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To investigate the spatial and temporal changes in fractional vegetation coverage (FVC) and their driving forces in different regions of the Inner Mongolia section of the Yellow River Basin, this paper observed the spatial trends and stability of FVC in these regions based on the MOD13Q1 information regarding the 2000–2020 period as a data source. It used the dimidiate pixel model to invert FVC, and based on the centre of gravity migration model, the coefficient of variation and the Mann–Kendall and Sen's slope estimator test, it studied the spatial variation trend and stability of FVC in the four relevant areas of the Inner Mongolia section; an attribution analysis using a geodetector was also conducted. The following results were found: (1) in terms of temporal FVC change in the relevant areas, from 2000 to 2020, the overall FVC showed an increasing trend, indicating an obvious hierarchy of change as per different seasonal scales (summer > growing season > fall > spring). There is a mutation point in FVC in different areas, and the FVC sequence is random. (2) Regarding spatial change, the overall FVC showed a trend of being high in the eastern regions and low in the western regions and low–high–low from the north to the south; the stability of the Hetao Irrigation District–Wuliangsuhai Area changed more significantly with the successive seasons, and the degraded areas of FVC were mainly distributed in the city centre of the Kundulun River–Daheihe River Area and in the Hetao Irrigation District in the summer. (3) In terms of driving factors, soil type had a relatively higher explanatory power regarding the Hetao Irrigation District–Wuliangsuhai Area, rainfall had a relatively higher explanatory power regarding the Morin River–Wuding River Area and the Kundulun River–Daheihe River Area, and land use had a relatively higher explanatory power regarding the Ten Kongtui–Heidaigou Area. [ABSTRACT FROM AUTHOR]

In this paper, recent research on terrestrial ecosystem predictability using the conditional nonlinear optimal parameter perturbation (CNOP-P) method is summarized. The main findings include the impacts of uncertainties in climate change on uncertainties in simulated terrestrial ecosystems, the identification of key physical parameters that lead to large uncertainties in terrestrial ecosystem modeling and prediction, and the evaluation of the simulation ability and prediction skill of terrestrial ecosystems by reducing key physical parameter errors. The study areas included the Inner Mongolia region, north–south transect of eastern China, and Qinghai–Tibet Plateau region. The periods of the studies were from 1961 to 1970 for the impacts of uncertainties in climate change on uncertainties in simulated terrestrial ecosystems, and from 1951 to 2000 for the identification of the most sensitive combinations of physical parameters. Climatic Research Unit (CRU) data were employed. The numerical results indicate the important role of nonlinear changes in climate variability due to the occurrences of extreme events characterized by CNOP-P in the abrupt grassland ecosystem equilibrium state and formation of carbon sinks in China. Second, the most sensitive combinations of physical parameters to the uncertainties in simulations and predictions of terrestrial ecosystems identified by the CNOP-P method were more sensitive than those obtained by traditional methods (e.g., one-at-a-time (OAT) and stochastic methods). Furthermore, the improvement extent of the simulation ability and prediction skill of terrestrial ecosystems by reducing the errors of the sensitive physical parameter combinations identified by the CNOP-P method was higher than that by the traditional methods. [ABSTRACT FROM AUTHOR]

Groundwater is a primary freshwater resource for human consumption and an essential source for industry and agriculture. Therefore, understanding its spatial and temporal trends and drivers is crucial for governments to take appropriate measures to manage water resources. This paper uses Gravity Recovery and Climate Experiment (GRACE) satellite data and the Global Land Data Assimilation System (GLDAS) to derive groundwater storage anomalies (GWSAs) and to analyze the spatial and temporal trends of GWSA in different regions of China (Xinjiang, Tibet, Inner Mongolia, North China Plain, South China, and Northeast China). It used groundwater-level observation data to verify the accuracy of GWSA estimates and analyzed the drivers of regional GWSA changes. The results showed that: (1) GWSA in South China increased at a rate of 4.79 mm/a from 2003 to 2016, and GWSA in other regions in China showed a decreasing trend. Among them, the decline rates of GWSA in Xinjiang, Tibet, Inner Mongolia, North China Plain, and Northeast China were −6.24 mm/a, −3.33 mm/a, −3.17 mm/a, −7.35 mm/a, and −0.75 mm/a, respectively. (2) The accuracy of the annual-scale GWSA estimates was improved after deducting gravity losses due to raw coal quality, and the correlation coefficient between GWSA and groundwater levels monitored by observation wells increased. (3) In Xinjiang, the annual water consumed by raw coal mining, industrial, and agricultural activities had a greater impact on GWSA than rainfall and temperature, so these human activities might be the main drivers of the continued GWSA decline in Xinjiang. Water consumption by raw coal mining and industry might be the main drivers of the continued decline in GWSA in Inner Mongolia and the North China Plain. The increase in groundwater storage in South China was mainly due to the recharge of rainfall. [ABSTRACT FROM AUTHOR]

