different climate data sets (in situ climate data and European Center for Medium-Range Weather Forecasts Reanalysis v5 data set (ERA5)) were similar, suggesting that ERA5 data can be used for VPM/VTM simulations at large spatial scales. From 2000 to 2021, annual GPP_VPM and T_VTM had no significant inter-annual trends at one savanna and three grassland sites but increased significantly at one savanna site. The results demonstrate the potential of using VPM (v3.0) and VTM (v2.0) to predict the seasonal dynamics and inter-annual variation of GPP and T in savannas and grasslands.

An early Late Cretaceous NW-SE compressional event that induced the uplift of the coastal mountains was recognized among the overall extensional regime in east China. While previous studies have explored the paleoelevation, paleogeographical extent, and possible climatic effects of coastal mountains, the exact timing of initial uplift has remained elusive. In this study, we applied detrital zircon U-Pb geochronology to sandstones from the Dasheng Group in the Yishu Rift Basin, east China. Our results suggest that the primary provenance of the Dasheng Group is intermediate-basic volcanic rocks (800–500 Ma, 330–215 Ma, and 150–122 Ma) derived from the Luxi Uplift and Sulu Orogenic Belt, and the secondary provenance is Mesoproterozoic-Paleozoic metamorphic rocks (2500–2300 Ma and 1850–1600 Ma) derived from the Jiaobei Terrane. The zircon age peaks of the Dasheng Group in the Yishu Rift Basin are nearly the same as those of the Lower Cretaceous Laiyang Group in the Jiaolai Basin. However, the proportion of pre-Mesozoic zircons decreases. For the Mesozoic zircons, although their main age peak is close to that of the Laiyang Group, their secondary age peak is similar to that of the Wangshi Group. We infer that the transitional characteristic of the Dasheng Group was caused by the initial uplift of the coastal mountains. Therefore, we speculate that the initial uplift of the coastal mountains occurred during the deposition of the Dasheng Group, and limit the maximum depositional age (MDA) of the Dasheng Group to 100–95 Ma.

Sequence stratigraphy and coal petrology can be used to comprehensively analyze the mechanism of extremely thick coal seams under the influence of the paleo-climate, paleo-environment, and accommodation space during a coal-forming period. Based on the vertical variations in coal quality, macerals, and lithology, key sequence surfaces were identified, including the terrestrialization surface (TeS), paludification surface (PaS), give-up transgressive surface (GUTS), accom-modation reversal surface (ARS), exposure surface (ExS), and flooding surface (FS) in thick coal seams of the Middle Jurassic Dameigou Formation in the Saishiteng Coalfield, northern Qaidam Basin. Using these key sequence surfaces, thick terrestrial coal seams can be divided into several wetting-up and drying-up cycles. In general, the vitrinite content, vitrinite/inertinite ratio (V/I), and gelification index (GI) increased from bottom to top, whereas the inertinite content decreased in the wetting-up cycles. The vertical stacking pattern considers the PaS as the bottom boundary, and the GUTS or ARS as the top boundary, representing an increasing trend in the accommodation space. However, the vitrinite content, V/I, and GI values decreased from the bottom to the top, whereas the inertinite content increased during the drying-up cycle. Another vertical stacking pattern started from the TeS, with the ExS or ARS as the top boundary, representing a decreasing trend in the accommodation space. The thick coal seams at the edge of the Saishiteng Coalfield are blocked by a large number of clastic sediments, whereas relatively few clastic sediments are found in the coalfield center; thus, a single extremely thick coal seam with good continuity can be formed. Based on the coal petrology and sequence stratigraphic analyses, a model of extremely thick coal seams superimposed on multiple peatlands was established from the basin margin to the basin center. Four to five drying-up and wetting-up cycles were predicted in accumulation variation. During a water transgression stage, new peat accumulates on the land, corresponding to a wetting-up cycle. In a water regression stage, new peat accumulates in the basin center, corresponding to a drying-up cycle. Analysis of the genesis of thick coal seams is important for the in-depth excavation of geological information during the coal-forming period and for coal resource exploration in terrestrial basins.

Amidst the rapid pace of urban development, rural communities continually face the challenges posed by erratic natural disasters and human-induced disturbances. Evaluating and improving the resilience of rural areas is crucial for achieving sustainable development. Examining the rural network framework serves as a method to achieve rural resilience. This study established a contact network encompassing 13 villages in Shiba town, Mingguang City, through the collection of time-distance data, questionnaire interview data, and map vector data to examine the spatial patterns of the rural network. The examination of structural resilience was conducted through the framework of complex network theory. The examination of the network’s transitivity and diversity through the frameworks of hierarchy, matching, transitivity, and aggregation reveals its resilience to disruption simulations, such as node failure. The findings indicate that the network exhibits a configuration marked by a dense central region, sparse connections in the north, and a lack of connectivity in the south. The network exhibits a flat structure, with nodes that are relatively uniform in nature. The network exhibits significant disassortativity, classifying it as a disassortative network, where villages with higher node degrees tend to connect with those having lower node degrees. The local transitivity of the network is significantly elevated, with approximately 90% of settlements necessitating just one transfer to establish direct communication. The network exhibits significant clustering effects, marked by robust connections among villages and a few isolated node villages. The transitivity of the network and its diverse spatial patterns show markedly different characteristics when subjected to interruption simulation. The study identified two primary nodes and one susceptible node. The findings from the study precisely reflect the characteristics of the rural network. This can provide theoretical perspectives for analyzing the resilience of rural network structures and support decision-making in rural planning and development.

Accurate forest cover maps are the basis for estimating forest biomass and are crucial for climate regulation and biodiversity conservation, especially in sub-humid and semi-arid regions such as Oklahoma, USA. To date, there is very limited data and knowledge of the spatial pattern and temporal dynamics of forest cover in Oklahoma, and current forest cover maps have large uncertainties. In this study, multi-sensor datasets, including the Phased Arrayed L-band Synthetic Aperture Radar (PALSAR-2), Landsat, and spaceborne Light Detection and Ranging (LiDAR), were combined to generate annual forest cover maps for the years 2015 to 2021. Specifically, both PALSAR-derived HV, HH-HV, and HH/HV and Landsat-derived Normalized Difference Vegetation Index (NDVI) were used together to generate annual maps of forest cover and three forest types (evergreen, deciduous, and mixed forest) at 30-m spatial resolution for each year. The canopy height and canopy coverage samples from the Global Ecosystem Dynamics Investigation (GEDI) and the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) were used to assess forest cover maps. We also compared the spatial distribution and forested area of several forest products. Our results show that using the forest definition (canopy height > 5 m, canopy coverage > 10% over an area of 0.5 ha) of the Food and Agriculture Organization of the United Nations (FAO), the accuracy of resultant PALSAR/Landsat forest cover map for 2019 were 77.4% (GEDI) and 95.6% (ICESat-2). The estimated forested area (51,916 km²) was moderately higher (7.2%) than the forested area from the USDA Forest Inventory and Analysis (FIA) statistics dataset (48,202 km²) in 2017. Between 2016 and 2020, Oklahoma’s forested area increased slightly by 1.9%. The PALSAR/Landsat forest maps are more accurate in western Oklahoma compared to other satellite-based forest products. The resultant annual maps of forest cover and three different forest types over Oklahoma can be used to support statewide forest management and conservation.

Total organic carbon (TOC) content is a crucial evaluation parameter in the process of shale gas exploration and development. Marine-continental transitional shale is characterized by strong heterogeneity and thin single-layer thickness. The discrete TOC data measured by experimental methods are unable to accurately reflect the reservoir characteristics of marine-continental transitional shale. In this paper, a multivariate nonlinear regression prediction model (R-MNR) was established, and the model was applied to predict the TOC content of shale for the first time. The ΔlgR model, multiple linear regression model (MLR), BP neural network model (BP model), and R-MNR model were built to predict the TOC of shale in Benxi Formation. The coefficient of determination (R²), mean-absolute-percentage-error (MAPE), root-mean-square-error (RMSE), and the number of input layer parameters (NILP) were employed to assess the efficacy of the model through the analytic hierarchy process (AHP) method. The total weight of R-MNR is 0.361, and that of BP model is 0.336. The weights of the two traditional models are 0.104 and 0.199, respectively. The results indicate that the R-MNR is comparable to the BP model in terms of prediction accuracy, and both models are significantly more accurate than the traditional prediction model. The R-MNR is capable of obtaining a clear TOC prediction formula, which is convenient for verification and promotion. During the training process of the R-MNR, the influence of each parameter and coupling relationship on the prediction results is elucidated, which enables researchers to gain a deeper understanding of the geophysical significance and geological process of the model. The result of this study suggests that the R-MNR can be employed to predict the TOC content of marine-continental transitional shale effectively in the future.