Responses to the Impact of Drought on Carbon and Water Use Efficiency in Inner Mongolia

Abstract

The dynamics of plants’ carbon and water use efficiency and their responses to drought are crucial to the sustainable development of arid and semi-arid environments. This study used trend analysis and partial correlation analysis to examine the carbon use efficiency (CUE) and water use efficiency (WUE) of Inner Mongolia’s vegetation from 2001 to 2020. MODIS data for gross primary productivity (GPP), net primary productivity (NPP), potential evapotranspiration (PET), evapotranspiration (ET), drought severity index (DSI), and plant type were used. Altered trends were observed for drought during 2001–2020 in the study area. The results revealed that 98.17% of the research area’s drought trend was from dry to wet and 1.83% was from wet to dry, and the regions with decreased drought regions were broadly dispersed. In 2001–2020, CUE in Inner Mongolia declined by 0.1%·year⁻¹, whereas WUE reduced by 0.008 g C·mm⁻¹·m⁻²·year⁻¹, but the total change was not significant. CUE decreased from west to east, whereas WUE increased from southwest to northeast. DSI and CUE had the highest negative connection, accounting for 97.96% of the watershed area, and 71.6% passed the significance test. The correlation coefficients of DSI and WUE were spatially opposite to those of CUE and DSI. In total, 54.21% of the vegetation cover exhibited a negative connection with DSI. The CUE and WUE of different vegetation types in Inner Mongolia were negatively correlated with the DSI index except for grasslands (GRA). Drought in Inner Mongolia mostly influenced the CUE of different plant types, which had a higher negative correlation than WUE. The study’s findings can inform climate change research on Inner Mongolia’s carbon and water cycles.

1. Introduction

Vegetation is an essential part of terrestrial ecosystems, regulating the water and carbon fluxes. While vegetation transpires water into the atmosphere, it also absorbs carbon dioxide (CO₂) from the air through photosynthesis, creates organic carbon, and stores it in its own tissues. Carbon use efficiency (CUE) is one of the factors that determines how much carbon dioxide (CO₂) is absorbed by vegetation [1,2]. For terrestrial ecosystems, it is a vital part of the carbon cycle and a primary indicator of how efficiently carbon is transported from the atmosphere to terrestrial biomass. Water use efficiency (WUE) quantifies the correlation between the amount of water used by terrestrial ecosystems and carbon emissions [3]. Numerous biotic parameters, such as the leaf area index and vegetation type, and abiotic ones, such as radiation, wind, precipitation, temperature, etc., have an impact on an ecosystem’s CUE and WUE [4,5,6]. The efficiency of water consumption in terrestrial ecosystems and carbon cycling are both affected by drought, which is an unpredictable interruption of the water cycle that has a direct influence on soil evapotranspiration and plants’ transpiration [7,8]. Although the sensitivity of different ecosystems to drought varies, drought can alter the circumstances of an ecosystem’s water supply and will have a significant impact on the carbon and water cycles [9]. A comprehensive knowledge of the impacts and feedback mechanisms of drought events on an ecosystem’s CUE and WUE is necessary for assessing and forecasting the ramifications of future climate change on these processes.