Wind erosion is a key global environmental problem. As an important protective measure to provide services to the ecosystems in wind-eroded areas, the wind erosion prevention service is of great significance to the management of wind and sand hazards and ecological environment restoration in the wind-eroded areas and the neighboring areas. Taking the Shiyang River basin as the study area, the quality of supplies for wind erosion prevention services was estimated using the RWEQ model for the years 2005, 2010, 2015, and 2020; the trajectories of air masses at wind speeds higher than the sand-causing wind speeds were simulated based on the forward trajectory module of the HYSPLIT model for a 24 h period; the spatial simulation of the flow of wind erosion prevention services on a daily scale with Minqin Station as the sand source was carried out; and the beneficiary areas of wind erosion prevention services were identified. Based on the RWEQ model, the spatial patterns of potential wind erosion, actual wind erosion, and wind and sand stabilization services were obtained, and the supply areas were divided. From 2005 to 2020, the wind erosion prevention service flow in the Shiyang River basin was distributed along a northwest–southeast direction, with a radial decrease from the center to the periphery, and with an extremely strong extraterritorial effect. The amount of wind erosion in the basin has a variable downward tendency over time and a spatial distribution pattern of high in the north and low in the south. The area of higher sand fixation is distributed in the eastern oasis area and desert junction zone. The HYSPLIT model was used to simulate the transport paths of wind and sand within 24 h during 2005–2020, the transmission paths of the wind erosion prevention service flow were obtained to be 59–134, and the flows were 2.55 × 104–3.85 × 106 t, displaying a changing trend of first decreasing, then increasing, and then decreasing. Gansu Province, Ningxia Hui Autonomous Region, and Inner Mongolia Autonomous Region are the most important areas benefiting from the wind erosion prevention service flow in the Shiyang River basin. The wind erosion prevention service flows in the basin benefit 47 cities in 9 provinces. [ABSTRACT FROM AUTHOR]

Based on the energy policy simulation model (EPS model) and the reality of Zhejiang Province and Inner Mongolia Autonomous Region, the carbon pricing policy scenario and the early retirement policy scenario of coal power generation units were constructed, respectively, and the policy effects simulated. The study explored whether Inner Mongolia Autonomous Region can learn from the low-carbon policies that have played a good role in Zhejiang Province in the process of achieving a carbon peak. The research found that: (1) Under the baseline scenario, both Zhejiang Province and Inner Mongolia Autonomous Region failed to achieve a carbon peak by 2030. (2) Under the scenarios of carbon pricing and early retirement of coal power generation units, the peak time of carbon in Zhejiang Province and Inner Mongolia Autonomous Region has been advanced, which shows the effectiveness of carbon pricing and early retirement of coal power generation units. (3) The above two policies have achieved good results in the overall implementation process of Zhejiang Province, but the carbon pricing policy has caused dramatic fluctuations in the power generation in Inner Mongolia Autonomous Region, and the early retirement policy of coal power units has failed to achieve the goal of reaching the peak carbon in Inner Mongolia Autonomous Region on schedule. [ABSTRACT FROM AUTHOR]

