**1. Introduction**

The variability of terrestrial biophysical variables influences the function of ecosystem components, which is likely to alter terrestrial ecological processes [1]. As one of the largest Chinese nature reserves, the Three-River Headwaters Region (TRHR) has a relatively high altitude and severe climate conditions, which makes its ecosystem extremely sensitive and vulnerable [2]. In the last few decades, due to intensified climate change and uncontrolled development activities, several ecological issues, including the recession of glaciers and tundra, wetland shrinkage, and grassland desertification, have emerged over the TRHR, resulting in complex biophysical interactions and an irreversible effect on the ecosystem [3]. Noticing the importance and urgency of environment protection, the Chinese governmen<sup>t</sup> has implemented a series of environmental protection policies over the TRHR since the early 21st century [4]. The Sanjiangyuan National Nature Reserve (SNNR) [5] as well as the Ecological Protection and Restoration Program (EPRP) [6] were established to conserve and rehabilitate the ecological environment, including retiring livestock, restoring degraded grassland, and ecological migration. Although these projects have greatly improved the resilience of the ecosystems, there are still large uncertainties in the spatiotemporal dynamics of the terrestrial biophysical variables. Therefore, comprehensive assessment of the terrestrial biophysical variation is a prerequisite for studying the interaction among ecological environment dynamics and provides instructive information about the hydrology, geographical ecology, and water resource management.

The air temperature (Ta) of the TRHR is undergoing significant warming, and has done over the last few decades [7,8]. Previous studies have shown that the rising trend of temperature over the TRHR is obviously larger than that in other regions in China [9,10]. The obvious warming trend, coupled with the accelerated carbon cycle between the land and atmosphere, has a significant impact on the biophysical processes, including the water cycle and energy exchange [11]. Recently, several studies based on ground observations found that the TRHR experienced a sustained warming and wetting trend over the past few decades [12]. For instance, Chong et al. [13] revealed that both Ta and precipitation (P) showed a significant upward trend (0.31 ◦C and 10.6 mm per decade, respectively) based on ground measurements from 21 meteorological sites distributed in the TRHR during 1956–2012. Significant warming and intensified *P* were also detected by Tong et al. [14], who suggested that Ta and P had increased by 0.9 ◦C and 102 mm in the past 20 years, respectively. The reduction of terrestrial relative humidity (RH) and solar radiation (Rs) were also captured during observations of the Tibetan Plateau, which correlate with rapid climate warming. However, in situ observations have their stubborn limitations as their representativeness of regional-scale climatic parameters remains problematic due to the terrestrial heterogeneity [15]. Fortunately, data assimilation techniques can provide optimal integrated information from site measurements, weather forecast products, and remote sensing data [16]. With the continuous accumulation of emerging forcing datasets produced by the data assimilation technique, it has become meaningful to further evaluate the long-term spatiotemporal information regarding climate change over the TRHR.

The pronounced climate warming along with the redistribution of precipitation patterns significantly influences the vegetation through a series of biophysical processes [17]. In this context, the remotely sensed normalized difference vegetation index (NDVI) has been widely used to detect the temporal variation of vegetation in the TRHR at multiple scales [18]. In past decades, the TRHR was under pressure to sustain increasing livestock grazing and suffered from an alpine grassland degradation problem. Liu et al. [19] reported that continuous and obvious grassland degradation had occured since the 1970s, experiencing fragmentation, desertification, and degradation to "black soil beach" [20]. In order to protect the grassland resource, a series of national nature reserve projects and ecological policies were established within the TRHR during the 21st century [21]. Recent studies have indicated that the slight increment in vegetation density (0.047/decade) is mainly attributed to the implementation of ecological restoration programs over the TRHR during 2001–2010 [22]. These findings were also demonstrated by Liu et al. [23], who found that the NDVI of the TRHR increased by 0.012/decade over the past 12 years (2000–2011), which is consistent with the ongoing "warm and

moist" trend. Understanding the variation in vegetation is often limited by the relatively brief dataset sequences, resulting in inconsistent accepted conclusions about the definite tendency of vegetation coverage in the TRHR. Therefore, it is critical to analyze the detailed variation of vegetation cover and the response of vegetation to climate change.

The fluctuation of climate and vegetation also has significant impacts on the surface water budget, particularly for evapotranspiration (ET), a crucial component of the terrestrial hydrological cycle [24]. ET is the sum of the evaporation from the land surface and the transpiration from plants into the atmosphere, and links the water budget, carbon sink, and energy exchange [25,26]. Therefore, the long-term variation of regional ET is of significance to monitor the biophysical processes and climate change. However, accurate simulations of the long-term ET of the TRHR remain a major challenge due to the lack of adequate and robust ground observations to determine regional ET over the TRHR. Moreover, datasets, such as the MOD16 product, from some global ET datasets are missing over the TRHR due to their existing gaps [27]. Recently, several satellite-based models and approaches have been developed to estimate the spatiotemporal ET in the TRHR over the last few decades [28]. For instance, based on a revised semi-empirical algorithm, Yao et al. [29] illustrated that there was no statistically significant trend in ET over the TRHR during the period 1982–2010. Xu et al. [30] found that ET showed a slight decreasing trend at the rate of 3.3 mm/decade from 2000 through 2014 in the TRHR by using an enhanced surface energy balance system (SEBS) algorithm. The simulated results were limited by the relatively short time span of the dataset and the uncertainties of model parameterization [31,32]. There are still large uncertainties about the spatiotemporal dynamics of ET over the complicated topography and heterogeneous surface of the TRHR. Thus, a robust assessment of the long-term variation of ET at a regional scale over the TRHR is in grea<sup>t</sup> demand for understanding the water cycle under an environment of rapid climate change.

As one of the most sensitive areas for climate change with complex terrain and high altitude, the TRHR is an ideal natural experimental area for investigating the response of terrestrial processes to climate change. Numerous studies have attempted to evaluate the interaction of the terrestrial biophysical variables (including climate, vegetation indices, and ET) by using different algorithms and datasets at multiple scales. For example, Zhang et al. [33] estimated the net primary productivity (NPP) of the TRHR using the Carnegie-Ames-Stanford approach (CASA) model, and found that the vegetation had a general increasing trend from 1982 to 2012, and pointed out that solar radiation was the primary factor controlling the increment of vegetation, with an average contribution of 0.73. Based on Gravity Recovery and Climate Experiment (GRACE) satellite data and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data, Xu et al. [30] suggested that soil moisture and total water storage were major determining drivers in vegetation greening. However, large discrepancies still exist in the spatiotemporal variation of terrestrial biophysical variables over the TRHR due to the differences in temporal series, spatial scale, algorithm, and data sources, which have hampered attempts to accurately evaluate long-term biophysical variation. Moreover, the spatial–temporal dynamics of climate change, vegetation growth, and water cycling have seldom been simultaneously discussed over the TRHR. As a result, little is accurately known about the spatiotemporal characterization of the response of terrestrial biophysical variables over the TRHR to climate change on large spatial scales and over long time periods.

In this study, we analyzed the spatiotemporal dynamics of terrestrial biophysical variables over the TRHR using a meteorological dataset, satellite-based vegetation index dataset, and a satellite-derived ET product from 1982 through to 2015, and investigated the main influencing factors accounting for biophysical variation. We had three major objectives. First, we analyzed the spatial patterns and trends of climate factors including Ta, P, RH, and Rs from 1982 through to 2015 over the TRHR of China. Second, we analyzed the spatiotemporal variation in the NDVI and ET from 1982 to 2015. Finally, we detected the response of vegetation and ET to climate change.

#### **2. Materials and Methods**
