Ecological Water Requirement of Natural Vegetation in the Tarim River Basin Based on Multi-Source Data

Abstract

The Tarim River Basin is one of the most ecologically fragile regions around the world in the arid areas of Northwest China. The study of natural vegetation ecological water requirement (EWR) is the basis for the promotion of regional ecological conservation and sustainable development of ecosystems when extreme environmental events occur frequently, which is of great significance for the formulation of scientific and rational ecological conservation strategies. In the study, we improved the vegetation EWR calculation method by introducing a dynamic soil moisture limitation coefficient (K_S) and a dynamic vegetation coefficient (K_C) that is coupled with a resistance correction factor (F_r) based on the Penman-Monteith method and analyzed its spatio-temporal variation characteristics. Additionally, this study utilized the latitude of ecosystem resilience (LER) to clarify the thresholds for vegetation EWR throughout the growing season in the study area and to analyze the water surplus and deficit (WSD) at different threshold levels. The results of the study show that: (1) Over the past 21 years, the EWR for vegetation has shown a downward trend, with the change in EWR for arbor-shrub forests being more significant than that for grasslands. The average EWR for arbor-shrub forests and grasslands is 36.76 × 10⁸ m³ and 459.59 × 10⁸ m³, respectively. (2) The minimum ecological water requirement (EWR_min) and optimal ecological water requirement (EWR_opt) for natural vegetation were 360.45 × 10⁸ m³ and 550.10 × 10⁸ m³, respectively. (3) In EWR_min conditions, the alpine plateau area as a whole showed a water surplus, and the plains area as a whole was in a state of water scarcity, but the precipitation in the study area as a whole could meet the basic survival needs of the vegetation. (4) In EWR_opt conditions, the plains and local alpine plateau areas are in a state of water scarcity, the area of water scarcity is gradually increasing, and the regional precipitation is unable to fully realize the objectives of ecological conservation and vegetation restoration.

1. Introduction

In arid areas, water plays a crucial role in driving ecological changes, which shapes the double directions of the ecological environment. It determines the conflict and competition between green oases and desertification, thereby safeguarding the stability and sustainable growth of the overall ecological system. However, due to the scarcity of precipitation and high evaporation in arid areas, the scarcity of water resources has become an objective reality and a difficult condition to reverse [1]. This has resulted in the unequal distribution of natural water resources by humans, exacerbating the conflict between ecological and economic consumption [2]. As the water needed by vegetation and other organisms is gradually occupied by human production and living water, the fragile basin ecological environment has suffered more serious damage. The research [3] shows that the water problems in arid areas are mainly reflected in two aspects: scarcity of water resources and degradation of the ecological system. The fulfillment of the vegetation ecological water requirement (EWR) is essential in maintaining the health of natural vegetation communities and supporting the normal ecological service [4], which is one of the core research to solve the two current problems of water resources. Quantitative analysis of EWR based on the significant differences in ecosystems and climatic conditions in different geographical areas is a key point to promote the transformation of water resources management from a crude mode to a refined and scientific one, as well as a crucial part of advancing the construction of ecological civilization in arid areas [5].

Since the second decade of the 21st century, with major conferences under the United Nations Framework Convention on Climate Change, such as the signing of the Paris Climate Agreement in 2015 and the adoption of the Sustainable Development Goals in the same year, the ecological security issue has gradually risen to be the core issue of the international community, and human consciousness of the difficult condition of reversing ecological damage and the long-term nature of ecological restoration has gradually deepened [6]. This realization shift has not only elevated the status of ecological protection in the global agenda but also promoted in-depth research and active response to ecological and environmental issues from all walks of life [7]. As the core of the ecosystems in arid areas, vegetation is crucial to the protection of ecological balance and biodiversity, which has made the issue of vegetation EWR a focal point of ecological research [8]. In the research of vegetation EWR, the common research methods include the area quota method, phreatic evaporation method, water balance method, Penman-Monteith (P-M) method based on remote sensing (RS) and geographic information systems (GIS), etc. The area quota method [9] has been the primary approach for estimating the EWR of crops and plantations. The phreatic evaporation method [10] ignores the constraints that the growth and development of vegetation are subject to the law of territorial differentiation in natural geography. The water balance method [11] is suitable for calculating the EWR in small to medium-closed basins. The P-M method based on RS and GIS has become the mainstream of the regional or global study of vegetation EWR because of its solid physical foundation and wide applicability [12]. However, the fixed foliar resistance assumption of the P-M method does not adequately consider the evapotranspiration characteristics in arid areas of vegetation under high stomatal control conditions [13], making it one of the largest sources of uncertainty in the regional-scale modeling process of EWR studies in arid regions. To overcome this limitation, it is particularly important to make some corrections to the P-M method for vegetation stomatal control. Cleugh et al. [14] and Yan et al. [15] proposed a method to convert stomatal conductance to surface conductance in combination with LAI to attenuate the effect of stomatal control on the accuracy of EWR for vegetation. While this method may be more theoretically accurate, it focuses on the effect of the vegetation’s own properties on stomatal conductance and requires complex modeling and a large number of vegetation-specific parameters, which is challenging for large-scale, long-time-series studies. Another method is to introduce a resistance correction factor (F_r) to adjust the surface conductance in the P-M equation. This method simulates adaptive changes in plant stomatal behavior at specific conditions based on more readily available environmental parameters, emphasizing the role of environmental conditions in regulating stomatal opening and closing behavior, making it more flexible and simpler than direct adjustment of surface conductance and more suitable for application in arid regions. In addition, none of the above approaches take ecosystem self-regulation and restoration objectives into account. In 2023, Hao et al. [16] used the latitude of ecosystem resilience (LER) to take the health of ecosystems and restoration goals as the core variables for assessing EWR and, for the first time, introduced the theory of ecosystem restoration into the study of EWR, which was combined with the improved Priestley-Taylor algorithm to assess the EWR at different threshold levels in the plains area of the Tarim River Basin.