To evaluate the detection performance of ozone lidars, the first comprehensive vertical ozone observation experiment in China was conducted at the Xilinhot National Climate Observatory in Inner Mongolia from August to December 2023. The ozone profiles and concentrations of four ozone lidars were systematically compared and assessed with ozone radiosonde measurements and ozone analyzer observations both at ground-based stations and on an Unmanned Aerial Vehicle. The results show that the relative deviations of four ozone lidars are less than 20% compared with ozone radiosonde measurements at a height between 150 and 400 m. Ozone lidars have better behavior between 400 m and 2000 m than the lower altitude, with the deviation within 10% and the correlation coefficient around 0.8. However, relative deviations of lidars increased with altitude above 2000 m. The surface ozone concentrations observed using ozone lidars agreed well with the ground-based ozone analyzer, especially during periods with ozone concentrations higher than 40 µg·m−3. The correlation coefficients for most models of ozone lidar are higher than 0.53. A further investigation of the influence of precipitation events on ozone lidar measurement has been conducted, which revealed that thick cloud layers, low cloud base, and an intensive precipitation event with large raindrop particles can result in high anomalies and reduce the inversion accuracy of the ozone lidar. During the experiment, four ozone lidars were assessed quantitatively according to the comprehensive performance, which could help to improve inversion algorithms and the system design of this promising technique. [ABSTRACT FROM AUTHOR]

Precise water management is essential for the efficient development of irrigated agricultural crops in the Hetao Irrigation Area of Inner Mongolia. Given the severe water scarcity in the region and the significant use of intercropping as a cropping method, the development of rational irrigation scheduling is crucial. The objective of this work was to combine the ISAREG model with wheat–sunflower intercropping crops in order to enhance the effectiveness of irrigation scheduling in intercropping systems. This was achieved by changing and verifying crucial parameters for simulating irrigation patterns in intercropping. We conducted an assessment of nine irrigation schedules for a wheat–sunflower intercropping system in order to provide a range of irrigation scenarios that effectively fulfill the water requirements of the system. In light of this, we suggested implementing restrictions on the dates and volumes of irrigation based on the demand for agricultural irrigation. This approach aimed to establish irrigation schedules that are highly efficient and tailored to the specific crops in the area. As a result, we achieved a water use efficiency rate of 100%, saved 28.78% of water resources, optimized crop irrigation schedules, and enhanced crop economics by 6.7%. This study presents a novel and efficient method to optimize agricultural irrigation schedules, boost agricultural water use efficiency, and maximize crop yields in order to promote sustainable agricultural development. [ABSTRACT FROM AUTHOR]

Near-surface PM2.5 estimates remain a global scientific research challenge due to their effect on human fitness and atmospheric environmental quality. However, practical near-surface PM2.5 estimates are impeded by the incomplete monitoring data. In this study, we propose the trigonometric polynomial fitting (TPF) method to estimate near-surface PM2.5 concentrations in south-central China during 2015. We employ 10-fold cross-validation (CV) to assess the reliability of TPF in estimating practical PM2.5 values. When compared to alternative methods such as the orthogonal polynomial fitting (OBF) method based on Chebyshev basis functions, Kriging interpolation, and radial basis function (RBF) interpolation, our results show that utilizing TPF31, with a maximum order of 3 in the x direction and a maximum order of 1 in the y direction, leads to superior efficiency through error minimization. TPF31 reduces MAE and RMSE by 1.93%, 24%, 6.96% and 3.6%, 23.07%, 10.43%, respectively, compared to the other three methods. In addition, the TPF31 method effectively reconstructs the spatial distribution of PM2.5 concentrations in the unevenly distributed observation stations of Inner Mongolia and the marginal regions of the study area. The reconstructed spatial distribution is remarkably smooth. Despite the non-uniform distribution of observation stations and the presence of missing data, the TPF31 method demonstrates exceptional effectiveness in accurately capturing the inherent physical attributes of spatial distribution. The theoretical and experimental results emphasize that the TPF method holds significant potential for accurately reconstructing the spatial distribution of PM2.5 in China. [ABSTRACT FROM AUTHOR]

Grassland has great potential for carbon sequestration; however, the relationship between carbon storage (CS) and greenhouse gas (GHG) budget and their influencing factors in the natural restoration process in grassland mining areas are rarely studied. In this study, taking Zhalainuoer mining area in Inner Mongolia as an example, the subsidence soil for 1-, 2-, 5-, 10-, and 15-year and non-subsidence soil were selected as the research objects to explore the relationship between CS and the GHG budget and their influencing factors. The results show that there is a significant negative correlation between CS and the GHG budget. Soil organic carbon storage accounts for 99% of CS. CS is positively correlated with SOM and AP, and with the bacteria Entotheonellaeota. The GHG budget is mainly affected by CO2 emission, which is positively correlated with subsidence time, plant biomass, and coverage, negatively correlated with the bacteria Actinobacteriota and Deinococcota, and positively correlated with Cyanobacteria. In summary, soil plays a major role in storing carbon. Carbon sequestration is a physiological process produced by plants and organisms. Subsidence affects soil CS by changing soil properties and thus affecting its aboveground vegetation and soil microorganisms. This study investigates the changes in soil carbon storage following subsidence caused by mining activities. The findings contribute to our understanding of the impact of mining subsidence on soil CS and can inform the development of low-carbon remediation technologies. [ABSTRACT FROM AUTHOR]

