Typical Region Research Articles

Areal artificial recharge has changed the interactions between surface water and groundwater

Artificial groundwater recharge is increasingly utilized globally for addressing regional groundwater issues due to its effectiveness and ease of application. However, due to the complexity of the interaction of groundwater systems with the external environment and its inherent latency, governance effects are difficult to quantify and are not fully understood yet. In this study, the efficiency of areal artificial groundwater recharge was determined and the interactions with surface water were analyzed by field surveys, sample analysis, water balance reconstruction, and model simulation. A typical groundwater recharge region in the Beijing-Tianjin-Hebei region, China, was focused. Additionally, the response of groundwater flow and water quality to areal artificial groundwater recharge was quantified. Results showed that areal artificial groundwater recharge changes the relationship between surface water and groundwater, with an efficiency of 70 % under high recharge and fluctuation, and decreasing to 20–30 % after the groundwater level was stabilized. The impact on the hydrodynamic field were primarily manifested as the formation of water mounds near river channels, which expand laterally up to 9.2 km from the recharging river channel one year after implementation.Then the impact increases to 14.9 km and continues to expend a year after cessation. The areal artificial groundwater recharge has simultaneously improved the water quality by 23 % in monitoring wells within a 10 km distance from the river channel. The results of this study quantify the impacts of area artificial recharge on groundwater and provide references for the improvement of water resources and water environment in those similar regions.

Doping dependence of scattering rate for linear-in-temperature resistivity in cuprate superconductors

The origin of the linear-in-temperature (T-linear) resistivity in cuprate superconductors remains a profound mystery in condensed matter physics. Here, we investigate the dependence of the T-linear resistivity coefficient on doping, i.e., A1(p), for three typical regions in the temperature versus doping phase diagram of hole-doped cuprates, from which the doping dependence of the scattering rate, i.e., α1(p), is further derived. It is found that for region I (p<p* and T>T*), α1(p) is almost a constant; for region II (p>p* and T>Tcoh), α1(p)∝p; for region III (p>p* and T<Tcoh), α1(p)∝p(pc−p), where T* is the onset temperature of the pseudogap phase, p* indicates the doping at which T* goes to zero, Tcoh marks the onset of antinodal quasiparticle coherence, and pc is the doping where the low-temperature linear behavior in the overdoped regime vanishes. Moreover, the deduced α1(p) relations are verified with the experimental data from previous reports. The discovered scattering rate versus doping relationship will shed light on the scattering mechanism underlying the T-linear resistivity in cuprate superconductors. Published by the American Physical Society 2024

The carrying capacity for vegetation of forest land across China: Near real-time monitoring and short-term forecasting based on satellite observation

Ecological restoration projects implemented over the past 20 years have substantially increased forest coverage in China, but the high tree mortality of new afforestation forest remains a challenging but unsolved problem. It is still not clear how much vegetation can be sustained by the forest lands with given water, energy and soil conditions, i.e., the carrying capacity for vegetation (CCV) of forest lands, which is the prerequisite for planning and implementing forest restoration projects. Here, we used a simplified method to evaluate the CCV across forest lands nationwide. Specifically, based on leaf area index (LAI) dataset, we use boosted regression tree and multiple linear regression model to analyze the CCV during 2001–2020 and 2021–2030 and explore the contribution of environmental factors. We find that there are three typical regions with lower CCV located in the Loess Plateau and the southern region of the Inner Mongolia Plateau, the Hengduan Mountain region, and the Tianshan Mountains. More importantly, the vegetation in the regions near the dry-wet climate transition zone show excess local carrying capacity for vegetation over the past two decades and they are more susceptible to potential climatic stress. In comparison, in the Greater Khingan Mountains and Hengduan Mountains, there is high potential to improve the forest growth. Temperature, precipitation and soil affects the CCV by shaping the vegetation in the optimal range. This indicates that more consideration should be given to restrictions of regional environmental constraints when planning afforestation and forest management. This study has important implications for guiding future forest scheme in China.