This study focuses on the Tarim River Basin (TRB) in Northwest China, utilizing RS and GIS techniques based on the P-M equation by introducing dynamic soil moisture limitation coefficient (K_S) and dynamic plant coefficient (K_C) that is coupled with the F_r to improve the calculation method of vegetation EWR in arid areas. The study reveals the spatio-temporal variations of EWR in the TRB. It utilizes the LER to identify the thresholds of vegetation EWR in different growing seasons and analyze the water surplus and deficit (WSD) situations. The findings provide valuable suggestions for managing and protecting the ecology in arid areas.

2. Materials and Method

2.1. Study Area

The TRB is located in the southern part of the Xinjiang Uygur Autonomous Region and mainly consists of the Hotan River, the Yerqiang River, the Kaikong River, the Aksu River, the Kashgar River, the Weigan River, the Keriya River, the Chelsea River, and the mainstream of the Tarim River (Figure 1b), with a basin area of 877,600 km². The terrain exhibits high elevation on the periphery and lower elevation in the center, gradually decreasing from west to east. The rivers flow from the periphery towards the center and from west to east as the terrain changes (Figure 1c). The region has a temperate continental climate. Water resources mainly originate from glaciers and snowmelt in the western high-cold region, flow through the desert oases, and eventually disappear into deserts or terminal lakes, which makes the water resources extremely scarce [17]. The region experiences an average annual precipitation range of 169 mm, with an average annual temperature of 10.7 °C and sunshine hours ranging from 2550 to 3500 h [17]. The natural vegetation types within the basin are primarily composed of arbor-shrub forests and grasslands. Arbor-shrub forests are mainly distributed in the plains below 1500 m, accounting for about 1% of the gross area in the TRB. Grasslands are mainly found in alpine plateaus above 1500 m in elevation, approximately 25% of the gross area. The principal vegetation consists of Populus euphratica Oliv, Tamarix chinensis lour, Alhagi camelorum Fisch, Glycyrrhiza uralensis Fisch, etc., in the study region.

2.2. Data Sources

The article utilizes multi-source data from 2000 to 2020, including measured meteorological data, normalized difference vegetation index (NDVI) images, soil hydrological characteristics data, precipitation data, and land use dataset.

The measured meteorological data were provided by the Chinese Meteorological Science data-sharing service network (http://data.cma.cn/, accessed on 18 June 2024), utilizing daily comprehensive meteorological data from 29 meteorological stations in the basin and its adjacent regions.

The NDVI images were obtained from the MOD13A1 data product provided by the Earth Science Data of the National Aeronautics and Space Administration (https://search.earthdata.nasa.gov/search, accessed on 18 June 2024), which has a temporal resolution of 16 days and a spatial resolution of 500 m.

Soil hydrological characteristic data were provided by the National Tibetan Plateau Data Center (https://data.tpdc.ac.cn, accessed on 18 June 2024), which include the field water capacity and soil wilting point for different soil types (with a spatial resolution of 1 km), as well as actual soil moisture data (with a temporal resolution of 24 h and a spatial resolution of 1 km).

The remote sensing precipitation data are provided by the Science Data Bank (https://www.scidb.cn/, accessed on 18 June 2024), with a temporal resolution of monthly and a spatial resolution of 1 km.

The land use dataset was adopted from the Resource and Environmental Science and Data Center of the Chinese Academy of Sciences (https://www.resdc.cn/, accessed on 18 June 2024) for every 5 years, 30 × 30 m.