To further promote dust control efforts in Chinese open-pit coal mines, this study focuses on the research of coal dust and rock dust produced by different explosions in the Haerwusu open-pit coal mine in China. By investigating the relationship between the physical and chemical characteristics of dust particles from explosions in open-pit mines and the wetting properties of dust, the main factors influencing the wetting properties of explosive dust are identified. This provides a theoretical basis for subsequent dust control work in open-pit coal mines. Simultaneously, to formulate more effective dust suppressants and reduce explosive dust pollution, this study conducts experiments on the surface tension, contact angles, and complex solution compatibility to select suitable surfactants. Ultimately, the effectiveness of the dust suppressants is evaluated through permeability experiments and indoor dust suppression experiments. The research findings are as follows: (1) The significant factors affecting the wetting properties of coal dust are the fixed carbon content and D50, while the significant factor affecting the wetting properties of rock dust is D50. (2) The formulated dust suppressants can increase the permeation height of coal dust by at least 10 times, increase moisture absorption by at least 4 times, and reduce the TSP concentration by at least 81.4%. [ABSTRACT FROM AUTHOR]

Desertification vulnerability and contributing factors are of global concern. This study analyzed the spatial and temporal distribution of net primary productivity (NPP), precipitation, and temperature from 1985 to 2015. The rain use efficiency (RUE) of vegetation was selected as an indicator; and desertification vulnerability and contributors were evaluated with the Mann–Kendall test (M–K test) and the Thornthwaite–Memorial model. The results showed that NPP was lower in that years that had lower precipitation and higher temperatures, and vice versa. NPP was spatially consistent with precipitation distribution and roughly opposite to the spatial distribution of the annual change rate of temperature. The desertification vulnerability decreased from west to east, among which both the western sub–region (WSR) and the central sub–region (CSR) had the largest proportion of regions with high desertification vulnerability. On the other hand, the eastern sub–region (ESR) mostly comprises areas with extremely low or low desertification vulnerability. The vulnerability contributors for desertification differed among each sub–region. The desertified regions in WSR and ESR were mainly influenced by human activity (HA), but primarily driven by the combined impact of Precipitation–Temperature (PT) and HA in CSR. The south–east part of the CSR was only affected by HA, whereas the lesser affected regions in the study area were affected by PT and HA simultaneously. The study provides recommendations for the improvement of regional ecological environments to prevent future disasters. [ABSTRACT FROM AUTHOR]

Based on the NDVI data of vegetation in Inner Mongolia from 1982 to 2015, the variational mode decomposition (VMD) method, which has been well applied in the field of signal decomposition, is introduced to study the periodicity of vegetation index in Inner Mongolia. The VMD method is used to extract the monthly and annual NDVI and the long time series cycle characteristics of temperature and precipitation in the same period from April 1982 to October 2015 in Inner Mongolia. The results show that temperature and precipitation are important factors affecting the growth of vegetation, and there are 6.99 and 3.49 months of the same oscillation cycle for monthly NDVI and temperature and precipitation time series; when the central frequency is the same, the amplitude of the monthly temperature and precipitation time series increases with the increase of the lag period. The annual scale NDVI has the same period of 16.95, 6.8a, and 4.85a with precipitation, and the same period of 6.8a and 4.85a with temperature. The Residue component shows that the overall NDVI and temperature in Inner Mongolia have shown a significant slow growth trend in the past 30 years. Although the precipitation has shown a significant slow decline trend in the same time period (p = 0.000), the grassland is still in the process of continuous improvement. [ABSTRACT FROM AUTHOR]

