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As a raw material for clean energy supply for the new generation, the soybean is conducive to the realization of global energy transition and sustainable development in the context of "carbon neutrality". However, global warming has been affecting soybean yields in recent years. How to clarify the correlation between meteorological factors and soybean yields, so as to ensure the security of soybean growth and development and the stability of renewable energy development, is a key concern of the government and academia. Based on the data of temperature, precipitation, sunshine duration and active accumulated temperature during the soybean growing season in Hulunbuir, Inner Mongolia Autonomous Region from 1951 to 2019, and soybean yield data of the city from 1985 to 2019, this paper adopted statistical methods such as the Trend Analysis Method, the Rescaled Range Analysis Method and so on to analyze the trends of yield changes, characteristics of abrupt changes and periodic patterns of climate factors and soybean yields in Hulunbuir. A Pearson Correlation Analysis and a Grey Relation Analysis were used to explore the correlation between climatic factors and soybean yields, followed by a comprehensive impact model of the combined effect of temperature and precipitation on soybean yields established by the Method of Integral Regression. The results showed that temperature and active accumulated temperature are the dominant factors affecting soybean yields in Hulunbuir, while the decrease in precipitation is unfavorable to the improvement of soybean yields. Meanwhile, temperature and precipitation have different effects on the growth and development of the soybean at different stages. The conclusion of this paper is of great practical significance for Hulunbuir to promote the sustainable development of clean energy. [ABSTRACT FROM AUTHOR]

The net primary productivity (NPP) of vegetation is an important indicator reflecting the vegetation dynamics and carbon sequestration capacity in a region. In recent years, China has implemented policies to carry out ecological protection. To understand the changes in the distribution of vegetation NPP in China and the influence of climate factors, the Carnegie–Ames–Stanford approach (CASA) model was used to estimate the NPP from 2001 to 2020. In this paper, several sets of measurement datasets and products were collected to evaluate the effectiveness of the model and suggestions were provided for the modification of the CASA model based on the evaluation results. In addition to the correlation analysis, this paper presents a statistical method for analyzing the quantitative effects in individual climatic factors on NPP changes in large regions. The comparison found that the model has a better estimation effect on grassland and needleleaf forest. The estimation error for the evergreen needleleaf forest (ENF) and deciduous broadleaf forest (DBF) decreases with the warming of the climatic zone, while the evergreen broadleaf forest (EBF) and deciduous needleleaf forest (DNF) do the opposite. The changes in total CASA NPP were consistent with the trends of other products, showing a dynamic increasing trend. In terms of the degree of correlation between the NPP changes and climatic factors, the NPP changes were significantly correlated with temperature in about 10.39% of the vegetation cover area and with precipitation in about 26.92% of the vegetation cover area. It was found that the NPP variation had a negative response to the temperature variation in Inner Mongolia grasslands, while it had a positive but small effect (±10 g C) in the Qinghai–Tibet Plateau grasslands. Precipitation had a facilitative effect on the grassland NPP variation, while an increase in the annual precipitation of more than 200 mm had an inhibitory effect in arid and semi-arid regions. This study can provide data and methodological reference for the ecological assessment of large-scale regional and climate anomalous environments. [ABSTRACT FROM AUTHOR]

The dry and windy climate and low ground cover in spring in the black soil region of Northeast China make the soil strongly affected by wind erosion, which seriously threatens the food security and ecological security of this region. In this paper, based on the daily observation data of 124 meteorological stations in study area from 1961 to 2020, seasonal and monthly wind erosion climate factor (C) in spring (March to May) were calculated by using the method proposed by the Food and Agriculture Organization of the United Nations (FAO), the wind erosion characterization in spring were systematically analyzed based on C by various statistical analysis methods. The results showed that in the past 60 years, spring wind erosion climate factor (CSp) and monthly C of the whole region and each province (region) all showed highly significant decreasing trend, but they began to show rebounded trend in the middle or late 2000s. CSp of the study area showed a significant upward trend since 2008 with an increase of 4.59 (10a)−1. The main contributors to this upward trend are the changes of C in March and in April. For the four provinces (regions), CSp in Heilongjiang, Jilin, Liaoning and eastern Inner Mongolia all showed rebounded since 2008, 2011, 2008 and 2009, respectively. The rebounded trend of CSp in eastern Inner Mongolia was the most obvious with a tendency rate of 11.27 (10a)−1, and its mutation occurred after 1984. The rebound trend of CSp in Heilongjiang Province takes the second place, with a trend rate of 4.72 (10a)−1, but there's no obvious time mutation characteristics. The spatial characteristics of CSp and monthly C are similar, showing decreasing characteristics centered on the typical black soil belt of Northeast China. Compared with 1961–1990, in the period from 1991 to 2020, the proportion of high value areas (CSp>35, monthly C>10) has decreased to varying degrees, while the proportion of low value areas (CSp ≤10, monthly C ≤4) has increased. The trends of seasonal and monthly C in 82.2%∼87.7% of the stations show significant decreases at 95% confidence level. CSp is closely related to wind speed at 2m height, temperature difference, minimum temperature and precipitation in the same period, of which the correlation between CSp and wind speed is the strongest, indicating that the main control factor for CSp in the study area is wind speed, but the impact of the change of temperature and precipitation on CSp cannot be ignored. [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]

Background: Desertification is one of the main obstacles to global sustainable development. Monitoring, evaluating and mastering its driving factors are very important for the prevention and control of desertification. As one of the largest deserts in China, the development of desertification in Otindag Sandy Land (OSL) resulted in the reduction in land productivity and serious ecological/environmental consequences. Although many ecological restoration projects have been carried out, the vegetation restoration of OSL and the impact mechanism of climate and human activities on desertification remain unclear. Methods: Taking OSL as the research area, this paper constructs the desertification index by using the remote sensing images and meteorological and socio-economic data, between 1986 and 2016, and analyzes the spatio-temporal evolution process and driving factors of desertification by using trend analysis and spearman rank correlation. Results: The results showed that: (1) Desertification in the OSL has fluctuated greatly during the past 30 years. Desertification recovered between 1986 and 1990, expanded and increased between 1990 and 2000, reduced between 2000 and 2004, developed rapidly between 2004 and 2007, and recovered again between 2007 and 2016; (2) The desertification of OSL is dominated by a non-significant change trend, accounting for 73.27%. In the significant change trend, the area of desertification rising trend is 20.32%, which is mainly located in the north and east, and the area of declining trend is 6.41%, which is mainly located in the southwest; (3) Desertification is the result of the superposition of climate and human activities. Climate change is the main influencing factor, followed by human activities, and the superposition effects of the two are spatio-temporal differences. Conclusions: These results shed light on the development of desertification in OSL and the relative importance and complex interrelationship between human activities and climate in regulating the process of desertification. Based on this, we suggest continuing to implement the ecological restoration policy and avoid the destruction of vegetation by large-scale animal husbandry in order to improve the situation of desertification. [ABSTRACT FROM AUTHOR]

The Grain for Green Project (GGP) has considerably improved the vegetation cover of the Loess Plateau, as well as changed the carbon and water coupling process of local vegetation to a certain extent. Water use efficiency (WUE) is a crucial measure for evaluating ecosystem responses to global climate change and is a key indicator of the carbon–water coupling between terrestrial ecosystems and the environment. A comprehensive understanding of the impact of vegetation reconstruction on WUE on the Loess Plateau is of great significance to the vegetation growth and contribution to sustainable of the Loess Plateau. In recent years, scholars have gained a more comprehensive understanding of the distribution and drivers of WUE on the Loess Plateau. However, through the study of carbon and water coupling in the Loess Plateau, it is found that the effects of different vegetation restoration levels on WUE are still to be studied in depth in terms of spatial and temporal heterogeneity and long timeseries. In this paper, we analyzed the trends of Normalized Difference vegetation cover (NDVI) and WUE from 2001 to 2010 and 2011 to 2020, respectively, to research at the WUE of the vegetation in this area in relation to vegetation restoration. It was found that the Loess Plateau's vegetation WUE rose from 2001 to 2020 at a rate of 0.023 g C kg−1 H2O per year, and that the increase from 2011 to 2020 was more significant than the growth from 2000 to 2010. The Loess Plateau's area with a growing trend in vegetation water use rate increased from 77.12% in 2001–2010 to 88.63% in 2011–2020, with the majority of the increased area occurring in the northeastern Inner Mongolia region. After 20 years of the reforestation project, the area where NDVI and WUE increased simultaneously accounted for 71.54% of the Loess Plateau, the area where NDVI increased but WUE decreased accounted for 10.95% of the Loess Plateau, and the area where NDVI increased but WUE decreased accounted for 7.15% of the Loess Plateau. The correlation between temperature precipitation and WUE was not significant for the whole Loess Plateau, further indicating that the increase in vegetation cover was the main reason for the increase in vegetation water efficiency. Therefore, the effect of vegetation cover on WUE should be fully considered when vegetation restoration is carried out on the Loess Plateau. [ABSTRACT FROM AUTHOR]