In recent years, many studies have been conducted by many scholars on drought indicators, remotely sensed drought monitoring, and meteorological and soil moisture-based measured drought information [10,11,12]. The different remote sensing methods used for drought monitoring have their own advantages and disadvantages. Though many drivers, including temperature, precipitation, and crop growth, can cause a drought, it is often difficult to accurately characterize drought information in a timely manner by a single element. Mu et al. [13] constructed a global-scale drought severity index (DSI) based on gross primary productivity (GPP), net primary productivity (NPP), potential evapotranspiration (PET), and evapotranspiration (ET) data from MODIS, which integrates the growth of plants. The DSI is a global-scale drought severity index that integrates plants’ growth and water stress responses to drought, and it is widely used for monitoring drought. Additionally, early assessments of the water usage efficiency and carbon content of vegetation were primarily based on field data [14]. Correlations of vorticity and thermal diffusion are two observational methods that have recently been used for investigating carbon and water usage efficiency in various plant communities [15,16,17,18]. For example, Zhou et al. [19] estimated carbon loss and carbon utilization in deciduous forests based on eddy correlations with micrometeorological observations. Although point-scale-based observation techniques are useful for studying local environments, they are insufficient for investigating the global carbon and water cycles of vegetation because of the latter’s great spatial and temporal variability. In recent years, remote sensing and data assimilation technology have developed rapidly, and the product data produced by using these technologies are rapid, macroscopic, and dynamic, which can overcome the shortcomings of traditional methods and enable the study of CUE and WUE to achieve a leap from points to the surface, providing the development of a new direction for research [20,21]. For example, when Zhang et al. [22] and Huang et al. [23] studied changes in CUE, they found that the CUE in sparsely vegetated areas had higher values than those in heavily vegetated areas. Li et al. [24] found that there is a high carbon sink in farmland ecosystems in semi-arid regions. Based on multi-model integration of NPP, ET data, and soil moisture data, Liu et al. [25] analyzed the variation in global WUE and found that GPP dominated the variation in WUE in wet areas, while ET dominated the variation in WUE in arid areas. Temperature and precipitation are frequently used in studies of the variables influencing the CUE and WUE of vegetation because they have direct impacts on the nutrient cycle of plants [26]. It has been shown that temperature and precipitation have an approximate parabolic relationship with CUE, while there is a threshold effect on the effect of WUE [27]. Drought mitigation strategies also have the potential to significantly alter vegetation’s CUE and WUE. Therefore, further studies are needed regarding the impacts of drought on dryland plants’ carbon and water cycles, as well as its severity and longevity [28].

A typical arid and semi-arid region, Inner Mongolia has a delicate environment and is particularly vulnerable to climate change. Water resources in Inner Mongolia have been getting scarcer and more degraded over the past few decades as a result of both human activity and climate change. This has had a serious impact on the resilience of local ecosystems and the efficiency of the growth of vegetation, and its ecological and environmental issues have been receiving more attention. Studies on Inner Mongolia’s changing plant cover, hydrological processes, and climate factors are becoming more in depth [29]. Meanwhile, the efficiency of different plant species in Inner Mongolia’s climate in using water and carbon has received less attention. With the goal of better comprehending the underlying patterns of the carbon and water cycles of terrestrial ecosystems in Inner Mongolia and their response to drought, CUE, WUE, and DSI indices were calculated from 2001 to 2020, based on MODIS NDVI, GPP, NPP, ET, and PET data, and the spatiotemporal dynamics of CUE, WUE, and their responses to drought in areas with different vegetation cover were investigated by correlation analyses. This research aimed to better understand the interplay between the carbon and water cycles in arid and semi-arid terrestrial ecosystems in the context of global climate change and to serve as a scientific reference for the efficient administration of soil and water resources and the sustainable growth of Inner Mongolia’s ecosystems. This study also seeks to shed light on the underlying patterns of carbon and water cycles in Inner Mongolia’s terrestrial ecosystems and their responses to drought.

2. Study Area and Methods

2.1. Study Area

Inner Mongolia is located at 37°24′–53°23′ N and 126°04′–97°12′ E (Figure 1a) and is situated at the northern border of China, with a total area of 1.183 × 10⁶ km², accounting for 12.3% of the total area of the country. Plateaus, plains, sandy regions, hills, and mountains make up most of the landscape, which is mostly located at elevations above 1000 m. The region has a temperate continental monsoon climate, which is characterized by short and hot summers, cold and long winters, strong winds and little rain in spring, sharp temperature drops in autumn, large day–night temperature differences, and long sunshine hours. Precipitation (35–530 mm) and temperatures (−5–10 °C) are low and unevenly distributed throughout the region, with the northeast receiving the least amount of rain and the southwest receiving the most. The studied region has a distribution of cold temperate coniferous forests, deciduous broadleaf forests, grasslands, and desert from the northeast to southwest caused by the clear climatic heterogeneity [30].