The ecological conservation and high-quality development of the Yellow River Basin (YRB) has been declared as a major national strategy of China. Surface water availability (precipitation minus evapotranspiration, PME) poses challenges to the sustainability of ecosystems of the YRB. Noteworthy is that the Mongolian section of the YRB (IMYRB) is a critical ecological barrier in Northern China. Soil moisture (SM) changes are highly sensitive to PME and important for regional ecological security. However, SM vs. PME interactions and relevant mechanisms within the IMYRB are poorly understood. We found significant decreases in SM and PME over the east IMYRB (r = 0.7, p < 0.05). During the wet (July, August, and September) and dry (April, May, and June) seasons, as well as the whole year, decreased SM drives increased PME through land-atmosphere interactions over more than 90% of the IMYRB. Reduction in SM decreased evapotranspiration over more than 80% of the IMYRB, increased surface temperature across more than 79% of the IMYRB, boosted atmospheric vertical ascent over more than 75% of the IMYRB, and enhanced moisture convergence and PME. This study highlights the land-atmosphere interactions over the IMYRB, implicating basin-scale impacts of climatic changes on water resources. [ABSTRACT FROM AUTHOR]

The dust event from 12 January to 17 January 2021 ("1.12 event") is the first dust process in 2021 and the earliest dust event in the last two decades. The dust event from 14 to 18 March 2021 ("3.15 event") was the strongest dust storm in the past decade. Distribution, transport, and impact on urban air quality of these two typical dust events were studied using multi-source satellite data, a HYSPLIT trajectory model, and a 3D concentration-weighted trajectory model. Results show that both dust events affected a wide range of areas, covering Northwest, North, Northeast, East, and Central-South China. A strong dust belt spanning Northwest, North, and Northeast China was formed in northern China on 15 March 2021. The distribution heights of the 1.12 and 3.15 events were 0–5 km and 0–10 km, respectively. Dust from western Inner Mongolia and southern Mongolia dominated the 1.12 event, while dust from southern Mongolia dominated the 3.15 event. Both of these dust sources had eastward and southeastward transport paths. The majority of the dust was near-ground in downstream cities from an altitude of 0–3 km. Most cities were affected by the dust backflow. The 1.12 event generated more severe particulate pollution in southern China than the 3.15 event. During high-value dust days, ozone pollution levels decreased at the majority of stations. Dust weather with low dust rising heights and dust backflow phenomena should be taken seriously in urban dust pollution forecasting and warning work. International collaboration is needed to improve China's desertification control. [ABSTRACT FROM AUTHOR]

With global warming, previous studies have found nonuniformity responses of precipitation because of regional differences. However, climate change affects the mean, extreme, and data structure of precipitation. Quantile regression, which can reflect every part of the trends of data, was used to detect responses of each part of precipitation in China. The V2.0 dataset of daily precipitation grid data (0.5° × 0.5°) from 1961 to 2020 in China was used as practical observation data. Daily precipitation in 2015–2100 from the China Model BCC-CSM2-MR of scenarios SSP2-4.5 and SSP5-8.5 were chosen as future climate changes with moderate and high radiative forcing, respectively. On the basis of the sign consistency of the slope coefficients with quantile regression, the results of quantiles q = 0.3, 0.5, 0.7 and 0.9 were selected to represent low, median, high and flood precipitation, respectively. Precipitation in four seasons was separately analyzed to observe seasonal characteristics in China. For the observation data, precipitation had obviously different responses in the low and high percentiles and was present in mainly spring and summer. In spring, in the middle and lower Yangtze Plains, the low and median precipitation increased, whereas the high and flood precipitation significantly decreased. In summer, Heilongjiang Province and northern Inner Mongolia showed decreasing trends in the low quantile and increasing trends in the high quantile, indicating a completely opposite trend adjustment. These regions deserve more attention. However, obviously different responses in low and high percentiles were not so evident in future climate changes. Self-consistency in model data may weaken the heteroscedastic characteristics of precipitation. [ABSTRACT FROM AUTHOR]