Extending across three major plateaus, namely the Qinghai-Tibetan Plateau, the Inner Mongolia-Xinjiang Plateau and the Loess Plateau, Northwest China has the complex terrain and spatio-temporal climate variations, and is affected by the interactions among different circulation systems, such as the summer monsoon, the westerlies and the plateau monsoon. The understanding of the climate variability, as well as its characteristics and evolution mechanisms in this area has been limited so far. In this paper, the precipitation characteristics and mechanisms in the eastern and western parts of Northwest China during the flood season are compared and analyzed based on the data from 192 national meteorological observational sites in Northwest China in 1961–2016. The results show that, divided by the northern boundary of the East Asian summer monsoon, there are huge differences in the precipitation variation characteristics between the eastern and western parts. The inter-annual variations, inter-decadal variations and total trends in the two parts all show a significant seesaw phenomenon. Moreover, it is found that the seesaw phenomenon of precipitation variation is closely related to the opposite variation between the East Asian summer monsoon index (MI) and the westerly circulation index (WI). In addition, the inverse variations on different time scales are only related to the contributions of precipitation at specific grades. Besides, in the two matching patterns of precipitation in the seesaw phenomenon, the middle and high latitudes are occupied by the "high-low-high" wave trains in the precipitation increases in the east of Northwest China (ENWC) and decreases in the west of Northwest China (WNWC) pattern, meaning precipitation increases in ENWC and decreases in WNWC. Whereas the opposite "low-high-low" wave trains at 500 hPa height are observed in the middle and high latitudes in the WH-EA pattern at 500 hPa height, meaning precipitation increases in WNWC and decreases in ENWC. Thus, the atmosphere circulation situation with two wave train types can support both the precipitation seesaw phenomenon and the opposite variation between MI and WI. Moreover, the seesaw phenomenon is shown to be related to the separate or joint effects of the South Asian High, ENSO and the plateau heating on the common but opposite effect on the summer monsoon and the westerlies, in which the South Asian High probably plays a more critical role. This study could deepen the scientific understanding of precipitation mechanisms and improve the weather forecast technology in Northwest China during the flood season. [ABSTRACT FROM AUTHOR]

• This paper aims at quantifying the factors affecting vegetation growth in the western of Inner Mongolia, and judging the relative contribution of natural factors and man-made factors. • It is obtained through geodetectors (GD) that Rainfall is the most important driving factor, with the q-value of 0.77 and GDP also having a huge impact on vegetation growth, q-value of 0.64. • Different from most studies, human activity also has a positive effect on vegetation growth. • The relative contribution of human activities reached 51.75%, slightly exceeding the influence of natural factors. Vegetation is an essential component of terrestrial ecosystems, and understanding the drivers of vegetation change is of great importance for ecological management. In recent years, vegetation growth has increased under the combined effect of global warming and human activities in Inner Mongolia. The net primary productivity (NPP) was used as an indicator to study the spatial and temporal changes in vegetation in the Qimeng Region (QR). The residual trend analysis method was used to analyze the relative contributions of climate variations (CV) and human activities (HA) to NPP changes across the QR, while their drivers were explored using a geographical detector approach to quantify the driving forces of NPP. The results show that (1) NPP exhibited a fluctuating growth trend from 2003 to 2020, with an overall growth rate of 2.91%/year. (2) Precipitation, GDP and population density were the dominant driving factors for the spatial distribution of NPP. The combined explanatory power of any two dominant factors exceeded the power of any dominant individual factor, and the interaction between climate and human factors had a significant effect on NPP. (3) The change in NPP was influenced by the combined effect of HA and CV, accounting for 37.69% of the total area, with the relative contribution of HA being 51.75%. Finally, the relative contribution of human activities was slightly higher than that of climate change, confirming the initial success of the Grain to Green Program as well as ecological conservation projects. This paper provides a scientific basis for the local government to carry out the conversion of cropland to forest. [ABSTRACT FROM AUTHOR]

The Qehan Lake Basin (QLB) and its system of lakes are located in a marginal monsoon zone and are extremely sensitive to global climate change. In this paper, using aerial photographs from different periods, in addition to MSS, TM, and ETM images, and combining these with regional topographic maps, we analyze lake area changes from 1958 to 2010 and the relation between Qehan Lake (QL) and climate variability. Our results indicate that there was a relatively high lake level in 1959, when the area and volume of the lake were 118.9 km and 151.9 × 10 m, respectively, but this level was subject to a shrinking trend until 2010, when the lake area was only 28.1 km, and the water volume was 41.1 × 10 m. West Qehan Lake (WQL) has experienced severe water shrinkage and lake level fluctuation. In 1958, WQL was 80.2 km in area and 124.1 × 10 m in volume. However, due to a rapid decrease in precipitation and increases in both temperature and evaporation, it began to dry up in 2002. The WQL Water area decreased by 1.82 km/a, and the lake level declined by 7 m during 1958-2002, so it became an ephemeral lake. [ABSTRACT FROM AUTHOR]

The northern edge of the East Asian monsoon region is also a transition zone of temperate continental and monsoon climate, which is a sensitive zone of global climate and ecological environment change. It is an urgent problem to find out the long-term climate change rules of this region. Using 101 tree core samples obtained from 54 trees, a 343-year tree-ring width chronology of the Platycladus orientalis (L.) Franco (1673–2016) in the Wula Mountains, Inner Mongolia, China was established. The analysis shows that the radial growth of P. orientalis is closely related to hydrothermal conditions, the average minimum temperature and relative humidity are the main factors that determine the growth of P. orientalis. Based on the strong correlation between the tree-ring indices and the annual average minimum temperature (r = −0.870, p < 0.001), we reconstructed an average annual minimum temperature series from 1673 to 2016 using a linear regression equation, and the reconstructed equation can explain 75.724% of the variance, respectively. According to the data from the past 343 years, the changes in the annual average minimum temperature series had the cycles of 58.6-, 7.7-, 3.4-, 3.1-, 2.2-, and 2.1-years; the series showed three warm periods (1824–1830, 1909–1922, and 1986–2011) and 4 cold periods (1703–1712, 1731–1738, 1754–1763, and 1937–1951). The low temperature periods recorded in the reconstructed series for the 17th and 18th centuries are consistent with the Little Ice Age that occurred in the northern hemisphere, and the heating rate significantly increased at the end of the 20th century. In addition, the cold and warm periods recorded by the reconstruction result is basically consistent with the extreme climate events recorded in the historical documents of the region and the reconstruction results in other regions. In this paper, the world's first chronology of P. orientalis tree ring width was established. A historical sequence of annual average minimum temperature of 343 years was reconstructed in a typical region of the East Asian monsoon climate. It fills in the blank of reconstruction sequence and variation characteristics of mean minimum temperature in the northern edge of East Asian monsoon and transition zone of temperate continental and monsoon climate in recent four centuries. [ABSTRACT FROM AUTHOR]

Context: Forest–grassland ecotones, the transitional zones between forests and grasslands, often harbor high levels of biodiversity. According to the portfolio theory, communities with such high biodiversity should exhibit high stability in ecosystem functioning. On the contrary, the theory of 'bi-stability' or 'multistability' predicts that ecosystems exhibiting multiple states or regimes (e.g. forest and grassland) are less stable. Understanding the stability of forest–grassland ecotone has broad implications for forest management and restoration. Objectives: In this study, by quantifying landscape heterogeneity and temporal invariability of regional climatic and vegetational metrics, we aimed to reveal how landscape diversity influences the stability of ecosystem functioning in the forest–grassland ecotone. Methods: We focused on the forest–grassland ecotone in northern China (i.e. from the forest belt in the Greater Khingan Mountains to the steppe grassland in Inner Mongolia) and used remote sensing data of land cover dynamics from 1992 to 2015 to analyze the landscape diversity and ecosystem dynamics at different grain sizes. To measure the temporal stability of climatic variables and vegetational dynamics, we calculated the interannual variation of temperature (T), wind speed (WS), precipitation (P), and the net primary productivity (NPP) on the grided sampling cells at different spatial scale over the same period. Results: Our results showed that landscape diversity across this forest–grassland ecotone was positively related to the stability of T and NPP, but negatively related to that of WS and P. These opposite patterns may be explained by the differential nature of ecosystem variables, namely whether they involve transporting substance across space (WS and P) or not (T and NPP). We also found that as the spatial scale increases, the diversity-stability relationships strengthened for all three climatic variables (i.e., T, P, WS). However, the stabilizing effects of landscape diversity on NPP first increased but then decreased with increasing grain size, peaking at ~ 30 km. Conclusions: Our empirical analyses demonstrate strong, but context-dependent diversity-stability relationships, shedding light on the paradox in the stability of ecosystem functioning in the ecotone. These findings also have implications for the management of landscape resources to maintain the sustainability of forest–grassland ecotones. [ABSTRACT FROM AUTHOR]

