A heavy rainstorm occurred in Henan Province, China, between 19 and 21 July, 2021, with a record-breaking 201.9 mm of precipitation in 1 h. To explore the key factors that led to forecasting errors for this extreme rainstorm, as well as the dominant contributor affecting its predictability, we employed the Global/Regional Assimilation and Prediction System-Regional Ensemble Prediction System (GRAPES-REPS) to investigate the impact of the upper tropospheric cold vortex, middle-low vortex, and low-level jet on predictability and forecasting errors. The results showed that heavy rainfall was influenced by the following stable atmospheric circulation systems: subtropical highs, continental highs, and Typhoon In-Fa. Severe convection was caused by abundant water vapor, orographic uplift, and mesoscale vortices. Multiscale weather systems contributed to maintaining extreme rainfall in Henan for a long duration. The prediction ability of the optimal member of GRAPES-REPS was attributed to effective prediction of the intensity and evolution characteristics of the upper tropospheric cold vortex, middle-low vortex, and low-level jet. Conversely, the prediction deviation of unstable and dynamic conditions in the lower level of the worst member led to a decline in the forecast quality of rainfall intensity and its rainfall area. This indicates that heavy rainfall was strongly related to the short-wave throughput, upper tropospheric cold vortex, vortex, and boundary layer jet. Moreover, we observed severe uncertainty in GRAPES-REPS forecasts for rainfall caused by strong convection, whereas the predictability of rainfall caused by topography was high. Compared with the European Centre for Medium-Range Weather Forecasts Ensemble Prediction System, GRAPES-REPS exhibits a better forecast ability for heavy rainfall, with some ensemble members able to better predict extreme precipitation.

An improved evaluation method for estimating gas content during the inversion process of deep-burial coal was established based on the on-site natural desorption curves. The accuracy of the US Bureau of Mines (USBM), Polynomial fitting, Amoco, and the improved evaluation methods in the predicting of lost gas volume in deep seams in the Mabidong Block of the Qinshui Basin were then compared. Furthermore, the calculation errors of these different methods in simulating lost gas content based on coring time were compared. A newly established nonlinear equation was developed to estimate the minimum error value, by controlling the lost time within 16 min, the related errors can be reduced. The improved evaluation was shown to accurately and rapidly predict the gas content in deep seams. The results show that the deep coal bed methane accumulation is influenced by various factors, including geological structure, hydrodynamic conditions, roof lithology, and coalification. Reverse faults and weak groundwater runoff can hinder the escape of methane, and these factors should be considered in the future exploration and development of coalbed methane.

Turkey is located in the Alpine-Himalayan seismic zone. The Anatolian plate has witnessed very severe and destructive earthquakes both in the past and today. In this study, statistical analyses of earthquakes that occurred between 1914 and 2019 along the Fethiye-Burdur fault zone, which is an active line, were conducted using geographic information systems. Analyses of standard distance, standard deviational eclipse, mean center, and median center were conducted to determine the geographic distributions of epicenters with a magnitude value of 3.5 and above. Quadrat and Average Nearest Neighbor analyses were used to reveal the spatial pattern. Anselin Local Moran I and Getis Ord Gi* method were used to determining where the earthquake epicenters are clustered locally. Kernel Density analyses were conducted to measure earthquake epicenters’ density. Quadrat analysis, Average Nearest Neighbor, Global Moran’s I, and Getis - Ord General G indices demonstrated that earthquakes are clustered in certain regions and are related to each other positionally. Anselin Moran’s I regional analyses revealed that high values were clustered in the West of Burdur city center and the district of Yeşilova, and similar results were obtained in the Getis Ord Gi* method.

It is crucial to investigate the characteristics of fire danger in the areas around Beijing to increase the accuracy of fire danger monitoring, forecasting, and management. Using meteorological data from 17 national meteorological stations in the areas around Beijing from 1981−2021, this study calculated the fire weather index (FWI) and analyzed its spatiotemporal characteristics. It was found that the high and low fire danger periods were in April−May and July−August, with spatial patterns of “decrease in the northwest−increase in the southeast” and a significant increase throughout the areas around Beijing, respectively. Next, the contributions of different meteorological factors were quantified by the multiple regression method. We found that during the high fire danger period, the northern and southern parts were affected by precipitation and minimum relative humidity, respectively. However, most areas were influenced by wind speed during the low fire danger period. Finally, comparing with the FWI characteristics under different SSP scenarios, we found that the FWI decreased during high fire danger period and increased during low fire danger period under different SSP scenarios (i.e., SSP245, SSP585) for periods of 2021−2050, 2071−2100, 2021−2100, except for SSP245 in 2071−2100 with an increasing trend both in high and low fire danger periods. This study implies that there is a higher probability of FWI in the low fire danger period, threatening the ecological environment and human health. Therefore, it is necessary to enhance research on fire danger during the low fire danger period to improve the ability to predict summer fire danger.

