Next Article in Journal
Research Review of Agricultural Machinery Power Chassis in Hilly and Mountainous Areas
Previous Article in Journal
Leaf Plasticity and Biomass Allocation of Arundo donax Under Combined Irrigation and Nitrogen Conditions in Salinized Soil
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Agriculture
Volume 15
Issue 11
10.3390/agriculture15111164
agriculture-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Twenty-Year Variability in Water Use Efficiency over the Farming–Pastoral Ecotone of Northern China: Driving Force and Resilience to Drought

by
Xiaonan Guo
1,2,†,
Meng Wu
3,†,
Zhijun Shen
2,
Guofei Shang
2,
Qingtao Ma
2,
Hongyu Li
2,
Lei He
1 and
Zhao-Liang Li
1,*
1
State Key Laboratory of Efficient Utilization of Arable Land in China, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
2
School of Land Science and Space Planning/Hebei International Joint Research Center for Remote Sensing of Agricultural Drought Monitoring, Hebei GEO University, Shijiazhuang 050031, China
3
Hebei Province City Agriculture Technology Innovation Centers, Shijiazhuang Agricultural Information Engineering Technology Research Center, Shijiazhuang Academy of Agricultural and Forestry Sciences, Shijiazhuang 050000, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agriculture 2025, 15(11), 1164; https://doi.org/10.3390/agriculture15111164
Submission received: 3 April 2025 / Revised: 24 May 2025 / Accepted: 26 May 2025 / Published: 28 May 2025
(This article belongs to the Section Ecosystem, Environment and Climate Change in Agriculture)
Browse Figures
Review Reports Versions Notes

Abstract

:
Water use efficiency (WUE), as an important metric for ecosystem resilience, has been identified to play a significant role in the coupling of carbon and water cycles. The farming–pastoral ecotone of Northern China (FPENC), which is highly susceptible to drought due to water scarcity, has long been recognized as an ecologically fragile zone. The ecological restoration projects in China have mitigated land degradation and maintain the sustainability of dryland. However, the process of greening in drylands has the potential to impact water availability. A comprehensive analysis of the WUE in the FPENC can help to understand the carbon absorption and water consumption. Using gross primary production (GPP) and evapotranspiration (ET) data from a MODerate resolution Imaging Spectroradiometer (MODIS), alongside biophysical variables data and land cover information, the spatio-temporal variations in WUE from 2003 to 2022 were examined. Additionally, its driving force and the ecosystem resilience were also revealed. Results indicated that the annual mean of WUE fluctuated between 0.52 and 2.60 gC kgH2O−1, showing a non-significant decreasing trend across the FPENC. Notably, the annual averaged WUE underwent a significant decline before 2012 (p < 0.05), and then showed a slight increased trend (p = 0.14) during the year afterward (i.e., 2013–2022). In terms of climatic controls, temperature (Temp) and soil volumetric water content (VSWC) dominantly affected WUE from 2003 to 2012; VPD (vapor pressure deficit), VSWC, and Temp showed comprehensive controls from 2013 to 2022. The findings suggest that a wetter atmosphere and increased soil moisture contribute to the decline in WUE. In total, 59.2% of FPENC was shown to be non-resilient, as grassland occupy the majority of the area, located in Mu Us Sandy land and Horqin Sand Land. These results underscore the importance of climatic factors in the regulation WUE over FPENC and highlight the necessity for focused research on WUE responses to climate change, particularly extreme events like droughts, in the future.

1. Introduction

Climate change is increasingly impacting our planet and its inhabitants. Increasing global land surface temperature is changing the precipitation patterns and heightening the frequency and severity of droughts, and as a result, it significantly affects the carbon and water cycles of terrestrial ecosystems [1,2,3]. Cropland is one of the most important terrestrial ecosystems, and it is both a victim of climate change and part of the solution. Climate change can exacerbate the soil evaporation, change the growing duration, and impact the yielding. Moreover, the extreme weather events such as droughts, floods, and heatwaves lead to crop failures and exacerbate soil erosion [4]. To address this global crisis, multi-faceted measures are required, combing technological innovations, policy interventions, behavioral changes, and nature-based solutions. Terrestrial ecosystems adapt by altering their structural composition and modifying their sensitivity to climatic variations [5]. Reforestation and afforestation can enhance the sequestration of CO2 and regulate water cycles. In addition, integrating trees with crops/livestock can enhance carbon storage and reduce soil erosion. Furthermore, precision farming, reduced tillage, and optimized fertilizer can be used to cut methane (CH4) and nitrous oxide (N2O) emissions.
In developing effective mitigation strategies and sustaining life on our planet, it is especially important to understand the climatic controls on the biogeochemical processes of plants, particularly the carbon and water cycles [6]. Water use efficiency (WUE), serves as a vital physiological indicator for assessing variations in carbon and water fluxes [7,8]. It plays an essential role in understanding plant resilience during drought conditions. A high WUE implies that plants minimize water loss by closing their stomata, thereby reducing evapotranspiration (ET) in response to drought. In contrast, a low WUE can indicate either adequate water availability or stunted plant growth [9]. WUE can be measured at various spatial scales, ranging from individual leaves to entire ecosystems, and across temporal scales from immediate readings to multi-year cycles [10]. Despite the availability of multiple definitions, the use of gross primary production (GPP) and ET for calculating ecosystem WUE has gained widespread acceptance in earlier research [6,11,12,13].
Environmental variables, such as temperature (Temp), precipitation (PPT), carbon dioxide concentration (CO2), net radiation (Rn), vapor pressure deficit (VPD), soil volumetric water content (VSWC), and their interactions with biological factors (such as leaf area index (LAI) and enhanced vegetation index (EVI)) significantly influence seasonal variations in GPP and ET, leading to interannual fluctuations in WUE [14,15,16,17,18,19]. Given the shifting climate and declining precipitation in the arid and semi-arid regions, there is growing research on examining the impacts of drought on terrestrial water and carbon cycles [9]. Ecosystems increase their WUE when shifting from wet to dry conditions and decrease it when transitioning from dry to wet [20]. Consequently, WUE has become a key metric for resilience analysis, helping to evaluate how terrestrial ecosystems react to hydroclimatic disturbance like drought [12]. Choosing drought-resilient species can enhance carbon sequestration, even during water-limited periods [6,21].
Understanding the biophysical controls, especially that climate change on WUE requires comparative studies across various ecosystems and regions [11,22]. Field measurement, such as eddy-covariance (EC) flux towers, allow for precise calculations of GPP and ET, thereby enabling accurate WUE assessments [23,24,25,26,27]. Nonetheless, ecosystems located in various geographical areas are expected to display distinct WUE patterns due to physiological variations and different environments [28,29]. Although modeling approaches offer a valuable tool for assessing long-term and large-scale WUE, they may introduce errors due to uncertainties within the models themselves [30]. Conversely, remote sensing can capture GPP and ET across extensive spatial and temporal scales. Numerous regional and global studies have utilized MODerate resolution Imaging Spectroradiometer (MODIS) GPP and ET to analyze spatio-temporal variations in WUE. This approach provides a balanced alternative between observations from eddy covariance and model simulations [31,32,33]. Additionally, cloud computing platforms like Google Earth Engine (GEE) facilitate access to long-term datasets, enabling studies on WUE variability at large scales [4,32].
The farming–pastoral ecotone of Northern China (FPENC), functioning as a transitional zone between the agricultural southeast and pastoral northwest, is situated within the arid and semi-arid regions of China [34]. Historically, the FPENC has been characterized by its ecological vulnerability, particularly to drought conditions stemming from water scarcity [35,36]. The impact of climate change has become increasingly evident over the past half-century; Northern China has experienced a notable rise in temperature. This rapid warming has exacerbated soil water consumption in the FPENC. Meanwhile, precipitation in the region has shown varying trends of increase over the past decade [34,37]. Human activities, particularly change in land use/land cover (LULC), have influenced precipitation patterns, evapotranspiration, and consequently alter ecosystem functions [38]. The Chinese government, therefore, has been carrying out a series of environment restoration projects, such as the Grain for Green Project. Hence, it is crucial to investigate the discrepancy of WUE among various vegetation types in the FPENC within the backdrop of climate change. However, the spatio-temporal variations of WUE and its controlling mechanisms remains insufficiently explored [39,40,41].
Utilizing MODIS GPP and ET products from 2003 to 2022 with the spatial resolution of MODIS GPP and ET is 500, alongside biophysical variables’ data and land cover information, this study investigated the characteristics of WUE variation in the FPENC over the past two decades with the support of the GEE platform. Additionally, the resilience of various ecosystems in the FPENC to drought was examined on a regional scale. The objectives are as follows: (1) to analyze the extent and spatio-temporal variations of WUE across the FPENC; (2) to evaluate the regulation mechanisms of biophysical variables on the WUE of each vegetation; (3) to uncover the resilience of various vegetation types to drought in FPENC. Our findings aim to enhance the understanding of WUE in the FPENC and help supply strategies for alleviating drought stress in arid regions.

