Squall line is a kind of common mesoscale disaster weather. At present, there are few studies on the elaborate detection of squall line by dual polarization radar. With the dual polarization upgrade of weather radar network, we need to study the relationship between squall line echoes of base data and polarization data to reveal new echo phenomena and formation mechanisms. The relationship between radar parameters and atmospheric physical processes also need to be examined. Based on the NUIST CDP radar, a squall line in the Yangtze and Huaihe River basin that occurred from July 30 to 31, 2014 is analyzed. The results show that polarization parameters have obvious advantages in the characteristics analysis of size, phase state, shape and orientation of the water condensate particles. The phase states of water condensate particles in convection cell can be distinguished through comparative discussion. Several phase states exist in the squall line, including small, medium and large raindrops, melting hails, dry hails and ice crystal particles and the ZDR column can be used to identify the location of the main updraft. In addition, the polarization parameters are more sensitive to the melting layer. The gust front is presented as a narrow linear echo in Z affected by strong turbulence. It is an obvious velocity convergence line in V and approximately 0.70 in rHV. The ZDR can be used as a criterion to distinguish the horizontal and vertical scale of turbulence. The deforming turbulence, which is affected by environmental airflow, will cause an abnormally high ZDR in the gust front and a negative ZDR before and after the gust front. The variation of ZDR depends on the turbulence arrangement, orientation and relative position between turbulence and radar. These dual polarization parameter characteristics offer insights into understanding the structure and evolution of the squall line.
In part I, the clear air echo in front of the squall line is caused by turbulence diffraction, which makes the ZDR echo characteristics different from particle scattering. To study the turbulence deformation phenomenon that is affected by environmental wind, the turbulence-related method is used to analyze the characteristics of three-dimensional turbulence energy spectrum density, and the parametric model of turbulence integral length scale and environmental wind speed is established. The results show that the horizontal scale of turbulence is generally larger than the vertical scale. The turbulence is nearly isotropic in the horizontal direction, presenting a flat ellipsoid with the vertical orientation of the rotation axis when there is no horizontal wind or the horizontal velocity is small. When horizontal wind exists, the turbulence scale increases along the dominant wind direction. The turbulence scale is positively correlated with the wind speed. The power function is used to fit the relationships of turbulence integral length scale and horizontal wind speed, which obtains the best fitting effect, and the goodness of fit (GF) is above 0.99 in each direction. The deforming turbulence can cause 8–9 dB ZDR anomalies in the echo of dual polarization radar, which the ratio of scales in the dominant wind and the vertical direction of deforming turbulence (Lu/Lw) is around 4.3. The variation in ZDR depends on the turbulence shape, orientation and the relative position between turbulence and radar. The shape of turbulence derived from radar detection results is consistent with that of the parametric model, which can provide a parametric scheme for turbulence research. The results reveal the mechanism of abnormal ZDR echo caused by deforming turbulence.
A rain-type adaptive pyramid Kanade–Lucas–Tomasi (A-PKLT) optical flow method for radar echo extrapolation is proposed. This method introduces a rain-type classification algorithm that can classify radar echoes into six types: convective, stratiform, surrounding convective, isolated convective core, isolated convective fringe, and weak echoes. Then, new schemes are designed to optimize specific parameters of the PKLT optical flow based on the rain type of the echo. At the same time, the gradients of radar reflectivity in the fringe positions corresponding to all types of rain echoes are increased. As a result, corner points that are characteristic points used for PKLT optical flow tracking in the surrounding area will be increased. Therefore, more motion vectors are purposefully obtained in the whole radar echo area. This helps to describe the motion characteristics of the precipitation more precisely. Then, the motion vectors corresponding to each type of rain echo are merged, and a denser motion vector field is generated by an interpolation algorithm on the basis of merged motion vectors. Finally, the dense motion vectors are used to extrapolate rain echoes into 0–60-min nowcasts by a semi-Lagrangian scheme. Compared with other nowcasting methods for four landfalling typhoons in or near Shanghai, the new optical flow method is found to be more accurate than the traditional cross-correlation and optical flow methods, particularly showing a clear improvement in the nowcasting of convective echoes on the spiral rainbands of typhoons.