2.3. Methods

2.3.1. EWR Model of Vegetation

To quantify the EWR of vegetation in the TRB, this study adopts the Thiessen polygon method, divides the study area into 29 sub-areas based on the distribution of meteorological stations, takes the evapotranspiration of the vegetation itself as the main water demand, and constructs the vegetation EWR model by comprehensively considering the soil moisture of different vegetation types and their growers [18], which is modeled as follows:

$$EWR=A_P\times ET\times {10}^{-3},$$

(1)

$$ET=K_S{K}_{C}E{T}_0.$$

(2)

where EWR is the vegetation ecological water requirements (m³), A_p is the vegetation cover area (m²), ET is the vegetation ecological water requirement quota (actual evapotranspiration) (mm), K_C is the vegetation coefficient, K_S is the soil moisture limitation coefficient [19], and ET₀ is the reference evapotranspiration rate (mm) [18].

2.3.2. Vegetation Coefficients (K_C) in the Growing Season

K_C can effectively reflect the degree of water demand of a plant, and it varies with vegetation types and growth stages. In this study, based on the characteristics of vegetation growth, the vegetation growth stages are divided into the initial growth stage, the developing growth stage, the mid-growth stage, and the latter growth stage [20]. The K_C for different stages is determined by plotting the vegetation coefficient curve [18]. For specific calculations, see references [12].

Considering that vegetation in arid regions has higher stomatal control than that under the standard conditions of the P-M model, this study introduces F_r to adjust the vegetation coefficient. The equation is as follows [18]:

$$K_{c,mid}=F_r{K}_{c\mathrm{x},mid},$$

(3)

$$F_r\approx {\displaystyle \frac{\Delta +\gamma (1+0.34u_2)}{\Delta +\gamma (1+0.34u_2\frac{r_1}{100})}},$$

(4)

$$\Delta ={\displaystyle \frac{4098[0.6108\exp (\frac{17.27T}{T+237.3})]}{{\left(T+237.3\right)}^2}},$$

(5)

$$\gamma =0.00163{\displaystyle \frac{Pa}{\lambda }},$$

(6)

$$u_2=u_z{\displaystyle \frac{4.87}{In(67.8Z-5.42)}}.$$

(7)

where $K_{c,mid}$ is the corrected vegetation coefficient of the mid-growth stage, F_r is the resistance correction factor $K_{cx,mid}$ is the uncorrected vegetation coefficient for the mid-growth stage [12], $\Delta$ is the slope of the saturation vapor pressure-temperature curve (kPa/°C), $\gamma$ is the psychrometric constant (kPa/°C), $u_2$ is the wind speed at 2 m height (m/s), $r_1$ is the average leaf surface resistance of the vegetation (s/m), (For xerophytic plants in the TRB, the value is taken from the reference value in the ASCE Hydrology Handbook [21], which is 800 s/m), T is the average of the minimum temperatures (T_min) and maximum temperatures (T_max) (°C), Pa is the atmospheric pressure at the measurement point (kPa), Z is the altitude of the measurement point (m), $u_z$ is the wind speed at the measurement point (m/s), $\lambda$ is the latent heat of vaporization of water (MJ/kg), $\lambda$ = 2.45.

In this study, the study area was divided into 29 subregions based on the distribution of meteorological stations, and the vegetation coefficients calculated in the 2020 Kumish subregion were combined with the vegetation coefficients measured by the vegetation physiological spectrometer in the Tuokexun area for comparative verification [22] (Figure 2). The results show that the correlation coefficient between pre-correction K_C and measured values is 0.56 and RMSE is 0.24, the correlation coefficients between post-correction K_C and measured values is 0.70 and RMSE is 0.13, and the corrected data are closer to the actual values with smaller deviation and higher reliability. In the Qumish subregion, the K_C in April was only 0.01, primarily affected by a 111-day continuous lack of precipitation that occurred from 16 January to 6 May 2020. Intervals and magnitudes of rainfall are key factors affecting the early growth stage of K_C. Due to this extreme climatic event, low values of vegetation coefficients were observed during the period. However, considering the spatio-temporal dynamics of the K_C in this study, the impact of such values on the overall research results is limited (

Table 1. Comparison of measured values with vegetation coefficients pre-correction and post-correction.

Vegetation Coefficients	April	May	June	July	August	September	October	Mean
Tuokexun	0.33	0.35	0.35	0.43	0.49	0.59	0.56	0.39
pre-correction	0.01	0.35	0.71	0.71	0.71	0.65	0.45	0.51
post-correction	0.01	0.22	0.44	0.44	0.44	0.42	0.37	0.33

).