Ecosystem water use efficiency (eWUE) is a useful metric to examine the interactions between water and carbon cycles in ecosystems. To reveal the response and adaptation characteristics of different vegetation types within the context of global warming on a regional scale, the spatiotemporal characteristics and influencing factors of the seasonal eWUE of various vegetation types in Inner Mongolia from 2001 to 2020 were explored. Based on MODIS gross primary productivity (GPP), evapotranspiration (ET) data and meteorological data, in this study, we estimated eWUE in different seasons in Inner Mongolia and used trend analysis and correlation analysis methods to analyze the relationship between eWUE in spring, summer and autumn and the temperature–precipitation. From 2001 to 2020, in this region, the GPP and ET in spring, summer and autumn showed increasing trends. In addition, the growth rates of GPP and ET in spring and summer were higher than those in autumn. Under the combined effect of GPP and ET, eWUE in different seasons showed a significant decreasing trend (p < 0.05)—this is ascribed to the extent of ET increasing more than GPP, especially in summer, with the most obvious decreasing rate. In terms of spatial trend, in spring and summer, there is a decreasing trend from northeast to southwest. The effects of precipitation and temperature on the eWUE in Inner Mongolia were mainly negatively correlated in the northeastern part of Inner Mongolia with higher altitudes during the spring and autumn seasons. In total, 95.096% of the total area had positive correlations between eWUE and temperature in spring. In summer, the region in which the WUE of the vegetation had an inverse relationship with both the temperature and the amount of precipitation was the largest compared to these regions in spring and autumn. [ABSTRACT FROM AUTHOR]

Wind power, as one of the primary clean energies, is an important way to achieve the goals of carbon peak and carbon neutrality. Therefore, high-resolution measurement and accurate forecasting of wind speed are very important in the organization and dispatching of the wind farm. In this study, several methodologies, including the mesoscale WRF (Weather Research and Forecasting(WRF) model, mathematical statistics algorithms, and machine learning algorithms, were adopted to systematically explore the predictability and optimization of wind speed of a Gobi grassland wind farm located in western Inner Mongolia. Results show that the rear-row turbines were significantly affected by upwind turbine wakes. The output power of upwind-group turbines was 591 KW with an average wind speed of 7.66 m/s, followed by 532 KW and 7.02 m/s in the middle group and 519 KW and 6.92 m/s in the downwind group. The higher the wind speed was, the more significantly the wake effect was presented. Intercomparison between observations and WRF simulations showed an average deviation of 3.73 m/s. Two postprocessing methods of bilinear interpolation and nearest replacement could effectively reduce the errors by 34.85% and 36.19%, respectively, with average deviations of 2.43 m/s and 2.38 m/s. A cycle correction algorithm named Average Variance–Trend (AVT) can further optimize the errors to 2.14 m/s and 2.13 m/s. In another aspect, the categorical boosting (CatBoost) artificial intelligence algorithm also showed a great performance in improving the accuracy of WRF outputs, and the four-day average deviation of 26–29 September decreased from 3.21 m/s to around 2.50 m/s. However, because of the influence of large-scale circulations, there still exist large errors in the results of various correction algorithms. It is therefore suggested through the investigation that data assimilation of the northwest and Mongolian plateau, boundary layer parameterization scheme optimization, and embedding of high-resolution topographic data could have great potential for obtaining more accurate forecasting products. [ABSTRACT FROM AUTHOR]

Dust weather is common and disastrous at the Tibetan Plateau. This study selected a typical case of dust weather and analyzed its main development mechanism in the northeast of the Tibetan Plateau, then applied six machine learning methods and a time series regression model to predict PM10 concentration in this area. The results showed that: (1) The 24-h pressure change was positive when the front intruded on the surface; convergence of vector winds with a sudden drop in temperature and humidity led by a trough on 700 hPa; a "two troughs and one ridge" weather situation appeared on 500 hPa while the cold advection behind the trough was strong and a cyclone vorticity was formed in the east of Inner Mongolia. (2) The trajectory of air mass from the Hexi Corridor was the main air mass path influencing Xining City, in this case, since a significant lag in the peak of PM10 concentration appeared in Xining City when compared with Zhangye City. (3) The Multiple Linear Regression was not only timely and effective in predicting the PM10 concentration but had great abilities for anticipating the transition period of particle concentration and the appearance date of maximum values in such dust weather. (4) The MA and MP in the clean period were much lower than that in the dust period; the PM10 of Zhangye City as an eigenvalue played an important role in predicting the PM10 of Xining City even in clean periods. Different from dust periods, the prediction effect of Random Forest Optimized by Bayesian hyperparameter was superior to Multiple Linear Regression in clean periods. [ABSTRACT FROM AUTHOR]