Spatial characteristic is an important indicator of remote sensor performance, and space-borne infrared hyperspectral sounder is the frontier of atmospheric vertical sounding technology. In this paper, the formation mechanism of the vertical spatial characteristics involved in the space-borne infrared hyperspectral sounding data are analyzed in detail, which shows that the vertical spatial characteristics of sounding data depends not only on the spectral channels and their waveband coverage, but also the specific atmospheric parameter and its specific variation interested. The indicators of vertical spatial characteristics are defined and their mathematical models are established based on the mechanism analyses. These models are applied to the vertical spatial characteristic evaluation of atmospheric temperature sounding for FY-4A GIIRS, which is the first space-borne infrared hyperspectral atmospheric sounder in geostationary orbit. It is concluded that FY-4A GIIRS can sound the vertical temperature distribution in whole troposphere and lower stratosphere with height<35 km. This study can provide basic information to support the improvement of infrared hyperspectral sounder and the trace of vertical spatial characteristics of atmospheric inversion products.
With the convection-permitting simulation of Super Typhoon Mangkhut (2018) with a 3 km resolution for 10.5 days using mesoscale numerical model, Weather Research and Forecasting Model Version 4.1 (WRFV4.1), the influences of local closure QNSE planetary boundary layer (PBL) scheme and non-local closure GFS planetary boundary layer scheme on super typhoon Mangkhut are mainly discussed. It is found that in terms of either track or intensity of typhoon, the local closure QNSE scheme is better than the non-local closure GFS scheme. Local and non-local closure PBL schemes have a large influence on both the intensity and the structure of typhoon. The maximum intensity difference of the simulated typhoon is 50 hPa. The intensity of typhoon is closely related to its variations in structure. In the rapid intensification stage, the typhoon simulated by the QNSE scheme has a larger friction velocity, stronger surface latent heat flux, sensible heat flux and vapor flux, related to a higher boundary height and stronger vertical mixing. The latent heat flux and sensible heat flux on the surface conveyed energy upward for the typhoon while the water vapor was transported upward through vertical mixing. While the water vapor condensed, the latent heat was released, which further warmed the typhoon eyewall, strengthening the convection. The stronger winds also intensified the vertical mixing and the warm-core structure, further strengthened the typhoon. The differences in surface layer schemes dominated the differences between the two simulations.
Previous studies on typhoon disaster risk zoning in China have focused on individual provinces or small-scale areas and lack county-level results. In this study, typhoon disaster risk zoning is conducted for China’s coastal area, based on data at the county level. Using precipitation and wind data for China and typhoon disaster and social data at the county level for China’s coastal area from 2004 to 2013, first we analyze the characteristics of typhoon disasters in China’s coastal area and then develop an intensity index of factors causing typhoon disasters and a comprehensive social vulnerability index. Finally, by combining the two indices, we obtain a comprehensive risk index for typhoon disasters and conduct risk zoning. The results show that the maximum intensity areas are mainly the most coastal areas of both Zhejiang and Guangdong, and parts of Hainan Island, which is similar to the distribution of typhoon disasters. The maximum values of vulnerability in the northwest of Guangxi, parts of Fujian coastal areas and parts of the Shandong Peninsula. The comprehensive risk index generally decreases from coastal areas to inland areas. The high-risk areas are mainly distributed over Hainan Island, south-western Guangdong, most coastal Zhejiang, the coastal areas between Zhejiang and Fujian and parts of the Shandong Peninsula.