Considering Inner Mongolia as the study area, the ecological theory of climate change, and human activities affecting a wide range of biodiversity patterns, MODIS multi-timeseries remote sensing image data were used and the interannual variation index was obtained by the method of fitting the curve to obtain the annual phenological and seasonal indicators. At the same time, the Landsat 8 standard deviation image was calculated to obtain the spatial variation index and generate spatial–temporal remote sensing indices to quantify the threat of climate change to biodiversity. In addition, the impact of human activities on biodiversity was quantified by generating a map of the human footprint in Inner Mongolia. The spatial–temporal remote sensing index and the human footprint index were integrated to identify areas protected from climate change and human activities, respectively. Eventually, the hotspot areas of biodiversity conservation in Inner Mongolia were obtained and priority protected area planning was based on the hotspot identification results. In this study, remote sensing technology was used to identify biodiversity conservation hotspots, which can overcome the limitations of insufficient species data from the past, improve the reliability of large-scale biodiversity conservation analyses, and be used for targeted management actions that have practical significance for biodiversity conservation planning. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI) is a key indicator of vegetation structure and function, and its products have a wide range of applications in vegetation condition assessment and usually act as important input parameters for ecosystem modeling. Grassland plays an important role in regional climate change and the global carbon cycle and numerous studies have focused on the product-based analysis of grassland vegetation changes. However, the performance of various LAI products and their discrepancies across different grassland types in drylands remain unclear. Therefore, it is critical to assess these products prior to application. We evaluated the accuracy of four commonly used LAI products (GEOV2, GLASS, GLOBMAP, and MODIS) using LAI reference maps based on both bridging and cross-validation approaches. Under different grassland types, the GLASS LAI performed better in meadow steppe (R2 = 0.26, RMSE = 0.41 m2/m2) and typical steppe (R2 = 0.32, RMSE = 0.38 m2/m2); the GEOV2 LAI performed better in desert steppe (R2 = 0.39, RMSE = 0.30 m2/m2). When we assessed their spatial and temporal discrepancies during the period from 2010 to 2019, the four LAI products overall showed a high spatial and temporal consistency across the region. Compared with GLASS LAI, the most consistent to least consistent correlations can be ordered by GEOV2 LAI (R2 = 0.94), MODIS LAI (R2 = 0.92), and GLOBMAP LAI (R2 = 0.87). The largest differences in LAI throughout the year occurred in July for all grassland types. Limited by the location and number of sample plots, we mainly focused on spatial and temporal variations. The spatial heterogeneity of land surface is pervasive, especially in vast grassland areas with rich grassland types, and the results of this study can provide a basis for the application of the product in different grassland types. Furthermore, it is essential to develop highly accurate and reliable satellite-based LAI products focused on grassland from the regional to the global scale according to these popular approaches, which is the next step in our work plan. [ABSTRACT FROM AUTHOR]

This study evaluates how resilient farmers' livelihoods are to climate change and what factors influence this resilience. To measure resilience, we constructed an indicator system based on the livelihood resilience analysis framework. We surveyed 42 experts and 630 farmers after a climate change disturbance in Aohan Banner, Inner Mongolia, from August to October 2021, and analyzed these data using the comprehensive index method. Meanwhile, we used a multiple linear regression model to analyze the key factors affecting farmer livelihood resilience across different livelihood types and towns. We found that farmers who primarily worked in agriculture had the highest resilience scores and that livelihood resilience differed by geographical location; specifically, livelihood resilience gradually declines from southern to northern areas and from forest and forest-grassland to grassland locations. The results also show that education level, agricultural technology training, transportation infrastructure, accessibility of information, awareness of climate change, climate change perception, change in livelihood strategies, family size, and the holding size of the arable area are positively associated with farmer livelihood resilience, while household head age is negatively associated with resilience. We therefore advise that policymakers should diversify agricultural livelihoods, afforest surrounding arable areas, improve transportation infrastructure, increase learning activities and skill training for farmers, and publicize climate change knowledge. [ABSTRACT FROM AUTHOR]

• Designing an integrated regional water environment modeling framework • Concurrent analysis of seasonal water and its driving forces across space and time • Differentiating how different blocks of driving factors interact with water scarcity • Driving factor impacts on water scarcity increase substantially over time • Local resource endowment and utilization critically impact spatial heterogeneity Increased grazing and agricultural production, industrialization, population growth, and consequent land use land cover (LULC) changes considerably increase water consumption. Global climate change exaggerates the uncertainty of water sources and supplies. Unfortunately, most current examinations are either confined within disciplinary silos or not integrated for considering wide-ranging socioenvironmental, management, and policy factors. The paper develops an integrated regional water environment modeling framework, examining how climate, LULC, socioenvironmental, and policy factors interact with the water environment. It also adopts a block-based econometric panel data analysis to quantify this framework. The paper extracts seasonal water area and LULC data through image processing from 2000 to 2014 in the Hulun-Buir watershed, Inner Mongolia of China. The paper quantitatively analyzed the interactions between seasonal water changes and major driving factors, such as climatic, land-use, socioeconomic, policy, space, and time. Many of these driving factors were interacting with the seasonal water environment and showing long-term causal relationships. The socioeconomic variables explained 71% of the variance of seasonal water change, the environmental and climatic factors about 9%, the regional disparities around 13%, and the yearly differences about 4%. The findings confirm that it is critical to carry out a time-series examination of causal relationships between seasonal water change and its manifold driving factors at the scale of regional watershed studies. This integrated watershed modeling framework is suitable for adaptation in other geographic areas or for integrated studies of other socio-environmental systems. Graphical Abstract [Display omitted]. [ABSTRACT FROM AUTHOR]

In this paper, we proposed a framework for evaluating the performance of ecosystem strategies prepared for enhancing vulnerability reduction in the face of hazards due to climate change. The framework highlights the positive effects of human activities in the coupled human and natural system (CHANS) by introducing adaptive capacity as an evaluation criterion. A built-in regional vulnerability to a certain hazard was generated based upon interaction of three dimensions of vulnerability: exposure, sensitivity and adaptive capacity. We illustrated the application of this framework in the temperate farming-grazing transitional zone in the middle Inner Mongolia of the northern China, where drought hazard is the key threat to the CHANS. Specific indices were produced to translate such climate variance and social-economic differences into specific indicators. The results showed that the most exposed regions are the inner land areas, while counties located in the eastern part are potentially the most adaptive ones. Ordos City and Bayannur City are most frequently influenced by multiple climate variances, showing highest sensitivity. Analysis also indicated that differences in the ability to adapt to changes are the main causes of spatial differences. After depiction of the spatial differentiations and analysis of the reasons, climate zones were divided to depict the differences in facing to the drought threats. The climate zones were shown to be similar to vulnerability zones based on the quantitative structure of indexes drafted by a triangular map. Further analysis of the composition of the vulnerability index showed that the evaluation criteria were effective in validating the spatial differentiation but potentially ineffective because of their limited time scope. This research will be a demonstration of how to combine the three dimensions by quantitative methods and will thus provide a guide for government to vulnerability reduction management. [ABSTRACT FROM AUTHOR]

Water availability is one of the most important environmental controls on vegetation phenology, especially in semi-arid regions. It is often represented in terms of soil moisture in small-scale studies, whereas it tends to be represented by precipitation in large-scale (e.g., regional) studies. Clearly, soil moisture is the more appropriate indicator for root water uptake and vegetation growth/phenology. Its potential advantage and applicability needs to be demonstrated at regional scales. The paper presents a data-based regional study of the effectiveness of novel water and temperature-based indices to predict spring vegetation green-up dates based on the Normalized Difference Vegetation Index (NDVI) observations in the grasslands of Inner Mongolia, China. The macro-scale hydrological model, VIC (Variable Infiltration Capacity), is employed to generate a soil moisture database across the region. In addition to a standard index based on temperature, two potential hydrology-based indices for prediction of spring onset dates are defined, based on the simulated soil moisture data as well as on observed precipitation data. Results indicate that the correspondence between the NDVI-derived green-up onset date and the soil-moisture-derived potential onset date exhibits a significantly better correlation as a function of increasing aridity compared to that based on precipitation. In this way the soil-moisture-based index is demonstrated to be superior to the precipitation-based index in terms of capturing grassland spring phenology. The results also showed that both of the hydrological (water-based) indices were superior to the thermal (temperature-based) index in determining the patterns of grass green-up in the Inner Mongolia region, indicating water availability to be the dominant control on average. The understanding about the relative controls on grassland phenology and the effectiveness of alternative indices to capture these controls are important for future studies of vegetation phenology change under climate change. [ABSTRACT FROM AUTHOR]

Evaluating the variations in terrestrial water storage anomalies (TWSA) associated with climate forcing and human activities in the Mongolian Plateau is crucial for assessing water scarcity and predicting potential pressures on water resources in the future. In this study, we assessed the impacts of climatic and anthropogenic drivers on the change in TWSA on the Mongolian Plateau by using the Independent Component Analysis (ICA) to examine Gravity Recovery and Climate Experiment (GRACE) based TWSA data and comparing the ICA modes with hydrometeorological data and statistical data related to human activities. The results showed that TWSA in the Mongolian Plateau has experienced significant depletion (−2.3 ± 0.62 mm/year) from 2002 to 2017, with a severe decline (−3.4 ± 0.78 mm/year) in Inner Mongolia, China, and a moderate depletion rate in Mongolia (1.44 ± 0.56 mm/year). The results of the statistical analysis indicated that climate change was the dominant driver for the decline in TWSA from 2002 to 2007, resulting in a decrease in TWSA in Mongolia and Inner Mongolia at rates of −5.17 ± 1.13 mm/year and −5.01 ± 2.0 mm/year, respectively. From 2008 to 2017, the intensity of human activities has increased in Mongolia, but climate-driven effects greatly offset the anthropogenic changes, leading to an increasing trend in TWSA in Mongolia during this period. Nevertheless, in Inner Mongolia, the anthropogenic water depletion, such as irrigation, coal mining, and grazing, outweighed the climate contributions on the variations in TWSA, causing the TWSA in Inner Mongolia to decline at a rate of 1.08 ± 0.54 mm/year during 2007–2011. [ABSTRACT FROM AUTHOR]