2.2. Data Sources and Preprocessing

2.2.1. GPP, NPP, NDVI, and ET Data

GPP (MOD17A2Hv006), NPP (MOD17A3HGFv-006), NDVI (MOD13A2Hv006), and ET (MOD16A2v006) data from the MODIS products utilized in this study were retrieved from the National Aeronautics and Space Administration (NASA) website (http:/modis.gsfc.nasa.gov/data, accessed on 1 January 2023). The time series spans 20 years, from 2001 to 2020. The MODISET, PET, and GPP products have a spatial and temporal resolution of 500 m (8 d)⁻¹; the NPP product has a spatial and temporal resolution of 500 m·year⁻¹; the NDVI product has a spatial and temporal resolution of 1000 m (16 d)⁻¹; and the data format is HDF. For computation of the vegetation CUE, WUE, and DSI indices, the data from the research region for 20 years were batch stitched, projection converted, and formatted by the MRT tool and resampled to 1000 m.

2.2.2. Land Cover Products

The land cover products were derived from the MCD12Q1 land cover data from the NASA MODIS website (https://modis.gsfc.nasa.gov/, accessed on 1 January 2023), with a spatial resolution of 500 m. The raw data were formatted and reprojected using MRT software, and the data were reprojected and resampled to make them consistent with MODIS NPP, GPP, ET, PET, and NDVI data projections and resolutions. The classification system of the IGBP (International Geosphere-Biosphere Program, IGBP) was used. In order to reduce the classification errors and the possible effects of land cover changes, only the elements in the images where the land cover types remained unchanged from 2001 to 2020 were retained in this paper (Figure 1b).

2.3. Methodology

2.3.1. DSI

According to earlier research, the DSI can both monitor meteorological dryness and indicate agricultural drought, since it integrates NDVI and the ratio of ET to PET, which may indicate the status of vegetation and signal crop water shortages [31]. The specific equations are

$$Z_{NDVI}=\frac{NDVI-\overline{NDVI}}{{\delta }_{NDVI}}$$

(1)

$$Z_{ET/PET}=\frac{ET/PET-\overline{ET/PET}}{{\delta }_{ET/PET}}$$

(2)

$$Z=Z_{NDVI}+Z_{ET/PET}$$

(3)

$$DSI=\frac{Z-\overline{Z}}{{\delta }_Z}$$

(4)

where NDVI and ET/PET are the NDVI and ET/PET values of a period during the study period; $\overline{\mathrm{NDVI}}$ and ${\delta }_{NDVI}$ are the mean and standard deviation of NDVI during the study period, respectively; $\overline{ET/PET}$ and ${\delta }_{ET/PET}$ are the mean and standard deviation of ET/PET during the study period, respectively; and $\overline{Z}$ and ${\delta }_Z$ are the mean and standard deviation of Z during the study period, respectively. The larger the DSI value, the wetter a period is, and the opposite trend indicates a dry period [31].

2.3.2. CUE and WUE

After preprocessing the NPP, GPP, and ET products of MODIS, the CUE and WUE for Inner Mongolia was calculated using the following equations [32]:

$$CUE=\frac{NPP}{GPP}$$

(5)

$$WUE=\frac{GPP}{ET}$$

(6)

where the unit of NPP and GPP is g C·m⁻², and ET stands for ecosystem evapotranspiration, in mm. CUE is a dimensionless quantity, and WUE is in units of g C·mm⁻¹·m⁻².