Diversity of Bacterial Communities in Sediment in Inland Water Bodies in Relation to Environmental Factors and Human Impacts: A Case Study on Typical Regions in Vietnam

Environmental changes and human impact can alter biodiversity in negative manners that affect mankind’s sustainable development. Particularly, such effects on inland waters are even more concerning, as those ecosystems play essential roles in sustaining human life as well as relevant wildlife. Thus, in this study, we investigated such effects on microbial diversity in inland waters in Vietnam using bacterial communities in sediment as indicators. To do this, we collected sediment samples from various locations in three respective regions (Red river in the north, Ba river in the central area, and Mekong delta flood zone in the south) using standard methods, extracted their total DNA, sequenced their V3-V4 16S rRNA gene fragments using an Illumina Miseq platform and analyzed the sequences to infer the diversity of the bacterial communities in the samples. These communities were compared in terms of richness (alpha diversity) and composition (beta diversity), and the correlations between their diversity levels and environmental factors, as well as human activities, were analyzed by using standard statistical tools. Our results showed that the communities were different from each other solely by region, in richness and critically in composition, although there were some communities distinctively different from all the others. Among environmental factors, only water salinity (and conductivity) had negative correlations with alpha-diversity indices of the bacterial communities, and phosphate concentration and turbidity had positive correlations, while the other factors had almost no correlations, which partially explains the region-specific diversity. These results also suggest that climate change incidences, especially sea intrusion, can have significant effects on microbial diversity in inland waters. The impact of human activities did not appear severe, solely affecting bacterial community richness, but not significantly affecting bacterial community composition. However, apparently reduced bacterial diversity in several sites with intense human impacts and distinct environmental conditions should be noted and deserve further investigation.

Medial temporal lobe gray matter microstructure in preclinical Alzheimer's disease.

Typical MRI measures of neurodegeneration have limited sensitivity in early disease stages. Diffusion MRI (dMRI) microstructural measures may allow for detection in preclinical stages. Participants had dMRI and either beta-amyloid PET or plasma biomarkers of Alzheimer's pathology within 18 months of MRI. Microstructure was measured in portions of the medial temporal lobe (MTL) with high neurofibrillary tangle (NFT) burden based on a previously developed post mortem 3D-map. Regressions examined relationships between microstructure and markers of Alzheimer's pathology in preclinical disease and then across disease stages. There was higher isometric volume fraction in amyloid-positive compared to amyloid-negative cognitively unimpaired individuals in high tangle MTL regions. Similarly, plasma biomarkers and 18F-flortaucipir were associated with microstructural changes in preclinical disease. Additional microstructural effects were seen across disease stages. Combining a post mortem atlas of NFT pathology with microstructural measures allows for detection of neurodegeneration in preclinical Alzheimer's disease. Highlights Typical markers of neurodegeneration are not sensitive in preclinical Alzheimer's. dMRI measured microstructure in regions with high NFT. Microstructural changes occur in medial temporal regions in preclinical disease. Microstructural changes occur in other typical Alzheimer's regions in later stages. Combining post mortem pathology atlases with in vivo MRI is a powerful framework.

Energy performance of buildings with multi-ribbed composite wall structural system

Multifunctional multi-ribbed composite wall (MRCW) structural system has been increasingly used due to its dual load bearing and energy saving capacities. Most of existing studies were focused on its mechanical properties. This paper aims to evaluate its energy saving effect. To this end, two models are constructed using EnergyPlus for the same residential building in Beijing, China, but with different systems of MRCW and shear wall. The dynamic simulation of hourly energy consumption throughout one year is carried out at the building operation stage. The results show that the heating energy consumption is higher than the cooling energy consumption. The total energy consumption of the MRCW system can be reduced by 11.4 % compared with that of the shear wall system. Further, the annual energy consumption of the residential building models in five typical climate regions in China with different orientations is simulated. The results show that the energy saving of MRCW system range from 5 % to 13 % in the five regions when compared with shear wall system, and the absolute value of energy saving is the largest in severe cold and cold regions. The north-south orientation is the best energy-saving orientation. Based on this study, it can be concluded that, other than carrying load, MRCW system can also save energy compared with shear wall system, especially in severe cold and cold regions.

