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• Quantitatively describes the groundwater recharge process for lakes. • Constructed a model for the groundwater table-lake area relationship in groundwater-dependent lakes in arid regions. • Suggested a groundwater depth of 1.73 m to maintain stable lake area within the Hetao Irrigation District. • Constructed model can serve as an effective reference tool for groundwater management and the protection of lake ecosystems. The groundwater table constitutes a significant factor in sustaining the ecosystems of lakes in arid regions. To elucidate the mechanism by which variations in groundwater tables affect lake areas, this paper presents a case study of a typical groundwater-fed lake in China's Hetao Irrigation District. An analytical model correlating groundwater tables with lake areas was developed to simulate and predict the impact of changes in groundwater tables on lake areas. The findings indicate that the analytical model adeptly simulates the lake area dynamics of the groundwater-fed lake cluster (Taerhu lake cluster) from 1998 to 2018, achieving a Nash-Sutcliffe efficiency (NSE) value exceeding 0.6 and a root mean square error (RMSE) value below 0.1 square kilometers. Maintaining a groundwater depth at 1.73 m ensures the stability of the lake area; a decrease in groundwater depth to 1.93 m would result in a 79.5 % reduction in lake area. The model constructed in this study offers a crucial tool for the ecological protection of lakes and groundwater management in arid regions. [ABSTRACT FROM AUTHOR]

[Display omitted] • Spring drought factors include snow cover, precipitation, temperature, and soil moisture. • Established a variance analysis projection pursuit model (ANOVA PPM) • ANOVA PPM achieves a balance between overall dispersion and local aggregation. Due to the influence of snow cover and soil freeze–thaw processes in the early spring, the characteristics of spring drought in cold regions are different from those in other regions. This paper takes Qiqihar, a cold and semiarid grain production area in Northeast China, as the study area. Based on factors such as snow cover in the early spring period and precipitation, temperature, evapotranspiration, and soil moisture during the spring sowing period, a projection pursuit model with analysis of variance (ANOVA PPM) was used to analyze the spatiotemporal variation characteristics of regional spring drought. For the ANOVA PPM, based on the grouping of the projection points, local aggregation is measured by the mean square within groups, and overall dispersion is measured by the mean square between groups, thus achieving comparability and balance between overall dispersion and local aggregation. The spatial analysis of spring drought showed that, from a spatial perspective, there were significant spatial differences in spring drought in different subregions, and spring drought was closely related to latitude, with significant differences occurring at different latitudes. The northern region is relatively light, while the central and southern regions are more severe. From a temporal perspective, there are significant differences in spring droughts between different decades (1980 s, 1990 s, 2000 s and 2010 s). Overall, spring drought first intensifies and then decreases. [ABSTRACT FROM AUTHOR]

• The runoff, sediment load and runoff erosion power decreased significantly. • Compared with runoff, runoff erosion power-sediment relationship is better. • The power function of runoff erosion power and sediment is better than linear. • Erosion in particular geographic locations responds strongly to runoff erosion power. In recent years, the acceleration of urbanization in the Yangtze River Economic Belt (YREB) has brought about problems such as soil erosion, ecosystem degradation and decline in ecological service functions. Therefore, this paper selects the Fujiang River Basin (FRB) in Upper Yangtze River (UYR), and analyzes the relationship between runoff erosion power (REP) and sediment transport (ST) on the basis of analyzing the variation characteristics of hydrological elements. Based on SWAT model, a variety of scenarios are set up to quantitatively analyze the impact of different land use changes on runoff and ST and REP in the basin. The results indicated that: (1) The annual precipitation in FRB from 1960 to 2018 did not change significantly, while the annual runoff, annual ST and annual REP decreased significantly. Compared with runoff and runoff depth, the relationship between sediment transport modulus (STM) and REP is the best, especially the power function between them is better than the linear function. (2) The results of the evaluation indexes of the simulation results of the SWAT model based on the measured runoff and sediment data from 2008 to 2018 are pass and above, that is, the simulation results are considered reliable. (3) Under the five scenarios of conversion of sloping farmland to forest, conversion of dry land to paddy field and conversion of grassland to bushland and forest land, the runoff and ST in the basin decreased but the overall range was small. The simulation results of SWAT model show that the erosion of specific areas in the basin shows the greatest degree of weakening with the decrease of REP. [ABSTRACT FROM AUTHOR]