A new digital elevation model (DEM) upscaling method based on high accuracy surface modeling (HASM) is proposed by combining the elevation information of DEM and the valley lines extracted from DEM with different flow accumulation thresholds. The proposed method has several advantages over traditional DEM upscaling methods. First, the HASM ensures the smoothness of the upscaled DEM. Secondly, several DEMs with different topographic details can be obtained using the same DEM grid size by incorporating the valley lines with different flow accumulation thresholds. The Jiuyuangou watershed in China’s Loess Plateau was used as a case study. A DEM with a grid size of 5 m obtained from the local surveying and mapping department was used to verify the proposed DEM upscaling method. We established the surface complexity index to describe the complexity of the topographic surface and quantified the differences in the topographic features obtained from different upscaling results. The results show that topography becomes more generalized as grid size and flow accumulation threshold increase. At a large DEM grid size, an increase in the flow accumulation threshold increases the difference in elevation values in different grids, increasing the surface complexity index. This study provides a new DEM upscaling method suitable for quantifying topography.

Remote sensing image scene classification and remote sensing technology applications are hot research topics. Although CNN-based models have reached high average accuracy, some classes are still misclassified, such as “freeway,” “spare residential,” and “commercial_area.” These classes contain typical decisive features, spatial-relation features, and mixed decisive and spatial-relation features, which limit high-quality image scene classification. To address this issue, this paper proposes a Grad-CAM and capsule network hybrid method for image scene classification. The Grad-CAM and capsule network structures have the potential to recognize decisive features and spatial-relation features, respectively. By using a pre-trained model, hybrid structure, and structure adjustment, the proposed model can recognize both decisive and spatial-relation features. A group of experiments is designed on three popular data sets with increasing classification difficulties. In the most advanced experiment, 92.67% average accuracy is achieved. Specifically, 83%, 75%, and 86% accuracies are obtained in the classes of “church,” “palace,” and “commercial_area,” respectively. This research demonstrates that the hybrid structure can effectively improve performance by considering both decisive and spatial-relation features. Therefore, Grad-CAM-CapsNet is a promising and powerful structure for image scene classification.

The Permian Lucaogou Formation represents one of the most important hydrocarbon source rock intervals in the Junggar Basin, although the sedimentary paleoenvironment and organic matter enrichment mechanism of the Lucaogou Formation remain controversial. We studied the temporal evolution of the sedimentary paleoenvironment in the Lucaogou Formation by analyzing the elemental composition and total organic carbon content of 27 hydrocarbon source rock samples from the J305 well in the Jimsar Sag. Using these data, we found that the Lucaogou Formation overall was deposited in a semisaline to saline, reducing lake basin under an arid climate. We identified five organic matter-enriched intervals, which can be correlated with the parameters that indicate a wetter climate and a more anoxic lake environment. To compare sedimentary environments spatially, we compiled environmental indicators from 10 cores and outcrops in three sags around the Bogda Mountains. The compilation shows that the organic matter-enriched Jimsar Sag experienced a more arid climate and a more saline and anoxic lake environment during the deposition of the Lucaogou Formation, which was possibly controlled by the paleogeographic position. We conclude that the spatially arid climate and anoxic environment induced organic matter burial in the Jimsar Sag, while temporal events of a more humid climate and more anoxic environment triggered the enrichment of organic matter in some intervals of the Lucaogou Formation.