Calibration for Improving the Medium-Range Soil Temperature Forecast of a Semiarid Region over Tibet: A Case Study

The high complexity of the parameter–simulation problem in land surface models over semiarid areas makes it difficult to reasonably estimate the surface simulation conditions that are important for both weather and climate in different regions. In this study, using the dense site datasets of a typical semiarid region over Tibet and the Noah land surface model with the constrained land parameters of multiple sites, an enhanced Kling–Gupta efficiency criterion comprising multiple objectives, including variable and layer dimensions, was obtained, which was then applied to calibration schemes based on two global search algorithms (particle swarm optimization and shuffled complex evaluation) to investigate the site-scale spatial complexities in soil temperature simulations. The calibrations were then compared and further validated. The results show that the Noah land surface model obtained reasonable simulations of soil moisture against the observations with fine consistency, but the negative fit and huge spatial errors compared with the observations indicated its weak ability to simulate the soil temperature over regional semiarid land. Both calibration schemes significantly improved the soil moisture and temperature simulations, but particle swarm optimization generally converged to a better objective than shuffled complex evaluation, although with more parameter uncertainties and less heterogeneity. Moreover, simulations initialized with the optimal parameter tables for the calibrations obtained similarly sustainable improvements for soil moisture and temperature, as well as good consistency with the existing soil reanalysis. In particular, the soil temperature simulation errors for particle swarm optimization were unbiased, while those for the other method were found to be biased around −3 K. Overall, particle swarm optimization was preferable when conducting soil temperature simulations, and it may help mitigate the efforts in surface forecast improvement over semiarid regions.

Novel CAD Diagnosis Method Based on Search, PCA, and AdaBoostM1 Techniques.

Background: Cardiovascular diseases (CVDs) are the primary cause of mortality worldwide, resulting in a growing number of annual fatalities. Coronary artery disease (CAD) is one of the basic types of CVDs, and early diagnosis of CAD is crucial for convenient treatment and decreasing mortality rates. In the literature, several studies use many features for CAD diagnosis. However, due to the large number of features used in these studies, the possibility of early diagnosis is reduced. Methods: For this reason, in this study, a new method that uses only five features-age, hypertension, typical chest pain, t-wave inversion, and region with regional wall motion abnormality-and is a combination of eight different search techniques, principal component analysis (PCA), and the AdaBoostM1 algorithm has been proposed for early and accurate CAD diagnosis. Results: The proposed method is devised and tested on a benchmark dataset called Z-Alizadeh Sani. The performance of the proposed method is tested with a variety of metrics and compared with basic machine-learning techniques and the existing studies in the literature. The experimental results have shown that the proposed method is efficient and achieves the best classification performance, with an accuracy of 91.8%, ever reported on the Z-Alizadeh Sani dataset with so few features. Conclusions: As a result, medical practitioners can utilize the proposed approach for diagnosing CAD early and accurately.

A Multi-Feature Fusion Method for Urban Functional Regions Identification: A Case Study of Xi’an, China

Research on the identification of urban functional regions is of great significance for the understanding of urban structure, spatial planning, resource allocation, and promoting sustainable urban development. However, achieving high-precision urban functional region recognition has always been a research challenge in this field. For this purpose, this paper proposes an urban functional region identification method called ASOE (activity–scene–object–economy), which integrates the features from multi-source data to perceive the spatial differentiation of urban human and geographic elements. First, we utilize VGG16 (Visual Geometry Group 16) to extract high-level semantic features from the remote sensing images with 1.2 m spatial resolution. Then, using scraped building footprints, we extract building object features such as area, perimeter, and structural ratios. Socioeconomic features and population activity features are extracted from Point of Interest (POI) and Weibo data, respectively. Finally, integrating the aforementioned features and using the Random Forest method for classification, the identification results of urban functional regions in the main urban area of Xi’an are obtained. After comparing with the actual land use map, our method achieves an identification accuracy of 91.74%, which is higher than other comparative methods, making it effectively identify four typical urban functional regions in the main urban area of Xi’an (e.g., residential regions, industrial regions, commercial regions, and public regions). The research indicates that the method of fusing multi-source data can fully leverage the advantages of big data, achieving high-precision identification of urban functional regions.

High-resolution soil erosion mapping in croplands via Sentinel-2 bare soil imaging and a two-step classification approach