• This paper constructed a framework for green water assessment in the Loess Plateau gully region using MIKE SHE model. • A new concept and extraction method for green water components was defined based on existing research. • A new concept of the potential of green water was defined in relation to the dried soil layers' classification indicators. • We assessed the dependence on green water components and potential for environmental changes. Exponentially increasing water demand and increasingly uncertain hydrological conditions due to climate change (CC) and land use/cover change (LUCC) undoubtedly pose significant challenges to the sustainability of water systems in arid and semi-arid regions worldwide, and even more so in the rain-fed agricultural area of the Loess Plateau (LP) in China, where green water supports all ecological landscapes and most agriculture. Therefore, an accurate assessment of green water resources is crucial for regional development. This paper constructed a comprehensive framework for assessing green water resources based on the MIKE SHE model, defined a new concept of green water components (GWCs) that can be extracted based on existing studies, proposes the potential of green water (GW P), and present a research methodology. To validate this research framework, we applied it to a typical basin in the LP gully area of the Nanxiaohegou (NXHG) basin. The model was calibrated using runoff data from basin outlet stations to extract four GWCs: high-efficiency green water (GW H), low-efficiency green water (GW L), unused green water (GW U), ineffective green water (GW I). We then assessed the GW P of the basin and analyzed its trends, consistency, and dependence on changing environments. The assessment framework is simple and easy to use, providing valuable information for regional revegetation and basin water management, and is highly recommended for replication in other regions. [ABSTRACT FROM AUTHOR]

• Managed vegetation represents a powerful tool for climate adaptation and mitigation, offering significant potential for positive environmental impact. • In the context of the Arabian Peninsula, managed vegetation has the remarkable capability to cool the surface temperature by over 5°Celsius, particularly during the hottest days of the year. • Achieving sustainability in water management remains a critical challenge that requires careful attention and proactive measures. The growth of vegetation in desert areas is expected to cause considerable changes to the local climate. Compared to bare ground desert, the balance of different processes related to vegetation such as reduced albedo and increased evapotranspiration can either warm up or cool down the surface climate. Quantifying these changes is essential for impact assessments of climate mitigation and adaptation measures, especially in regions increasingly hit by heat waves such as the Arabian Peninsula. In this respect, the analysis of in situ observations is challenging owing to limited data, uneven vegetation distribution, complex terrain, and other confounding factors. Thus, this study, using long-term satellite data at the daily temporal resolution, estimates the impact of vegetation on land surface temperature for several agricultural regions in the Kingdom of Saudi Arabia. Results demonstrate that managed vegetation has a significant cooling effect during the day and particularly during hot weather events, reducing the highest temperatures by > 5 °C. During heat waves, managed vegetation can provide an additional cooling effect of about 3 °C (2 to 4 °C depending on the location). This effect is robustly assessed though a tailored statistical indicator that is suitable for application over complex terrain that are found in the Saudi agricultural sites. However, a situation is identified where the vegetation vigor collapsed and the cooling effect vanished, which was possibly related to issues associated with water management sustainability. Thus, herein, this paper discusses several potential solutions to achieve sustainable irrigation among those that were recently proposed in the scientific literature, such as desalinization from renewable energy and land-use change options that could enhance local precipitation. This study supports the decision-making process of the green initiatives undertaken in the Middle East and similar efforts in other arid regions. [ABSTRACT FROM AUTHOR]

• Based on the principles of SPEI, we develop a GDAI. • The relationship between GDAI and AI is closer. • The correlation between GDAI and SPEI is also better. Climate change has increased the risk of drought in both arid and non-arid regions of the globe. Therefore, in order to monitor and evaluate global drought and aridity, based on the principle of standardized precipitation evapotranspiration index (SPEI), this paper selected precipitation and potential evapotranspiration data and used a frequency calculation method to develop a multi-scale global drought aridity index (GDAI) that concurrently considers spatial and temporal variation characteristics: the spatiotemporal standardized precipitation evapotranspiration index. The index was compared with the aridity index (AI) and SPEI. The results showed that the correlation coefficients between GDAI and AI were greater than 0.8 in most areas, and the relationship was closer and could reveal the global aridity and wetness zoning. In addition, the correlation between GDAI and SPEI was also better, which could capture the temporal change characteristics of drought. Moreover, this study also compared the grades of GDAI and SPEI with the actual drought grades in Yunnan from September 2009 to August 2010, and found that GDAI was better than SPEI, and GDAI was the most accurately evaluated at the 3 month time scale, with an accuracy rate of 86.11%. Therefore, this index is more helpful for the assessment and monitoring of global drought and aridity. [ABSTRACT FROM AUTHOR]