Ecological protection and high-quality development of the Yellow River Basin (YRB), China, aroused remarkable concerns from China's Central Government, and has been a major national strategy. The Inner Mongolia reach of the Yellow River Basin (IM-YRB) is a typical dryland with pervasive vegetation restoration through the actions of the ecological projects that have been conducted in recent years. However, how climate changes and human activities, such as land use and land cover (LULC) changes, jointly impact vegetation variations in this region remains poorly understood. Here, using an explainable machine learning technique, we evaluated linkages between the kernel normalized difference vegetation index (kNDVI) and air temperature, precipitation, soil moisture, and LULC changes, and relevant marginal contributions of these four drivers to the observed vegetation changes. The grassland fraction on a pixel level was selected as the quantitative LULC variable due to its key role in regional LULC. We found that interannual kNDVI changes in most areas of this study region were negatively sensitive to temperature, but positively sensitive to precipitation and soil moisture, with area fractions of 71.74%, 96.93%, and 89.33%, respectively. The area fraction of negative kNDVI sensitivity to LULC was roughly equivalent to that of positive kNDVI sensitivity. The contributions of air temperature, precipitation, soil moisture, and LULC to overall kNDVI changes were 21.54%, 33.32%, 32.19%, and 12.95%, respectively. Moisture conditions also play a critical role in vegetation changes, which was reflected by the fluctuating growth of kNDVI as interannual changes in precipitation. Nonetheless, kNDVI changes are also affected by LULC, and LULC became the dominant factor behind the kNDVI anomalies over the grassland restoration regions from barren over the IM-YRB. This research provides theoretical support for dryland vegetation restoration under the influence of climate change. [ABSTRACT FROM AUTHOR]

This study mainly examined the relationships among primary productivity, precipitation and temperature by identifying trends of change embedded in time-series data. The paper also explores spatial variations of the relationship over four types of vegetation and across two precipitation zones in Inner Mongolia, China. Traditional analysis of vegetation response to climate change uses minimum, maximum, average or cumulative measurements; focuses on a whole region instead of fine-scale regional or ecological variations; or adopts generic analysis techniques. We innovatively integrate Empirical Mode Decomposition (EMD) and Redundancy Analysis (RDA) to overcome the weakness of traditional approaches. The EMD filtered trend surfaces reveal clear patterns of Enhanced Vegetation Index (EVI), precipitation, and temperature changes in both time and space. The filtered data decrease noises and cyclic fluctuations in the original data and are more suitable for examining linear relationship than the original data. RDA is further applied to reveal partial effect of precipitation and temperature, and their joint effect on primary productivity. The main findings are as follows: (1) We need to examine relationships between the trends of change of the variables of interest when investigating long-term relationships among them. (2) Long-term trend of change of precipitation or temperature can become a critical factor influencing primary productivity depending on local environments. (3) Synchronization (joint effect) of precipitation and temperature in growing season is critically important to primary productivity in the study area. (4) Partial and joint effects of precipitation and temperature on primary productivity vary over different precipitation zones and different types of vegetation. The method developed in this paper is applicable to ecosystem research in other regions. [ABSTRACT FROM AUTHOR]

Clarifying the spatiotemporal variation of crop irrigation water requirement (IWR) under the background of climate change is an essential basis for water resource management, determining the irrigation quota and adjusting the planting structure. Using 61 years of climate data from 205 stations in Northwest China, this study investigated the spatiotemporal variations of climatic factors and IWR during the growth period of five main grain crops (spring wheat, winter wheat, spring maize, summer maize, and rice) and explored the dominant climatic driving factors of IWR variation. Results showed that (1) the IWR of grain crops showed distinct differences. Rice was the highest water consumption crop (mean of 753.78 mm), and summer maize was the lowest (mean of 452.90 mm). (2) The variation trends and average values of IWR of different grain crops have spatial heterogeneity across Northwest China. For most crops, high values and increasing trends of IWR were mainly located in eastern Xinjiang, western Gansu, and western Inner Mongolia. (3) Tmax (maximum temperature), Tmin (minimum temperature), and Peff (effective precipitation) showed an increasing trend during the growth period of each grain crop, while U10 (wind speed at 10 m height), SD (solar radiation), and RH (relative humidity) presented decreasing trends. (4) SD, Tmax, and U10 promoted the increase of grain crops' IWR, while Peff and RH inhibited it. The impacts of climatic factors on the grain crop IWR differed among different regions. Peff was the most influential factor to the IWR of all grain crops in most areas. Therefore, under the premise of a significant increase in T and uncertain precipitation mode in the future, it is urgent to take effective water-saving measures according to the irrigation needs of the region. To cope with the adverse impact of climate change on the sustainable development of agriculture in the northwest dry area, to ensure regional and national food security. [ABSTRACT FROM AUTHOR]

The arid and semiarid grassland ecosystems are characterized by limited water resources and are generally vulnerable to climate change. Understanding how plants in arid and semiarid ecosystems respond to global climatic variation is crucial for ecological restoration under a changing climate. Although the effects of climate on aboveground biomass (AGB) and belowground biomass (BGB) have been widely explored, how AGB and BGB respond to climatic variation is seldom disentangled. There is still a need to understand how plant communities respond to global climate change. In this study, we conducted a transect survey across grasslands in Inner Mongolia to capture changes in AGB and BGB in plant communities. Then, we used structural equation modelling (SEM) to explore the relationship between environmental factors and the root:shoot ratio to understand how plant communities respond to ecological drought under global climate change. Our results showed that low precipitation use efficiency (LPUE) results in a high root:shoot (HRS) ratio, and BGB was more sensitive to environmental changes. By contrast, high precipitation use efficiency (HPUE) led to a low root:shoot (LRS) ratio, and environmental factors had a greater impact on AGB. For the LPUE pattern, soil water content (SWC), pH, and soil total nitrogen (STN) mainly affected the HRS ratio. Soil water content and STN influenced the HRS ratio through a positive effect on BGB. For the HPUE pattern, SWC, STN, and plant abundance (PA) predominantly regulate the LRS ratio, while biodiversity (plant abundance) affects the LRS ratio by positively affecting AGB. Our results highlight the differential impact of precipitation use efficiency on aboveground and belowground biomass allocation. This is important for monitoring the impact of drought events on plant biomass, improving productivity assessment models in arid and semi‐arid regions, and assessing local carbon storage accurately. [ABSTRACT FROM AUTHOR]

Quantitative assessment of the dynamics of carbon and water use efficiency on the Loess Plateau in the context of complex climate change and its driving mechanisms is important for the improvement of the regional ecological environment and the enhancement of ecological service functions. In order to assess the impact of climate change and human activities on the carbon and water use efficiency of the Loess Plateau, this study investigates the spatial and temporal rates of change in CUE, WUE, and meteorological factors at the image metric scale using one-dimensional linear fit regression, investigates the spatial correlation between CUE and meteorological factors using partial correlation analysis, and quantifies the relative contributions of human activities to CUE and WUE using residual analysis. The following are the study's conclusions: (1) The CUE and WUE of the Loess Plateau decreased geographically from 2000 to 2020, and both the CUE and WUE of the Loess Plateau exhibited a non-significant declining trend (p > 0.05), with the CUE falling at a rate of 0.001/10a (a: year) and the WUE decreasing at a rate of 0.047/10a (a: year). (2) From 2000 to 2020, the mean values of the CUE and WUE of the Loess Plateau were 0.60 and 1.75, respectively, with a clear spatial difference. (3) CUE was favorably linked with precipitation in 56.51% of the Loess Plateau, dispersed throughout the south-central portion of the Loess Plateau and Inner Mongolia, whereas the biased association with temperature was not statistically significant and often negative. (4) An examination of residuals revealed that human activities affected the trend of CUE and WUE to some degree. Only the WUE residuals of evergreen broadleaf forests exhibited a strong upward trend that was considerably influenced by people. In conclusion, this study used remote sensing image data and meteorological data to systematically analyze the spatial and temporal dynamic patterns of carbon use efficiency and water use efficiency on the Loess Plateau over the past 21 years, as well as the characteristics of their responses to climate change and human activities, thereby providing theoretical guidance for the study of carbon and water cycles in terrestrial ecosystems on the Loess Plateau. [ABSTRACT FROM AUTHOR]