Erosion-induced lateral soil redistribution leads to spatially heterogenous soil composition, which can be captured through the distinctive spectral reflectance of soils under varying levels of erosion influence. This points to the potential of using remotely sensed soil spectra to detect severe erosion and deposition hotspots in exposed croplands and, importantly, further differentiate the intra-class spectral variability of moderate erosion that often occupies the largest proportion. Here, we aim to develop a two-step erosion classification and mapping approach based on multitemporal compositing of Sentinel-2 bare soil images of a typical agricultural region (11,500 km2) of northeast China. A random forest classifier was firstly trained against the ground-truth data derived from very high resolution (VHR) imagery in Google Earth, with an overall accuracy of 91 % that allowed for clear delineation of severe erosion and deposition areas based on their distinct topographic and spectral features particularly in the red and red-edge bands. In the second step, the remaining area of moderate erosion (60.30 %) was further differentiated using Iterative Self-Organizing cluster unsupervised classification to yield a five-class soil erosion map at 10 m spatial resolution. The accuracy of the predicted map was successfully validated by independent Caesium-137 (137Cs) and soil organic carbon observations at catchment and regional scales, as revealed by significant inter-class differences in soil redistribution rates estimated from 137Cs inventory. The severe erosion class had a soil loss rate of 5.5 mm yr−1, suggesting that previous assessments have underestimated erosion severity. The spatial accordance of crop growth with soil erosion intensity, particularly in localized settings, further highlighted the potential of bare soil imaging for mapping the spatiotemporal development of soil erosion and its response to targeted sustainable cropland management efforts.

Occurrence, source apportionment and ecological risk of bisphenol analogues in river sediments in areas with different land use patterns

Bisphenol analogues (BPs) have gained increasing attention in recent years due to their ubiquitousness and potential endocrine disrupting properties in environments. However, little information is available on their spatiotemporal distribution, source apportionment and ecological risk in river sediments, especially the case in river basins with a high population density and those typical regions with agricultural-urban gradient, where land use patterns and intensity of human activity are varying. In this study, field investigations of BPs in the sediment of the entire Qinhuai River Basin, a typical agricultural-suburban agricultural-urban gradient area, were conducted before and after the flood period. Thirty-two sites were sampled for six types of BPs, resulted in no significant difference in the concentration of ΣBPs between the two periods, with ΣBPs ranging from 3.92 to 151 ng/g and 2.16–59.0 ng/g, respectively. Bisphenol A (BPA) was the main contributor. Whereas a multivariate analysis of variance (MANOVA) suggested that the composition structure of BPs had been influenced by water periods. The land use patterns had an impact on the distribution of ΣBPs in river sediments, which was more significant in after the flood period, with ΣBPs in urban rivers was 1.85 times, 3.44 times, and 3.08 times higher than the suburban rivers, agricultural rivers, and reservoirs, respectively. Yet land use types did not significantly alter the composition structure of BPs. The correlation analysis between BPs and the physicochemical properties of sediments showed a significant positive correlation between BPA and total organic carbon (TOC). The positive matrix factorization model (PMF) suggested that BPs in sediments of the basin might be influenced by industrial coatings, textiles, electronics and biopharmaceuticals, as well as urban wastewater or solid waste generated from daily life. The ecological risk assessment posed by BPA, based on the risk quotient, indicated that the ecological risk of BPA in sediments was low for three indicator benthic organisms: crustaceans, worms, and mollusks. However, the risk of BPA in river sediments varied among different land use patterns, with the risk ranking as follows: reservoirs < agricultural rivers < suburban rivers < urban rivers.

Effect of freeze-thaw cycles on aggregate breakdown of typical black soil during transportation

During the snowmelt period, the external erosive forces are dominated by freeze-thaw cycles and snowmelt runoff. These forces may affect soil structure and aggregate stability, thereby influencing snowmelt erosion. The process of snowmelt runoff can lead to the breakdown of aggregates during their transportation. However, few studies examined the effects of freeze-thaw cycles on the breakdown of aggregates during transportation. Focusing on 5-7 and 3-5 mm soil aggregates of typical black soil region in Northeast China, we analyzed the composition of water-stable aggregates, mean weight diameter (MWD), normalized mean weight diameter (NMWD), as well as breakdown rate of soil aggregates (BR) under different freeze-thaw cycles (0, 1, 5, 10, 15 and 20 times) and different transport distances (5, 10, 15, 20, 25 and 30 m). We further investigated the contribution (CT) of both freeze-thaw cycles and transport distances to BR. The results showed that: 1) After freeze-thaw cycles, the 5-7 and 3-5 mm aggregates were mainly composed of particles with a diameter of 0.5-1 mm. With increasing frequency of freeze-thaw cycles, the MWD generally showed a downward trend. Moreover, under the same number of freeze-thaw cycles, the NMWD of 3-5 mm aggregates was higher than that of 5-7 mm aggregates. 2) As the transport distance increased, the BR of 5-7and 3-5 mm aggregates gradually increased. Compared that under control group, the BR under one freeze-thaw cycle increased by 59.7%, 32.2%, 13.7%, 6.2%, 13.4%, 7.5%, and 60.0%, 39.0%, 18.4%, 13.0%, 6.3%, 6.1% at the condition of 5, 10, 15, 20, 25 and 30 m transport distances, respectively. However, with increasing frequency of freeze-thaw cycles, the BR increased slowly. 3) The breakdown of soil aggregates was mainly influenced by the transport distance (CT=54.6%) and freeze-thaw cycles (CT=26.2%). Freeze-thaw cycles primarily altered the stability of soil aggregates, which in turn affected the BR. Therefore, during the snowmelt period, freeze-thaw cycles reduced the stability of soil aggregates, leading to severe breakdown of soil aggregates during snowmelt runoff process. This made the soil more susceptible to migration with snowmelt runoff, which triggered soil erosion. Therefore, more attention should be paid on the prevention of soil erosion during snowmelt period.