2.3.3. Analysis of F_r’s Response to Changes in Meteorological Factors

The extreme aridity of the TRB climate has made xerophytic plants the dominant ecological vegetation type. Compared to the reference crop from the P-M equation, xero-phytic plants have a higher leaf resistance to reduce transpiration loss, thereby improving water use efficiency and survival capability [18]. In the EWR estimation model for arid regions, F_r is a key intermediate variable that plays an important role in adjusting and modifying the impact of plant physiological resistance on water use. Due to the significant impact of different climatic conditions on the F_r value, it is extremely important to provide reasonable suggested values for F_r based on specific climatic conditions. This approach is crucial for improving the accuracy of EWR estimations. This study constructs the F_r function model and uses the SHAP library in Python to create Partial Dependence Plots (PDP) and Individual Conditional Expectation plots (ICE) to explore the complex relationships between F_r and various factors. The core formulas for plotting PDPs and ICEs are as follows [23]:

$${\widehat{f}}_S\left(x_S\right)={\displaystyle \int \widehat{f}(x_S,X)}dP(X)$$

(8)

where $\widehat{f}$ is the F_r function model, $x_S$ is the specific factor, and $X$ is the other factors in the $\widehat{f}$ model.

2.3.4. Thresholds Determination for EWR of Vegetation

Quantifying the threshold for EWR of vegetation is key to maintaining ecosystem stability and is a constraining factor in the effectiveness of coordinated governance of mountains, rivers, forests, farmlands, lakes, grasslands, and deserts [24]. Based on the theory of ecosystem resilience, Hao et al. [16] quantified the threshold for EWR of vegetation using NDVI data, overcoming the difficulties in determining parameters with traditional methods and interference from subjective human factors. Grounded in the methodology proposed by Hao et al., this study fully considered the physiological characteristics and stage characteristics of the vegetation in the research area and quantified the thresholds of EWR for different stages. The calculation equation is as follows:

$$LAT={\displaystyle \frac{1}{n}}{\displaystyle \sum \max }\left|\left(NDV{I}_{ni}-\overline{NDVI}\right)/\overline{NDVI}\right|,$$

(9)

$${\epsilon }_{NDVI\_ET}=median\left[{\displaystyle \frac{(E{T}_i-\overline{ET})/\overline{ET}}{(NDV{I}_i-\overline{NDVI})/\overline{NDVI}}}\right],$$

(10)

$$k={\epsilon }_{NDVI\_ET}\times LAT,$$

(11)

$$EW{R}_{s\min }=A_P\times \min \left[E{T}_{si}\times (1-k)\right]>0$$

(12)

$$EW{R}_{sopt}=A_P\times mean\left[E{T}_{si}\times (1+k)\right]>0.$$

(13)

where LAT is the change rate of NDVI, n is the number of stable stages in the NDVI series, $NDV{I}_{ni}$ is the annual NDVI of the nth stable stage, $\overline{NDVI}$ is the multi-year average NDVI, ${\epsilon }_{NDVI\_ET}$ is the elasticity coefficient of ET concerning the rate of change of NDVI, $\overline{ET}$ is the multi-year average ET (mm), $E{T}_i$ is the annual values of the ET (mm), $NDV{I}_i$ is the annual values of the NDVI, k is the rate of change of ET undesirable vegetation system conditions, $E{T}_{si}$ is the ET for the i_th growing season of the year (mm), $EW{R}_{s\min }$ and $EW{R}_{sopt}$ is the minimum and optimal ecological water requirement, respectively, for the growth season of natural vegetation (m³).

2.3.5. Water Surplus and Deficit Index

To characterize the WSD conditions of vegetation, we use the EWR of vegetation as the water demand index and the effective precipitation as the water supply index to construct a water surplus and deficit index. The equation is as follows [25]:

$$\left\{\begin{array}{l}{P}_a={\displaystyle \frac{P(4.17-0.2P)}{4.17}},P<8.3\\ P_a=4.17+0.1P,P\ge 8.3\end{array}\right.,$$

(14)

$$WSD=m\times P_{\mathrm{a}}\times A_P-\mathrm{EWR},$$

(15)

$$I=({\displaystyle \frac{WSD}{EWR}}).$$

(16)

where P_a is the daily effective precipitation (mm/d); P is the measured daily precipitation (mm/d); WSD is the ecological water surplus or deficit for vegetation (m³); m is the number of days with precipitation, and I is the water surplus and deficit index. I > 0, I = 0, and I < 0 indicate that during the vegetation growth period, there is a water surplus, water supply-demand balance, and water deficit, respectively.

3. Results

3.1. F_r Response to Changes in Meteorological Factors

To avoid the overestimation of EWR in the study area due to the fixed leaf resistance assumption of the P-M equation, this study introduces F_r to correct the K_c,mid. Based on the actual estimated values of F_r according to the 29 meteorological stations in July from 2000 to 2020, the study explores its response to changes in 2 m wind speed, air pressure, maximum temperature, and minimum temperature, identifies the most sensitive factors, and provides recommended values of F_r under different ranges of the factor.