Purpose: Nitrogen (N) deposition and warming may influence microbially mediated processes and functioning of ecosystem. Our study aimed to examine how soil bacterial and fungal community composition, diversity, and interactions respond to N deposition and warming. Materials and methods: High-throughput sequencing and bioinformatic analysis were performed to explore microbial community composition and diversity, and cohesion analysis was adopted to assess the microbial interactions after 11 consecutive years ammonium nitrate supplementation and warming in a desert steppe of Inner Mongolia, Northern China. Results: Our results demonstrated nitrogen supplementation, warming, and N supplementation plus warming affected the bacterial and fungal community structures, and the effects were soil depth-dependent. N supplementation improved the relative abundance of copiotrophic groups (Bacteroidetes and Betaproteobacteria) and restrained oligotrophic groups (Chloroflexi, Deltaproteobacteria, and Acidobacteria) at 0–2 cm depth. N supplementation plus warming significantly increased the relative abundance of Ascomycota at 2–5 cm depth. N supplementation and N supplementation plus warming significantly reduced bacterial diversity. The redundancy analysis demonstrated that pH and N availability significantly contributed to the variation in bacterial and fungal community, respectively. N supplementation and warming simplified bacterial connectivity and improved positive cohesion: negative cohesion ratio, while warming increased fungal connectivity and reduced positive cohesion: negative cohesion ratio. Conclusion: The effects of N supplementation and warming on bacterial and fungal community structure were soil depth-dependent. N supplementation and N supplementation plus warming reduced bacterial diversity. N supplementation and warming simplified bacterial interactions. The bacterial community was more sensitive to nitrogen supplementation and warming than fungal community. [ABSTRACT FROM AUTHOR]

Background: Inner Mongolia, desertification is happening due to climate change and land use alterations. In order to evaluate desert restoration effectiveness, this study compares number of species and species diversity in restored (with planted trees), unrestored area, and the reference ecosystem (Ref-E, typical steppe and woody steppe). Results: The Ref-E had the most plant species (64 taxa), while the unrestored area had the fewest (5 taxa). Among restored areas (restored in 2012, 2008, 2005), older restoration sites had more species (18-42). Similarly, species richness (3.93-0.41) and diversity (1.99-0.40) were highest in the Ref-E and lowest in unrestored areas, with older restored sites having higher values. Conclusions: More plant species and diversity in older restoration areas suggest progress toward ecosystem stabilization, approaching the Ref-E. Therefore, tree planting in Inner Mongolia's Hulunbuir semi-arid desert is a successful restoration effort. [ABSTRACT FROM AUTHOR]

• A dynamic two-stage SBM model with non-discretionary variables is proposed. • China's CPLR efficiency under four scenarios of extreme temperatures is measured. • The influences of extreme temperatures on the CPLR efficiency are highlighted. • A comparative analysis of CPLR efficiency under four scenarios is provided. With the influence of climate changes resulting in more extremely days, we might expect to face a rise in associated coal productivity losses and land destruction. Numerous studies have focused on measuring the efficiency of coal production system. Yet scant studies have considered their internal structure, reconstructed lands, and especially the impact of extreme temperatures. Then, this paper divided such a system into stages of coal production (CP) and land reconstruction (LR), named the CPLR system. This study proposes a modified two-stage dynamic slacks-based measure model with non-discretionary variables for measuring the CPLR efficiency under four scenarios of extreme temperatures. The results are as follows. (1) The CPLR system for 24 provinces under four scenarios spanning 2012 to 2017 had relatively low efficiency except for Fujian, Inner Mongolia and Sichuan, which is mainly caused by the bad performance of the LR system. (2) Extreme temperatures must be considered in measuring CPLR efficiency and its stage efficiency, or otherwise the underestimation of CPLR efficiency and CP efficiency will become in 18 provinces. The negative impacts of extreme temperatures on the efficiency of reconstructed lands and raw coal production should be noticed, especially for Anhui. [ABSTRACT FROM AUTHOR]

Quantitative assessment of the terrestrial water storage (TWS) changes and the major driving factors have been hindered by the lack of direct observations in Inner Mongolia, China. In this study, the spatial and temporal changes of TWS and groundwater storage (GWS) in Inner Mongolia during 2003–2021 were evaluated using the satellite gravity data from the Gravity Recovery and Climate Experiment (GRACE) and the GRACE Follow On combined with data from land surface models. The results indicated that Inner Mongolia has experienced a widespread TWS loss of approximately 1.82 mm/yr from 2003–2021, with a more severe depletion rate of 4.15 mm/yr for GWS. Meteorological factors were the driving factors for water storage changes in northeastern and western regions. The abundant precipitation increased TWS in northeast regions at 2.36 mm/yr. Anthropogenic activities (agricultural irrigation and coal mining) were the driving factors for water resource decline in the middle and eastern regions (especially in the agropastoral transitional zone), where the decrease rates were 4.09 mm/yr and 3.69 mm/yr, respectively. In addition, the severities of hydrological drought events were identified based on water storage deficits, with average severity values of 17 mm, 18 mm, 24 mm, and 33 mm for the west, middle, east, and northeast regions, respectively. This study established a basic framework for water resource changes in Inner Mongolia and provided a scientific foundation for further water resources investigation. [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]

Ephedra sinica Stapf is an important traditional medicinal plant. However, in recent years, due to climate change and human activities, its habitat area and distribution area have been decreasing sharply. In order to provide better protection for E. sinica, it is necessary to study the historical and future potential zoning of E. sinica. The maximum entropy model (MaxEnt) was used to simulate the potential geographical distribution patterns of E. sinica under historical and future climatic conditions simulated using two Shared Socio-economic Pathways. The main results were also analyzed using the jackknife method and ArcGIS. The results showed that: (1) the potential suitable distribution area of E. sinica in China is about 29.18 × 105 km2—high-suitable areas, medium-suitable areas, and low-suitable areas cover 6.38 × 105 km2, 8.62 × 105 km2, 14.18 × 105 km2, respectively—and E. sinica is mainly distributed in Inner Mongolia; (2) precipitation and temperature contribute more to the distribution of E. sinica; (3) under two kinds of SSPs, the total suitable area of E. sinica increased significantly, but the differences between 2021–2040, 2041–2060, 2061–2080, and 2081–2100 are not obvious; (4) the barycentre of E. sinica moves from the historical position to its southwest. The results show that E. sinica can easily adapt to future climates well, and its ecological value will become more important. This study provides scientific guidance for the protection, management, renewal and maintenance of E. sinica. [ABSTRACT FROM AUTHOR]

Water use efficiency (WUE) reflects the balance between carbon assimilation and water consumption in terrestrial ecosystems. Considering the fragile conditions of global water resources, the evaluation of regional WUE variation characteristics and response mechanisms is critical for promoting sustainable ecological development and water resource utilization. Based on gross primary productivity (GPP) and evapotranspiration (ET) datasets, combined with vegetation and meteorological data, this study examined the spatiotemporal variations, annual variation contribution rate, and driving mechanism of WUE in Inner Mongolia from 2001 to 2020. The main results are as follows: (1) The interannual and spatial variations of GPP, ET, and WUE all exhibited increasing trends, with WUE increasing in approximately 70% of the region and significantly increasing in 22.35% of pixels exhibiting a significant increase. Areas with the most significant increases were located in the Horqin sandy land and Mu Us sandy land. (2) The highest WUE values were observed in the summer season, followed by autumn and winter, and the lowest in spring. (3) Among all vegetation types, the typical steppe ecosystems contributed most to the interannual variability (IAV) of GPP, ET, and WUE, with values of 169.89%, 141.09%, and 193.42%, respectively. While the coniferous forest contributed least or negatively to GPP, ET, and WUE IAV, with values of −36.28%, 28.20%, and −32.86%, respectively. (4) The primary driver of WUE variation was found to be GPP, which contributed 59.36%, mainly in the central and western regions. The remaining 40.64% was attributable to ET, concentrated in the northeast region. (5) Human activities significantly affected WUE, with a contribution (about 53.52%) larger than that of climate change (nearly 46.48%). Increased precipitation improves vegetation WUE and is the most important climate factor influencing WUE variations. These findings will aid the formulation of vegetation protection and water resource management strategies in water-stressed areas. [ABSTRACT FROM AUTHOR]

The paper investigates how climate changes and socioeconomic transformations affected grassland productivity from 2000 to 2017 in counties in Inner Mongolia of China. The grassland productivity and climatic change variables included 162 readings (9 periods in the growing season × 18 years), while ten socioeconomic variables contained 18 yearly readings. The mismatch between the environmental observations and socioeconomic data compounds quantitative analysis of causal relations between them. We applied several time-series processing methods to generate five datasets. We fit them with conventional, dependent-lagged, and dynamic-lagged panel regression models to investigate which combinations augment causal relationships between grassland productivity, climate, and socioeconomic changes. The results confirmed that the conventional model with the original data failed to reveal causal relationships between these factors. The panel regression models with the original data tended to intensify the causal effects of climate factors on grassland productivity. The models with the transformed datasets disclosed more socioeconomic variables showing causal relationships with grassland productivity. The dependent-lagged model with the empirical mode decomposition transformed data produced the highest R-squared value and lowest prediction error. GDP, state fixed asset investment, highway construction, grain plantation, and livestock density were primary factors affecting grassland productivity. In brief, the integration of time-series data mining and time-lagged panel analysis effectively unveiled the causes of grassland deterioration under climate and socioeconomic changes. [Display omitted] • Augment causality hidden in env-socio-economic systems with data assimilation. • Assimilate temporal environmental and socioeconomic data into compatible time-series. • Experiment with four groups of computational data mining methods on data assimilation. • Compare combinations of data assimilation methods and lagged panel regression models. [ABSTRACT FROM AUTHOR]