Estimation of Maize Residue Cover Using Remote Sensing Based on Adaptive Threshold Segmentation and CatBoost Algorithm

Maize residue cover (MRC) is an important parameter to quantify the degree of crop residue cover in the field and its spatial distribution characteristics. It is also a key indicator of conservation tillage. Rapid and accurate estimation of maize residue cover (MRC) and spatial mapping are of great significance to increasing soil organic carbon, reducing wind and water erosion, and maintaining soil and water. Currently, the estimation of maize residue cover in large areas suffers from low modeling accuracy and poor working efficiency. Therefore, how to improve the accuracy and efficiency of maize residue cover estimation has become a research hotspot. In this study, adaptive threshold segmentation (Yen) and the CatBoost algorithm are integrated and fused to construct a residue coverage estimation method based on multispectral remote sensing images. The maize planting areas in and around Sihe Town in Jilin Province, China, were selected as typical experimental regions, and the unmanned aerial vehicle (UAV) was employed to capture maize residue cover images of sample plots within the area. The Yen algorithm was applied to calculate and analyze maize residue cover. The successive projections algorithm (SPA) was used to extract spectral feature indices from Sentinel-2A multispectral images. Subsequently, the CatBoost algorithm was used to construct a maize residue cover estimation model based on spectral feature indices, thereby plotting the spatial distribution map of maize residue cover in the experimental area. The results show that the image segmentation based on the Yen algorithm outperforms traditional segmentation methods, with the highest Dice coefficient reaching 81.71%, effectively improving the accuracy of maize residue cover recognition in sample plots. By combining the spectral index calculation with the SPA algorithm, the spectral features of the images are effectively extracted, and the spectral feature indices such as NDTI and STI are determined. These indices are significantly correlated with maize residue cover. The accuracy of the maize residue cover estimation model built using the CatBoost model surpasses that of traditional machine learning models, with a maximum determination coefficient (R2) of 0.83 in the validation set. The maize residue cover estimation model constructed based on the Yen and CatBoost algorithms effectively enhances the accuracy and reliability of estimating maize residue cover in large areas using multispectral imagery, providing accurate and reliable data support and services for precision agriculture and conservation tillage.

Quantifying the water use efficiency of karst ecosystems and response to environmental factors

Study regionThe study area is located in Guizhou province, the centre of the karst region in southwest China. Study focusAs a typical karst region, Guizhou has long faced challenges with water scarcity. Quantifying water use efficiency (WUE) is crucial for the efficient use of water resources and sustainable management. This study examined the spatio-temporal evolution patterns and its driving factors on WUE in Guizhou using Theil-Sen's slope, partial correlation methods, and the geographic detector model. New hydrological insights for the region(1) The multi-annual mean WUE of non-karst region (NKR, 2.75 gC kg−1 H2O yr−1) is higher than that of karst region (KR, 2.10 gC kg−1 H2O yr−1) during 2001–2020. (2) The rate of increase of WUE was three times higher in KR than in NKR. (3) Precipitation and temperature were identified as the key factors influencing WUE. Interactions between each environmental factors were observed to have both bi-variate and non-linear enhancement effects. Notably, in the NKR, soil moisture, vegetation type and leaf area index were the dominant factors influencing WUE, with interactions between environmental factors had both enhanced and weakened effect on WUE. (4) The synergistic effects of these interactions exceeded the individual or cumulative effects of the two environmental factors. These findings contribute to our understanding of the environmental factors driving changes in WUE in the KR.