2.3.3. Trend Analysis

In order to statistically analyze the changes in vegetation’s carbon and water consumption efficiency and the drought index on the Mongolian plateau, a one-dimensional linear regression was carried out [33]. These calculations were made using the following formula:

$${\theta }_{\mathit{Slope}}=\frac{n\times {\displaystyle {\sum }_{i=1}^{n}i\times X_i-{\displaystyle {\sum }_{i=1}^{n}i\times {\displaystyle {\sum }_{i=1}^n{X}_i}}}}{n{\displaystyle {\sum }_{i=1}^n{i}^2-{\left({\displaystyle {\sum }_{i=1}^{n}i}\right)}^2}}$$

(7)

where n is the total number of years and X_i is the remote sensing data in the ith year.

2.3.4. Correlation Analysis

To further investigate the impact of drought on carbon and water use efficiency, the correlation coefficients of the drought severity index (DSI) with CUE and WUE were calculated on an image-by-image basis using Pearson’s correlation coefficients. Correlation analysis can reveal the closeness of the inter-relationship between two or more variables. This method can test the strength and directional distribution of the correlations between the factors of the study variables, is simple and easy to use, and has been widely used in various subject areas [34,35]. It is calculated as

$$R=\frac{{\displaystyle \sum _{i=0}^n\left(x_i-\overline{x}\right)\left(y-\overline{y}\right)}}{\sqrt{{\displaystyle \sum _{i=0}^n{\left(x_i-\overline{x}\right)}^2}}\sqrt{{\displaystyle \sum _{i=0}^n{\left(y-\overline{y}\right)}^2}}}$$

(8)

where R is the correlation coefficient, with value range [–1, 1]; x_i indicates the CUE and WUE values in year i; $\overline{x}$ indicates the average value of CUE and WUE in all years; y_i indicates the annual average value of DSI in year i; and $\overline{y}$ indicates the average value of DSI in all years.

3. Results

3.1. Spatial and Temporal Characteristics of Drought

Between the years 2001 and 2020, the mean slope of the interannual variation of the DSI in Inner Mongolia ranged from −0.163 to 0.171 (Figure 2a). The trend of drought in the research area was from dry to wet, which accounted for 98.17% of the total, and the locations with a clear tendency of reducing dryness were widely scattered over the area. The regions of Tongliao City, Chifeng City, the southern portion of Xing’an League, and the Kubuqi Desert in Ordos were the most severely impacted by the trend of drought from wet to dry, accounting for 1.83% of the total. The most severe drought conditions were seen in the northern portion of the Ordos Plateau, followed by Tongliao City and the neighboring Chifeng City, then Arongqi and Zalantun, both of which are agriculturally productive regions (Figure 2b).

According to the results of the change in the area and degree of drought in Inner Mongolia (Figure 3), there have been various degrees of drought in the region over the past 20 years. The drought area showed a decreasing trend, with the largest year being 2001, which accounted for 93.23% of the total. The normal area also showed a decreasing trend, with the largest year being 2011, which accounted for 34.95%. Finally, the wet area showed an increasing trend, with the largest year being 2013, which accounted for 85%. Additionally, Inner Mongolia saw much drought before 2009 and increased rain after that. In 2017, the amount of the drought and normal areas peaked; after 2017, the total amount of the drought and normal areas gradually declined.

3.2. Distribution of Vegetation’s CUE and WUE

The CUE decreased from west to east, with high-value areas located in the western Mawusu Sands and around the center of Xilinguole League, and the low-value areas mostly concentrated in locations with a low elevation in eastern Inner Mongolia (Figure 4a). Inner Mongolia’s CUE values varied from 0.147 to 1.208, with a mean of 0.656. Grassland had the greatest CUE among the four plant types, whereas DNF had the lowest (Figure 4c). The CUE of different vegetation kinds was GRA (0.681) > MF (0.616) > WSA (0.595) > DBF (0.584) > CRO (0.582) > SA (0.567) > DNF (0.541). WUE had clearly different geographical distribution features from CUE, and the general trend showed an increase from southwest to northeast (Figure 4b). WUE readings ranged from 0.394 to 3.273 g C·mm⁻¹·m⁻², with a multi-year average of 1.351 g C·mm⁻¹·m⁻². Unlike CUE, the general distribution of WUE in the research region was forest > farmland > grassland (Figure 4d).