From the ICE plot (Figure 3), $u_2$ is the primary factor influencing changes in F_r. When other factors are held constant, fluctuations in F_r are most pronounced with changes in $u_2$, significantly surpassing the variations caused by the other three factors, indicating a high sensitivity of F_r to changes in $u_2$. Specifically, the trend of F_r decreases significantly and becomes more dispersed as $u_2$ increases. Given that the actual range of values of $u_2$ is mainly concentrated between the range of 0–2.5 m/s, to more accurately analyze the response of F_r in this range, we set up 8 intervals of $u_2$ and plotted the PDP plots of $u_2$ for the 8 intervals (Figure 4) to accurately analyze the average level of response of F_r in different intervals, and listed the suggested values of the average level of F_r in the 8 intervals (

Table 2. Suggested values of F_r for different intervals of $u_2$ (m/s).

$u_2$	0~0.25	0.25~0.5	0.5~0.75	0.75~1	1~1.5	1.5~2	2~2.5	>2.5
Fr	0.92	0.82	0.74	0.68	0.6	0.53	0.46	0.42

).

3.2. Temporal Variation Characteristics for EWR of Vegetation

Based on the improved vegetation EWR model in arid areas, this study estimated the EWR of arbor-shrub forests as well as grasslands from 2000 to 2020 (Figure 5). As grasslands constitute a large proportion of the study area, accounting for 34%, their EWR is also higher, with an average annual water requirement reaching 459.59 × 10⁸ m³. In contrast, arbor-shrub forests cover approximately 2% of the total basin area, resulting in a smaller water demand of 36.76 × 10⁸ m³. Therefore, the EWR of grasslands constitutes the mainstay of the vegetation water requirement in the basin. From a temporal perspective, the EWR of both arbor-shrub forests and grasslands in the study area generally shows a decreasing trend, with decreases of 0.18 × 10⁸ m³/a and 0.5 × 10⁸ m³/a, respectively. This is mainly due to intensified human activities leading to natural vegetation areas gradually transitioning to other land use types and the reduction of natural vegetation cover.

3.3. Spatial Variation Characteristics for EWR of Vegetation

The spatial distribution of EWR in the TRB from 2000 to 2020 reveals a distinct pattern where EWR is high in the periphery and low in the center of the area (Figure 6). The high-value areas (>20 × 10⁴ m³) are primarily concentrated around parts of the upper reaches of the Hotan River, the Yerqiang River, the Weigan River, the mainstream of the Tarim River, and the Kaikong River; the low-value areas (<20 × 10⁴ m³) are primarily concentrated around the Kashgar River, Chelchens River’s tributaries, downstream of the Hotan River, downstream of the Yerqiang River, Aksu River, and Keriya River’s tributaries. This is largely because the high-value areas have more abundant water resources and stronger plant community transpiration capacity compared to the low-value areas.

Using the Sen + MK method to analyze the trend of EWR change over 21a, according to the confidence level α = 0.05, this study classified the trend of EWR change into five categories: significant decrease, insignificant decrease, insignificant increase, significant increase, and stable unchanged, statistics were also provided on the proportion of gross area occupied by the five types (Figure 7a). In general, the overall trend of EWR in the study area over the past 21a showed insignificant changes, and the proportion of areas with significant increases or decreases was not large, with 9.4% of the areas with significant decreases, 35.6% of the areas with insignificant decreases, 5.4% of the areas with stable invariant, 41.8% of the areas with insignificant increases, and 7.8% of the areas with significant increases.

Statistics on the proportion of change trends of different vegetation types (Figure 7b) showed that 33% of arbor-shrub forests’ total area had significant changes in EWR (including both significant increase and significant decrease), and 17% of grassland’s total area had significant changes in EWR, which was relatively more significant than grassland. This result indicates that arbor-shrub forests are more susceptible to external changing environmental factors (climate change or anthropogenic factors).

3.4. Thresholds for EWR by Growing Season for Different Vegetation Types

To maintain the stability of the ecosystem in the TRB, we estimated the thresholds for vegetation EWR in the basin based on the LER. The EWR_min and EWR_opt were 360.45 × 10⁸ m³ and 550.10 × 10⁸ m³, respectively (

Table 3. Thresholds (×10⁸ m³) for EWR of different vegetation types during each growing season.

Vegetation Type	Initial Stage		Developing Stage		Mid-Stage		Latter Stage		Growing Season
	(1)	(2)	(1)	(2)	(1)	(2)	(1)	(2)	(1)	(2)
arbor-shrub forests	0.01	1.53	1.77	3.41	15.48	22.21	4.51	6.24	21.76	33.39
grasslands	2.53	35.84	38.24	69.44	254.62	351.95	43.30	59.47	338.69	516.70
natural vegetation	2.54	37.37	40.01	72.85	270.10	374.16	47.81	65.72	360.45	550.10

Note: (1) for EWR_min, (2) for EWR_opt.