2. Material and Methods

2.1. Study Area

The FPENC, where farming activities of crop and livestock production co-exist, is located in the semi-arid area of Northern China (103°15′~124°37′ E, 34°48′~48°32′ N) (Figure 1). This region covers 9 provinces (autonomous regions), including Inner Mongolia, Heilongjiang, Jilin, Liaoning, Hebei, Shanxi, Shaanxi, Gansu, and Ningxia, encompassing 154 counties (banners, cities) with a total area of approximately 63.36 × 104 km2, equivalent to 6.6% of China’s total land area and a population of about 70 million. The altitude increases from northeast to southwest, with the lowest altitude less than 200 m and the highest being close to 4500 m, and most areas are situated at elevations above 1000 m. The region is characterized by a typical temperate semi-arid continental monsoon climate, with the following main features: the average annual temperature ranges from 0 to 8 °C, and annual precipitation between 250 and 500 mm, centered from around 400 to 450 mm. Annual precipitation variability is reported to be 15% and 30%. Therefore, agro-meteorological factors were used to define the FPENC’s boundaries in our study. We delineated the FPENC using 300 mm and 450 mm rainfall contours, incorporating county administrative divisions [34]. Over 70% of annual rainfall occurs between June and September (summer months often experience heavy rainstorms, while spring droughts are frequent). A ≥10 °C accumulated temperatures is 2000–3200 °C per day, sufficient for one crop per year (e.g., corn, potatoes). Winter extreme temperature lows of −20 to 30 °C (central Inner Mongolia can drop below −30 °C). The frost-free period was 120–180 days (longer in the east, shorter in the west). The aridity index (annual potential evaporation/precipitation) is 1.5–3.0. Annual evapotranspiration is 1800–2500 m, which is far exceeding precipitation, exacerbating drought risks. The soil type is mainly chestnut soil and brown soil, and the landform of the area is complex and diverse, with the Inner Mongolia Plateau as the main body, where grasslands, mountains, sandy lands, rivers, and lakes coexist. The main vegetation types are grasslands (GRAs) and cropland (CRO), which accounted for 74.6% and 22.1%, respectively. Additionally, savannas (SAVs), woody savannas (WSAs), closed shrublands (CSHs), deciduous broadleaf forests (DBFs), mixed forests (MFs), evergreen needleleaf forests (ENFs), deciduous needleleaf forests (DNFs), and open shrublands (OSHs) together account for 3.3% (Figure 1).

2.2. Data Sources

In this study, MODIS GPP (MOD17A2H) and MODIS ET (MOD16A2) products from 2003 to 2022 were utilized to calculate WUE. The MOD17A2H provides cumulative GPP data with an 8-day composite period at a resolution of 500 m, while the MOD16A2 product provides global terrestrial evapotranspiration data at the resolution and temporal frequency [6]. Additionally, we used the MODIS land cover type version 6.1 (MCD12Q1 v061), which also has a resolution of 500 m. Data from multiple sources were incorporated spanning the same period for the purpose of detecting the drivers of WUE. Air temperature at 2 m, and VSWC at 7 cm below the ground, with a resolution of 0.1 degrees, were obtained from ERA5-Land monthly averaged dataset (https://cds.climate.copernicus.eu). The net radiation was included in the ERA5-land dataset. The Climate Hazards Group Infrared Precipitation with station (CHIRPS) precipitation dataset was selected in our study, as the EAR5-land dataset has a relatively large deviation from the mainstream precipitation products in mainland China (Hong et al., 2021; Jiang et al., 2021) [42,43]. The CHIRPS is a 40-year quasi-global (50° S–50° N, 180° E–180° N), with a resolution of 0.05° at montly scale. Monthly VPD data from Terra-Climate, with a resolution of 1/24° (~4 km) was also used. The 8-day MODIS LAI product (MOD12A2H), with a 500 m resolution, along with the 16-day MODIS EVI product (MOD13A1) at the same resolution, were incorporated to evaluate the effect of vegetation phenology on WUE. The yearly Standardized Precipitation Evapotranspiration Index (SPEI) was used to identify the drought period. Detailed information on the datasets is depicted in Table 1.

2.3. Methods

The framework shown in Figure 2 describes the overall process of studying the spatio-temporal variability of WUE and its controlling mechanisms among various vegetation types across the FPENC. The process is divided into three main steps. The first step is to analyze the spatio-temporal variation of WUE in the FPENC based on MODIS GPP and ET product. Long-term trends were assessed using the non-parametric Theil–Sen slope and Mann–Kendall test. To eliminate the influence of autocorrelation, the seasonal and trend decomposition using the LOESS (STL) method was adopted [44] The second step was to detect the influence of biophysical drivers on WUE. Using random forest and multiple regression methods, five environment factors (PPT, Temp, VSWC, Rn, and VPD) and two biotic factors (LAI and EVI) were selected for the driving force analysis. The third step was to explore the characteristics of ecosystem resilience in different vegetation types over FPENC.

2.3.1. Estimation of WUE in the FPENC

Utilizing the GEE platform, WUE was calculated as the ratio of GPP to ET resulting in 8-day intervals with a resolution of 500 m. The mean monthly WUE was derived from the average of the 8-day WUE values for each month. If two different months were represented in an 8-day cumulative period, the month with the majority of days received the assigned value. The mean annual WUE for each year was calculated similarly.
Initially, we examined the spatial distribution and trends of annual mean WUE and related biophysical variables from 2003 to 2022. Vegetation types and land use categories were reclassified based on the MODIS land-cover map to select sampling locations across 10 different vegetation types (Figure 1). These locations were chosen to ensure that vegetation was present within a 500 m buffer. This approach facilitated the analysis of WUE variability in various vegetation groups in the FPENC at both monthly and annual intervals. The GPP, ET, and WUE values of each vegetation type were also computed for the expected littoral and swamp forests due to the lack of MODIS GPP and ET for their land use type.
Long-term trends were assessed using the non-parametric Theil–Sen slope method [45]. In addition, the non-parametric Mann–Kendall test [46,47] was employed to evaluate the significance of the identified trends in WUE. The Mann–Kendall test assumes that the data are serially independent. However, the times series of WUE may have autocorrelation, which might cause errors for the trend test, increasing the significance of data [44]. Therefore, the LOESS (STL) method was adopted to eliminate the influence of autocorrelation in the seasonal and trend decomposition of WUE in various vegetation types.

2.3.2. Exploration of the Biophysical Controls on WUE

In this study, seven biophysical factors—Temp, Rn, VSWC, PPT, VPD, LAI, and EVI—were identified as drivers of WUE. The datasets for these variables were aggregated to a monthly scale. We selected the samples for each vegetation evenly, and extracted WUE and variable data at sampling locations for various vegetation groups. The impact of the chosen biophysical drivers on the WUE of different vegetation types within the FPENC was assessed using the random forest (RF) method [6,48]. This non-parametric algorithm does not make assumptions about the data structure and is capable of handling auto-correlated datasets [49]. Based on the RF algorithm, the increase in node purity (IncNodePurity) indicated the importance of variables, the importance scores derived from RF analysis were utilized to indicate predictive strength of each factor in influencing WUE. The analysis was conducted in the R environment (R 4.1.0) using the randomForest package (Guo et al., 2023) [49].
Additionally, we employed multiple linear regression to assess the relationship between WUE and environmental variables (Temp, PPT, Rn, VSWC, VPD) across the FPENC, using the following equation:
y = a Temp + b PPT + c VPD + d Rn + e VSWC + ε
where y denotes the annual WUE for a specific cell and year, while Temp, PPT, VPD, Rn, and VSWC represent the average annual air temperature, precipitation, vapor pressure deficit, net radiation, and soil volumetric water content, respectively. The regression coefficients are indicated as a, b, c, d, and e, with ε representing the residual error term. The standardized regression coefficients were determined by taking the standard deviation of the independent variable and multiplying it by the reciprocal of standard deviation of the dependent variable [12].

2.3.3. Calculation the Ecosystem Resilience in the FPENC

Ecosystem resilience refers to the capacity of an ecosystem to preserve its structure and functions despite abrupt changes in hydro-climate conditions, such as transitions from dry to wet years or vice versa [12]. In this study, we define ecosystem resilience (Rd) as the ratio of averaged annual WUE during drought years (WUEd, determined by identifying the year with the heavy drought (SPEI < −1.5) per pixel) to the average annual WUE calculated from 2003 to 2022 (WUEm) [50,51]. The spatial resolution of SPEI with 0.5 ° was converted to 500 m based on the bilinear interpolation. An ecosystem is deemed resilient if it can maintain or enhance WUE, thereby supporting productivity in water-limited conditions during droughts; specifically, this occurs when WUEd is at least equal to WUEm, yielding an Rd value of 1 or higher. Thus, a greater Rd value (equal to or exceeding 1) signifies a resilient ecosystem, 0.9 < Rd < 1 indicates that the ecosystem was slightly non-resilient, and 0.8 ≤ Rd ≤ 0.9 indicates a moderately non-resilient ecosystem; however, when the Rd < 0.8, it indicates a severely non-resilient ecosystem [12].
R d = W U E d W U E m

3. Results

3.1. Trends in WUE

Figure 3 illustrates the spatial distribution of the 20-year average WUE and its trends in the study area. Over the past two decades, the mean WUE in the FPENC has fluctuated between 0.52 and 2.60 gC kgH2O−1, with most areas showing WUE values ranging from 0.92 to 1.76 gC kgH2O−1 (Figure 3a). Nearly all the area exhibited a non-significantly degraded trend over the FPENC, only the area in the west of Liaoning, north of Hebei and Shanxi, central Inner Mongolia was shown to be non-significantly improved, and some areas in Ningxia and Gansu showed a significant improvement (Figure 3b and Figure S1). The mean monthly WUE of the vegetation types of FPENC during the study period exhibited higher values mainly occurring from May to September, and the highest value was observed in June (2.55 gC H2O−1), followed by May (2.33 gC H2O−1), September (2.24 gC H2O−1), July (1.98 gC H2O−1), and August (1.94 gC H2O−1) (Figure 4, Figure 5 and Figure S4). The average WUE was highest in summer (with WUE higher than 2 gC H2O−1 in most areas) (Figure S5).
To evaluate the reliability of the numerical results, statistical tests were performed. The annual averaged WUE of the whole FPENC has exhibited a notable decreasing trend over the twenty years (R2 = 0.29, p < 0.01). Specifically, it demonstrated a significant decline during the first ten years (R2 = 0.39, p < 0.05), while in the last ten years, it showed a slight upward trend (2013–2022) (R2 = 0.16, p = 0.14) (Figures S2 and S3).
To gain deeper insights into how various vegetation types respond to climatic and biological factors, we examined the WUE trends for each vegetation type group. It showed an overall tendency of decrease Figure 6a,b). The annual WUE all showed a sharp decrease in 2010, and it showed an increasing trend afterwards (Figure 6b). In terms of ten vegetation types, the annual WUE of evergreen needle forest exhibited the highest value (1.87 ± 0.12 gC H2O−1), with the WUE of mixed forest ranking second afterwards (1.61 ± 0.1 gC H2O−1), and the open shrubland showing the lowest WUE (1.08 ± 0.14 gC H2O−1). Especially, the grassland, which occupied the majority area in the FPENC, had a relatively low value of WUE (1.16 ± 0.11 gC H2O−1) (Figure 6a, Table 2).