3.3. Spatial and Temporal Variation in Vegetation’s CUE and WUE

Analyzing the trajectory of NPP and GPP can help improve one’s understanding of the factors that contribute to changes in CUE. This is because both NPP and GPP have an influence on CUE. The combined effects of these two factors caused CUE to decrease at a rate of 0.1% per year between the years 2001 and 2020 in Inner Mongolia, even though both NPP and GPP showed statistically significant upward trends of growth (p < 0.05) throughout this same time period (Figure 5a). Only a small portion of the area (38.5%) passed the significance test (p < 0.05) (Figure 6b); only 10.58% of the areas showed a decreasing trend, primarily near Mawusu Sands and Hulun Lake. The rate of spatial variation of CUE in Inner Mongolia ranged from −0.139 to 0.074 (Figure 6a), with the area showing an increasing trend accounting for 89.42% of the vegetation cover in the study area. The WUE in Inner Mongolia decreased at a rate of 0.008 g C·mm⁻¹·m⁻²·year⁻¹ from 2001 to 2020, but the overall change was not significant (p > 0.05). This indicates that the interannual trend of WUE was relatively stable during the study period, which was jointly determined by the significant growth in ET and GPP (Figure 5b). The geographical variability of WUE in Inner Mongolia ranged from −0.789 to 0.235 g C·mm⁻¹·m⁻²·year⁻¹ and varied slightly (Figure 6c). Among the areas, 43.21% and 56.79% of the vegetation cover in the study area had increases and decreases in WUE, but only 15.2% of the area passed the significance test (p < 0.05), of which 7.63% of the area had significant decreases, mainly in the typical grassland in the northeastern part of Xilinguole League; 7.57% of the area showed significant increases, distributed in the Erdos region and in the Maowusu Sands of Inner Mongolia (Figure 6d).

3.4. Effects of Drought on the CUE and WUE of Vegetation

The correlation coefficients of CUE and DSI varied from −0.977 to 0.643 (Figure 7a), with substantial geographical variability. In 2.04% of the research region, CUE was positively linked with the drought index (DSI), primarily in the Loop Plain, near the Yinshan Mountains, and in the Daxinganling area. This suggests that an increase in the drought index in these areas led to an increase in the CUE of vegetation, i.e., an increase in carbon storage. The negative correlation between the drought index (DSI) and the CUE was the strongest, and the area with the largest negative correlation, accounting for 97.96% of the watershed area, was primarily distributed near Ordos, Xilinguole, and Hulun Lake, where 50.1% and 21.7% of the areas had highly significant correlations (p < 0.01) and significant correlations (p < 0.05), respectively (Figure 7b).

The correlations between WUE and DSI were negative and variable, from −0.973 to 0.953 (Figure 7c), and their geographical distribution was more or less the inverse of that of CUE and DSI. The Mawusu Sands in western Inner Mongolia, Xilinguole in central China, and the region around Chifeng and Tongliao in the east were the primary locations with positive and negative correlations between DSI and WUE, accounting for 54.21% of the vegetation cover. In 45.79% of the research area, mostly in the Loop Plain and the Daxinganling region, there was a negative correlation between WUE and the drought index (DSI). Only 17.4% of points in the region were statistically significant (p < 0.05), with only 6.2% of points showing a highly significant link (p < 0.01) (Figure 7d).

The correlation coefficients of CUE, WUE, and DSI for different vegetation types were compared, showing that except for GRA, CUE and WUE were negatively correlated with the DSI index. The correlation between CUE and DSI was −0.141 in DNF and −0.585 in GRA (Figure 8a). There was a 0.112 correlation between WUE and DSI in GRA, and a −0.212 correlation between CUE and DSI in MF. The strongest correlation was seen between WUE and DSI in GRA (Figure 8b). Overall, the negative link between CUE and drought in Inner Mongolia was much stronger than that between CUE and WUE.

