# 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\(\cdot\)mm\({}^{-1}\cdot\)m\({}^{-2}\cdot\)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. d 2023 12 583. 12.39.00.12030583 12.  
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. [PERSON] 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, [PERSON] 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.