• A Decision Support System for flash-flood risk management in the arid areas is designed. • An integrated GIS and Watershed Modelling System is applied for flood flow simulation. • AI and Classic Delphi are employed for smart sustainable management of flash-flood risks. • Flood risk is analysed using Failure Mode and Effects Analysis & Hazard & Operability techniques. This paper presents a novel framework for smart integrated risk management in arid regions. The framework combines flash flood modelling, statistical methods, artificial intelligence (AI), geographic evaluations, risk analysis, and decision-making modules to enhance community resilience. Flash flood is simulated by using Watershed Modelling System (WMS). Statistical methods are also used to trim outlier data from physical systems and climatic data. Furthermore, three AI methods, including Support Vector Machine (SVM), Artificial Neural Network (ANN), and Nearest Neighbours Classification (NNC), are used to predict and classify flash flood occurrences. Geographic Information System (GIS) is also utilised to assess potential risks in vulnerable regions, together with Failure Mode and Effects Analysis (FMEA) and Hazard and Operability Study (HAZOP) methods. The decision-making module employs the Classic Delphi technique to classify the appropriate solutions for flood risk control. The methodology is demonstrated by its application to the real case study of the Khosf region in Iran, which suffers from both drought and severe floods simultaneously, exacerbated by recent climate changes. The results show high Coefficient of determination (R2) scores for the three AI methods, with SVM at 0.88, ANN at 0.79, and NNC at 0.89. FMEA results indicate that over 50% of scenarios are at high flood risk, while HAZOP indicates 30% of scenarios with the same risk rate. Additionally, peak flows of over 24 m3/s are considered flood occurrences that can cause financial damage in all scenarios and risk techniques of the case study. Finally, our research findings indicate a practical decision support system that is compatible with sustainable development concepts and can enhance community resilience in arid regions. [ABSTRACT FROM AUTHOR]

• The oldest juniper studied in the Fann Mountains in western Tajikistan reached the age of 1320 years. • Moisture conditions are more strongly correlated with growth-ring widths than temperature. • Divergence between dendroclimatic reaction at low and high-elevation sites was confirmed. • Elevation can have stronger effects on the dendroclimatic signal than slope exposure. • Negative growth is triggered by dry conditions and dust storm events at low-elevation sites. This paper reports on a detailed dendrochronological and dendroclimatological study of juniper trees (Juniperus seravschanica Kom. and J. semiglobosa Regel), sampled at seven sites having different elevations (2200 – 3100 m) and exposures (south-north) in the Pamir-Alay mountains, north-western Tajikistan. We developed seven new, well-replicated ring-width chronologies extending back over the last 219–1320 years. Our results confirm that the main climate factor influencing radial growth in this semi-arid region is variability in precipitation. However, they also indicate that there is a divergence in dendroclimatic responses between low- and high-elevation sites, where different limiting factors play a dominant role. The radial growth of J. semiglobosa at the uppermost treeline locations is the most sensitive to winter precipitation. Tree-ring formation in J. seravschanica at low-elevation sites is strongly limited by spring-summer drought conditions and by winter temperatures. The results of PCA analysis indicate that the relationships between growth and climate are linked more to elevation than to exposure. It was also found that negative growth extremes at low elevation sites may be triggered by strong dust storm events. Our findings clearly show that precisely planned selections of topographically differentiated sites, including elevation and exposure, are crucial for the possible reconstruction of different climatic elements in the high mountains of Central Asia for the last hundreds to thousands of years. [ABSTRACT FROM AUTHOR]