MAX-DOAS observations of pollutant distribution and transboundary transport in typical regions of China

Studying the spatiotemporal distribution and transboundary transport of aerosols, NO2, SO2, and HCHO in typical regions is crucial for understanding regional pollution causes. In a 2-year study using multi-axis differential optical absorption spectroscopy in Qingdao, Shanghai, Xi'an, and Kunming, we investigated pollutant distribution and transport across Eastern China-Ocean, Tibetan Plateau-Central and Eastern China, and China-Southeast Asia interfaces. First, pollutant distribution was analyzed. Kunming, frequently clouded and misty, exhibited consistently high aerosol optical depth throughout the year. In Qingdao and Shanghai, NO2 and SO2, as well as SO2 in Xi'an, increased in winter. Elevated HCHO in summer in Shanghai and Xi'an, especially Xi'an, suggests potential ozone pollution issues. Subsequently, pollutant transportation across interfaces was studied. At the Eastern China-Ocean interface, the gas transport flux was the largest among other interfaces, with the outflux exceeding the influx, especially in winter and spring. The input of pollutants from the Tibetan Plateau to central-eastern China was larger than the output in winter and spring, with SO2 having the highest transport flux in winter. The pollution input from Southeast Asia to China significantly exceeded the output, with spring and winter inputs being 3.22 and 3.03 times the output, respectively. Lastly, the transportation characteristics of a pollution event at Kunming were studied. During this period, pollutants were transported from west to east, with the maximum SO2 transport flux at an altitude of 2.87 km equaling 27.74 µg/(m2·s). It is speculated that this pollution was caused by the transport from Southeast Asian countries to Kunming.

Multivariate and scale-dependent controls of deep soil carbon after afforestation in a typical loess-covered region

Afforestation is beneficial to improving soil carbon pools. However, due to the lack of deep databases, the variations in soil carbon and the combined effects of multiple factors after afforestation have yet to be adequately explored in >1 m deep soils, especially in areas with deep-rooted plants and thick vadose zones. This study examined the multivariate controls of soil organic carbon (SOC) and inorganic carbon (SIC) in 0–18 m deep under farmland, grassland, willow, and poplar in loess deposits. The novelty of this study is that the factors concurrently affecting deep soil carbon were investigated by multiwavelet coherence and structural equation models. On average, the SOC density (53.1 ± 5.0 kg m−2) was only 12% of SIC density (425.4 ± 13.8 kg m−2), with depth-dependent variations under different land use types. In the soil profiles, the variations in SOC were more obvious in the 0–6 m layer, while SIC variations were mainly observed in the 6–12 m layer. Compared with farmland (SOC: 17.0 kg m−2; SIC: 122.9 kg m−2), the plantation of deciduous poplar (SOC: 28.5 kg m−2; SIC: 144.2 kg m−2) increased the SOC and SIC density within the 0–6 m layer (p < 0.05), but grassland and evergreen willow impacted SOC and SIC density insignificantly. The wavelet coherence analysis showed that, at the large scale (>4 m), SOC and SIC intensities were affected by total nitrogen-magnetic susceptibility and magnetic susceptibility-water content, respectively. The structural equation model further identified that SOC density was directly controlled by total nitrogen (path coefficient = 0.64) and indirectly affected by magnetic susceptibility (path coefficient = 0.36). Further, SOC stimulated the SIC deposition by improving water conservation and electrical conductivity. This study provides new insights into afforestation-induced deep carbon cycles, which have crucial implications for forest management and enhancing ecosystem sustainability in arid regions.

To Embed Geosites In An Area wıth Great Tourism Activity: A Proposal for Köyceğız, Türkiye

Köyceğiz Region offers a fascinating beauty with its lakes, long sandy beaches, rivers, as well as mountains and hills covered with pine, sweetgum and mastic tree forests. Therefore, there is an intense visitor activity in the region. In addition, there are well-marked, year-round hiking and cycling routes around the mountains and lakes in Köyceğiz. On some of these routes, eco-sites have been identified and explanatory panels have been placed. However, geosites have not been identified in the region for the geological structures that carry all this ecological diversity. In fact, the Köyceğiz Region is one of the typical regions that presents geological stories from the paleotectonic and neotectonic periods together. Our proposal in this study is to select geosites on existing nature routes that reflect the geological story of the region and to develop explanatory panels for them. In this study, we have identified geosites located on nature tourism routes in the region and described their geoscientific content. By installing panels on geosites, the geo-ecological systems located on the routes will be able to explain themselves to travelers. This is a new geo-eco-tourism approach with a small budget, aimed at existing visitors in the region, without trying to attract visitors to the area.