3.2. Trends in Biophysical Variables

Seven biophysical variables, including EVI, LAI, PPT, Rn, Temp, VPD, and VSWC, were selected to analyze the biophysical controls on WUE (Figure 7, Figure 8, Figure 9 and Figure 10). The EVI and LAI showed an increasing trend during the 20 years (Figure 8a,b, Figure 9a,b and Figure 10a,b), with annual average EVI in most areas ranging from 0~0.30, and LAI ranging from 0~1.60 m2 m−2 (Figure 7a,b). And they had lower values in grassland, open shrubland, and cropland, higher values remained in mixed forest and deciduous broadleaf forest (Figure 9a,b). The annual average PPT varied from 265.2~594.0 mm in most areas from 2003 to 2022 (Figure 7c). Furthermore, PPT exhibited an increasing trend over most of FPENC (Figure 8c and Figure 9c), and the eastern part of FPENC showed a severe increase (Figure 8c). The PPT was higher in mixed forest, deciduous broadleaf forest, and closed shrubland than that in the grassland and cropland (Figure 10c). The western area of FPENC enjoys a larger annual average Rn than that in the eastern area of FPENC (Figure 7d). The Bashang area which is situated in the central part of FPENC showed a slight increase in Rn and the other area showed a decreasing trend during the study period (Figure 8d). Rn showed lower values in cropland, open shrubland, and deciduous needle forest (Figure 10d). The annual average temperature ranged from 2.0 to 8.9 °C (Figure 7e) and showed an increasing trend across 71.3% of the FPENC area (Figure 8e and Figure 9e). Temp showed an obvious decreasing trend before 2012, and it increased among all the vegetation types afterwards (Figure 9e and Figure 10e). Moreover, the increasement of Temp was larger in the central and northeastern areas of FPENC during the study period. The higher latitude of deciduous needle forest led the lower Temp. The range of VPD was from 0.6 to 1.0 kPa (Figure 7f). VPD was higher in shrubland and grassland and was lowest in the deciduous needle forest (Figure 9f). Overall, the VPD showed a decreasing trend over the 20 years in the FPENC (Figure 9f), and it had nearly the same distribution with Temp, with the decreasing area mainly located in the eastern and central part, and only a small part showed an increasing trend, indicating a drier atmosphere (Figure 8f). VSWC ranged from 0.20 to 0.30 m3 m−3 (Figure 7g), and nearly the entire study area displayed a slight decrease in VSWC (Figure 8g). The VSWC stayed with relative lower value before 2011, and it increased significantly in 2012. The VSWC was significantly lower in grassland and deciduous broadleaf forest than the other vegetation types (Figure 10g).
The results in Table 2 also showed the same tendency with Figure 10, especially the EVI, LAI, VPD, and VSWC in different vegetation types. The lowest values of EVI and LAI occurred in open shrubland, followed by grassland and cropland. The PPT in closed shrubland and mixed forest had annual means of 556.96 mm and 529.65 mm, respectively. PPT was lowest in open shrubland with an average annual total of 407.37 mm. The distribution trends of VSWS and precipitation are consistent across different vegetation types (Table 2).

3.3. Regulations of the Biophysical Variables on WUE

3.3.1. The Correlation Between the WUE and Biophysical Variables

The heatmap illustrates the correlations between WUE and various biophysical variables, including EVI, LAI, PPT, Rn, Temp, VPD, and VSWC in the FPENC over the period 2003–2022 (Figure 11), WUE showed a positive relationship with Rn, Temp, and VPD, and showed a negative relationship with EVI, LAI, PPT, and VSWC. Especially, WUE is strongly negatively correlated with LAI (−0.52), indicating that better vegetation health and density may not be significantly conducive to the improvement of WUE. There is an obvious positive correlation between WUE and VPD (0.52), suggesting the VPD would induce stomata closure and open, which dominate the variation of WUE. Conversely, VSWC showed a moderately negative correlation with WUE (−0.44), implying that drier soil conditions can enhance water use efficiency, highlighting the role of soil moisture in supporting plant growth and reducing water stress.

3.3.2. Biophysical Regulations on WUE of Each Vegetation Type

Figure 12 accessed the relative importance of key driving factors on each vegetation cover type. As for CRO, VPD is the most crucial factor with an IncNodePurity value of approximately 3.0, indicating its key role in regulating the WUE of cropland. Rn and PPT followed VPD, collectively affecting cropland water use efficiency (Figure 12a). The high sensitivity of cropland to VPD might be related to its high transpiration rate and sensitivity to water conditions. For CSH, Temp is the primary influencing factor. Temperature directly affects plant transpiration rates and photosynthetic efficiency, thereby impacting water use efficiency (Figure 12b). As for DBF, WUE is mainly influenced by Temp and VSWC. These two factors play a crucial role in regulating WUE in these forests, as temperature and soil water conditions directly affect tree transpiration and photosynthesis, thus influencing water use efficiency (Figure 12c). WUE is primarily affected by VSWC in DNF, indicating that soil water conditions are a key factor for water use in this kind of forest. Moreover, LAI and EVI are also important factors for DNF, reflecting the impact of vegetation structure and growth conditions on WUE (Figure 12d). For ENF, EVI is the most important factor. EVI reflects vegetation health and productivity, indicating that healthy vegetation significantly influences water use efficiency. WUE is mainly affected by VPD for GRA, and Temp and Rn followed afterwards, reflecting the combined effect of environmental conditions on WUE of grassland (Figure 12f). Temp is a key factor in regulating water use efficiency in mixed forests, with other important factors including LAI and Rn (Figure 12g). Rn and EVI together exhibited weak controls on WUE for OSH, as the IncNodePurity was relatively small (Figure 12h). WUE is mainly influenced by Temp and VPD in SAV, both with IncNodePurity values of around 3.0. Temp and VPD directly affect plant transpiration rates and water use efficiency (Figure 12i). In WSA, WUE is primarily affected by Temp, with an IncNodePurity value of about 12, indicating that temperature is the determining factor for water use efficiency in semi-arid grasslands. VPD and LAI follow behind Temp, reflecting the importance of these factors in semi-arid environments (Figure 12j).
Overall, the main driving factors affecting WUE differ across vegetation cover types, with Temp and VPD being key factors in most vegetation types. Moreover, VSWC and LAI also showed an important influence on WUE. Additionally, other factors like Rn, PPT, and EVI also play significant roles in certain ecosystems.

3.3.3. Climatic Controls on WUE at Regional Scale

We further explored the influence of environmental variables (Temp, PPT, Rn, VSWC, and VPD) on WUE during different periods at a regional scale. It was found that from 2003 to 2012, Temp dominates WUE in the central area, and VPD plays the dominant role on WUE in the eastern FPENC, and in the western area, the VSWC play the dominant role (Figure 13a). From 2013 to 2022, VSWC plays a dominant role on WUE in the eastern and western part of FPENC, and Temp dominantly controls the WUE in the eastern (mainly the northeastern Inner Mongolia and western of Jilin province) and western part of FPENC (mainly in Gansu and Ningxia) (Figure 13b). From 2003 to 2022, the VSWC played the dominant role on WUE, mainly in the eastern area of Inner Mongolia and western Liaoning province, and the north of Shaanxi and Shanxi province (Figure 13c).

3.4. Ecosystem Resilience

The WUEd in the FPENC ranged from 0.09 to 4.09 gC H2O−1, with most values in the range of 0.60–2.40 (Figure 14a). The WUEm ranged from 0.52 to 2.60 gC H2O−1 (Figure 3a). The ecosystem resilience (Rd) ranged from 0.07 to 2.60 gC H2O−1 (Figure 14b), and the classification of resilient level indicates that the ecosystems in the eastern area of FPENC showed more resilience than that in the western part (Figure 14c). On the whole, the non-resilient level occupied about 59.2% of the whole area, while the level of “resilience” accounted for 40.8% of the area in the FPENC. The area with a severely non-resilient level was mainly located on the border of Northern Shaanxi and Southern Inner Mongolia, western Inner Mongolia. These areas are also the heart of Mu Us Sand Land and Horqin Sandy Land, which is very dry and lacks water. The WUEd for each vegetation type was lower than the WUEm grassland and open shrubland, which resulted in the non-resilient ecosystem (Figure 14d). Rd of ENF was 1.09, which reveals that the ecosystems were slightly resilient. Furthermore, the Rd in DBF and DNF and CSH were higher than 1.5, indicating more robust resilience (Figure 14d).

4. Discussion

4.1. Assessment of Data Precision

The MODIS ET and GPP products were used to calculate the WUE in FPENC. Though the spatial resolution of 500 m and use of 8-day composites may obscure fine-scale variations in WUE, especially for heterogeneous land covers, the method has been used previously [6], and proved to be highly accurate. The MODIS GPP have proved to be underestimated in arid areas [52], and thus may lead to the underestimation of WUE; therefore, we tested the MODIS-based WUE with other data sources for further verification. We compared the annual average WUE values from the MODIS data and the EC dataset, with one shrubland and six grasslands selected [23]. In addition, we added another four EC sites located in the FPENC [53,54,55]. The EC measuring the WUE was higher than the MODIS based on the WUE with the root mean square error (RMSE) being 0.27 gC kg−1H2O−1, the R-square (R2) being 0.83, and the relative error (bias) being 0.20 gC kg−1H2O−1 (Figure S6). Though the results showed the MODIS WUE was lower than the EC-observed values, the verification showed that our research can capture the long-term trend and spatio-temporal variation of WUE; thus, it would be valuable to guide the vegetation afforestation in FPENC. With the fast development of a remote sensing product, a higher spatio-temporal resolution of WUE may be used to carry out further analysis in FPENC.
As for the meteorological data source, Temp, VSWC, and Rn were derived from ERA5-land dataset, which has been proven to have high precision [56]. Several studies have highlighted that the ERA5-land dataset has a relatively large deviation from the mainstream precipitation products in mainland China [42,43], so we used the CHIRPS precipitation dataset. Previous study in the semi-arid grasslands in Inner Mongolia has proved that the CHIRPS precipitation dataset reveals better accuracy than the EAR5-land with regard to both RMSE and bias [56]. As for our study, we compared the monthly PPT data from CHIRPS with observed data selected from 33 meteorological stations located in the FPENC for the year 2022, and the results show that the R2 was 0.84 and RMSE was 24.24 mm (Figure S8), indicating a higher precision of the CHIRPS PPT. The TerraClimate VPD used in our research has been identified as having good precision with station-based VPD [5]. Therefore, high-precision data sources ensure the accuracy of analysis on the spatio-temporal characteristics of WUE and its driving forces in the FPENC.