The impact of global climate change on ecosystem structure has attracted much attention from researchers. However, how climate change and meteorological conditions influence community phylogenetic structure remains poorly understood. In this research, we quantified the responses of grassland communities' phylogenetic structure to long- and short-term meteorological conditions in Inner Mongolia, China. The net relatedness index (NRI) was used to characterize phylogenetic structure, and the relationship between the NRI and climate data was analyzed to understand the dynamics of community phylogenetic structure and its relationship with extreme meteorological events. Furthermore, multiple linear regression and structural equation models (SEMs) were used to quantify the relative contributions of meteorological factors before and during the current growing season to short-term changes in community phylogenetic structure. In addition, we evaluated the effect of long-term meteorological factors on yearly NRI anomalies with classification and regression trees (CARTs). We found that 1) the degree of phylogenetic clustering of the community is relatively low in the peak growing season, when habitat filtering is relatively weak and competition is fiercer. 2) Extreme meteorological conditions (i.e., drought and cold) may change community phylogenetic structure and indirectly reduce the degree of phylogenetic clustering by reducing the proportion of dominant perennial grasses. 3) Meteorological conditions before the growing season rather than during the current growing season explain more variation in the NRI and interannual NRI anomalies. Our results may provide useful information for understanding grassland community species assembly and how climate change affects biodiversity. [ABSTRACT FROM AUTHOR]

The formation and development of blowouts is an important surface indication of sand drift activity in the Hulun Buir sandy grassland and the main cause of landscape pattern heterogeneity. In this study, dynamic changes and determinants of blowouts in this region were studied via analysis of multi‐period high‐resolution remote sensing data from 1959 to 2018. The different stages of activity and dominant factors of the fixation and activation of blowouts were analysed with Lancaster's M‐index. The results show that the activity of blowouts in Hulun Buir sandy grassland can be divided into three stages from 1959 to 2018: (I) growth and expansion stage, from 1959 to 2004; (II) fixation and grassland recovery stage, from 2005 to 2010; (III) redevelopment stage, from 2011 to 2018. The two geomorphic units of depressions and depositional lobes exhibit different responses to changes in climatic factors. The depositional lobes are more sensitive to the change in external factors with better feedback and their areas and numbers change with the variation of stage, while depressions exhibit relatively slow changes. The wind regime is the dominant factor explaining the variation of activity of blowouts, with precipitation and potential evapotranspiration as secondary factors for this region. Roads have little effect on the development and deformation of blowouts, but the movement of cattle and sheep across the region has a big influence. Therefore, the desertification process of Hulun Buir grassland is driven by both natural and human factors. [ABSTRACT FROM AUTHOR]

Questions: Drought timing during the growing season can profoundly influence plant diversity and cover, with subsequent impacts on ecosystem functions and services in terrestrial environments. However, the different responses of plant communities to drought in different periods of the growing season remain uncertain. Location: The research was conducted in a semi‐arid grassland at the Duolun Restoration Ecology Research and Demonstration Station, in Inner Mongolia, China. Methods: The responses of plant species richness and cover to early‐ and late‐season drought with a four‐year field experiment were examined in a semi‐arid grassland in northern China. Soil moisture at different depths was measured to explore the underlying mechanisms of the drought effects on plant richness and cover. Results: Over the four years, late‐ rather than early‐season drought suppressed plant richness. Decreased species richness (−1.65 species) under late‐season drought was mainly due to suppressed perennial forb richness (−1.88 species). Late‐season drought also exerted greater negative effects on plant cover than early‐season drought. Effects of late‐season drought on plant cover depended on annual precipitation. Reduced plant cover (−7.5%) under early‐season drought stemmed from declines in shrubs and semi‐shrubs, particularly Artemisia frigida cover (−4.9%), which is sensitive to water deficits because of its shallow root depth (14.01 cm). Declined plant cover (−39.0%) under late‐season drought was mainly caused by suppressed perennial forb (−21.5%) and shrub and semi‐shrubs cover (−17.4%). The combined effects of early‐ and late‐season drought on plant cover were greater than the treatment alone, likely due to aggravating water deficits under growing‐season drought. Conclusions: Our results suggest late‐season drought is more critical for shifting plant communities. than early‐season drought. Our findings highlight the regulatory role of drought timing on plant community composition and development in arid and semi‐arid regions. [ABSTRACT FROM AUTHOR]

Under global warming, according to results obtained from offline drought indices driven by projections of general circulation models (GCMs), future droughts in China will worsen but the results are not consistent. We analyzed changes in droughts covering the entire hydrologic cycle using outputs of GCMs of the 6th Coupled Model Intercomparison Project (CMIP6) for SSP2-4.5 and SSP5-8.5 climate scenarios, and compared the results with that of popular, offline drought indices [the self-calibrating Palmer Drought Severity Index (scPDSI), Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Precipitation Actual Evapotranspiration Index (SPAEI)]. Among meteorological, agricultural, and hydrological drought indices tested under both SSP scenarios, the results obtained from SPAEI and scPDSI agree better with univariate drought indices than SPEI. scPDSI generally agrees well with agricultural droughts (Standardized Soil Moisture Index with the surface soil moisture content; SSIS). Future droughts estimated using soil moisture analysis are more widespread than that from precipitation and runoff analysis in humid regions of South China by the end of the twenty-first century. In arid northwestern China and Inner Mongolia, drought areas and severity based on scPDSI and SSIS forced with the SSP scenarios show obvious decreasing trends, in contrast to increasing trends projected in South China. Trends projected using SPEI contradict those projected by other drought indices in non-humid regions. Therefore, selecting appropriate drought indices is crucial in project representative future droughts and provides meaningful information needed to achieve effective regional drought mitigation strategies under climate warming impact. [ABSTRACT FROM AUTHOR]

Global surface temperature is increasing significantly under climate change with increasingly uneven distribution of precipitation, which has been making an important impact on the sustainable development of the dryland agriculture in semi-arid regions. The Inner Mongolia Autonomous Region (IMAR) has the largest area ratio of semi-arid regions in China, and spring wheat is one of its major grain crops, and the negative impacts of climate change pose a serious threat to its production. In order to reduce this threat, we assessed the impact thresholds and risks posed by the main climatic factors to spring-wheat production, and put forward quantitative adaptive countermeasures. The results showed that, from 1961 to 2012, the appropriate impact thresholds of average temperature, precipitation, fertilization level, and water supply level were 15.6 °C, 333 mm, 319.5 kg/ha, and 743.7 mm, respectively. Compared with the average values in 1996–2012, the maximum fertilization level and water supply level could be increased by 162.2 kg/ha and 406.5 mm, respectively, to improve the adaptive yield of spring wheat. The risks of adverse impacts of temperature rise, precipitation decrease, and their combined effect on spring-wheat production were found to be 11.1%, 9.3%, and 10.3%, respectively. The whole IMAR showed a high risk of adverse impacts of temperature rise and the combined action of temperature rise and precipitation decrease on spring-wheat production. In particular, the spring-wheat production in the northeastern, eastern, and central areas were found to be at the highest risk to the impacts of climate change. Based on the determined appropriate thresholds of fertilization level and water supply level, it was determined that in the northeastern area, the maximum fertilization level and water supply level could be increased by 229.9 kg/ha and 441.4 mm, respectively, to increase the adaptive yield of spring wheat. In the eastern area, the two factors could be increased by 142.2 kg/ha and 464.3 mm, respectively. In the central area, they could be increased by 242.2 kg/ha and 513.7 mm, respectively. In the southwestern area, they could be increased by 41.4 kg/ha and 201.7 mm, respectively. [ABSTRACT FROM AUTHOR]

• A hydrological modeling approach was proposed to distinguish the relative contribution of climate changes and human activities on Daihai Lake's recession. • In the absence of natural runoff and human-induced water withdrawal observations, the natural state's lake water storage changes were reconstructed. • Without considering the high water years, the anthropogenic factor took the responsibility for the lake shrinkage about 58%, while the climatic factor took roughly about 42% in period 1974–2018. • This investigation serves as a useful example of applying a distributed model to the assessment of lake hydrology, especially for the lake catchments with limited observations. In recent decades, water levels in many closed inland lakes over north-western China experienced severe declines, among which the Daihai Lake in Inner Mongolia, China was particularly significant. Remotely sensed investigations indicated that the Daihai Lake had shrunk approximately 64% in an area with the dramatic decline of water level approximately 9 m from the 1980 s to 2010 s, which has caused serious concern on the sustainable development of the surrounding region. Consequently, several previous studies have concentrated on the possible causes of lake shrinkage. However, predicting climate variability and anthropogenic activities' relative contributions to lake water decline is challenging due to the lack of direct water consumption upstream and watershed-scale natural runoff observations. In this paper, a hydrological modeling approach was presented to quantitatively evaluate and separate the relative contributions of climate variations and intensifying anthropogenic activities. The simulation accuracy was evaluated through comparisons with observed data and cross-validation with previous studies. Based on a baseline period, the natural streamflow draining into the lake was reconstructed using the model. The results revealed that the water loss of the Daihai Lake was caused predominately by human activities rather than climate variability. Specifically, for wet conditions, the climate factors posed a negative effect on the water level decline of the lake and delayed the shrinking process of the lake area. The anthropogenic factors took a dominant role (roughly 58%) of lake shrinkage at the annual average scale for normal and dry conditions, while the climate factors accounted for approximately 42%. Hence, the rational policy for water resource management, ecological restoration, and dam and reservoir constructions/operations should be the essential precautions for sustainable development of the Daihai Lake and its surroundings. Additionally, the hydrological modeling approach adopted in this investigation serves as a useful example of the quantitative assessment of climate variations and human activities, especially for the ungauged catchments. [ABSTRACT FROM AUTHOR]

Quantitatively, analyzing the driving mechanism of vegetation coverage change is of important significance for regional ecological environment evaluation and protection. Based on time series NDVI data and meteorological data of the Yellow River Basin (Inner Mongolia Section), the trend and significance of climate factors and vegetation coverage in the YRB (IMS) and four sub-regions (the Hetao Irrigation district, the Ten Tributaries region, the Hunhe river basin, and the Dahei river basin) from 2000 to 2018 were ascertained. We used geographic detectors to quantitatively analyze the effects of detection factors on vegetation coverage change. The results indicated that the spatial pattern of vegetation variation and climate change had obvious spatial heterogeneity. During 2000–2018, the regions with vegetation improvement (72.87%) were much greater than that with degradation (26.55%) in the YRB (IMS). Annual precipitation change (4.55%) was a key driving factor to the vegetation coverage change in the YRB (IMS). Among the four sub-regions, the land use conversion type demonstrated the largest explanatory power, but the q values of the four sub-regions were different from each other. The results of the interaction showed that land use change and annual precipitation change were the major driving factors that influenced regional vegetation coverage change. This study has an important reference value for improving the basin's ecological environment. [ABSTRACT FROM AUTHOR]

The typical steppe in Inner Mongolia is an important component of the Eurasian steppes. It plays a dominant role in preventing desertification and against sandstorms, but highly sensitive and vulnerable to climate change. Based on long-term observed precipitation data and remotely sensed Normalized Difference Vegetation Index (NDVI) images, the non-stationary behavior of summer precipitation and its linkage with vegetation change were investigated, by means of incorporating time-varying and physical based explanatory covariates in non-stationary modeling. Results indicated that time-dependent models exhibited good performance to reproduce the temporal variations of eco-hydrological variables. The non-stationarity of summer precipitation was prominently visible for the majority of sites during the period from 1957 to 2017, with the mean behavior described as a linear or nonlinear time-varying pattern. In general, the steppe has experienced a decreasing trend in summer precipitation, but whether the decline tends to maintain or weaken or strengthen depends on the spatial location of the site studied. Differences appeared in the changes of vegetation in summer from 1998 to 2017 in different sub-regions. Evidences for the presence of stationary evolution was found in most sub-regions in the middle part, together with a linear increase in the westernmost sub-regions while a non-linear decrease in the easternmost sub-regions. Covariate analyses further highlighted the role of precipitation variabilities in the modeling of the NDVI-related vegetation dynamics over the steppe. The potential relations of summer precipitation to vegetation growth were characterized as both linear and non-linear positive forms. In particular, precipitation extremes could be responsible for the occurrences of exceptional cases in vegetation condition. The fluctuations in summer precipitation have crucial significance for future predictions of vegetation succession. Findings from this study would lead to additional insights to understanding the effect of climate change on grassland ecosystem processes. [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]

The development of animal husbandry in China is facing enormous pressure from increasing demand of meat consumption, climate change, degrading grassland, and changing national policies. This paper presents the latest findings from an anthropological field investigation interviewing local herders, traders, and local officials from farming-pastoral ecotone in Inner Mongolia and shed lights on how locals have adapted their lives to climate change and the new national husbandry policies of the twenty-first century. Based on the anthropological interviews, we discussed the future opportunities and challenges of animal husbandry development in farming-pastoral ecotone. Results suggested that national ecological conservation policies and meat price have much larger impacts on animal husbandry than climate change, as perceived by locals. Family Fencing Policy, a relatively new policy aiming to avoid overgrazing and restore vegetation, was neither well accepted nor well implemented by the local herders. This is partially explained by the poor fencing technology, insufficient support facilities, as well as the high costs and low-profit margins in the animal husbandry. We conclude by suggesting that pastoralism in Northern China may greatly benefit from the development of rural cooperatives and active participation of locals in policy designing and implementation. [ABSTRACT FROM AUTHOR]

Global climate change is altering the amounts of ice and snow in winter, and this could be a major driver of soil microbial processes. However, it is not known how bacterial and fungal communities will respond to changes in the snow cover. We conducted a snow manipulation experiment to study the effects of snow removal on the diversity and composition of soil bacterial and fungal communities. A snow manipulation experiment was carried out on the meadow steppe in Hulunbuir, Inner Mongolia, China, during the winter period October 2019–March 2020. Soil samples were collected from the topsoil (0–10 cm) in mid-March 2020 (spring snowmelt period). Snow removal significantly reduced soil moisture and soil ammonium concentration. Lower snow cover also significantly changed the fungal community structure and beta diversity. Snow removal did not affect the bacterial community, indicating that fungal communities are more sensitive to snow exclusion than bacterial communities. The relative importance analysis (using the Lindeman–Merenda–Gold method) showed that available nitrogen (AN), soil water content (SWC), total organic carbon (TOC), microbial biomass carbon (MBC), and microbial biomass nitrogen (MBN) together explained 94.59% of the variation in soil fungal beta diversity, where AN was identified as the most important predictor. These finding provide insights into potential impacts of climate warming and associated reduced snow cover on soil microbial communities and processes. [ABSTRACT FROM AUTHOR]

Abstract: Renewable energy is one of the possible solutions when addressing climate change. Today, large-scale renewable energy integration needs to include the experience to balance the discrepancy between electricity demand and supply. The electrification of transportation may have the potential to deal with this imbalance and to reduce its high dependence on oil production. For this reason, it is interesting to analyse the extent to which transport electrification can further the renewable energy integration. This paper quantifies this issue in Inner Mongolia, where the share of wind power in the electricity supply was 6.5% in 2009 and which has the plan to develop large-scale wind power. The results show that electric vehicles (EVs) have the ability to balance the electricity demand and supply and to further the wind power integration. In the best case, the energy system with EV can increase wind power integration by 8%. The application of EVs benefits from saving both energy system cost and fuel cost. However, the negative consequences of decreasing energy system efficiency and increasing the CO2 emission should be noted when applying the hydrogen fuel cell vehicle (HFCV). The results also indicate that developing renewable energy is crucial for transportation electrification. [Copyright &y& Elsevier]

Abstract: Inner Mongolia is an important ecological barrier in northern China. The vegetation coverage and changes in the region will directly affect many important economic regions in China. In this paper, based on the long time series NOAA/AVHRR NDVI dataset and vegetation-climate regions map in Inner Mongolia, we utilized the Mann - Kendall non-parametric test to analyze vegetation change treads in different vegetation-climatic regions from 1982 to 2006. The results showed that different vegetation-climate regions had different vegetation change treads. Therein, Central and Western parts of Inner Mongolia had an ascending trend, while, the eastern part had a descending trend. Inner Mongolia span different climatic regions, and plants lived there had various adaptive capacities under different environments. Therefore, its change trends also were quite different. All these illustrate that, under the background of climate change, the sensibility of vegetation are significant different and some plants in different regions have showed their adaptive capacities. [Copyright &y& Elsevier]

Abstract: Knowledge about changes of regional carbon density during late Quaternary is of great significance for understanding regional and global carbon cycles. Taking the Hunshandake Sandy Land, located in eastern Inner Mongolia of China as an example, this paper aims to reconstruct Late Quaternary environmental changes, and then to discuss the fluctuations of organic carbon density and storage in the desert environment of northern China. The study area with a mean annual precipitation ca. 300 mm is characterized by the occurrence of fixed and semi-fixed sand dunes reaching a maximal height of 30 m. The research methods included field observations, examinations of grain sizes, as well as carbon and calcium carbonate contents of the sediment samples. The chronology is based on Optically Stimulated Luminescence (OSL) dating and radiocarbon analyses. This work reveals that the organic carbon density in the surface layer of the Hunshandake Sandy Land changed between at least 2500 g/m2 during the Holocene Climatic Optimum and 1500 g/m2 when aeolian processes dominate. The dune construction was restricted by periglacial processes during the Last Glacial Maximum (LGM). The Holocene Climatic Optimum, indicated by extensive formation of kastanozem soils, occurred between 5 ka and 3 ka in the study area. Although desertification might cause a 40% loss of total organic carbon storage within several dozen of years in the study area, a considerable amount of this carbon was re-deposited in the regions of downwind directions. [Copyright &y& Elsevier]