). Among them, the high-water use stage of vegetation during the growing season was concentrated in the mid-growth stage, which was primarily attributed to the prolonged duration and substantial vegetative cover of the mid-growth stage. According to the ratio for EWR of vegetation in each growing season under different threshold levels (Figure 8), as the water demand transitioned from the minimum to the appropriate water demand, the percentage of EWR for the vegetation changed significantly across the growing seasons. Specifically, in the EWR_opt, the percentage of EWR increased significantly in the initial growth stage of vegetation and changed less in the remaining three periods, which was mainly related to the physiological characteristics of vegetation, reflecting the dynamic change of the percentage of water demand in the process of vegetation growth and development under different threshold levels. Therefore, at different threshold levels, managers should rationally regulate water resources according to the characteristics of vegetation growth stages.

3.5. Analysis for WSD of Vegetation EWR at Different Threshold Levels

To discuss the satisfaction of vegetation EWR under a single precipitation factor in the TRB, according to the estimation of EWR thresholds, this study set up two scenarios, EWR_min (

Table 4. Growing season WSD (×10⁸ m³) and I for different vegetation EWR_min conditions.

Vegetation Type	Initial Stage		Developing Stage		Mid-Stage		Latter Stage		Growing Season
	(1)	(2)	(1)	(2)	(1)	(2)	(1)	(2)	(1)	(2)
arbor-shrub forests	0.67	203.33	1.66	0.94	−6.99	−0.45	−2.47	−0.55	−7.13	−0.33
grasslands	30.02	11.86	26.75	0.70	78.60	0.31	3.90	0.09	139.26	0.41
natural vegetation	30.69	0.92	28.40	0.42	71.61	0.21	1.43	0.03	132.13	0.27

Note: (1) for WSD, (2) for I.

) and EWR_opt (

Table 5. Growing season WSD (×10⁸ m³) and I for different vegetation EWR_opt conditions.

Vegetation Type	Initial Stage		Developing Stage		Mid-Stage		Latter Stage		Growing Season
	(1)	(2)	(1)	(2)	(1)	(2)	(1)	(2)	(1)	(2)
arbor-shrub forests	−0.85	−0.56	0.01	0.00	−13.72	−0.62	−4.20	−0.67	−18.76	−0.56
grasslands	−3.29	−0.09	−4.46	−0.06	−18.73	−0.05	−12.28	−0.21	−38.75	−0.07
natural vegetation	−4.15	−0.11	−4.44	−0.06	−32.45	−0.09	−16.48	−0.25	−57.52	−0.10

Note: (1) for WSD, (2) for I.

), to characterize the EWR conditions for achieving ecological preservation and vegetation restoration, and for fulfilling the basic survival needs of the vegetation, respectively, and analyzed the WSD situation in each growth period.

In the EWR_min condition, the overall surplus of precipitation in the study area was 132.1 × 10⁸ m³, indicating that the overall precipitation in the TRB could meet the basic survival needs of the vegetation (Table 4). However, from different vegetation types, arbor-shrub forests showed a water deficit overall, and grasslands showed a water surplus overall. This is mainly due to the high spatial heterogeneity of precipitation, and there are localized areas where the precipitation cannot meet the basic survival needs of the vegetation. As shown in Figure 9a, the ecological water balance for vegetation changes from surplus to deficit with decreasing altitude. In the alpine plateau region, the vegetation can completely rely on precipitation to ensure its basic survival, except for the localized water shortage in the Yerqiang River Basin. The area with severe ecological water deficit is mainly located in the plains, where precipitation is scarce, with an average annual precipitation of only 77 mm, making it extremely difficult to support the basic survival of vegetation, especially in the middle and late stages of the growth of arbor-shrub forests, which suffer from serious water shortage and have a high dependence on surface and groundwater. However, this part of the water is easily affected by water resource development, and vegetation is more sensitive to anthropogenic disturbances.

In EWR_opt conditions, the vegetation was basically in water deficit during different growth stages, and the total water deficit during the vegetation growth stage was 57.52 × 10⁸ m³, with a water deficit index of 10%, and the precipitation in the study area was unable to fully achieve the goals of ecological preservation and vegetation restoration, and the order of the severity of water deficit in each growth stage was latter stage > initial stage > mid-stage > developing stage (Table 5). The vegetation ecological water deficit rate generally follows a pattern of high levels in spring and fall but lower levels in summer. This is mainly because vegetation in spring (April–May) enters into the development stage. The EWR of vegetation increases, but precipitation does not increase significantly, and thus water shortage occurs. In summer (June–August), the vegetation enters the stable growth stage; although EWR has a large increase compared to spring, precipitation is more abundant, so the water shortage situation is alleviated; in the fall (September–October), the vegetation enters the late growth stage, although the demand for water decreases, but precipitation also decreases accordingly, and the overall situation is still a water shortage situation. According to the spatial distribution (Figure 9b), the spatial pattern of ecological water scarcity in the alpine plateau region changed more obviously than that of vegetation water demand in the EWR_min condition, with the total area of water scarcity becoming larger and ecological water scarcity also occurred in localized areas of the Hotan River Basin and the Keriya River Basins. This phenomenon is mainly because precipitation on the southern slopes of the Tianshan Mountains is more abundant than on the northern slopes of the Kunlun Mountains, and the precipitation in the area of change is at a low level in the alpine plateau region.