4.2. The WUE Characteristics

Our study determined that the average annual WUE in the FPENC was 1.32 ± 0.11 gC kg−1H2O−1. Using a similar approach, Xue et al. (2015) and Huang et al. (2017) reported the ecosystem WUE from a global scale, with Xue et al. finding an annual WUE of 1.7 gC kg−1H2O−1, which also exceeds our findings [11,57]. The value in our study is lower than the values reported in a previous study of Indian forest (1.72–2.02 gC kg−1H2O−1) [6,58], and also falls short of 1.55 gC kg−1H2O−1 reported for China [9]. And it was higher than the annual average WUE (1.24 gC kg−1H2O−1) in Loess Plateau [8]. Another research paper in an aridity area of China (the area contains the FPENC) showed that the average WUE during growing season was 1.67 ± 0.98 gC kg−1H2O−1, which was slightly higher than our results. The discrepancies between our findings and previous studies may stem from two reasons, one is the differences in environmental conditions and vegetation types, different research areas may exhibit differences in environment features and vegetation types, leading the difference in WUE [9,21,23]. Another reason is the data source. We focused on the pixel analysis using the MODIS product in the FPENC, and other studies studied the WUE in aridity of China based on the meta-analysis at 31 EC sites [23]. Previous research has reported the underestimation of MODIS GPP and ET in arid area [52].
WUE is greatly influenced by vegetation types, prior research has established that forests typically demonstrate a higher WUE compared to croplands, while grasslands exhibit the lowest levels [21,52]. In our research, the hierarchy of WUE among various vegetation types was as follows: ENF > MF > CSH > WSA > DNF > DBF > GRA >SAV > CRO > OSH. This ranking slightly deviates from previous study that reported WUE in the order of DBF > DNF > EBF > CRO > OSH > ENF > MF > GRA [9]. The discrepancies between our findings and previous studies may stem from differences in the research area, which lead to the discrepancy in environmental conditions, plant species, and cultivation practices. Our research showed that WUE in ENF and MF remained at a relatively high value compared to other vegetation types, corroborating findings from earlier studies [21,23,50]. In our study, the WUE for cropland was measured at 2.0 gC kg−1H2O, lower than values observed in a sub-humid area [52], highlighting that temperature and precipitation significantly influence cropland WUE. The WUE observed in FPENC grasslands fell within the ranges established in previous research [23,59].

4.3. The Biophysical Controls of WUE

We selected both climatic and biotic variables with the aid of the RF algorithm to detect the main factors in influencing WUE of the ten vegetation types. Our study indicates that the biophysical variables were able to address the interannual variations in WUE quite effectively. The results revealed that Temp was the most critical factor influencing WUE, followed by VPD, LAI, and VSWC. However, the relative importance of these drivers varied among different vegetation types. Temp affects key plant physiological processes in plants, such as photosynthesis and transpiration, thereby influencing WUE [40]. VPD impacts the transpiration rate, thereby controlling plant water usage. It was identified as the primary factor affecting cropland and grassland, while VSWC emerged as the leading driver for DNF. Given that temperature is a major determinant of WUE in FPENC ecosystems, rising global temperature could significantly impact the WUE of vegetation.
When assessing the impact of environmental factors on WUE, the results illustrated in Figure 12a indicate that from 2003 to 2012, WUE in FPENC was primarily influenced by Temp and VSWC, with Temp dominantly controlling the WUE in the center FPENC, and VSWC controlled WUE in the western part. A decrease in Temp during this period was associated with a decline in WUE, as shown by their positive correlation (Figure 10). Conversely, precipitation appeared to have a limited effect on WUE which contrasts with the findings of the study conducted by Xiao et al. (2013), who suggested that annual temperature had a more substantial impact on WUE compared to annual precipitation [21,37]. Additionally, the observed negative relationship between VSWC and WUE suggests that increasing soil water content corresponded to lower WUE levels. Therefore, the combination of declining Temp and rising VSWC contributed to the decrease in WUE during the first decade. However, from 2013 to 2022, WUE showed a slight increase (Figure S2); this can be explained by the combined effects of multiple factors (especially Temp, VSWC, and VPD), leading the WUE to slightly increase during the last ten years, and this occurred especially in the southeastern of FPENC. Over the entire study period, WUE was primarily governed by VPD and VSWC (Figure 12c). The observed decrease in VPD and increase in VSWC suggest a trend towards a wetter environment, which together resulted in lower WUE. Furthermore, given the strong positive correlation between Temp and VPD, we conclude that WUE in FPENC from 2003 to 2022 was mainly influenced by Temp and VSWC, being consistent with the controlling mechanisms identified for different vegetation types in Figure 11.

4.4. Ecosystem Resilience

WUEd was lower in the central and western areas of FPENC (Figure 13a), indicating that the ecosystems in this region were delicate and faced challenges in adjusting to drought disturbances. In total, 37.1% of the area showed the Rd < 0.8 (i.e., severely non-significant resilient), mainly located in northwestern FPENC, this is occupied with grassland. This result is consistent with Guo et al. (2019), who reported that Rd in Northwest China ranged from 0.4 to 0.5, ranking in the level of severely non-resilient [9,60]. The highest Rd values in northeast FPENC are likely attributed to the deciduous forests (DBF and MF) [9,61,62].
Drought disruption directly affects the transpiration process of vegetation and soil evaporation, thereby influencing photosynthesis and water use efficiency [63,64,65]. Drought is one of the most severe natural resource challenges that the farming–pastoral ecotone of Northern China (FPENC) is facing. In order to assess the ecosystem resilience to drought, this study refers to the definition of the ecosystem resilience as the ratio of WUEd (WUE in drought years) to the annual average WUE (WUEm). SPEI can monitor the assessment of dry and wet conditions under climate change, with significant advantages in areas with significant temperature changes, making it suitable for climate change research. Therefore, we detected the drought years with SPEI < −1.5, and then calculated the average annual WUE during drought years to the average annual WUE. The resilience area based on the SPEI metrics was 40.8%, indicating that the vegetation recovery project has improved the FPENC ecological environment quality.

4.5. Limitations

As climatic factors like precipitation and temperature typically exhibited delayed impacts on vegetation growth and as a result, drought also shows lagging effects on ecosystem WUE. In the future, we should explore the drought’s delayed impact on WUE with multiple methods, together with a different drought index.
Previous studies reported, despite the biophysical variables, the timing and duration of drought also influenced the increase or decrease in WUE [17,27,66]. Summer drought usually caused the decrease in WUE, especially for the coniferous and broadleaf forest. One contributing factor is the increased vapor pressure deficit during drought conditions, which causes stomatal closure due to excessive water through transpiration, subsequently reducing canopy photosynthetic capacity [67,68]. However, the effects of spring drought or autumn drought on WUE are less clear. The duration of drought also impacts an ecosystem’s WUE. Prolonged droughts lasting longer than one month result in decreased seasonal and annual GPP and WUE.
Additionally, our study did not analyze the influencing of CO2 on WUE and Rd. Previous study has indicated that CO2 fertilization can enhance ecosystem resilience, particularly on the Tibetan plateau. The study also showed the CO2 can positively impact WUE over the long term [9]. Although our study observed a decreasing trend in WUE, this may suggest that CO2 had minimal influence. However, a more detailed analysis about the impact of CO2 on WUE is warranted in future investigations. Furthermore, human activity impact can also be explored in the near future.

5. Conclusions

The spatio-temporal variation and its driving force of WUE across the FPENC during the two decades with the support of a GEE platform was examined. In addition, the ecosystem resilience over the FPENC was also explored. This study reveals a non-significant declining trend in WUE (0.52–2.60 gC kgH2O−1) over the FPENC in the period of 2003–2022. Notably, WUE decreased markedly from 2003 to 2012, followed by a slight increase from 2013 to 2022. Among vegetation types, the evergreen needle forest exhibited the highest WUE, while grassland maintained relatively low values. In terms of climatic controls on WUE, Temp, VPD, and VSWC played the dominant role in controlling the WUE from 2003 to 2022. During the drought years, 40.18% of the whole FPENC showed ecosystem resilience, mainly in the vegetation of DBF, DNF, CRO, CSH, ENF, MF, SAV, and WSA, and grassland and open shrubland were shown to be moderately non-resilient. This study can enhance our understanding of WUE characteristics at both ecosystem and regional scales within the farming–pastoral ecotone of Northern China and shed light on ecosystem resilience amid global warming.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agriculture15111164/s1, Figure S1: The slope of inter-annual water use efficiency variations during 2003 to 2022 in the farming-pastoral ecotone of Northern China; Figure S2: Annual averages of water use efficiency and its trends during 2003 to 2010, 2011-2022 and 2003 to 2022 in the farming-pastoral ecotone of Northern China; Figure S3: The annual WUE during the study period from 2003 to 2022 in the farming-pastoral ecotone of Northern China; Figure S4: Monthly average water use efficiency during 2003 to 2022 in the farming-pastoral ecotone of Northern China; Figure S5: The spatial distribution of characteristics of annual averages of water use efficiency (WUE) in each season: (a) Spring, (b) Summer, (c) Autumn and (d) Winter during the study period from 2003 to 2022; Figure S6: Comparison of the auunal water use efficiency (WUE) obsered from EC sites and the calculation from the MODIS GPP and ET; Figure S7: Comparison of precipitation obeseved from meteorological stations and the CHIRPS data.