4. Discussion

4.1. Spatial and Temporal Trends of CUE and WUE

The CUE of vegetation in Inner Mongolia was determined in this study by using MODIS data from 2001 to 2020. The results indicated that Inner Mongolia had a multi-year mean value of CUE of 0.656, which agrees with the assumption of Du et al. (2021) [36] that the mean value of CUE in dry and semi-arid regions is more than 0.5. Multi-year mean CUE decreased spatially from west to east in Inner Mongolia because of the region’s higher altitude, higher humidity, and colder temperatures, which all lead to lower ecosystem energy consumption via autotrophic respiration and more efficient carbon sequestration and transfer in the vegetation [37]. Since grassland ecosystems are less complicated than forest ecosystems, and since photosynthesis fixes more solar energy during the development of vegetation while burning more energy for respiration, the CUE of grassland is higher than that in forests [38,39,40,41]. Consistent with the findings of Li et al. [42] on the distribution of NPP/GPP, the general distribution of CUE demonstrated that regions with sparse vegetation were greater than dense vegetation areas. The regional distribution of WUE exhibited an upward tendency from the southwest to the northeast, and, in contrast to CUE, WUE was noticeably greater in forest environments than in grassland habitats. The reason for this is because grassland ecosystems have less plant cover, poorer productivity, and higher surface evapotranspiration [43]. There was a correlation between the geographic distribution of the different ecological zones and the distribution of WUE. The northern part of Hulunbeier, Inner Mongolia, has coniferous and mixed forests, with sufficient water and heat resources and high vegetation cover; under the same conditions, this ecological zone has stronger photosynthesis, and thus will produce more organic matter than other regions. Moreover, the changes in temperature were small, reducing evapotranspiration [44,45,46,47]. In contrast, desert grasslands have little vegetation cover, low productivity, and higher soil evaporation, resulting in relatively low WUE values in these areas [48].

4.2. Effects of Drought on CUE and WUE

Drought affects CUE and WUE mainly through stomatal and nonstomatal factors. The stomatal factor is the water stress imposed by drought on plants, which, in turn, changes stomatal conductance to affect CUE and WUE. Stomatal conductance is the degree of stomatal opening and closing on the surface of plant leaves, which controls transpiration, respiration, and photosynthesis [49]. Under drought conditions, stomatal conductance decreases in plants to reduce transpiration water losses, but at the cost of a lower CO₂ concentration in the leaves as well. Since the diffusion resistance of water vapor is greater than that of carbon dioxide [50], when stomatal conductance decreases, the decrease in water vapor escaping from the leaves is less than the decrease in carbon dioxide uptake, leading to an increase in WUE; accordingly, CUE and WUE decrease when stomatal conductance increases [51]. Nonstomatal factors refer to the inhibition of the physiological functions of plants’ photosynthetic organs by drought, the decrease in photosynthetic enzyme activity, the decrease in the photosynthetic rate, and the decrease in carbon sequestration capacity, which ultimately affect CUE and WUE [52,53].

The drought index (DSI) was negatively correlated with the CUE of vegetation in 97.96% of the regions in Inner Mongolia, while it was negatively correlated with water use efficiency in only 54.21% of the regions, indicating that the occurrence of drought events was the main limiting factor for the reduction in the CUE of vegetation in Inner Mongolia, and the occurrence of drought events could increase the average ratio of the dark respiration rate to the photosynthesis rate of vegetation while reducing vegetation’s photosynthesis. The occurrence of drought events reduces photosynthesis in vegetation and increases the average ratio of the dark respiration rate to the photosynthesis rate, which leads to a reduction in the CUE of vegetation. The impact of drought events on ecosystems varies [54,55,56], and even under the same severe drought conditions, the effects on ecosystems can vary greatly depending on the ecosystem and the climatic context.