Soil organic carbon (SOC) is a critical variable used to determine the carbon balance. However, large uncertainties arise when predicting the SOC stock in soil profiles in Chinese grasslands, especially on desert rangelands. Recent studies have shown that desert ecosystems may be potential carbon sinks under global climate change. Because of the high spatial heterogeneity, time-consuming sampling methods, and difficult acquisition process, the relationships the SOC storage and distribution have with driving factors in desert rangelands remain poorly understood. Here, we investigated and developed an SOC database from 3162 soil samples (collected at depths of 0−10 cm and 10−20 cm) across 527 sites, as well as the climate conditions, vegetation types, and edaphic factors associated with the sampling sites in the desert rangelands of northern Xinjiang, north-west China. This study aims to determine the SOC magnitude and drivers in desert rangelands. Our findings demonstrate that the SOC and SOC density (SOCD) were 0.05−37.13 g·kg⁻¹ and 19.23−9740.62 g·m⁻², respectively, with average values of 6.81 ± 5.31 g·kg⁻¹ and 1670.38 ± 1202.52 g·m⁻², respectively. The spatial distributions of SOC and SOCD all showed gradually decreasing trends from south-west to north-east. High-SOC areas were mainly distributed in the piedmont lowlands of the Ili valley, while low-SOC regions were mainly concentrated in the north-west area of Altay. The redundancy analysis results revealed that all environmental factors accounted for approximately 37.6% of the spatial variability in SOC; climate factors, vegetation factors, and soil properties explained 15.0%, 1.7%, and 12.3%, respectively. The structural equation model (SEM) further indicated that evapotranspiration, average annual precipitation, and the SWC were the dominant factors affecting SOC accumulation, mainly through direct effects, although indirect effects were also delivered by the vegetation factors. Taken together, the results obtained herein updated the SOC data pool available for desert rangelands and clarified the main driving factors of SOC variations. This study provided supporting data for the sustainable use and management of desert rangelands and the global ecosystem carbon budget.

After the construction of cascade reservoirs in the upper reaches of the Three Gorges Reservoir (TGR), the sediment load outflow of the upper Yangtze River Basin (YRB) has been significantly altered, decreasing from 491.8 Mt/yr (1956–2002) to 36.1 Mt/yr (2003–2017) at Yichang station. This has widely affected river hydrology, suspended sediment grain size distribution, and channel morphology. This study analyzed hydrological variations in water discharge and sediment load of the upper YRB over the past 62 years (1956–2017) by employing a double mass curve. The variations in the source areas of sediment yielding for the upper YRB were quantified, and field measurement data of the cross-channel profile were collected to investigate the sedimentation process in the TGR from 2003 to 2017. More than 90% of the sediment load reduction in the upper YRB may be explained by human activities. The Jinshajiang River was no longer the largest sediment source area for the Zhutuo station (accounting for 5.23%) in the 2013–2017 time span, and the sediment rating rates for the inflow and outflow of the TGR shifted to negatively correlated. A longitudinal fining trend was revealed in the suspended sediment size. Still, the mean median grain size of suspended sediment in the TGR had an increasing trend in the 2013–2017 period. This result may be closely related to sediment regulation in reservoirs and incoming sediment load reduction. Sedimentation in the TGR decreased sharply from 299.8 Mt/yr in 2003–2012 to 47.2 Mt/yr in 2013–2017, but the sedimentation rate of the TGR remained at > 80% annually. Moreover, some cross sections in the fluctuating backwater zone experienced scouring.

This study aims to propose an empirical prediction model of hydraulic aperture of 2D rough fractures through numerical simulations by considering the influences of fracture length, average mechanical aperture, minimum mechanical aperture, joint roughness coefficient (JRC) and hydraulic gradient. We generate 600 numerical models using successive random additions (SRA) algorithm and for each model, seven hydraulic gradients spanning from 2.5 × 10⁻⁷ to 1 are considered to fully cover both linear and nonlinear flow regimes. As a result, a total of 4200 fluid flow cases are simulated, which can provide sufficient data for the prediction of hydraulic aperture. The results show that as the ratio of average mechanical aperture to fracture length increases from 0.01 to 0.2, the hydraulic aperture increases following logarithm functions. As the hydraulic gradient increases from 2.5 × 10⁻⁷ to 1, the hydraulic aperture decreases following logarithm functions. When a relatively low hydraulic gradient (i.e., 5 × 10⁻⁷) is applied between the inlet and the outlet boundaries, the streamlines are of parallel distribution within the fractures. However, when a relatively large hydraulic gradient (i.e., 0.5) is applied between the inlet and the outlet boundaries, the streamlines are disturbed and a number of eddies are formed. The hydraulic aperture predicted using the proposed empirical functions agree well with the calculated results and is more reliable than those available in the preceding literature. In practice, the hydraulic aperture can be calculated as a first-order estimation using the proposed prediction model when the associated parameters are given.