Author Contributions

Conceptualization, X.G. and Z.-L.L.; methodology, X.G. and M.W.; software, X.G. and Z.S.; validation, G.S., Q.M. and H.L.; writing—original draft preparation, X.G. and M.W.; Z.-L.L. and L.H. provided conceptual and editorial advice and rewrote significant parts of the manuscript; funding acquisition, M.W. and Z.-L.L. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Natural Science Foundation of Hebei Province (No. C2023403003, D2022403013), the Postdoctoral Research Foundation of China (No. 2020M670543), the National Key Research & Development Program of China (2022YFB3903505), Shijiazhuang Science and Technology Program (247790759A). We also would like to thank the editors and anonymous reviewers for providing valuable comments and suggestions on the manuscript.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The data are available on request from the authors, the raw data supporting the conclusions of our manuscript will be made available by the authors on request.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Bernacchi, C.J.; Vanloocke, A. Terrestrial Ecosystems in a Changing Environment: A Dominant Role for Water. Annu. Rev. Plant Biol. 2015, 66, 599. [Google Scholar] [CrossRef] [PubMed]
  2. Dai, A. Increasing drought under global warming in observations and models. Nat. Clim. Change 2013, 3, 52–58. [Google Scholar] [CrossRef]
  3. He, L.; Wang, J.; Ciais, P.; Ballantyne, A.; Yu, K.; Zhang, W.; Xiao, J.; Ritter, F.; Liu, Z.; Wang, X.; et al. Non-symmetric responses of leaf onset date to natural warming and cooling in northern ecosystems. PNAS Nexus 2023, 2, 308. [Google Scholar] [CrossRef]
  4. Valjarevic, A.; Morar, C.; Brasanac-Bosanac, L.; Cirkovic-Mitrovic, T.; Djekic, T.; Mihajlovic, M.; Milevski, I.; Culafic, G.; Lukovic, M.; Niemets, L.; et al. Sustainable land use in Moldova: GIS & remote sensing of forests and crops. Land Use Policy 2025, 152, 107515. [Google Scholar]
  5. Zhang, Y.Y.; Wang, Q.T.; Zhang, X.Y.; Guo, Z.C.; Guo, X.N.; Ma, C.H.; Wei, B.C.; He, L. Pre-Season Precipitation and Temperature Have a Larger Influence on Vegetation Productivity than That of the Growing Season in the Agro-Pastoral Ecotone in Northern China. Agriclture 2025, 15, 219. [Google Scholar] [CrossRef]
  6. Nandy, S.; Saranya, M.; Srinet, R. Spatio-temporal variability of water use efficiency and its drivers in major forest formations in India. Remote Sens. Environ. 2022, 269, 112791. [Google Scholar] [CrossRef]
  7. Huang, M.; Piao, S.; Sun, Y.; Ciais, P.; Cheng, L.; Mao, J.; Poulter, B.; Shi, X.; Zeng, Z.; Wang, Y. Change in terrestrial ecosystem water-use efficiency over the last three decades. Glob. Change Biol. 2015, 21, 2366–2378. [Google Scholar] [CrossRef]
  8. Ma, R.; Cui, X.; Wang, D.; Wang, S.; Wang, H.; Yao, X.; Li, S. Spatial and Temporal Characteristics of Water Use Efficiency in Typical Ecosystems on the Loess Plateau in the Last 20 Years, with Drivers and Implications for Ecological Restoration. Remote Sens. 2022, 14, 5632. [Google Scholar] [CrossRef]
  9. Guo, L.; Sun, F.; Liu, W.; Zhang, Y.; Wang, H.; Cui, H.; Wang, H.; Zhang, J.; Du, B. Response of Ecosystem Water Use Efficiency to Drought over China during 1982–2015: Spatiotemporal Variability and Resilience. Forests 2019, 10, 598. [Google Scholar] [CrossRef]
  10. Gu, C.; Tang, Q.; Zhu, G.; Ma, J.; Gu, C.; Zhang, K.; Sun, S.; Yu, Q.; Niu, S. Discrepant responses between evapotranspiration- and transpiration-based ecosystem water use efficiency to interannual precipitation fluctuations. Agric. For. Meteorol. 2021, 303, 108385. [Google Scholar] [CrossRef]
  11. Huang, L.; He, B.; Han, L.; Liu, J.; Wang, H.; Chen, Z. A global examination of the response of ecosystem water-use efficiency to drought based on MODIS data. Sci. Total Environ. 2017, 601, 1097–1107. [Google Scholar] [CrossRef] [PubMed]
  12. Jia, B.H.; Luo, X.; Wang, L.H.; Lai, X. Changes in Water Use Efficiency Caused by Climate Change, CO2 Fertilization, and Land Use Changes on the Tibetan Plateau. Adv. Atmos. Sci. 2023, 40, 144–154. [Google Scholar] [CrossRef]
  13. Kang, M.; Cho, S. Progress in water and energy flux studies in Asia: A review focused on eddy covariance measurements. J. Agric. Meteorol. 2021, 77, 2–23. [Google Scholar] [CrossRef]
  14. Dekker, S.C.; Groenendijk, M.; Booth, B.B.; Huntingford, C.; Cox, P.M. Spatial and temporal variations in plant water-use efficiency inferred from tree-ring, eddy covariance and atmospheric observations. Earth Syst. Dyn. 2016, 7, 525–533. [Google Scholar] [CrossRef]
  15. Gao, Y.; Zhu, X.; Yu, G.; He, N.; Wang, Q.; Tian, J. Water use efficiency threshold for terrestrial ecosystem carbon sequestration in China under afforestation. Agric. For. Meteorol. 2014, 195, 32–37. [Google Scholar] [CrossRef]
  16. Li, X.Y.; Zou, L.; Xia, J.; Wang, F.Y.; Li, H.W. Identifying the Responses of Vegetation Gross Primary Productivity and Water Use Efficiency to Climate Change under Different Aridity Gradients across China. Remote Sens. 2023, 15, 1563. [Google Scholar] [CrossRef]
  17. Ma, J.; Jia, X.; Zha, T.; Bourque, C.P.A.; Tian, Y.; Bai, Y.; Liu, P.; Yang, R.; Li, C.; Li, C.; et al. Ecosystem water use efficiency in a young plantation in Northern China and its relationship to drought. Agric. For. Meteorol. 2019, 275, 1–10. [Google Scholar] [CrossRef]
  18. Song, Q.-H.; Fei, X.-H.; Zhang, Y.-P.; Sha, L.-Q.; Liu, Y.-T.; Zhou, W.-J.; Wu, C.-S.; Lu, Z.-Y.; Luo, K.; Gao, J.-B. Water use efficiency in a primary subtropical evergreen forest in Southwest China. Sci. Rep. 2017, 7, 43031. [Google Scholar] [CrossRef]
  19. Wang, H.; Li, X.; Xiao, J.; Ma, M. Evapotranspiration components and water use efficiency from desert to alpine ecosystems in drylands. Agric. For. Meteorol. 2021, 298, 108283. [Google Scholar] [CrossRef]
  20. Zou, J.; Ding, J.; Welp, M.; Huang, S.; Liu, B.J.S. Assessing the response of ecosystem water use efficiency to drought during and after drought events across Central Asia. Sensors 2020, 20, 581. [Google Scholar] [CrossRef]
  21. Xiao, J.; Sun, G.; Chen, J.; Chen, H.; Chen, S.; Dong, G.; Gao, S.; Guo, H.; Guo, J.; Han, S.J.A.; et al. Carbon fluxes, evapotranspiration, and water use efficiency of terrestrial ecosystems in China. Agric. For. Meteorol. 2013, 182, 76–90. [Google Scholar] [CrossRef]
  22. Li, F.; Xiao, J.; Chen, J.; Ballantyne, A.; Jin, K.; Li, B.; Abraha, M.; John, R. Global water use efficiency saturation due to increased vapor pressure deficit. Science 2023, 381, 672–677. [Google Scholar] [CrossRef] [PubMed]
  23. Bai, Y.; Zha, T.; Bourque, C.P.A.; Jia, X.; Ma, J.; Liu, P.; Yang, R.; Li, C.; Du, T.; Wu, Y. Variation in ecosystem water use efficiency along a southwest-to-northeast aridity gradient in China. Ecol. Indic. 2020, 110, 105932. [Google Scholar] [CrossRef]
  24. Brümmer, C.; Black, T.A.; Jassal, R.S.; Grant, N.J.; Spittlehouse, D.L.; Chen, B.; Nesic, Z.; Amiro, B.D.; Arain, M.A.; Barr, A.G.; et al. How climate and vegetation type influence evapotranspiration and water use efficiency in Canadian forest, peatland and grassland ecosystems. Agric. For. Meteorol. 2012, 153, 14–30. [Google Scholar] [CrossRef]
  25. Xu, X.; Jiao, F.; Gong, H.; Xue, P.; Lin, N.; Liu, J.; Zhang, K.; Qiu, J.; Lin, D.; Yang, Y.; et al. Observed divergence in the trends of temperature controls on Chinese ecosystem water use efficiency. Ecol. Indic. 2023, 157, 111241. [Google Scholar] [CrossRef]
  26. Wang, F.; Zhang, F.; Gou, X.; Fonti, P.; Xia, J.; Cao, Z.; Liu, J.; Wang, Y.; Zhang, J. Seasonal variations in leaf-level photosynthesis and water use efficiency of three isohydric to anisohydric conifers on the Tibetan Plateau. Agric. For. Meteorol. 2021, 308–309, 108581. [Google Scholar] [CrossRef]
  27. Xie, J.; Chen, J.; Sun, G.; Zha, T.; Yang, B.; Chu, H.; Liu, J.; Wan, S.; Zhou, C.; Ma, H.; et al. Ten-year variability in ecosystem water use efficiency in an oak-dominated temperate forest under a warming climate. Agric. For. Meteorol. 2016, 218–219, 209–217. [Google Scholar] [CrossRef]
  28. Farquhar, G.D.; Ehleringer, J.R.; Hubick, K.T. CARBON ISOTOPE DISCRIMINATION AND PHOTOSYNTHESIS. Annu. Rev. Plant Physiol. Plant Mol. Biol. 1989, 40, 503–537. [Google Scholar] [CrossRef]
  29. Zhu, Q.; Jiang, H.; Peng, C.; Liu, J.; Wei, X.; Fang, X.; Liu, S.; Zhou, G.; Yu, S. Evaluating the effects of future climate change and elevated CO2 on the water use efficiency in terrestrial ecosystems of China. Ecol. Modell. 2011, 222, 2414–2429. [Google Scholar] [CrossRef]