Forecasting the intensity of typhoons is a difficult problem in numerical weather prediction. It is subject to many factors, among which the selection of physical parameterization schemes for the model is a hot topic of research. In this study, the effects of horizontal mixing length (represented by h_diff) and planetary boundary layer (PBL) schemes were investigated. Six idealized and four operational sensitivity experiments were designed based on simulation of the typhoon Lingling, which occurred over the western Pacific in 2019, using the Hurricane Weather Research and Forecasting model. The results of the idealized experiments showed that, as h_diff was increased, the slope of the typhoon eye area also increased, and its intensity became stronger. On the other hand, the results of the sensitivity experiments indicated that the intensity of the simulated typhoon was sensitive to the choice of PBL scheme, with the forecast bias of the QNSE (Quasi-Normal Scale Elimination) scheme being smaller than that of the GFDL (Geophysical Fluid Dynamics Laboratory) scheme. Angular momentum budget analyses indicated that, when increasing the h_diff, the convergence of angular momentum was larger in the boundary layer, which led to a faster spin-up of the vortex, further increasing the intensity of the typhoon. From the calculated horizontal and vertical vortex spread it was found that, when the h_diff was increased, the corresponding horizontal and vertical diffusion eddies also showed an increasing trend, which was also the reason for the strengthening of the typhoon. Meanwhile, the forecast bias decreased significantly with increasing horizontal mixing length when using the same PBL scheme.
Global maps of bioclimatic variables currently exist only at very coarse spatial resolution (e.g. WorldClim). For ecological studies requiring higher resolved information, this spatial resolution is often insufficient. The aim of this study is to estimate important bioclimatic variables of Mongolia from Earth Observation (EO) data at a higher spatial resolution of 1 km. The analysis used two different satellite time series data sets: land surface temperature (LST) from Moderate Resolution Imaging Spectroradiometer (MODIS), and precipitation (P) from Climate Hazards Group Infrared Precipitation with Stations (CHIRPS). Monthly maximum, mean, and minimum air temperature were estimated from Terra MODIS satellite (collection 6) LST time series product using the random forest (RF) regression model. Monthly total precipitation data were obtained from CHIRPS version 2.0. Based on this primary data, spatial maps of 19 bioclimatic variables at a spatial resolution of 1 km were generated, representing the period 2002–2017. We tested the relationship between estimated bioclimatic variables (SatClim) and WorldClim bioclimatic variables version 2.0 (WorldClim) using determination coefficient (R2), root mean square error (RMSE), and normalized root mean square error (nRMSE) and found overall good agreement. Among the set of 19 WorldClim bioclimatic variables, 17 were estimated with a coefficient of determination (R2) higher than 0.7 and normalized RMSE (nRMSE) lower than 8%, confirming that the spatial pattern and value ranges can be retrieved from satellite data with much higher spatial resolution compared to WorldClim. Only the two bioclimatic variables related to temperature extremes (i.e., annual mean diurnal range and isothermality) were modeled with only moderate accuracy (R2 of about 0.4 with nRMSE of about 11%). Generally, precipitation-related bioclimatic variables were closer correlated with WorldClim compared to temperature-related bioclimatic variables. The overall success of the modeling was attributed to the fact that satellite-derived data are well suited to generated spatial fields of precipitation and temperature variables, especially at high altitudes and high latitudes. As a consequence of the successful retrieval of the bioclimatic variables at 1 km spatial resolution, we are confident that the estimated 19 bioclimatic variables will be very useful for a range of applications, including species distribution modeling.
The landscape index is a quantitative index which reflects characteristics of structure composition and spatial pattern in landscape studies, it is, therefore, expected to describe the spatial pattern of scientific research in bibliometric analysis. In this study, a novel attempt to regard scientific research as a kind of ‘landscape’ was made, and landscape indices were improved for bibliometric analysis to measure the spatial pattern of scientific research. For illustrating the feasibility of our method, global geoscience research from 1994 to 2018 was presented as a case. Moreover, spatiotemporal migration of landscape centroids was visualized. The results indicated that global geoscience publications increased steadily and articles were highly concentrated at the country level. The top 10 countries published 69.93% of total articles and 84.68% of geoscience articles were from top 20 productive countries. The spatial migration of centroids was mainly reflected in the longitude because of significant increasing of articles in eastern countries, especially in China with the growth rate of 747.14%. At the patch scale, the change trend of improved landscape indices verified the spatiotemporal changes of global distribution of geoscience articles. At the landscape scale, the strengthening of global international collaboration is the main driving forces of spatial heterogeneity of global geoscience research. This study is expected to help readers to understand global trends of geoscience research in the past 25 years, and to promote the development of bibliometric analysis towards the directions of spatialization and visualization.