4. Discussion

4.1. Rationality of Improving the EWR Models for Vegetation in Arid Areas

It is essential for the sustainable utilization of water resources and the growth of the national economy to estimate the vegetation EWR [26]. The P-M model is widely recognized in estimating the vegetation EWR, but its accuracy greatly depends on two key parameters: K_C and K_S. Currently, most studies use fixed values for K_C and K_S [27]. However, the P-M model’s universality and transferability are limited due to significant variations in climate, soil, and vegetation types across regions. With the rapid development of RS and GIS, the data of NDVI, land use, and soil hydrological characteristics in large-scale regions have been supplemented, and the ability to calculate spatio-temporal dynamic Kc and Ks has been available at this stage. Against this background, Chi et al. [12] combined RS and GIS to estimate the EWR of vegetation in the Ergune River Basin by calculating the spatio-temporal dynamic K_C and K_S using the P-M model. However, they did not consider the impact of vegetation stomatal control degree on the actual evapotranspiration in areas severely affected by water scarcity. In the arid region EWR estimation model, the introduction of F_r avoids the overestimation of EWR in the study area by the fixed foliar resistance assumption of the P-M formula. Building on previous research, this study introduces a correction factor, F_r, to adjust Kc, which further improves the precision of Kc and enhances the EWR of the vegetation model in arid areas. To verify the reasonableness of the research results, this study compares the estimation results for ET of vegetation in the TRB with the calculation results of Li [28] and Chen [29]. The results show that the results of this study are similar to those of Chen, while the estimated value of Li is lower than that of this study. The key difference between the results of Li’s study and the present study is that he used fixed values of K_C and K_S in the application of the P-M model and ignored their dynamic changes. In summary, the improving EWR model of vegetation for arid areas in this study has a mature and complete theoretical foundation and is more applicable for estimating ecohydrological water requirements in areas with large research regions, a lack of ground observation data, or complex ecological structures.

4.2. Analysis of EWR Spatial and Temporal Changes in the TRB

The TRB has been affected by strong human activities and climate change over the past 21 years, resulting in a shrinking of the natural vegetation area and a decreasing trend in the EWR of vegetation. Spatially, the EWR of vegetation is generally characterized by “high in the periphery and low in the center”, showing a pattern of uneven increase and decrease in vegetation water demand. In many areas on the northern slopes of the Kunlun Mountains, such as the Yarkant River, Hotan River, Keriya River, and the Chelchen River Basin, although they are facing the problem of insufficient suitable ecological water requirements, and the scarce precipitation can hardly satisfy the needs of ecological protection; however, global warming has led to an increase in the amount of snow and ice melting in their regions [30], which has enriched the amount of available water resources and contributed to the increase in the vegetation cover [31], resulting in an increase in the EWR of the vegetation. The southern slopes of the Tianshan Mountains (Kaikong River Basin, Weigan River Basin, Aksu River Basin) and Kashgargar River Basin, which are rich in precipitation, are able to satisfy the suitable ecological water requirement of the vegetation, and the overall ecological environment is better, and the changes in the EWR of the vegetation are more subject to the combined influence of climate change and human activities [32]. In addition, the EWR pattern of vegetation in the region of the Tarim River mainstem, which is densely populated and has frequent human activities [33], is complex and variable and is subject to the common constraints of a variety of factors.

4.3. Analysis of Water Fulfillment for Vegetation in the TRB

The natural vegetation in the basin is an important barrier to protecting the ecology of the basin and fulfilling the EWR_min condition of vegetation is a rigid demand for the actual management and decision-making of the ecosystem, while fulfilling the EWR_opt condition of vegetation is a flexible demand [34]. Analyzing the water satisfaction of vegetation in the minimum water demand condition shows that the precipitation in the study area can fulfill the basic survival demand of vegetation, but the arbor-shrub forests suffered from serious water deficit stress in the middle and late growth stages. In EWR_opt conditions, precipitation in the study area could not fully realize the goals of ecological conservation and vegetation restoration. All types of vegetation generally face water shortages during the growth season, with the initial and later stages of the growth experiencing the most serious shortages. According to the different goals of ecological environmental protection, the problems that need to be focused on are also different. To ensure basic vegetation survival, it is necessary to focus on the water deficit problem in the initial and late growth stage of arbor-shrub forests to prevent the reverse succession of the environment in the oasis area and desert area. To achieve the goals of ecological conservation and vegetation restoration, it is necessary to focus on the water deficit problem in the initial and later growth stages and to promote the positive succession of the vegetation community. Water resources in arid areas are limited, and the water cycle is seriously interfered with by high-intensity human activities. Changing the water resources management mode from “supply is determined by demand” to “demand is determined by supply” is a crucial component in the sustainable utilization of water resources in water-scarce areas [35]. According to the precipitation supply, it is difficult to realize the goal of comprehensive ecological conservation and restoration of vegetation without external water sources in the study area. Therefore, this study suggests adopting the management mode that focuses on solving the water shortage problem of arbor-shrub forests during the middle and late stages of growth to ensure basic survival, supplemented by regulating the water resources surplus according to the actual development demand and the local realization of ecological conservation and vegetation restoration, and the management mode that rationally regulates the water resources. In recent years, nearly 70 ecological gates have been constructed in the mainstem of the TRB as of 2022 to address the growing ecological problems. This has improved the morphological connectivity of the mainstem of the Tarim River as a whole [36], ensuring the continuous recharge of water sources from the oasis area along the mainstem. This reduces the dependence of vegetation on precipitation and positively contributes to vegetation protection, promoting the structure and function of the ecosystem.