  30. Zhuang, Y.; Zhao, W. Dew formation and its variation in Haloxylon ammodendron plantations at the edge of a desert oasis, northwestern China. Agric. For. Meteorol. 2017, 247, 541–550. [Google Scholar] [CrossRef]
  31. Kim, H.W.; Hwang, K.; Mu, Q.; Lee, S.O.; Choi, M. Validation of MODIS 16 global terrestrial evapotranspiration products in various climates and land cover types in Asia. KSCE J. Civ. Eng. 2012, 16, 229–238. [Google Scholar] [CrossRef]
  32. Liu, Y.; Zhou, Y.; Ju, W.; Chen, J.; Wang, S.; He, H.; Wang, H.; Guan, D.; Zhao, F.; Li, Y.; et al. Evapotranspiration and water yield over China’s landmass from 2000 to 2010. Hydrol. Earth Syst. Sci. 2013, 17, 4957–4980. [Google Scholar] [CrossRef]
  33. Mu, Q.; Zhao, M.; Running, S.W. MODIS global terrestrial evapotranspiration (ET) product (NASA MOD16A2/A3). Algorithm Theor. Basis Doc. Collect. 2013, 5, 381–394. [Google Scholar]
  34. Zhang, G.L.; Chen, X.; Zhou, Y.; Zhao, H.L.; Jin, Y.L.; Luo, Y.C.; Chen, S.Y.; Wu, X.Y.; Pan, Z.H.; An, P.L. Land use/cover changes and subsequent water budget imbalance exacerbate soil aridification in the farming-pastoral ecotone of northern China. J. Hydrol. 2023, 624, 129939. [Google Scholar] [CrossRef]
  35. Jiang, L.; Huang, X.; Wang, F.; Liu, Y.; An, P. Method for evaluating ecological vulnerability under climate change based on remote sensing: A case study. Ecol. Indic. 2018, 85, 479–486. [Google Scholar] [CrossRef]
  36. Zhou, W.C.; Liu, Z.J.; Wang, S.S. Spatiotemporal Dynamics of the Cropland Area and Its Response to Increasing Regional Extreme Weather Events in the Farming-Pastoral Ecotone of Northern China during 1992–2020. Sustainability 2023, 15, 13338. [Google Scholar] [CrossRef]
  37. He, L.; Li, Z.L.; Wang, X.M.; Xie, Y.W.; Ye, J.S. Lagged precipitation effect on plant productivity is influenced collectively by climate and edaphic factors in drylands. Sci. Total Environ. 2021, 755, 142506. [Google Scholar] [CrossRef]
  38. Sajikumar, N.; Remya, R. Impact of land cover and land use change on runoff characteristics. J. Environ. Manag. 2015, 161, 460–468. [Google Scholar] [CrossRef]
  39. Feng, X.R.; Zhang, T.; Feng, P.; Li, J.Z. Evaluation and tradeoff-synergy analysis of ecosystem services in Luanhe River Basin. Ecohydrology 2022, 15, e2473. [Google Scholar] [CrossRef]
  40. He, L.; Wang, J.; Penuelas, J.; Zohner, C.M.; Crowther, T.W.; Fu, Y.; Zhang, W.; Xiao, J.; Liu, Z.; Wang, X.; et al. Asymmetric temperature effect on leaf senescence and its control on ecosystem productivity. Proc. Natl. Acad. Sci. Nexus 2024, 3, pgae477. [Google Scholar] [CrossRef]
  41. Yang, B.Y.; Zhang, T.; Li, J.Z.; Feng, P.; Miao, Y.J.J. Optimal designs of LID based on LID experiments and SWMM for a small-scale community in Tianjin, north China. J. Environ. Manag. 2023, 334, 117442. [Google Scholar] [CrossRef]
  42. Hong, T.L.; Li, H.Y.; Chen, M.Q. Comprehensive Evaluations on the Error Characteristics of the State-of-the-Art Gridded Precipitation Products Over Jiangxi Province in 2019. Earth Space Sci. 2021, 8, e2021EA001787. [Google Scholar] [CrossRef]
  43. Jiang, Q.; Li, W.Y.; Fan, Z.D.; He, X.G.; Sun, W.W.; Chen, S.; Wen, J.H.; Gao, J.; Wang, J. Evaluation of the ERA5 reanalysis precipitation dataset over Chinese Mainland. J. Hydrol. 2021, 595, 125660. [Google Scholar] [CrossRef]
  44. Almeida, T.A.B.; Montenegro, A.A.d.A.; Mackay, R.; Montenegro, S.M.G.L.; Coelho, V.H.R.; de Carvalho, A.A.; da Silva, T.G.F. Hydrogeological trends in an alluvial valley in the Brazilian semiarid: Impacts of observed climate variables change and exploitation on groundwater availability and salinity. J. Hydrol. -Reg. Stud. 2024, 53, 101784. [Google Scholar] [CrossRef]
  45. Li, S.J.; Wang, G.J.; Sun, S.L.; Hagan, D.F.T.; Chen, T.X.; Dolman, H.; Liu, Y. Long-term changes in evapotranspiration over China and attribution to climatic drivers during 1980–2010. J. Hydrol. 2021, 595, 126037. [Google Scholar] [CrossRef]
  46. Kendall, M.G. Rank Correlation Methods; Charles Grifin: London, UK, 1975. [Google Scholar]
  47. Mann, H.B. Nonparametric tests against trend. Econometrica 1945, 13, 245–259. [Google Scholar] [CrossRef]
  48. Nandy, S.; Srinet, R.; Padalia, H. Mapping Forest Height and Aboveground Biomass by Integrating ICESat-2, Sentinel-1 and Sentinel-2 Data Using Random Forest Algorithm in Northwest Himalayan Foothills of India. Geophys. Res. Lett. 2021, 48, e2021GL093799. [Google Scholar] [CrossRef]
  49. Guo, X.; Xiao, J.; Zha, T.; Shang, G.; Liu, P.; Jin, C.; Zhang, Y. Dynamics and biophysical controls of nocturnal water loss in a winter wheat-summer maize rotation cropland: A multi-temporal scale analysis. Agric. For. Meteorol. 2023, 342, 109701. [Google Scholar] [CrossRef]
  50. Sharma, A.; Goyal, M.K. District-level assessment of the ecohydrological resilience to hydroclimatic disturbances and its controlling factors in India. J. Hydrol. 2018, 564, 1048–1057. [Google Scholar] [CrossRef]
  51. Sharma, A.; Goyal, M.K. Assessment of ecosystem resilience to hydroclimatic disturbances in India. Glob. Change Biol. 2018, 24, e432–e441. [Google Scholar] [CrossRef]
  52. Wang, X.; Ma, M.; Li, X.; Song, Y.; Tan, J.; Huang, G.; Zhang, Z.; Zhao, T.; Feng, J.; Ma, Z.; et al. Validation of MODIS-GPP product at 10 flux sites in northern China. Int. J. Remote Sens. 2013, 34, 587–599. [Google Scholar] [CrossRef]
  53. Liu, P.; Zha, T.S.; Jia, X.; Tian, Y.; Hao, S.R.; Li, X.H. Divergent responses of canopy and ecosystem water use efficiency to environmental conditions over a decade in a shrubland ecosystem dominated by Artemisia ordosica. Agric. For. Meteorol. 2025, 368, 110551. [Google Scholar] [CrossRef]
  54. Liu, P.; Zha, T.; Jia, X.; Black, T.A.; Jassal, R.S.; Ma, J.; Bai, Y.; Wu, Y. Different Effects of Spring and Summer Droughts on Ecosystem Carbon and Water Exchanges in a Semiarid Shrubland Ecosystem in Northwest China. Ecosystems 2019, 22, 1869–1885. [Google Scholar] [CrossRef]
  55. Dong, G.; Zhao, F.Y.; Chen, J.Q.; Qu, L.P.; Jiang, S.C.; Chen, J.Y.; Shao, C.L. Divergent forcing of water use efficiency from aridity in two meadows of the Mongolian Plateau. J. Hydrol. 2021, 593, 125799. [Google Scholar] [CrossRef]
  56. Ma, X.Q.; Leng, P.; Liao, Q.Y.; Geng, Y.J.; Zhang, X.; Shang, G.F.; Song, X.N.; Song, Q.; Li, Z.L. Prediction of vegetation phenology with atmospheric reanalysis over semiarid grasslands in Inner Mongolia. Sci. Total Environ. 2022, 812, 152462. [Google Scholar] [CrossRef]
  57. Xue, B.L.; Guo, Q.H.; Otto, A.; Xiao, J.F.; Tao, S.L.; Li, L. Global patterns, trends, and drivers of water use efficiency from 2000 to 2013. Ecosphere 2015, 6, 174. [Google Scholar] [CrossRef]
  58. Wang, Y.P.; Baldocchi, D.; Leuning, R.; Falge, E.; Vesala, T. Estimating parameters in a land-surface model by applying nonlinear inversion to eddy covariance flux measurements from eight FLUXNET sites. Glob. Change Biol. 2007, 13, 652–670. [Google Scholar] [CrossRef]
  59. Niu, S.; Xing, X.; Zhang, Z.; Xia, J.; Zhou, X.; Song, B.; Li, L.; Wan, S. Water-use efficiency in response to climate change: From leaf to ecosystem in a temperate steppe. Glob. Change Biol. 2011, 17, 1073–1082. [Google Scholar] [CrossRef]
  60. Cai, X.; Li, Z.; Liang, Y. Tempo-spatial changes of ecological vulnerability in the arid area based on ordered weighted average model. Ecol. Indic. 2021, 133, 108398. [Google Scholar] [CrossRef]
  61. Sun, S.; Song, Z.; Wu, X.; Wang, T.; Wu, Y.; Du, W.; Che, T.; Huang, C.; Zhang, X.; Ping, B.; et al. Spatio-temporal variations in water use efficiency and its drivers in China over the last three decades. Ecol. Indic. 2018, 94, 292–304. [Google Scholar] [CrossRef]
  62. Liu, Y.; Xiao, J.; Ju, W.; Zhou, Y.; Wang, S.; Wu, X. Water use efficiency of China’s terrestrial ecosystems and responses to drought. Sci. Rep. 2015, 5, 13799. [Google Scholar] [CrossRef]
  63. Peng, D.; Zhang, B.; Wu, C.; Huete, A.R.; Gonsamo, A.; Lei, L.; Ponce-Campos, G.E.; Liu, X.; Wu, Y. Country-level net primary production distribution and response to drought and land cover change. Sci. Total Environ. 2017, 574, 65–77. [Google Scholar] [CrossRef]
  64. Teuling, A.J.; Van Loon, A.F.; Seneviratne, S.I.; Lehner, I.; Aubinet, M.; Heinesch, B.; Bernhofer, C.; Grünwald, T.; Prasse, H.; Spank, U. Evapotranspiration amplifies European summer drought. Geophys. Res. Lett. 2013, 40, 2071–2075. [Google Scholar] [CrossRef]