Using the Boreal Ecosystem Productivity Simulator (BEPS), Liu et al. [57] determined the WUE of China’s terrestrial ecosystems, finding that, from 2000 to 2011, the WUE in northeastern China and central Inner Mongolia decreased. Guo et al. [58] observed that WUE rose during drought in the forested regions of northeastern China, northeastern Inner Mongolia, and southern China, but fell in the forested regions of northwestern and central China. During 2009 and 2010, a drought in southwestern China reduced plant growth in the provinces of Yunnan, northern Guangxi, Guizhou, and eastern Sichuan, according to research by Li et al. [59]. According to the findings of a study carried out by Yang et al. [60], which investigated the associations between global WUE and humidity indices, the value of WUE rose in arid ecosystems during droughts but declined in semi-arid/sub-humid ecosystems over the same time period. This difference is likely attributable to the varying degrees of sensitivity of these ecosystem types to shifts in the hydro-climatic conditions. Drought has a significant impact on northern China’s dryland vegetation for a number of reasons, including the fact that forest and grassland ecosystems react differently to drought, and because the forests in the region are more vulnerable to water scarcity and carbon depletion than grasslands [61]. The increased autotrophic respiration depletion and the decreased productivity of different vegetation types in the context of increased regional drought events are the main reasons for the reduced carbon and water use efficiency and the weaker carbon sequestration capacity of Inner Mongolia’s ecosystems. This also predicts that the ability of the ecosystems in the region to increase productivity and carbon sinks in the future will need to be at the cost of consuming more water.

4.3. Uncertainties and Limitations

There is not enough of a correlation between the drought index and the changes in the CUE and WUE of vegetation to draw any firm conclusions from this study. This is because the carbon and water cycles involve many coupling factors (including climatic conditions, CO₂ concentrations, soil conditions, and human activities), and are affected by uncertainties such as the accuracy of remote sensing used for monitoring and the complexity of ecological processes. If we want to understand and predict the responses and functional changes in ecosystems under global changes and human disturbances, we need to increase our understanding of the processes and mechanisms of the interactive effects of the key factors on the CUE and WUE of vegetation at different scales, and we need to reveal the quantitative the impact of the components of climate change and human activities.

5. Conclusions

The specific conclusions are as follows:

(1) From 2001 to 2020, the climate in the study area was mainly dry to wet. Vegetation showed carbon and water use efficiency of 0.656 and 1.351 g C·mm⁻¹·m⁻², respectively. CUE decreased from west to east, whereas WUE increased from southwest to northeast. The CUE of different vegetation types was GRA (0.681) > MF (0.616) > WSA (0.595) > DBF (0.584) > CRO (0.582) > SA (0.567) > DNF (0.541), while WUE was DNF > MF > WSA > DBF > SA > CRO > GRA.

(2) From 2001 to 2020, CUE in Inner Mongolia declined by 0.1%·year⁻¹, whereas WUE fell by 0.008 g C·mm⁻¹·m⁻²·year⁻¹ but was not significant. Except for the high-altitude mountainous areas in western Inner Mongolia and the eastern margin, CUE increased. CUE declined in only 10.58% of the areas, mostly around the Mawusu Sands and Hulun Lake. WUE fell in 56.79% of the areas, with 7.63% dropping considerably, mostly in the northeastern typical grasslands of Xilinguole League.

(3) The negative correlation between the drought index (DSI) and CUE was the largest, accounting for 97.96% of the watershed area, and the areas passing the significance test accounted for 71.6%. The correlation coefficients of WUE and DSI were spatially opposite to the correlations between CUE and DSI. The drought index (DSI) and WUE had the highest positive association and the largest negative correlation for 54.21% of the vegetation cover area. The CUE and WUE of Inner Mongolian vegetation types were adversely linked with the DSI except in GRA. Drought in Inner Mongolia mostly influenced the CUE of different plant types, which had a higher negative correlation than WUE.