Desertification and the expansion of deserts are regarded as a major threat to human livelihoods and ecosystem services, as well as achieving Sustainable Development Goals. Humans have been recognized as significant agents in the expansion of desert‐like conditions, through the agency of desertification/land degradation, and through contributions to global climate change. Yet, how desertification changes and whether human or climate change drives these processes and trends over the whole Mongolia Plateau is still not clear. Here we first use MODIS data for 2000–2019 to assess the long‐term vegetation changes across the Mongolian Plateau, both in Inner Mongolia of PR China and the Mongolian People's Republic. We then appied a General Linear Model to analyze the additional climate and human attribute data sets to explore relationships between the observed desert extent reduction and their potential drivers. Results show that from 2000–2019, desert extent declined by over 7%, comprised of an almost 11% reduction in Inner Mongolia and 5% reduction in Mongolia, and a reduction in the degree of desert conditions, as measured by vegetation cover grades, across the whole region (37.5% decline ‐ one grade in PR China, 28.75% in Mongolia). The majority of desert area reduction occurs in the northern and eastern areas of the Mongolia Plateau. We found that the main drivers of changing desert extent is not because of a reduction in human pressures on the land, but an increasing trend of both precipitation and temperature, which explains over 75% of all the observed changes. Besides, urbanization, and a reduction of population pressures on the land, contribute to most of the remaining changes. Our results suggest that although the climate has been the primary driver of changing desert area and severity, human interventions have played an important role, although the way it intervenes and the intensity varies in Inner Mongolia and Mongolia. Human's active response to land degradation in the desert contributes to more land restoration in Inner Mongolia than in Mongolia. [ABSTRACT FROM AUTHOR]

• In the Mongolian Plateau, wildfires occurred frequently from 2001 to 2015. • Advancing spring phenology more obvious in the Inner Mongolian than in Mongolia. • Preseason temperature has the greatest impact on the spring phenology. • In a typical fire area, the SOS in the fire zone shifted to earlier in the year. The variation of land surface phenology (LSP) in natural ecosystems has received much attention at the regional and global scales. In this paper, we explore the relationship between variations in the start of the growing season (SOS) and their direct and indirect drivers for the Mongolian Plateau. Our results show that wildfire events mainly occurred in 2001, 2002, 2006, 2008, 2011, and 2016. The frequency of Mongolian wildfires is lower in the south and west, but higher in the east and north. The frequency of wildfires in Inner Mongolia (China) shows a spatial gradient, with a higher frequency in the northeast and a lower frequency in the southwest. Late SOS was most widely distributed in the southeast of Mongolia and the south of Inner Mongolia. The vegetation in the grassland or forest areas showed a comparatively early SOS, between the 110th and 130th day-of-year (DOY). The advancing trends of the SOS were mainly distributed around the northeast Mongolian Plateau, which is more obvious in Inner Mongolia (China) than in Mongolia. Under the control of precipitation (pre) and solar radiation, the preseason average temperature (T mean) in most areas of the study area is from 0 to 3 months. In the Mongolian Plateau, negative partial correlations between the SOS and preseason T mean was mainly concentrated in the northern and northeastern parts of the study area, while positive correlations were mainly distributed in the central and southeastern parts. We also found that in addition to climate factors, wildfires have an impact on the SOS. After wildfires, the enhanced vegetation index (EVI), T mean and SOS showed a significant difference in the greening period between the reference region and the burned area. Compared with the reference zone, the SOS in the fire zone shifted to being earlier. This study, therefore, provides a scientific basis and reference for understanding variations in the SOS in the study area and the response of the SOS to climate change and wildfires. [ABSTRACT FROM AUTHOR]

Lake Hulun is the fifth‐largest lake in China, playing a substantial role in maintaining the balance of the grassland ecosystem of the Mongolia Plateau, which is a crucial ecological barrier in North China. To better understand the changing characteristics of Lake Hulun and the driving mechanisms, it is necessary to investigate the water storage changes of Lake Hulun on extended timescales. The main objective of this study is to reconstruct the water storage time series of Lake Hulun over the past century. We employed a machine learning approach termed the extreme gradient boosting tree (XGBoost) to reconstruct the water storage changes over a one‐century timescale based on the generated bathymetry and satellite altimetry data and investigated the relationships with hydrological and climatic variables in long term. Results show that the water storage changes from 1961 to 2019 were featured by four fluctuation phases, with the highest water storage observed in 1991 (14.02 Gt) and the lowest point in 2012 (5.18 Gt). The century‐scale reconstruction result reveals that the water storage of Lake Hulun reached the highest point in the 1960s within the period of 1910–2019. The lowest stage occurred in the sub‐period of the 1930s–1940s, which was even lower than the alerted shrinkage stage in 2012. The predictive model results indicate the effective performance of the XGBoost model in reconstructing century‐scale water storage variations, with the mean absolute error of 0.68, normalized root mean square error of 0.11, Nash–Sutcliffe efficiency of 0.97, and correlation coefficient of 0.94. The annual fluctuations of water storage were mostly affected by precipitation, followed by vapor pressure, temperature, potential evapotranspiration, and wet day frequency. The dominating characteristics of different variables vary evidently with different sub‐periods. The atmospheric circulations of the Arctic Oscillation, El Nino Southern Oscillation, Pacific Decadal Oscillation, and North Atlantic Oscillation have tight associations with the water storage variations of Lake Hulun, which change with different study periods. Plain Language Sumamry: To better understand the changing characteristics of Lake Hulun and the driving mechanisms, it is necessary to investigate the water storage changes of Lake Hulun on extended timescales (e.g., century timescale). The main objective of this study is to reconstruct the water storage time series of Lake Hulun over the past one century. We employed a machine learning approach termed the extreme gradient boosting tree (XGBoost) to reconstruct the one‐century water storage changes and investigated the relationships with hydrological and climatic variables in long term. The century‐scale reconstruction result reveals that the water storage reached the highest point in the 1960s within the period 1910‐2019. The lowest stage occurred in the 1930s‐1940s, which was even lower than the alarted shrinkage stage in 2012. The annual fluctuations of water storage were mostly affected by precipitation, followed by vapor pressure, wet day frequency, potential evapotranspiration, and temperature. However, the dominating characteristics of different variables vary evidently with different sub‐periods. This study is expected to provide an efficient technical solution of reconstructing long‐term lake water storage records and to advance our scientific understanding of the characteristics of lake water balances in response to climate change and variability in the Mongolia Plateau. Key Points: The complete bathymetry map of Lake Hulun was constructed based multi‐source remote sensing dataA machine learning approach was employed to reconstruct the century‐scale water storage of Lake HulunThe potential links of one‐century variations of lake water storage with climatic variables and atmospheric circulations [ABSTRACT FROM AUTHOR]

Unanticipated environmental shocks impact the livelihoods of many resource users around the world. These shocks are likely to be more unpredictable as the effects of climate change continue to mount. Yet how households adapt to these changing climatic conditions especially in the context of rapidly changing market conditions in most areas of the world, is poorly understood. These interactions have wide implications for both smallholder livelihoods and sustainable use of natural resources. In this paper, we examine the relationship between environmental shocks and stocking rates in livestock herds in the Inner Mongolian grasslands of northern China. We uniquely examine three types of shocks and how households adapt livestock production strategies in response to each. Separately and in aggregate, we look at three common shocks in Inner Mongolia: droughts, snowstorms, and locust outbreaks. We use a difference-in-differences approach to estimate changes in stocking rates among households that experience shocks versus those that do not with a panel dataset from 2009 to 2014 of 597 households. While we find no clear impact from locusts, our results suggest droughts and snowstorms have opposite effects: droughts are associated with increases in herd sizes, but snowstorms result in decreased herds. We suggest these differences are due to interactions between shocks, emerging options to borrow on credit, and livestock markets. Household adaptation to climate change will be strategic and take advantage of both available resources as well as market conditions. • Climate change is causing stronger and more unpredictable environmental shocks. • We use a difference-in-differences model to estimate the impact of shocks on stocking rate. • Drought relates to bigger herds; snowstorms relate to smaller herds. • Households adapt to shocks taking advantage of both available resources and market conditions. • Shaping well-functioning market may reduce opportunistic resource exploitation. [ABSTRACT FROM AUTHOR]

Spatially high-resolution precipitation data is important for the users to study basin-scale hydro-logy and climate change. Inner Mongolia is a vast and sparsely populated region with sparse rainfall distribution. The Tropical Rainfall Measuring Mission (TRMM) satellites provide a remote-sensing data source covering the whole globe but is generally in coarse spatial resolution and cannot satisfy the needs of users for driving hydrological models or making sustainable plans for local areas. Downscaling is needed to achieve resolution refinement on the TRMM data. In this study, for the area of Inner Mongolia, four different models including exponential regression model (ERM), multiple linear regression model (MLR), generalized linear regression model (GLM), geographically weighted regression model (GWR) were used to obtain annual average precipitation data in a high resolution (1.0 km2), based on the 0.25-degree TRMM data during 2005–2016. The predictors for the downscaling were screened using stepwise regression. Results indicated that: (1) Compared with the GWR, only the MLR performs better in the northeastern part of Inner Mongolia; (2) According to the spatial pattern of precipitation and the verification based on the measured precipitation, GWR is the most suitable model for the region; (3) Due to the varied climates in different parts of Inner Mongolia, the idea of common regression, which uses a set of constant parameters across different areas, has a poor capability in capturing the high-value areas of precipitation, and local regression which uses varying parameters has a better performance, comparatively; (4) In the northwest Inner Mongolia, due to low vegetation coverage and sparse gauges, all the four models performs poorly. [ABSTRACT FROM AUTHOR]