• A new hybrid model DC-LSTM for daily streamflow forecasting is developed. • The model is examined for streamflow forecasting in different climate regions. • Integrating LSTM and dynamic classification can improve the performance of streamflow forecasting. • Box -Cox data transformation can further improve model performance in arid regions. Daily streamflow forecasting is a major determinant of ecological processes in running waters, healthy stream ecology and surrounding environment, and accurate streamflow forecasting provides a powerful foundation for ecological assessment, management, and decision-making. Recently, data-driven models for different flow regimes have shown excellent potential in streamflow forecasting. However, the boundaries between different flow regimes were selected arbitrarily without considering the changes in boundaries that often occur over time in the real world. Therefore, in this paper, an integrated modelling approach that couples a dynamic classification method with a long short-term memory networks (LSTM) model without data transformation (the DC-LSTM model) and an LSTM with Box-Cox data transformation (the DC-B-LSTM model) is developed to improve the performance of streamflow forecasting considering different flow regimes. The boundaries of dynamic classification are dynamic changing interval values of related hydrological variables improved from traditional classification method just using static single-variable threshold, so dynamic classification can more fully explore the relationship and information of hydrological data. The performance of both the DC-LSTM and DC-B-LSTM models is compared to that of the LSTM model without data classification (the traditional LSTM model) and with data classification using a traditional static method (the C-LSTM model) based on data from 8 stations within 4 river basins in different climate regions in the United States. The results show that both the DC-LSTM and DC-B-LSTM models out-perform the traditional LSTM models (with or without static data classification) for all river basins considered. Furthermore, the DC-B-LSTM model displays better performance than the DC-LSTM model in arid areas. [ABSTRACT FROM AUTHOR]

[Display omitted] • The proportion of vegetation improvement area in QLMs reached 65.06% in the past 21 years, while 52% of the area will be continuously improved in the future. • The distribution of the NDVI in the QLMs maintained a high spatial agglomeration. • Annual sunshine duration is the main meteorological factor affecting vegetation change in Qilian Mountains. • The interaction between different driving factors can improve the explanatory power of vegetation changes compared with a single driving factor. Vegetation is an important indicator reflecting ecological environment stability, and monitoring vegetation change and understanding its potential driving force have important guiding significance for the adjustment and implementation of ecological restoration measures. As an ecological security barrier in northwest China, the Qilian Mountains (QLMs) play an important role in promoting the green development of social and economic development. Currently, the comprehensive driving effects of natural and anthropogenic factors on vegetation change in the QLMs are not clear. Based on the normalized difference vegetation index (NDVI), the spatiotemporal characteristics and trend changes in vegetation in the QLMs were systematically analyzed from 2000 to 2020 in this paper. The effects of natural and anthropogenic driving factors on vegetation change were explored using a geographic detector (GeoDetector). The results showed that the vegetation has improved continuously in the past 21 years, but the overall vegetation coverage was still low. The vegetation distribution was highly clustered, with a decreasing trend from east to west. Annual sunshine duration (q statistic = 0.3347) and distance to the rivers (q statistic = 0.2649) had the greatest explanatory power for vegetation change, while slope, aspect, and landform type had the least explanatory power. The interaction between elevation and sunshine duration, temperature and precipitation, temperature and sunshine duration, elevation and precipitation had the most explanatory power for vegetation change. Finally, we determined the ranges or categories of driving factors that were most suitable for vegetation growth by using a risk detector. The results of this study can help us further understand the potential driving mechanism of vegetation coverage variation in the QLMs and provide theoretical guidance for relevant managers to formulate ecological restoration measures and land management policies in the next step. It is of great significance to maintain the stability of the fragile ecological environment and prevent land degradation in arid and semiarid areas of Northwest China. [ABSTRACT FROM AUTHOR]

• Spatiotemporal variation in vegetation on the Loess Plateau was investigated. • Precipitation was the greatest driving factor. • There was a bivariate or nonlinear enhancement between all factors. • Key factors' adaptation range or type suitable for vegetation cover was identified. Vegetation, being a core component of ecosystems, is known to be influenced by natural and anthropogenic factors. Studying vegetation cover dynamics variation and its drivers is critical to understanding the interactions between vegetation and ecosystems. The Loess Plateau (LP) is located in a semi-arid and semi-humid region with severe soil erosion and fragile ecology. This paper used the annual maximum Normalized Difference Vegetation Index (NDVI ymax) and growing season mean NDVI (NDVI gsmean) as the vegetation cover indicator. The vegetation cover variation of LP from 2000 to 2020 was analyzed using Sen's slope and Mann-Kendall test. Then the influence of natural and anthropogenic factors on the driving mechanisms of spatial vegetation differentiation was explored by the Geodetector model. The results revealed that the growth trends of NDVI ymax and NDVI gsmean were 0.075/10 years and 0.038/10 years, respectively, and the areas with improved vegetation cover accounted for 92.67 % and 88.58 % of the total area. The vegetation cover of the southeastern and northern parts of the LP decreased significantly; however, the central, southwestern, and northeastern parts exhibited a remarkable improvement. Precipitation, vegetation type, soil type, temperature, and land use type were the key driving forces, ranked differently on NDVI ymax and NDVI gsmean. However, precipitation was the most critical factor both on NDVI ymax and NDVI gsmean. The interaction detection showed non-linear and mutual enhancement, with no independent factor. The findings of our study can assist in identifying the vegetation cover status of the LP, as well as the driving forces, which can provide theoretical support for the formulation of environmental conservation policies. [ABSTRACT FROM AUTHOR]

• Both GPP and GPP max in Chinese grasslands showed increases at different space–time scales. • Trend analysis and breakpoint tests were used for the first time to monitor GPP change simultaneously in Chinese grasslands. • GPP max × CUP is a robust indicator to explain annual-scale variation in GPP. At the ecosystem level, Gross Primary Productivity (GPP) was defined as the organic compounds formed by plants that absorb atmospheric CO 2 through photosynthesis and sequester carbon in plant bodies. How to accurately assess the spatiotemporal evolution of ecosystem carbon indicators has become a critical issue to be addressed. Chinese grasslands are located in a typical arid and semi-arid climate zone and are sensitive to global changes, which will inevitably have serious impacts on the function and structure of Chinese grasslands. Based on this, our paper takes Chinese grassland ecosystems as the research area, and used multi-source GPP dataset from terrestrial ecosystem model simulations and remote sensing satellite observations to quantitatively analyze the spatiotemporal evolution patterns of GPP in Chinese grasslands over the past 40 years. We combined trend analysis and breakpoint test, and then to analyze the mechanism components of GPP space–time variability. The main results are as follows: (1) The model and remote sensing estimated GPP results showed that more than 80% of Chinese grasslands show a significant increasing trend over the past 40 years, with growth rates ranging from 0.68 to 3.13 g C/m−2 year−1. (2) GPP max also shows that more than 80% of Chinese grasslands are growing rapidly, with an overall growth rate of more than 0.1 g C/m−2 year−1 in each region. The overall long-term trends and interannual variability of multi-source GPP and GPP max are generally consistent, yet vegetation dynamics in local areas are still uncertain. (3) The breakpoint test showed that 'monotonically increasing' was the largest breakpoint type of GPP in Chinese grasslands (33.09%), and the direction of change of GPP in Chinese grasslands before and after the breakpoint was also increasing. (4) GPP max × CUP explained 91% of the temporal variability of annual-scale GPP in Chinese grasslands from a mechanistic view, and we found that the peak photosynthetic growth and the length of the phenological period synergistically controlled the interannual variability of GPP in Chinese grasslands. Under the scenario of rapid global change, our study accurately assessed the long-term dynamics of GPP and its mechanism-driven of grassland ecosystems in China, which is helpful for estimating and predicting the carbon budget of grassland ecosystems in China, and has important guiding significance for policy formulation to mitigate climate change. [ABSTRACT FROM AUTHOR]

[Display omitted] • As compared to precipitation, soil moisture greatly influences GPP. • There is a solid spatial agreement among PUE and SWUE with a step-wise gradient from southwest to northeast. • PUE and SWUE are relatively low in extremely arid and humid regions. • In relatively moderate areas, PUE and SWUE increase with the abundance of moisture conditions. • Meadow grassland has the highest PUE and SWUE, followed by typical grassland and desert steppe. Grassland ecosystem dominates in the Mongolian Plateau, mostly located in its arid and semi-arid regions. Although the ecosystem is an important source for agriculture, it is also a fragile system ecologically. This system is one of the most sensitive areas to global climate change. Precipitation (PPT) and soil moisture (SM) are important water sources in the grassland ecosystem, and their changes would greatly affect vegetation growth. This paper generates the precipitation use efficiency (PUE) and soil water use efficiency (SWUE) of Mongolian Plateau grassland based on multi-source remote sensing data to investigate the spatio-temporal distribution pattern and identify the driving factors. Results showed four main findings. Firstly, two water use efficiency (WUE) indicators show a generally increasing trend from 2000 to 2018, with average PUE and SWUE 1.07 gC·m−2·mm−1 and 1.03 gC/kg·H 2 O, respectively. They have similar spatial distribution patterns, consistent with the available water resources, decreasing from northeast to southwest. However, grassland vegetation growth is more sensitive to soil moisture than precipitation, and the dynamic change of SWUE is smoother and more significant than PUE. Secondly, due to the higher species richness, better vegetation biological characteristics and less severe growth environment, meadow grassland has the highest PUE and SWUE, followed by typical grassland and desert steppe. Thirdly, PUE and SWUE are relatively low in extremely arid and humid regions. In areas with relatively moderate water conditions (PPT in 148–360 mm, SM in 0.14–0.35 cm3/cm3), two indicators increase with the abundance of moisture conditions and reach the maximum. Fourthly, there is a positive linear relationship between PUE (SWUE) with precipitation and a unimodal correlation between PUE (SWUE) with temperature across the entire grassland. However, a varying correlation exists in different grassland ecosystems, especially meadow grassland. Together with analyzing past and future trends, this study provides strong evidence to reflect the impact of global climate change and the management and protection of the grassland ecosystems in arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