Landforms are an important element of natural geographical environment, and textures are the research basis for the spatial differentiation, evolution features, and analysis rules of the landform. Using the regional difference of texture to describe the spatial distribution pattern of macro landform features is helpful to the landform classification and identification. Digital elevation model (DEM) image texture, which gives full expression to texture difference, is key data source to reflect the surface features and landform classification. Following the texture analysis, landform features analysis is assistant to different landforms classification, even in landform boundary. With the increasing accuracy requirement of landform information acquisition in geomorphic thematic mapping, hierarchical landform classification has become the focus and difficulty in research. Recently, the pattern recognition represented by Convolutional Neural Network has made great achievements in landform research, whose multichannel feature fusion structure satisfies the network structure of different landform classification. In this paper, DEM image texture was taken as the data source, and gray level co-occurrence matrix was applied to extract texture measures. Owing to the similarity of similar landform and the difference of different landform in a certain scale, a comprehensive texture factor reflecting landform features was proposed, and the spatial distribution pattern of landform features was systematically analyzed. On this basis, the coupling relationship between texture and landform type was explored. Thus, the deep learning method of Convolutional Neural Network is used to train the texture features, and the second-class landform classification is carried out through softmax. The classification results in small relief and mid-relief low mountains, overall accuracy are 84.35% and 69.95% respectively, while kappa coefficient are 0.72 and 0.40 respectively, were compared to that of traditional unsupervised landform classification results, and the superiority of Convolutional Neural Network classification was verified, it approximately improved 6% in overall accuracy and 0.4 in kappa coefficient.
The annual peak growth and trend shift of vegetation are critical in characterizing the carbon sequestration capacity of ecosystems. As the well-known area with the fastest vegetation growth in the world, the Loess Plateau (LP) lands find an enhanced greening trend in the annual and growing-season. However, the spatiotemporal dynamics of vegetation peak growth and breakpoints characteristics on time series still needs to be explored. Here, we performed tendency analysis to characterize recent variations in annual peak vegetation growth through a satellite-derived vegetation index (NDVImax, Maximum Normalized Difference Vegetation Index) and then applied breakpoint analysis to capture abrupt points on the time series. The results demonstrated that the vegetation peak trend had been significantly increasing, with a growth rate at 0.68×10–2·a–1 during 2001–2018, and most pixels (70.81%) have a positive linear greening trend over the entire LP. In addition, about 83% of the breakpoint type on the monthly NDVI time series is a monotonic increase at the pixel level, and most pixels (57%) have detected breakpoints after 2010. Our results also showed that the growth rate accelerates in the northwest and decelerates in the southeast after the breakpoint. This study indicates that combining abrupt analysis with gradual analysis can describe vegetation dynamics more effectively and comprehensively. The findings highlighted the importance of breakpoint analysis for monitor timing and shift using time series satellite data at a regional scale, which may help stakeholders to make reasonable and effective ecosystem management policies.