As an essential part of the hydrological cycle, precipitation is usually associated with floods and droughts and is increasingly being paid attention to in the context of global warming. Analyzing the change trends and correlation of temperature and extreme precipitation indicators can effectively identify natural disasters. This study aimed to detect the correlation and change trends of temperature and extreme precipitation indicators in Inner Mongolia from 1960 to 2019. Panel vector autoregression (PVAR) models based on Stata software were used to detect the correlation between temperature and extreme precipitation indicators at 35 climatological stations throughout Inner Mongolia. The temperature and extreme precipitation indicator trends were analyzed using the Mann–Kendall test and Sen's slope method. The spatial distribution characteristics of the annual precipitation and rainfall intensity were more significant in the southeast and more minor in the northwest, while an increase in the annual wet days was noticeable to the northeast. The Granger cause tests of the temperature and the extreme precipitation indicators showed a correlation between each indicator and temperature at the significance level of 1%. The temperature positively correlated with only the rainfall intensity while negatively correlating with the remaining indicators. There is no doubt that trend analysis showed significant increasing trends in rainfall intensity at all stations, which means increased risk in extreme precipitation events. By contrast, the annual precipitation and annual wet days showed significant decreasing trends, which means that the precipitation is concentrated, and it is easier to form extreme precipitation events. The study can provide a basis for decision-making in water resources and drought/flood risk management in Inner Mongolia, China. [ABSTRACT FROM AUTHOR]

Vegetation net primary productivity (NPP) is an important aspect of the global carbon cycle, and its change is closely related to climate change. This study analyzed the spatial-temporal variation of the standardized precipitation evapotranspiration index (SPEI) and NPP in the Mongolian Plateau, and investigated the effect of drought on NPP. To this end, NPP was simulated using the Carnegie-Ames-Stanford Approach (CASA) model. The results showed that from 1982 to 2014, NPP exhibited an upward trend in different seasons, and a significant increasing trend in most areas in the growing season and spring. The degree of drought also showed an increasing trend in each season. Moreover, the decrease in NPP and SPEI in Mongolia was larger than that in Inner Mongolia. Vegetation showed a positive correlation with SPEI in the growing season and summer, but a negative correlation in the other seasons. Moreover, the impact of drought on vegetation in the growing season showed a lag effect, whereas the lag response was inconspicuous during the early stages of the growing season. Different vegetation NPP responded strongly to the SPEI of the current month and the previous month. [ABSTRACT FROM AUTHOR]

Sand and dust storms (SDSs) cause major disasters in northern China. They have serious impacts on human health, daily life, and industrial and agricultural production, in addition to threatening the regional ecological environment and social economy. Based on meteorological observational data and the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 dataset for spring 2000–2021, we used the Lamb–Jenkinson circulation classification method to classify the three major areas influencing SDSs in northern China. We also used the k-means clustering method to classify the overall circulation pattern in northern China. Our results show that the circulation types favoring SDSs in the southern basin of Xinjiang are southwesterly winds (SW), cyclones (C), and anticyclones (A). The circulation types favoring SDSs in western Inner Mongolia and southern Mongolia are northwesterly winds (NW), northerly winds (N), cyclones (C), and anticyclones (A). The circulation types favoring SDSs in central Inner Mongolia are northwesterly winds (NW), northerly winds (N), southwesterly winds (SW), and anticyclones (A). The 500 hPa and surface circulation patterns in China can be divided into nine types. Among them, five dominant circulation patterns favor strong SDSs: a cold high-pressure region and cold front (T1), a Mongolian cyclone (T2), a mixed type of Mongolian cyclone and cold front (T3), a thermal depression and cold front (T5), and a cold front (T8). During 2000–2004, the T8 circulation pattern occurred most frequently as the main influencing circulation. From 2005 to 2010, the T3 and T8 circulation patterns dominated. Circulation patterns T1 and T3 dominated during 2011–2015 and 2016–2020, respectively. We analyzed the main circulation patterns for four SDS events occurring in 2021 by combining the Lamb–Jenkinson and k-means methods. The SDS events in 2021 were closest to the T3 circulation pattern and were mainly influenced by Mongolian cyclones and surface cold fronts. The main propagation paths were westerly and northwesterly. [ABSTRACT FROM AUTHOR]