[Display omitted] • SIF data that characterizes the growth of sandy vegetation is more sensitive. • SIF can well capture the changes in surface moisture and groundwater. • Vegetation growth has a strong correspondence with SM and a relatively weak correlation with TWS. • Vegetation growth in the late period of the mutation year is generally better than that in the early period. • Similarities and differences of vegetation-moisture relationship in four sandy lands. Hulunbuir, Otindag, Horqin and Mu Us are the four greater sandy lands in China. They are typical ecologically fragile areas with limited ability to resist interference and are susceptible to changes in the external environment. With climate change in recent decades, these areas have experienced severe land desertification and decreased groundwater levels. The ecological environment is at risk of further deterioration, and continuous vegetation restoration and management should be adapted to the local climate and maintain the water balance with natural precipitation. This paper combines multi-source satellite observations to explore the relationship between vegetation growth and water supply. The results show four main points. (1) Although the vegetation growth in Otindag and Horqin had degraded before 2010, it improved after 2010. The vegetation growth and soil moisture (SM) of the four sandy lands both generally increased from 2003 to 2016. The SM of the Horqin increased significantly (84% anomaly > 1.4 × 10−2 cm3/cm3). Other than the Hulunbuir, the other sandy lands show a downward trend in terrestrial water storage (TWS), especially the Mu Us declined the most (97% anomaly < −40 mm). (2) Solar-induced chlorophyll fluorescence (SIF) has specific advantages in characterizing the sandy vegetation productivity and water composition changes compared to vegetation index (EVI) or gross primary productivity (GPP). (3) The regional pattern of the vegetation-moisture relationship is mainly affected by the precipitation-driven SM and depends on the types of underlying vegetation functions. (4) Both SM and TWS have approximately one-month persistence in the four sandy lands, especially the Mu Us has the longest persistence (0.94 months and 1.1 months). The persistence in the early period is generally longer than that in the late period, implying that the resilience of water components after the mutation year is stronger than before it. This study provides a scientific basis for the management and restoration of sandy lands. It is recommended that different vegetation restoration and protection strategies should be developed for specific sandy land moisture conditions in arid/semi-arid regions of northern China, which includes the improvement of water use efficiency to maintain soil moisture and the reduction of excessive groundwater uses. [ABSTRACT FROM AUTHOR]

[Display omitted] • Grassland degradation is the main reason for the decline in carbon storage. • The carbon storage of farmland has increased significantly. • The risk of carbon storage reduction in the future is still high. The carbon storage of terrestrial ecosystems is closely related to global climate change. Therefore, assessing the impact of land-use changes on carbon storage is instructive to reduce global carbon emissions. Taking an arid region in northwest China as an example, this paper analyzed the land-use changes in arid region from 1980 to 2015. It used the CA-Markov model to predict the land-use pattern from 2020 to 2050, the carbon storage and sequestration module in Integrated Valuation of Ecosystem Services and Tradeoffs (Invest) mode to assess and predict ecosystem carbon storage over the past 35 years and in the next 30 years and discusses the impact of land-use changes on the carbon storage of terrestrial ecosystems. The results showed that: (1) The high-value areas of carbon storage in arid region were primarily concentrated in grassland, forests and farmland, and the low-value areas of carbon storage were mainly in desert areas. (2) The carbon storage in arid region showed a downward trend. From 1980 to 2015, the carbon reserves of terrestrial ecosystems in arid region decreased by 90.95 Tg, and the net loss of carbon reserves from 2020 to 2050 was 14.72 Tg. Grassland degradation was the main reason for the reduction of carbon storage in arid region. (3) The carbon storage of farmland had increased significantly, but the expansion of farmland dominated by high carbon density grassland, wetland and forest, had reduced the overall carbon storage of the arid region. [ABSTRACT FROM AUTHOR]