Long-term rainfall data are crucial for flood simulations and forecasting in karst regions. However, in karst areas, there is often a lack of suitable precipitation data available to build distributed hydrological models to forecast karst floods. Quantitative precipitation forecasts (QPFs) and estimates (QPEs) could provide rational methods to acquire the available precipitation data for karst areas. Furthermore, coupling a physically based hydrological model with QPFs and QPEs could greatly enhance the performance and extend the lead time of flood forecasting in karst areas. This study served two main purposes. One purpose was to compare the performance of the Weather Research and Forecasting (WRF) QPFs with that of the Precipitation Estimations through Remotely Sensed Information based on the Artificial Neural Network-Cloud Classification System (PERSIANN-CCS) QPEs in rainfall forecasting in karst river basins. The other purpose was to test the feasibility and effective application of karst flood simulation and forecasting by coupling the WRF and PERSIANN models with the Karst-Liuxihe model. The rainfall forecasting results showed that the precipitation distributions of the 2 weather models were very similar to the observed rainfall results. However, the precipitation amounts forecasted by WRF QPF were larger than those measured by the rain gauges, while the quantities forecasted by the PERSIANN-CCS QPEs were smaller. A postprocessing algorithm was proposed in this paper to correct the rainfall estimates produced by the two weather models. The flood simulations achieved based on the postprocessed WRF QPF and PERSIANN-CCS QPEs coupled with the Karst-Liuxihe model were much improved over previous results. In particular, coupling the postprocessed WRF QPF with the Karst-Liuxihe model could greatly extend the lead time of flood forecasting, and a maximum lead time of 96 h is adequate for flood warnings and emergency responses, which is extremely important in flood simulations and forecasting.
This study uses geographically weighted regression to determine the spatial distribution of the effective utilization coefficient of irrigation water in Zhejiang Province, China, owing to the influences of spatial attributes on the irrigation efficiency. The sample set of this study comprised 165 agricultural test sites. A multivariate linear regression model and a geographically weighted regression model were established using the effective utilization coefficient of agricultural irrigation water as the dependent variable in addition to a suite of independent variables, including the actual irrigation area, the percentage of farmland using water-saving irrigation, the type of irrigation area, the net water consumption per mu, the water intake method, the terrain slope, and the soil field capacity. Results revealed a positive spatial correlation and noticeable agglomeration features in the effective utilization coefficient of irrigation water in Zhejiang Province. The geographically weighted regression model performed better in terms of fit and prediction accuracy than the multivariate linear regression model. The obtained findings confirm the suitability of the geographically weighted regression model for determining the spatial distribution of the effective utilization coefficient of irrigation water in Zhejiang, and offer a new approach on a regional scale.
Four types of granite specimens were prepared and treated by chemical corrosion for 5 and 30 days, which were then used to carry out triaxial compression tests under different confining pressures σ3. Type A is the intact sample with no preexisting flaws. Types B and C are the samples containing two relatively low-dip flaws and two relatively high-dip flaws, respectively. Type D is the sample including both relatively low-dip and relatively high-dip flaws. The influences of pH value of chemical solutions, flaw distribution, corrosion time and σ3 on triaxial stress-strain curves and ultimate failure modes are analyzed and discussed. The results show that the pH value of the chemical solution, corrosion time and the arrangement of preexisting flaws play crucial roles in the cracking behaviors of granite specimens. Type A specimens have the largest peak axial deviatoric stress, followed by Type C, Type D, and Type B specimens, respectively. It is because the decrease in the inclination of preexisting flaws induces the weakening effect due to the decrease in the shadow area along the compaction direction. Under a σ3 of 5 MPa, the peak axial deviatoric stress drops by approximately 40.89%, 29.08%, 4.08%, and 23.53% for pH = 2, 4, 7, and 12, respectively. For intact granite (Type A) specimens, the ultimate failure mode displays a typical shear mode. The connection of two secondary cracks initiated at the tips of preexisting cracks is always the ultimate failure and crack coalescence mode for Type B specimens. The ultimate failure and crack coalescence mode of Types C and D specimens are significantly affected by pH value of the chemical solution, corrosion time and σ3, which is different from those of Types A and B specimens due to the differences in flow distributions.