  65. Zhao, M.; Running, S.W. Drought-Induced Reduction in Global Terrestrial Net Primary Production from 2000 Through 2009. Science 2010, 329, 940–943. [Google Scholar] [CrossRef]
  66. Xie, J.; Zha, T.; Zhou, C.; Jia, X.; Yu, H.; Yang, B.; Chen, J.; Zhang, F.; Wang, B.; Bourque, C.P.A.; et al. Seasonal variation in ecosystem water use efficiency in an urban-forest reserve affected by periodic drought. Agric. For. Meteorol. 2016, 221, 142–151. [Google Scholar] [CrossRef]
  67. Law, B.E.; Falge, E.; Gu, L.; Baldocchi, D.D.; Bakwin, P.; Berbigier, P.; Davis, K.; Dolman, A.J.; Falk, M.; Fuentes, J.D.; et al. Environmental controls over carbon dioxide and water vapor exchange of terrestrial vegetation. Agric. For. Meteorol. 2002, 113, 97–120. [Google Scholar] [CrossRef]
  68. Reichstein, M., Tenhunen; Valentini, R. Severe drought effects on ecosystem CO2 and H2O fluxes at three mediterranean evergreen sites: Revision of current hypotheses? Glob. Change Biol. 2002, 8, 999–1017. [Google Scholar] [CrossRef]
Agriculture 15 01164 g001
Figure 1. The location of study area, with samples from the vegetation.
Figure 1. The location of study area, with samples from the vegetation.
Agriculture 15 01164 g001
Agriculture 15 01164 g002
Figure 2. A framework of the present study.
Figure 2. A framework of the present study.
Agriculture 15 01164 g002
Agriculture 15 01164 g003
Figure 3. (a) The annual average water use efficiency (WUE) and (b) the spatial distribution of its trend during 2003–2022 over the study area.
Figure 3. (a) The annual average water use efficiency (WUE) and (b) the spatial distribution of its trend during 2003–2022 over the study area.
Agriculture 15 01164 g003
Agriculture 15 01164 g004
Figure 4. Monthly means of water use efficiency (WUE) from 2003 to 2022 over the study period.
Figure 4. Monthly means of water use efficiency (WUE) from 2003 to 2022 over the study period.
Agriculture 15 01164 g004
Agriculture 15 01164 g005
Figure 5. Boxplot of the mean values of monthly water use efficiency (WUE) over the FPENC from 2003 to 2022.
Figure 5. Boxplot of the mean values of monthly water use efficiency (WUE) over the FPENC from 2003 to 2022.
Agriculture 15 01164 g005
Agriculture 15 01164 g006
Figure 6. (a) Inter-annual variations of water use efficiency (WUE) in different vegetation types over the study area and (b) the monthly averages of water use efficiency and its trends from 2003 to 2010, 2011 to 2022, and 2003 to 2022 in the farming–pastoral ecotone of Northern China. ENF: evergreen needle forest, DNF: deciduous needle forest, DBF: deciduous broadleaf forest, MF: mixed forest, CSH: closed shrubland, OSH: open shrubland, WSAs: woody savannas, SAVs: savannas, GRA: grassland, and CRO: cropland.
Figure 6. (a) Inter-annual variations of water use efficiency (WUE) in different vegetation types over the study area and (b) the monthly averages of water use efficiency and its trends from 2003 to 2010, 2011 to 2022, and 2003 to 2022 in the farming–pastoral ecotone of Northern China. ENF: evergreen needle forest, DNF: deciduous needle forest, DBF: deciduous broadleaf forest, MF: mixed forest, CSH: closed shrubland, OSH: open shrubland, WSAs: woody savannas, SAVs: savannas, GRA: grassland, and CRO: cropland.
Agriculture 15 01164 g006
Agriculture 15 01164 g007
Figure 7. Spatial distribution of annual averages for the biophysical variables, (a) enhanced vegetation index (EVI), (b) leaf area index (LAI), (c) precipitation (PPT), (d) net radiation (Rn), (e) temperature (Temp), (f) vapor pressure deficit (VPD), and (g) soil volumetric water content (VSWC) at 7 cm below the ground during the study period of 2003–2022.
Figure 7. Spatial distribution of annual averages for the biophysical variables, (a) enhanced vegetation index (EVI), (b) leaf area index (LAI), (c) precipitation (PPT), (d) net radiation (Rn), (e) temperature (Temp), (f) vapor pressure deficit (VPD), and (g) soil volumetric water content (VSWC) at 7 cm below the ground during the study period of 2003–2022.
Agriculture 15 01164 g007
Agriculture 15 01164 g008
Figure 8. Spatial distribution of long-term trends in biophysical variables, (a) enhanced vegetation index (EVI), (b) leaf area index (LAI), (c) precipitation (PPT), (d) net radiation (Rn), (e) temperature (Temp), (f) vapor pressure deficit (VPD) and (g) soil volumetric water content (VSWC) at 7 cm below the ground during the study period of 2003–2022.
Figure 8. Spatial distribution of long-term trends in biophysical variables, (a) enhanced vegetation index (EVI), (b) leaf area index (LAI), (c) precipitation (PPT), (d) net radiation (Rn), (e) temperature (Temp), (f) vapor pressure deficit (VPD) and (g) soil volumetric water content (VSWC) at 7 cm below the ground during the study period of 2003–2022.
Agriculture 15 01164 g008
Agriculture 15 01164 g009
Figure 9. The annual means of (a) enhanced vegetation index (EVI), (b) leaf area index (LAI), (d) net radiation (Rn), (e) air temperature (Temp), (f) vapor pressure deficit (VPD), (g) volumetric soil water content (VSWC). And the annual totals of (c) precipitation (PPT) during 2003–2022 in the farming–pastoral ecotone of Northern China. The dashed lines represent the trend of each variable. The blue shaded area represents the standard deviation.
Figure 9. The annual means of (a) enhanced vegetation index (EVI), (b) leaf area index (LAI), (d) net radiation (Rn), (e) air temperature (Temp), (f) vapor pressure deficit (VPD), (g) volumetric soil water content (VSWC). And the annual totals of (c) precipitation (PPT) during 2003–2022 in the farming–pastoral ecotone of Northern China. The dashed lines represent the trend of each variable. The blue shaded area represents the standard deviation.
Agriculture 15 01164 g009
Agriculture 15 01164 g010
Figure 10. The annual means of (a) enhanced vegetation index (EVI), (b) leaf area index (LAI), (c) precipitation, (d) net radiation (Rn), (e) air temperature (Temp), (f) vapor pressure deficit (VPD), (g) volumetric soil water content (VSWC), and the annual totals of (c) precipitation (PPT) in each of different vegetation types during 2003–2022 over the study period. CRO: cropland, ENF: evergreen needle forest, OSH: open shrubland, CSH: closed shrubland, GRA: grassland, SAVs: savannas, MF: mixed forest, DBF: deciduous broadleaf forest, MF: mixed forest, WSAs: woody savannas and DNF: deciduous needle forest.
Figure 10. The annual means of (a) enhanced vegetation index (EVI), (b) leaf area index (LAI), (c) precipitation, (d) net radiation (Rn), (e) air temperature (Temp), (f) vapor pressure deficit (VPD), (g) volumetric soil water content (VSWC), and the annual totals of (c) precipitation (PPT) in each of different vegetation types during 2003–2022 over the study period. CRO: cropland, ENF: evergreen needle forest, OSH: open shrubland, CSH: closed shrubland, GRA: grassland, SAVs: savannas, MF: mixed forest, DBF: deciduous broadleaf forest, MF: mixed forest, WSAs: woody savannas and DNF: deciduous needle forest.
Agriculture 15 01164 g010
Agriculture 15 01164 g011
Figure 11. The heatmap of the correlation between WUE and biophysical variables (EVI, enhanced vegetation index; LAI, leaf area index; PPT, precipitation; Rn, net radiation; Temp, air temperature; VPD, vapor pressure deficit; VSWC, volumetric soil water content) over the study period. The annual means (except for PPT, the annual totals of PPT were used) over 2003–2022 were used during the correlation plot.
Figure 11. The heatmap of the correlation between WUE and biophysical variables (EVI, enhanced vegetation index; LAI, leaf area index; PPT, precipitation; Rn, net radiation; Temp, air temperature; VPD, vapor pressure deficit; VSWC, volumetric soil water content) over the study period. The annual means (except for PPT, the annual totals of PPT were used) over 2003–2022 were used during the correlation plot.
Agriculture 15 01164 g011
Agriculture 15 01164 g012
Figure 12. Importance ranking of driving factors in predicting water use efficiency (WUE of each vegetation type in the study period based on a random forest model. (a) CRO: cropland, (b) CSH: closed shrubland, (c) DBF: deciduous broadleaf forest, (d) DNF: deciduous needle forest, (e) ENF: evergreen needle forest, (f) GRA: grassland, (g) MF: mixed forest, (h) OSH: open shrubland, (i) SAVs: savannas and (j) WSAs: woody savannas.
