1. Introduction
Vegetation is a fundamental element of terrestrial ecosystems. It is the natural link between soil, water, and the environment and plays an essential role in the terrestrial carbon balance and the regulation of the climate system [
1]. Terrestrial water storage (TWS) usually includes the total amount of water stored in groundwater, soil water, surface water (lakes, rivers, and reservoirs), glaciers, and snow. It is a major component of the global water cycle [
2,
3,
4,
5]. It also plays a vital role in protecting the ecological environment and forming extreme hydrological events. Meanwhile, terrestrial water is also an important prerequisite for keeping vegetation green, regulating about half of the vegetation growth in global ecosystems and thus strongly influencing the global carbon cycle [
6,
7,
8,
9]. In addition, vegetation as an essential component of terrestrial ecosystems is important for regulating water balance at regional and global scales [
10]. Therefore, a clear understanding of terrestrial water and vegetation is needed. Similarly, a clear understanding of the interactions between terrestrial water and vegetation greenness is particularly crucial for predicting future water cycles, especially in arid and semi-arid regions [
11]. In this regard, the Normalized Difference Vegetation Index (NDVI) is considered to be a good indicator for identifying vegetation areas and long-term changes in their condition [
12,
13,
14,
15]. An increase in NDVI usually indicates enhanced vegetation growth and a decrease in NDVI usually indicates reduced plant growth [
9]. The study of vegetation response mechanisms to changes in terrestrial water storage has become an important element of regional ecological conservation and healthy development, which has significant ecological, social, and economic impacts.
In recent years, it has been shown how land greenness affects the global water cycle and regional terrestrial water balance, such as by altering the terrestrial water cycle [
16]. Papagiannopoulou et al. [
17] showed that water availability is the primary driver of global vegetation greenness anomalies, and that 61% of vegetation has limited land surface water resources despite the relative importance of Northern Hemisphere temperatures during the growing season. At the same time, the impact of climate warming has strengthened the water dependence of mountain vegetation [
18,
19]. The increase in vegetation cover also means more water demand [
20].
The time-variable gravity field models from the Gravity Recovery and Climate Experiment (GRACE) or GRACE Follow-On (GRACE-FO) provide a unique opportunity [
21] to quantify and investigate changes in terrestrial water storage (TWS) on regional and global scales. They have become an essential hydrological tool for quantifying basin-scale TWS [
22]. The TWS obtained from GRACE has become a valuable data source for studying vegetation–soil–moisture relationships [
23]. Yang et al. [
24] reported that the TWS observed by GRACE is a better measure to explain the dynamics of Australian vegetation than precipitation. Xie et al. [
25] demonstrated a consistent trend of statistical significance between vegetation greenness and GRACE TWS on a global scale. They showed that increasing vegetation greenness is an important cause of declining water storage in northern and western China. Andrew et al. [
26] further explored the relationship between the TWS anomaly (TWSA) and vegetation using the discrete wavelet transform technique. Their results showed that the decomposed TWSA explained the changes in vegetation better than the original TWSA. Different vegetation types have different degrees of one-way or two-way causality between NDVI and TWS [
27]. It has also been shown that vegetation captures only part of the total precipitation, and precipitation provides only indirect information on plant water status [
24,
27]. In summary, TWS is a more direct indicator of soil moisture available for plant growth, and therefore, correlates with vegetation changes to a greater extent. However, most of the literature aims to investigate the relationship between changes at large scales, and very little literature has examined the response between NDVI and TWS of vegetation in small-scale inland basins.
Studies have shown that vegetation change and water resources interact and restrict each other [
28,
29]. Human activities also intensify the balance between them [
30]. Li et al. [
31] demonstrated that changes in vegetation conditions can lead to significant changes in water storage, especially in the Yellow River basin, where the effect of vegetation changes is more pronounced, and different vegetation types contribute differently. The WRB, as part of the Yellow River Basin, also has this effect. With the implementation of the “Grain for Green Project” and the “Three North Protection Forest Project” [
32], the vegetation cover rate has increased significantly [
33]. Correspondingly, the increased vegetation consumes more water resources [
34], especially the excessive consumption of soil water [
35]. The contradiction between the excessive consumption of soil water and the sustainability of the restored vegetation is becoming more and more prominent, further threatening the sustainability of the ecosystem and socio-economic development. Therefore, it is of great scientific significance and social value to monitor WRB water reserves and explore the response relationship between water reserves and vegetation, which can rationally utilize and effectively manage regional water resources and improve the ecological environment.
This study investigates the relationship between WRB vegetation and TWS using NDVI products and GRACE and GRACE-FO time-variable gravity field models. The second part of the paper introduces the study area and data; the third part explains the data processing methods used; the fourth part gives the corresponding results; the fifth part gives a specific analysis of the results; and the sixth part gives the conclusion.
6. Conclusions
Weihe River is the largest tributary of the Yellow River of China. To better understand the ecological change in WRB, this study investigated the spatial and temporal variation of NDVI of vegetation in the WRB from April 2002 to May 2020 and the variation of TWS, and discussed the response relationship between them. The results demonstrate the following.
The NDVI in the WRB shows a prominent increasing trend, with higher NDVI values in the south and smaller NDVI values in the northwest, gradually increasing from northwest to southeast. In addition, the overall trend of NDVI is more obvious, and the browning rate is higher than the greening rate. Only 23.1% of the areas show a decreasing trend of NDVI, among which, the extremely significant decreasing areas are located in the densely populated urban areas. Meanwhile, the extremely significant increasing areas account for 30.1% and are located in the Loess Plateau area in the northwest, which proves that the vegetation cover is increasing at a great rate. Water storage in the WRB shows a decreasing trend, with the annual average water storage showing a gradual decrease from the northwest to the southeast, and the sharpness of the decline in water storage gradually aggravates from the west to the northeast. It is probably the result of multiple factors. In particular, rainfall was the main factor. Temperature and evaporation dispersion also have an effect.
The correlation analysis shows a consistent trend and intra-annual pattern between TWS and NDVI, which indicates some consistency between the changes in water storage and NDVI in the region. There is a great correlation between them in time scale, and the lag of NDVI to TWS is about 3 months. On the spatial scale, there is a significant difference in the response relationship between them, mainly showing a negative correlation, and the significant degree is different, mainly as follows: the correlation is weak in the hilly areas of the Loess Plateau with low vegetation coverage and high altitude areas, and densely populated cities such as Xi’an; the middle agricultural planting area is a low correlation; the significant correlation areas are the grass gathering area in the middle and the forest area in the south, which also indicates that the correlation degree is related to vegetation types.
Overall, the existing results have confirmed the basic feasibility of using GRACE TWS as a tool to explore the hydrological impact of plant greenness and the interaction between vegetation greenness and land water conditions. In particular, TWS is considered as an ideal index to study the impact of vegetation change on land water conditions [
25]. Therefore, the interaction between TWS and vegetation in the WRB studied in this paper is feasible and helps to improve the understanding of the relationship between terrestrial water and vegetation change in the region. However, this study only provides preliminary progress of the relationship between TWS and vegetation NDVI in the WRB, which needs to be investigated further. The next step is to further study the specific reasons for the correlation between TWS and NDVI.