The Middle Cretaceous Mishrif Formation of the Cenomanian–Turonian age is an important Middle Eastern reservoir. Previous studies have shown that carbonates in the Mishrif Formation are affected by karstification and are concentrated at the top of the formation. However, there is a lack of systematic research on the effects of karstification on the formation’s reservoir characteristics. Based on core samples, scanning electron microscopy of thin sections, and geochemical analysis of C, O, and Sr isotopes from wells in the Missan Oil Fields, unconformity was identified at the top of the B21 subzone of the Mishrif Formation. Core and cast-thin-section observations indicate that there is a significant lithological difference above and below the unconformity, and dissolution pores and voids are well-developed beneath it. Vadose silt fills the intragranular pores and geopetal fabric is developed inside some mouldic pores beneath the unconformity surface. The isotopic values of 87Sr/86Sr (0.707270–0.707722) and d18O (−5.94‰—−2.32‰) suggest that the top of the B21 subzone of the Mishrif Formation has been severely affected by syngenetic karst. Karstification is the key to high-quality reservoir formation and the reservoir’s oil-bearing heterogeneity. This study will inform oil and gas exploration in the Missan Oil Fields and in other areas with similar reservoir characteristics.
A varied class method is applied to calculate the effective discharges and their variations after the Three Gorges Dam (TGD) construction based on the mean daily flow discharge and suspended sediment concentration field data from 1981 to 2016. For comparison, the bankfull discharges are also determined according to the cross-section profiles and flow discharge-stage relations. Our results show that a bimodal effective discharge curve usually exists at the fixed sites, which generates two effective discharges (Qe1 and Qe2) within the moderate flow range. Under the quasi-equilibrium circumstances of the pre-dam period, effective discharges are closely related to the mean annual runoff, with a narrow range of regional variations in occurrence frequency. Our analyses draw the conclusion that the relatively higher unsaturation degrees of the pre-dam effective discharges caused by dam interception and riverbed coarsening are the primary cause of the increase in effective discharges from Yichang to Shashi, while the more frequent low and medium discharges due to flow regulation drive the decrease in effective discharges from Jianli to Datong. The slightly elevated flood levels and descending bankfull levels collaboratively result in the decrease of bankfull discharges from Yichang to Shashi, while the lowered bed elevation causes the increase in bankfull discharges from Luoshan to Datong. Overall, the bankfull discharge in the Middle and Lower Yangtze River is larger than effective discharge and approaches the 1.5- year recurrence interval discharge.
The stability of dawsonite is an important factor affecting the feasibility evaluation of CO2 geological storage. In this paper, a series of experiments on the interaction of CO2-water-dawsonite-bearing sandstone were carried out under different CO2 pressures. Considering the dissolution morphology and element composition of dawsonite after the experiment and the fluid evolution in equilibrium with dawsonite, the corrosion mechanism of dawsonite led by CO2 partial pressure was discussed. The CO2 fugacity of the vapor phase in the system was calculated using the Peng–Robinson equation of state combined with the van der Waals 1-fluid mixing rule. The experimental results indicated that the thermodynamic stability of dawsonite increased with the increase of CO2 partial pressure and decreased with the increase of temperature. The temperature at which dawsonite dissolution occurred was higher at higher
Fracture system is an important factor controlling tight oil accumulation in the Triassic Chang 8 Member, southwestern Ordos Basin, China. A systematic characterization of the multi-scale natural fractures is a basis for the efficient tight oil production. Based on outcrops, seismic reflections, well cores, well logs (image and conventional logging), casting thin sections, and scanning electron microscope observation, the multi-scale fractures occurrences and their influences on Chang 8 tight sandstone reservoirs are revealed. The results show that three periods of strike-slip faults and four scales of natural fractures developed, namely mega-scale (length > 7 × 10 7 mm), macro-scale (3.5 × 105 < length < 7 × 10 7 mm), meso-scale (10 < length < 3.5 × 10 5 mm), and micro-scale (length < 10 mm) fractures. The mega- and macro-scale fractures developed by strike-slip faults are characterized by strike-segmentation and lateral zonation, which connect the source and reservoir. These scale fractures also influence the distribution and effectiveness of traps and reservoirs, which directly influence the hydrocarbon charging and distribution. The meso fractures include the tectonic, diagenetic, as well as hydrocarbon generation-related overpressure types. The meso- and micro-scale fractures improve the sandstone physical properties and also the tight oil well production performance. This integrated study helps to understand the distribution of multi-scale fractures in tight sandstones and provides a referable case and workflow for multi-scale fracture evaluation.
The establishment of the National Key Ecological Function Areas (NKEFAs) is an important measure for national ecological security, but the current ecological and environmental evaluation of NKEFAs lacks research on the air quality in the NKEFAs. This study presented the current status of the air quality in the NKEFAs and its driving factors using the geographic detector q-statistic method. The air quality in the NKEFAs was overall better than individual cities and urban agglomeration in eastern coast provinces of China, accounting for 9.21% of the days with air quality at Level III or above. The primary air pollutant was PM10, followed by PM2.5, with lower concentrations of the remaining pollutants. Pollution was more severe in the sand fixation areas, where air pollution was worst in spring and best in autumn, contrasting with other NKEFAs and individual cities and urban agglomerations. The main influencing factors of air quality index (AQI) in the NKEFAs were land use type, wind speed, and relative humidity also weighted more heavily than factors such as industrial pollution and anthropogenic emissions, and most of these influence factors have two types of interactive effects: binary and nonlinear enhancements. These results indicated that air pollution in the NKEFAs was not related with the emission by intensive economic development. Thus, the policies taking the NKEFAs as restricted development zones were effective, but the air pollution caused by PM10 also showed the ecological status in the NKEFAs, especially at sand fixation areas was not quite optimistic, and more strict environmental protection measures should be taken to improve the ecological status in these NKEFAs.
Over the past 2000 years, a high-resolution pollen record from the Yushenkule Peat (46°45′–46°57′N, 90°46′–90°61′E, 2374 m a.s.l.) in the south-eastern Altai Mountains of northwestern China has been used to explore the changes in vegetation and climate. The regional vegetation has been dominated by alpine meadows revealed from pollen diagrams over the past 2000 years. The pollen-based climate was warm and wet during the Roman Warm Period (0–520 AD), cold and wet during the Dark Age Cold Period (520–900 AD), warm and wet during the Medieval Warm Period (900–1300 AD), and cold and dry during the Little Ice Age (1300–1850 AD). Combined with other pollen data from the Altai Mountains, we found that the percentage of arboreal pollen showed a reduced trend along the NW-SE gradient with decreasing moisture and increasing climatic continentality of the Altai Mountains over the past 2000 years; this is consistent with modern distributions of taiga forests. We also found that the taiga (Pinus forest) have spread slightly, while the steppe (Artemisia, Poaceae and Chenopodiaceae) have recovered significantly in the Altai Mountains over the past 2000 years. In addition, the relatively warm-wet climate may promote high grassland productivity and southward expansion of steppe, which favors the formation of Mongol political and military power.
Three-dimensional (3D) reconstruction of the equivalent pore network model (PNM) using X-ray computed tomography (CT) data are of significance for studying the CO2-enhanced coalbed methane recovery (CO2-ECBM). The docking among X-ray CT technology, MATLAB, with COMSOL software not only can realize the 3D reconstruction of PNM, but also the CO2-ECBM process simulation. The results show that the Median filtering algorithm enabled the de-noising of the original 2D CT slices, the image segmentation of all slices was realized based on the selected threshold, and the PNM can be constructed based on the Maximum Sphere algorithm. The mathematical model of CO2-ECBM process fully coupled the expanded Langmuir equation. At the same time for CO2 injection, CH4 pressure tends to decrease with the increase of CO2 pressure, but its difference is not obvious. The CH4 pressure in the slice center changed a lot, while at the edge it changed a little under different CO2 pressures. The injected CO2 was transported to matrix along the macro and micro-fractures with continuous flow. The injected CO2 first replaced the adsorbed CH4 by covering the inner surface of macro-pores and meso-pores to form the single molecular layer adsorption of CO2. Then they migrated to micro-pores by Fick’s diffusion, sliding flow, and surface diffusion. Furthermore, the CO2 replaced CH4 adsorbed by volumetric filling in micro-pores, and formed the multi-molecular layer adsorption of CO2. The gas pressure and migration path between CO2 and CH4 are opposite. This study can provide a theoretical basis for studying digital rock physics technology and enrich the development of CO2-ECBM technology.