Figure 12. Importance ranking of driving factors in predicting water use efficiency (WUE of each vegetation type in the study period based on a random forest model. (a) CRO: cropland, (b) CSH: closed shrubland, (c) DBF: deciduous broadleaf forest, (d) DNF: deciduous needle forest, (e) ENF: evergreen needle forest, (f) GRA: grassland, (g) MF: mixed forest, (h) OSH: open shrubland, (i) SAVs: savannas and (j) WSAs: woody savannas.
Agriculture 15 01164 g012
Agriculture 15 01164 g013
Figure 13. The dominant environmental factor of WUE during the period of (a) 2003–2012, (b) 2013–2022, and (c) 2003–2022 in the study area. The selected environmental variables were Temperature (Temp), precipitation (PPT), net radiation (Rn), vapor pressure deficit (VPD), and soil volumetric water content (VSWC).
Figure 13. The dominant environmental factor of WUE during the period of (a) 2003–2012, (b) 2013–2022, and (c) 2003–2022 in the study area. The selected environmental variables were Temperature (Temp), precipitation (PPT), net radiation (Rn), vapor pressure deficit (VPD), and soil volumetric water content (VSWC).
Agriculture 15 01164 g013
Agriculture 15 01164 g014
Figure 14. (a) The WUE in drought years (WUEd), (b) the ecosystem resilience (Rd), and (c) the classification of the Rd; (d) the WUE in drought years (WUEd), the annual average WUE (WUEm), and the ecosystem resilience (Rd) for the ten vegetation types during the study period (from 2003 to 2022): woody savannas (WSAs), savannas (SAVs), mixed forest (MF), open shrubland (OSH), grassland (GRA), evergreen needle forest (ENF), deciduous broadleaf forest (DBF), closed shrubland (CSH), and cropland (CRO). Resilient: Rd ≥ 1; slightly non-resilient: 0.9 < Rd < 1; moderately non-resilient: 0.8 ≤ Rd ≤ 0.9; and severely non-resilient: Rd < 0.8.
Figure 14. (a) The WUE in drought years (WUEd), (b) the ecosystem resilience (Rd), and (c) the classification of the Rd; (d) the WUE in drought years (WUEd), the annual average WUE (WUEm), and the ecosystem resilience (Rd) for the ten vegetation types during the study period (from 2003 to 2022): woody savannas (WSAs), savannas (SAVs), mixed forest (MF), open shrubland (OSH), grassland (GRA), evergreen needle forest (ENF), deciduous broadleaf forest (DBF), closed shrubland (CSH), and cropland (CRO). Resilient: Rd ≥ 1; slightly non-resilient: 0.9 < Rd < 1; moderately non-resilient: 0.8 ≤ Rd ≤ 0.9; and severely non-resilient: Rd < 0.8.
Agriculture 15 01164 g014
Table 1. Detail specifications of the datasets and products used in this study.
Table 1. Detail specifications of the datasets and products used in this study.
No.DataDataset/ProductPeriodResolutionData Source
SpatialTemporal
1.Gross primary
productivity (GPP)		MODIS GPP(MOD17A2H)2003–2022500 m8 daysProvider:NASA LP DAAC at the USGS EROS Center
(https://lpdaac.usgs.gov/products/mod17a2hv061/ (accessed on 2 April 2025)
Image collection ID:MODIS/006/MOD17/A2H)
2.Evapo-transpiration
(ET)		MODIS ET (MOD16A2)2003–2022500 m8 daysProvider:NASA LP DAAC at the USGS EROS Center
(https://lpdaac.usgs.gov/products/mod16a2v061/ (accessed on 2 April 2025)
Image collection ID:MODIS/006/MOD16/A2)
3.Leaf area index (LAI)MODIS LAI (MOD15A2H)2003–2022500 m8 daysGoogle Earth Engine Provider:NASA LP DAAC at the USGS EROS Center (https://lpdaac.usgs.gov/products/mod15a2hv061/ accessed on 2 April 2025)
Image collection ID:MODIS/006/MOD15A2H
4.Enhanced vegetation
index (EVI)		MODIS EVI (MOD13A1)2003–2022500 m16 daysProvider:NASA LP DAAC at the USGS EROS Center
(https://lpdaac.usgs.gov/products/mod13a1v061/) (accessed on 2 April 2025)
Image collection ID:MODIS/006/MOD13A1)
5.RainfallClimate Hazards Group Infrared Precipitation with Station (CHIRPS) monthly averaged data2003–20220.05 degree Provider: Climate Hazards Center-UC SANTA BARBARA (https://www.chc.ucsb.edu/data/chirps (accessed on 2 April 2025)
Index of /products/CHIRPS-2.0/) Image collection ID: Image collection ID: UCSB-CHG/CHIRPS/MONTHLY)
6.TemperatureERA-5 Land monthly averaged data 2 m temperature2003–20220.1 degreeMonthlyProvider:Climate data store
(https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land-monthly-means?tab=overview (accessed on 2 April 2025)
Image collection ID:ECMWF/ERA5 LAND
MONTHLY)
7.Soil moistureERA-5 Land monthly averaged data-volumetric
soil water content (VSWC) (0–7 cm depth)		2003–2022
8.Net radiationERA-5 Land monthly averaged data-resultant
of the surface net solar and thermal radiation
data		2003–2022
9.Vapor pressure
deficit (VPD)		TerraClimate: Monthly Climate and Climatic
Water balance for global terrestrial surfaces
VPD		2003–20222.5 arc minutesMonthlyProvider:Terraclimate
(https://climatedataguide.ucar.edu/climate-data/terraclimate-global-high-resolution-gridded-temperature-precipitation-and-other-water (accessed on 2 April 2025)
mage collection ID:IDAHO EPSCOR
TERRACLIMATE)
10Land use classesMODIS Land Cover Type MCD12Q12022500 mAnnualProvider:NASA LP DAAC at the USGS EROS Center
(https://lpdaac.usgs.gov/products/mcd12q1v061/ (accessed on 2 April 2025))
11Stardardized precipitation evapotranspiration (SPEI) 2003–20220.5°AnnualProvider: Global SPEI database (http://spei.csic.es/database.html (accessed on 2 April 2025))
Table 2. The multi-year means of annual averaged enhanced vegetation index (EVI), leaf area index (LAI), temperature (Temp), net radiation (Rn), vapor pressure deficit (VPD), the volumetric water content (VSWC) at 7 cm below the ground, water use efficiency (WUE), and annual totals of precipitation (PPT) for each vegetation type and the whole of FPENC. CRO: cropland, CSH: closed shrubland, DBF: deciduous broadleaf forest, DNF: deciduous needle forest, ENF: evergreen needle forest, GRA: grassland, MF: mixed forest, OSH: open shrubland, SAVs: savannas and WSAs: woody savannas.
Table 2. The multi-year means of annual averaged enhanced vegetation index (EVI), leaf area index (LAI), temperature (Temp), net radiation (Rn), vapor pressure deficit (VPD), the volumetric water content (VSWC) at 7 cm below the ground, water use efficiency (WUE), and annual totals of precipitation (PPT) for each vegetation type and the whole of FPENC. CRO: cropland, CSH: closed shrubland, DBF: deciduous broadleaf forest, DNF: deciduous needle forest, ENF: evergreen needle forest, GRA: grassland, MF: mixed forest, OSH: open shrubland, SAVs: savannas and WSAs: woody savannas.
Vegetation TypesEVILAITempPPTRnVSWCVPDWUE
m2 m−2°CmmW m−2m3 m−3kPagC H2O−1
WSA0.24 12.41 5.55 527.54228.41 0.31 0.63 1.39 ± 0.08
SAV0.26 12.22 5.59 527.17 225.90 0.31 0.67 1.2 ± 0.07
MF0.26 17.02 6.38 529.65234.15 0.31 0.58 1.61 ± 0.10
OSH0.14 2.57 8.06 407.37 212.14 0.12 0.84 1.08 ± 0.18
GRA0.17 4.82 6.21 454.63 225.91 0.25 0.73 1.16 ± 0.11
ENF0.24 12.60 7.25 487.98 236.59 0.27 0.69 1.87 ± 0.12
DNF0.20 11.98 2.35 529.07208.03 0.33 0.48 1.16 ± 0.14
DBF0.27 16.49 5.17 523.24 225.54 0.15 0.64 1.28 ± 0.09
CSH0.26 15.05 7.30 556.96 230.27 0.30 0.72 1.38 ± 0.11
CRO0.20 6.84 6.27 483.98 215.11 0.28 0.67 1.30 ± 0.12
FPENC0.23 11.20 6.01 502.76 224.21 0.26 0.67 1.32 ± 0.11
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Guo, X.; Wu, M.; Shen, Z.; Shang, G.; Ma, Q.; Li, H.; He, L.; Li, Z.-L. Twenty-Year Variability in Water Use Efficiency over the Farming–Pastoral Ecotone of Northern China: Driving Force and Resilience to Drought. Agriculture 2025, 15, 1164. https://doi.org/10.3390/agriculture15111164

AMA Style

Guo X, Wu M, Shen Z, Shang G, Ma Q, Li H, He L, Li Z-L. Twenty-Year Variability in Water Use Efficiency over the Farming–Pastoral Ecotone of Northern China: Driving Force and Resilience to Drought. Agriculture. 2025; 15(11):1164. https://doi.org/10.3390/agriculture15111164

Chicago/Turabian Style

Guo, Xiaonan, Meng Wu, Zhijun Shen, Guofei Shang, Qingtao Ma, Hongyu Li, Lei He, and Zhao-Liang Li. 2025. "Twenty-Year Variability in Water Use Efficiency over the Farming–Pastoral Ecotone of Northern China: Driving Force and Resilience to Drought" Agriculture 15, no. 11: 1164. https://doi.org/10.3390/agriculture15111164

APA Style

Guo, X., Wu, M., Shen, Z., Shang, G., Ma, Q., Li, H., He, L., & Li, Z.-L. (2025). Twenty-Year Variability in Water Use Efficiency over the Farming–Pastoral Ecotone of Northern China: Driving Force and Resilience to Drought. Agriculture, 15(11), 1164. https://doi.org/10.3390/agriculture15111164

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop