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.

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.

There is high confidence that extreme precipitation will increase in most areas if the globe continues to warm. In the US, NOAA Atlas 14 (NA14) is the most authoritative source for heavy rainfall frequency values used in infrastructure planning and design. However, NA14 assumes a stationary climate and uses only historical observations to estimate values. Thus, use of such values for design may lead to underperformance of long-lived infrastructure, thereby placing people and property at increased risk from flooding. Analyses of global climate model (GCM) simulations suggest that projected extreme precipitation changes will be positive nearly everywhere in the US and will be larger for shorter durations, lower annual exceedance probabilities (AEPs), and higher emissions. Herein, we provide adjustment factors that can be applied to observations-based precipitation frequency values to estimate potential future changes under selected global warming levels. These are derived from two statistically downscaled daily precipitation datasets (STAR and LOCA2) developed using modern methods that focus in part on modeling the high tail of the precipitation distribution with a high degree of fidelity. These datasets, each consisting of 16 ensemble members downscaled from a common set of 16 CMIP6 GCMs, provide estimates for durations of daily and longer. The set of adjustment factors are extended using seven models from the NA-CORDEX suite of dynamically downscaled simulations by analyzing the change in adjustment factors from daily to hourly durations. There is an average increase in the adjustment factors of about 1.3. This factor is applied to the daily adjustment factors from STAR and LOCA2 to produce estimates for the hourly duration.

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.

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.

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.

Based on standardized precipitation index data, a systematic analysis was conducted of the spatiotemporal variations of drought events in China from 1978 to 2018. Drought events were identified using the run theory applied to the standardized precipitation index data set, and key variables such as drought frequency, duration, and intensity were quantified. Additionally, drought vulnerability, exposure, and resilience were calculated to comprehensively assess the regional drought risk. The spatiotemporal transmission characteristics and pathways of drought risk were further explored using the Markov chain model and its extended version based on spatial lag theory. The results revealed significant differences in the spatial and temporal distribution of drought events across China, with north-west China experiencing a particularly high frequency, duration, and intensity of droughts. Overall, the pattern of drought risk presented a gradient, being higher in the north-west and lower in the south-east. The risk was relatively stable from year to year, with few large fluctuations. Moreover, a strong spatial similarity in drought risk was observed among neighboring provinces, but there was no obvious spatial lag effect. This study provides a valuable scientific foundation for effective drought disaster risk management and the formulation of response measures.

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.

Policies designed to reduce transportation emissions are known to be co-beneficial due to reductions in planet-warming greenhouse gases like carbon dioxide (CO₂) and health-harmful air pollutants, such as nitrogen dioxide (NO₂). The growing recognition of persistent racial and ethnic disparities in air pollution exposure and associated health impacts has increased demand for policy interventions aimed at systematically reducing such inequities. Here, we use a regulatory-grade air quality model focused on the Chicago region to find that medium- and heavy-duty vehicle (MHDV) tailpipe emissions account for ~22% of the area’s ambient NO₂ concentrations. Exposure to MHDV-tailpipe NO₂ in our domain is associated with 1330 (95% confidence interval (CI): 330, 2000) annual premature deaths and 1580 (95% CI: −310, 3870) new cases of pediatric asthma, disproportionately affecting census tracts with higher percentages of residents of color. Given the inequitable impacts of MHDV NO₂ exposure, we also use our model to assess the air quality, health, and equity outcomes if a policy scenario based on California’s Advanced Clean Trucks (ACT) regulation were instantaneously adopted in Illinois. We find that ACT adoption would lead to ~48% of on-road MHDVs having zero tailpipe emissions by 2050; an instantaneous transition to this policy would reduce annual mean population-weighted NO₂ concentrations by 0.98 ppb (parts per billion) (−8.4%), resulting in reductions of 500 (95% CI: −120, −750) premature deaths and 600 (95% CI: 120, −1440) fewer new pediatric asthma cases annually – with the largest health benefits observed in neighborhoods with higher percentages of residents of color. Our study highlights the benefits of implementing policy interventions focused on zero-emission MHDVs to address air pollution exposure and health impact disparities.