Evaluation of the cooling performance of a crushed-rock interlayer embankment with longitudinal ventilation ducts in a pan-Arctic permafrost region

In pan-Arctic permafrost regions, longitudinal ventilation ducts are often installed in embankments to cool the underlying permafrost and thereby ensure road stability. However, the current design methods for embankments with longitudinal ventilation ducts (ED) normally simplify the conjugate heat transfer process between the duct wall and air in the duct. In this study, based on the chimney effect and the theory of fluid flow and heat transfer, we propose an improved numerical method for evaluating the cooling performance of the ED. Using this method, we conduct an assessment of the influences of duct configuration on the efficiency of ventilation duct cooling in winter. The results indicate that increasing the diameter of the duct and the height of the duct outlet can effectively improve the radial cooling range of the ventilation duct. However, the cooling performance of the ventilation duct becomes worse when the length of the duct is increased. To address the limited cooling performance of the ED, we design a crushed-rock interlayer embankment installed with longitudinal ventilation ducts (CED). It is found that the CED is more effective at maintaining the long-term thermal stability of the road in a typical pan-Arctic permafrost region when compared to the ED and the crushed-rock interlayer embankment. Furthermore, a cost analysis of the four different embankment designs reveals that the CED is a financially viable option for embankment construction in permafrost regions.

The carbon emissions risk evolution and low-carbon optimization in a typical mountainous region on the western edge of the Sichuan Basin, China

Land use and cover change (LUCC) is a major driver of this rapid increase in atmospheric carbon. To reasonably plan various types of land use areas and mitigate the rate of growth of carbon emissions, this study takes Yaan, Sichuan province, as a case study. First, the calculation of Yaan's land use carbon emissions (LUCE) was approached by taking land use structure into account. Subsequently, the spatiotemporal distribution of LUCE was evaluated by employing the carbon emission risk index and the Moran index. Finally, the multi-objective linear programming (MOP), the Markov chain, and the PLUS model were used to predict the spatial distribution of LUCC in 2030, including natural development scenarios (NDS) and low-carbon optimization development scenarios (LODS). According to the findings, the impervious surface is identified as the principal contributor to LUCE, while the forest is recognized as the principal absorbers of carbon. The carbon emissions in typical mountainous areas are distributed in cities and generally concentrate towards the plains in the northeast direction. Under the LODS, LUCE decrease significantly. For both NDS and LODS, the overall trend of land development direction in Yaan is “northeast-southwest” from 2020 to 2030. These results could provide some suggestions for low-carbon land use in cities like Yaan.

Monitoring Snow Cover in Typical Forested Areas Using a Multi-Spectral Feature Fusion Approach

Accurate snow cover monitoring is greatly significant for research on the hydrology model and regional climate variation, especially in Northeast China where forests cover almost forty percent of the total area. However, effectively monitoring snow cover under the forest canopy is still challenging with either in situ or remote sensing observations. The global SNOWMAP algorithm pertinent to the fixed normalized difference snow index (NDSI) threshold is, therefore, no longer applicable in a typical forested region of Northeast China. In order to achieve the goal of improving the accuracy of monitoring snow cover in areas with forest, utilizing MOD09GA and MOD13A1 products, a new approach of snow mapping was developed in this study, and it exploits the fusion and coupling of spectral features by integrating and analyzing the normalized difference forest snow index (NDFSI), the normalized difference vegetation index (NDVI), and the NDSI index. Then, Landsat 8 OLI images of high resolution were used to evaluate snow cover mapping precision. The experimental results indicated that the NDFSI index combined with the NDVI index showed great potential for extracting the snow cover distribution in forested regions. Compared with the snow distribution obtained from the Landsat 8 images, the average bias and FAR (false alarm ratio) values of snow cover mapping obtained by this algorithm were 1.23 and 13.54%, which were reduced by 1.98 and 29.36%, respectively. The overall accuracy of 81.31% was reached, which is improved by 20.19%. Thus, the snow classification scheme combining multiple spectral features from MODIS data works effectively in improving the precision of automatic snow cover mapping in typical forested areas of Northeast China, which provides essential support and significant perspectives for the next step of establishing a runoff model and rationally regulating forest water resources.