4.4. Uncertainty Analysis

The research ideas and methodologies employed here hold potential reference value for comparable arid regions. Nevertheless, given the complexity of the study area, methods, and data involved, there remains a degree of uncertainty in the findings.

• Given the operational convenience of the single-plant coefficient method in large-scale regional studies, it was chosen as the means of calculating Kc in this study. However, given the significant variation in actual vegetation cover during the growing season, subsequent studies could explore how to efficiently utilize the double-plant coefficient method to achieve higher accuracy in estimating water demand while maintaining reasonable computational inputs [37].
• Although multi-source data enriches the available data sources, there is some uncertainty due to the effects of their own means of access and data algorithms that are not unique, limited measured calibration data, inconsistent spatial and temporal matches, and large variations in data quality. In addition, the measured stations in the study area are sparse and spatially poorly represented, mostly in the plains, which may have some impact on the accuracy of the calculation results. In order to compensate for this deficiency, future research can consider introducing remote sensing meteorological data with higher accuracy.
• The TRB faces a severe conflict between water resources supply and demand, which leads to the complexity of effective utilization and coordination of water resources. When calculating EWR, this study also ignored economic and social factors, which is a shortcoming in previous studies [38]. However, considering the complex interrelationships between the economy, society, and environment, as well as the uncertainty factor, there is a need to conduct further in-depth research into the impact of these factors on EWR.

5. Conclusions

With the help of RS and GIS, the calculation method based on the P-M equation for EWR of vegetation in arid areas in the study was improved by introducing a dynamic K_S and a dynamic K_C that is coupled with a F_r, and its spatio-temporal variation characteristics were analyzed. Additionally, this study utilized the LER to clarify the thresholds for EWR of vegetation throughout the growing season in the study area and to analyze the WSD at different threshold levels. The main conclusions of the study can be summarized as follows:

• u₂ is the main factor affecting the change of F_r; with the increase of u₂, F_r tends to decrease, and there is a negative correlation between u₂ and F_r.
• The average EWR of arbor-shrub forests and grasslands were 36.76 × 10⁸ m³ and 459.59 × 10⁸ m³, respectively, and grasslands were the mainstay of the vegetation EWR in the basin. From 2000 to 2020, the EWR of arbor-shrub forests decreased by 0.18 × 10⁸ m³/a, while that of grasslands decreased by 0.5 × 10⁸ m³/a, and this change is primarily attributed to the reduction in the area of natural vegetation.
• The EWR in the study area follows a pattern of being high in the periphery and low in the center. Arbor-shrub forests showed more significant changes in EWR than grasslands, indicating that arbor-shrub forests are more susceptible to external environmental factors.
• The EWR_min and EWR_opt required in the natural vegetation growth season were 360.45 × 10⁸ m³ and 550.10 × 10⁸ m³, respectively. In the EWR_opt condition, the percentage of EWR in the initial growth stage of the vegetation increased significantly, and the changes in the remaining three periods were small. At different threshold levels, managers should rationally regulate water resources according to the characteristics of vegetation growth stages.
• Precipitation in the study area can meet the basic survival needs of the vegetation but falls short of fully achieving ecological conservation and vegetation restoration objectives. In EWR_min conditions, the total precipitation surplus in the study area was 132.1 × 10⁸ m³. However, due to the spatial heterogeneity of precipitation, the alpine plateau area as a whole shows a water surplus, and the plain area as a whole is in a water shortage, especially in the middle and late stages of growth for arbor-shrub forests suffer from serious water deficit stress, and are more sensitive to anthropogenic factors of interference; In EWR_opt conditions, the total area of water shortage area becomes larger, the area change is mainly concentrated in the alpine plateau area, and the precipitation could not realize the goal of ecological conservation and vegetation restoration, with a total water deficit of 57.52 × 10⁸ m³, and the vegetation ecological water deficit rate generally follows a pattern of high levels in spring and fall, but lower levels in summer.